structure
stringlengths 50
3.96k
| text
stringlengths 49
3.87k
| image
imagewidth (px) 288
1.28k
| download_url
stringlengths 33
34
| instance_name
stringlengths 18
19
| date
stringclasses 53
values | additional_info
stringlengths 785
2.42k
| date_scrapped
stringlengths 28
28
| file_filters
stringlengths 546
45.5k
| compilation_info
stringclasses 27
values | rendering_filters
stringlengths 168
184
| assets
sequencelengths 0
0
| category
stringclasses 1
value | uuid
stringlengths 38
38
| length
stringlengths 2
4
| difficulty
stringclasses 3
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
\begin{algorithm}[H]
\caption{{\sc Bernoulli Factory for Matching}}
\label{alg:bernoulli_matching}
\begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning tree of the complete graph $K_n$.
\State Let $T$ be the set of edges $(i,j)$ of the tree oriented toward vertex $1$.
\State For each edge $(i,j) \in T$ sample the $x_{i \pi(j)}$-coin. If any sample is $0$, restart.
\State Output the matching $\{(i, \pi(i))\}_{i \in [n]}$.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{{\sc Bernoulli Factory for Matching}}
\begin{algorithmic}
\State Pick uniformly at random a permutation $\pi$ over $[n]$.
\State For each $i \in [n]$ sample the $x_{i \pi(i)}$-coin. If any sample is $0$, restart.
\State Pick uniformly at random a spanning tree of the complete graph $K_n$.
\State Let $T$ be the set of edges $(i,j)$ of the tree oriented toward vertex $1$.
\State For each edge $(i,j) \in T$ sample the $x_{i \pi(j)}$-coin. If any sample is $0$, restart.
\State Output the matching $\{(i, \pi(i))\}_{i \in [n]}$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2011.03865" | "2011.03865.tar.gz" | "2024-02-19" | {
"title": "combinatorial bernoulli factories",
"id": "2011.03865",
"abstract": "a bernoulli factory is an algorithmic procedure for exact sampling of certain random variables having only bernoulli access to their parameters. bernoulli access to a parameter $p \\in [0,1]$ means the algorithm does not know $p$, but has sample access to independent draws of a bernoulli random variable with mean equal to $p$. in this paper, we study the problem of bernoulli factories for polytopes: given bernoulli access to a vector $x\\in p$ for a given polytope $p\\subset [0,1]^n$, output a randomized vertex such that the expected value of the $i$-th coordinate is \\emph{exactly} equal to $x_i$. for example, for the special case of the perfect matching polytope, one is given bernoulli access to the entries of a doubly stochastic matrix $[x_{ij}]$ and asked to sample a matching such that the probability of each edge $(i,j)$ be present in the matching is exactly equal to $x_{ij}$. we show that a polytope $p$ admits a bernoulli factory if and and only if $p$ is the intersection of $[0,1]^n$ with an affine subspace. our construction is based on an algebraic formulation of the problem, involving identifying a family of bernstein polynomials (one per vertex) that satisfy a certain algebraic identity on $p$. the main technical tool behind our construction is a connection between these polynomials and the geometry of zonotope tilings. we apply these results to construct an explicit factory for the perfect matching polytope. the resulting factory is deeply connected to the combinatorial enumeration of arborescences and may be of independent interest. for the $k$-uniform matroid polytope, we recover a sampling procedure known in statistics as sampford sampling.",
"categories": "cs.ds cs.dm cs.gt math.co math.pr",
"doi": "",
"created": "2020-11-07",
"updated": "2024-02-19",
"authors": [
"rad niazadeh",
"renato paes leme",
"jon schneider"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2011.03865"
} | "2024-03-15T03:22:39.599567" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main-Bernoulli.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007493876,
"profanity_score": 0.014805742,
"threat_score": 0.0076119336,
"sexually_explicit_score": 0.011795434,
"flirtation_score": 0.23112918
},
"imsart.cls": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0034408006,
"insult_score": 0.008234881,
"profanity_score": 0.016479544,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.33692676
},
"tex_journal/ack_journal.tex": {
"toxicity_score": 0.017466826,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.0052166977,
"insult_score": 0.009032886,
"profanity_score": 0.014566627,
"threat_score": 0.008375733,
"sexually_explicit_score": 0.011087708,
"flirtation_score": 0.21878932
},
"preamble.sty": {
"toxicity_score": 0.010869644,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007322875,
"profanity_score": 0.014566627,
"threat_score": 0.0074954215,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.22498475
},
"tex_journal/appendix_necessary_conditions_journal.tex": {
"toxicity_score": 0.011246625,
"severe_toxicity_score": 0.00078201294,
"identity_attack_score": 0.0034038029,
"insult_score": 0.0072278744,
"profanity_score": 0.013302735,
"threat_score": 0.007081157,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.2562556
},
"tex_journal/intro_journal.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"tex_journal/prelim_journal.tex": {
"toxicity_score": 0.008796243,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0025158543,
"insult_score": 0.0067623714,
"profanity_score": 0.012995303,
"threat_score": 0.007029374,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.34695542
},
"tex_journal/missingproofs.tex": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0007677078,
"identity_attack_score": 0.0025713511,
"insult_score": 0.007607877,
"profanity_score": 0.012961143,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.007726009,
"flirtation_score": 0.32522786
},
"tex_journal/appendix_algebraic_proof_journal.tex": {
"toxicity_score": 0.009047564,
"severe_toxicity_score": 0.0021743774,
"identity_attack_score": 0.0014891637,
"insult_score": 0.0070378734,
"profanity_score": 0.019622196,
"threat_score": 0.0057186154,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.24196765
},
"tex_journal/non_generic_impossibility_journal.tex": {
"toxicity_score": 0.026145924,
"severe_toxicity_score": 0.0017642975,
"identity_attack_score": 0.008250522,
"insult_score": 0.009678889,
"profanity_score": 0.019212283,
"threat_score": 0.0084793,
"sexually_explicit_score": 0.022765188,
"flirtation_score": 0.4438807
},
"tex_journal/appendix_impossibility_journal.tex": {
"toxicity_score": 0.032863233,
"severe_toxicity_score": 0.0017929077,
"identity_attack_score": 0.010729378,
"insult_score": 0.011920903,
"profanity_score": 0.017504321,
"threat_score": 0.007819066,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.38663936
},
"tex_journal/non_generic_factory_journal.tex": {
"toxicity_score": 0.01438814,
"severe_toxicity_score": 0.00088214874,
"identity_attack_score": 0.0050317086,
"insult_score": 0.008652883,
"profanity_score": 0.013678487,
"threat_score": 0.006925808,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.28204504
},
"tex_journal/necessary_condition_journal.tex": {
"toxicity_score": 0.009990019,
"severe_toxicity_score": 0.00074863434,
"identity_attack_score": 0.0030708222,
"insult_score": 0.0069713728,
"profanity_score": 0.012687869,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.009967142,
"flirtation_score": 0.36041215
},
"tex_journal/generic_factory_journal.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0059936526,
"insult_score": 0.009146886,
"profanity_score": 0.016616182,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.017221333,
"flirtation_score": 0.35249746
},
"tex_journal/open_journal.tex": {
"toxicity_score": 0.007288316,
"severe_toxicity_score": 0.0007534027,
"identity_attack_score": 0.0027193425,
"insult_score": 0.006372869,
"profanity_score": 0.012243799,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.00855169,
"flirtation_score": 0.22490336
},
"tex_journal/matching_factory_journal.tex": {
"toxicity_score": 0.008670582,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.0027008436,
"insult_score": 0.0067908717,
"profanity_score": 0.012380436,
"threat_score": 0.0069387536,
"sexually_explicit_score": 0.0094363475,
"flirtation_score": 0.23135975
},
"imsart.sty": {
"toxicity_score": 0.04735767,
"severe_toxicity_score": 0.0023460388,
"identity_attack_score": 0.0047727237,
"insult_score": 0.020063626,
"profanity_score": 0.026863953,
"threat_score": 0.0072753434,
"sexually_explicit_score": 0.020759964,
"flirtation_score": 0.34987062
},
"imsart-nameyear.bst": {
"toxicity_score": 0.0201057,
"severe_toxicity_score": 0.0013446808,
"identity_attack_score": 0.0036997858,
"insult_score": 0.010172892,
"profanity_score": 0.018119188,
"threat_score": 0.007340072,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.32274416
},
"tex_journal/appendix_matching_journal.tex": {
"toxicity_score": 0.011749268,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.0024603575,
"insult_score": 0.0076838774,
"profanity_score": 0.012722028,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.0073426575,
"flirtation_score": 0.25054014
},
"imsart-number.bst": {
"toxicity_score": 0.019477395,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0037552824,
"insult_score": 0.00979289,
"profanity_score": 0.018119188,
"threat_score": 0.00756015,
"sexually_explicit_score": 0.01374168,
"flirtation_score": 0.37246096
},
"main-Bernoulli.tex": {
"toxicity_score": 0.022610499,
"severe_toxicity_score": 0.0019264221,
"identity_attack_score": 0.0050317086,
"insult_score": 0.0103248935,
"profanity_score": 0.023311393,
"threat_score": 0.007637825,
"sexually_explicit_score": 0.023590868,
"flirtation_score": 0.38638145
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.44030004110152,
"hash": "fff7e6e666666666",
"most_frequent_color_ratio": 95.44030004110152
}
} | [] | "algorithm" | "63ecf243-37ae-4356-9144-bccb55ec54a9" | 593 | easy |
|
\begin{algorithmic}[1]
\State \Return $\arg\max_{c\in \mathbf{C}}{ P\left( Y \lvert \mathbf{X},c\right) P\left( c\right) }$
\end{algorithmic}
| \begin{algorithmic}
[1]
\State \Return $\arg\max_{c\in \mathbf{C}}{ P\left( Y \lvert \mathbf{X},c\right) P\left( c\right) }$
\end{algorithmic} | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated statistical prior represented in trellis, e.g., finite state machines and markov processes. we introduce a variation of list viterbi algorithm (lva) to enable accurate recovery using much fewer tests than objectives, which efficiently gains from the correlated prior statistics structure. our numerical results demonstrate that the proposed multi-stage gt (msgt) algorithm can obtain the optimal maximum a posteriori (map) performance with feasible complexity in practical regimes, such as with covid-19 and sparse signal recovery applications, and reduce in the scenarios tested the number of pooled tests by at least $25\\%$ compared to existing classical low complexity gt algorithms. moreover, we analytically characterize the complexity of the proposed msgt algorithm that guarantees its efficiency.",
"categories": "cs.it math.it q-bio.qm stat.ap",
"doi": "",
"created": "2024-02-15",
"updated": "",
"authors": [
"ayelet c. portnoy",
"alejandro cohen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.10018"
} | "2024-03-15T04:32:28.811580" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"GT_with_prior_statistics_for_ISIT.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"GT_with_prior_statistics_for_ISIT.tex": {
"toxicity_score": 0.0074139764,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.002192123,
"insult_score": 0.00654387,
"profanity_score": 0.013644327,
"threat_score": 0.0063238298,
"sexually_explicit_score": 0.01209032,
"flirtation_score": 0.31215188
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.56252899463186,
"hash": "003f3f3f073f3f3f",
"most_frequent_color_ratio": 93.56252899463186
}
} | [] | "algorithm" | "6bf7ab91-418f-4220-90ec-9884d22c9527" | 142 | easy |
|
\begin{algorithmic}[1]
\For{each client index $i = 1,2,\dots,n$ \textbf{in parallel}}
\State initialize $\mathbf{u}_i$, $\mathbf{D}^{(i)}$;
\EndFor
\For{$a=1,2,\dots,t_1$}
\State $S_a$ $\leftarrow$ randomly select $n_s$ from $n$ clients
\For{each client index $i \in S_a$ \textbf{in parallel}}
\State download $\mathbf{C}$ from the server;
\State $\mathbf{C}^{(i)}$, $\hat{r}_i$ $\leftarrow$ ClientUpdate($\mathbf{u}_i$, $\mathbf{C}$, $\mathbf{D}^{(i)}$);
\State upload $\mathbf{C}^{(i)}$ to the server;
\EndFor
\State $\mathbf{C} \leftarrow \frac{1}{n_s} \sum^{n_s}_{i=1} \mathbf{C}^{(i)}$;
\Comment Server Aggregation
\EndFor
\State \bf return: $\hat{\mathbf{R}} = [\hat{r}_1, \hat{r}_2, \dots, \hat{r}_n]^T$
\end{algorithmic}
| \begin{algorithmic}
[1]
\For{each client index $i = 1,2,\dots,n$ \textbf{in parallel}}
\State initialize $\mathbf{u}_i$, $\mathbf{D}^{(i)}$;
\EndFor
\For{$a=1,2,\dots,t_1$}
\State $S_a$ $\leftarrow$ randomly select $n_s$ from $n$ clients
\For{each client index $i \in S_a$ \textbf{in parallel}}
\State download $\mathbf{C}$ from the server;
\State $\mathbf{C}^{(i)}$, $\hat{r}_i$ $\leftarrow$ ClientUpdate($\mathbf{u}_i$, $\mathbf{C}$, $\mathbf{D}^{(i)}$);
\State upload $\mathbf{C}^{(i)}$ to the server;
\EndFor
\State $\mathbf{C} \leftarrow \frac{1}{n_s} \sum^{n_s}_{i=1} \mathbf{C}^{(i)}$;
\Comment Server Aggregation
\EndFor
\State \bf return: $\hat{\mathbf{R}} = [\hat{r}_1, \hat{r}_2, \dots, \hat{r}_n]^T$
\end{algorithmic} | "https://arxiv.org/src/2301.09109" | "2301.09109.tar.gz" | "2024-02-07" | {
"title": "federated recommendation with additive personalization",
"id": "2301.09109",
"abstract": "building recommendation systems via federated learning (fl) is a new emerging challenge for advancing next-generation internet service and privacy protection. existing approaches train shared item embedding by fl while keeping the user embedding private on client side. however, item embedding identical for all clients cannot capture users' individual differences on perceiving the same item and thus leads to poor personalization. moreover, dense item embedding in fl results in expensive communication cost and latency. to address these challenges, we propose federated recommendation with additive personalization (fedrap), which learns a global view of items via fl and a personalized view locally on each user. fedrap enforces sparsity of the global view to save fl's communication cost and encourages difference between the two views through regularization. we propose an effective curriculum to learn the local and global views progressively with increasing regularization weights. to produce recommendations for an user, fedrap adds the two views together to obtain a personalized item embedding. fedrap achieves the best performance in fl setting on multiple benchmarks. it outperforms recent federated recommendation methods and several ablation study baselines.",
"categories": "cs.lg",
"doi": "",
"created": "2023-01-22",
"updated": "2024-02-07",
"authors": [
"zhiwei li",
"guodong long",
"tianyi zhou"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2301.09109"
} | "2024-03-15T07:15:57.429137" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"iclr2024_conference.bst": {
"toxicity_score": 0.014765122,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0030153254,
"insult_score": 0.008709883,
"profanity_score": 0.019553876,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.01686747,
"flirtation_score": 0.5479165
},
"main.tex": {
"toxicity_score": 0.015016444,
"severe_toxicity_score": 0.0013828278,
"identity_attack_score": 0.0052166977,
"insult_score": 0.008386881,
"profanity_score": 0.016821137,
"threat_score": 0.008259222,
"sexually_explicit_score": 0.016395653,
"flirtation_score": 0.35838574
},
"main.bbl": {
"toxicity_score": 0.0126288915,
"severe_toxicity_score": 0.0011491776,
"identity_attack_score": 0.004069764,
"insult_score": 0.007607877,
"profanity_score": 0.014874061,
"threat_score": 0.007624879,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.20600256
},
"math_commands.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0074368757,
"profanity_score": 0.011526455,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.0059272056,
"flirtation_score": 0.37736848
},
"natbib.sty": {
"toxicity_score": 0.05750068,
"severe_toxicity_score": 0.0035858154,
"identity_attack_score": 0.008139528,
"insult_score": 0.02173949,
"profanity_score": 0.043415543,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.028662905,
"flirtation_score": 0.41074932
},
"FedRAP.bib": {
"toxicity_score": 0.008984734,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0020163832,
"insult_score": 0.0068763723,
"profanity_score": 0.013268576,
"threat_score": 0.0062979385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.24059892
},
"iclr2024_conference.sty": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0094128875,
"profanity_score": 0.01220964,
"threat_score": 0.0058739646,
"sexually_explicit_score": 0.0057207854,
"flirtation_score": 0.47478196
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"main.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.16284829721361,
"hash": "133f0787871f3c3f",
"most_frequent_color_ratio": 93.16284829721361
}
} | [] | "algorithm" | "6c8fa35d-b77a-4884-899b-d2a0ed5356bc" | 729 | medium |
|
\begin{algorithm}
\caption{$L$-lag-test (empirical version)}
\label{alg:llt}
\begin{algorithmic}[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$, $\alpha$, $L$, $B$
\For{$k = 1, \ldots, n-L$}
\State $X'_k \gets \left(X_k \ldots, X_{k+L}\right)$
\State $Y'_k \gets \left(Y_k, \ldots, Y_{k+L}\right)$
\EndFor
\State $N \gets \lfloor (n-L)/d\rfloor$
\For{$b = 1, \ldots, B$}
\State $\left(X^*, Y^*\right) \gets \texttt{IBB}\left((X'_1, \ldots, X'_{n-L}), (Y'_1, \ldots, Y'_{n-L}), d\right)$
\State $D_b \gets Nd \, \mathrm{dcov}_{n-L}\left(X^*, Y^*\right)$\label{algline:dcov1}
\EndFor
\State $c_\alpha^* \gets$ empirical upper $\alpha$-quantile of $\{D_1, \ldots, D_B\}$
\If{$(n-L) \, \mathrm{dcov}_{n-L}(X', Y') > c_\alpha^*$}\label{algline:dcov2}
\State $\texttt{Decision} \gets$ `Reject $H_0$'
\Else
\State $\texttt{Decision} \gets$ `Do not reject $H_0$'
\EndIf
\Ensure \texttt{Decision}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{$L$-lag-test (empirical version)}
\begin{algorithmic}
[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$, $\alpha$, $L$, $B$
\For{$k = 1, \ldots, n-L$}
\State $X'_k \gets \left(X_k \ldots, X_{k+L}\right)$
\State $Y'_k \gets \left(Y_k, \ldots, Y_{k+L}\right)$
\EndFor
\State $N \gets \lfloor (n-L)/d\rfloor$
\For{$b = 1, \ldots, B$}
\State $\left(X^*, Y^*\right) \gets \texttt{IBB}\left((X'_1, \ldots, X'_{n-L}), (Y'_1, \ldots, Y'_{n-L}), d\right)$
\State $D_b \gets Nd \, \mathrm{dcov}_{n-L}\left(X^*, Y^*\right)$ \EndFor
\State $c_\alpha^* \gets$ empirical upper $\alpha$-quantile of $\{D_1, \ldots, D_B\}$
\If{$(n-L) \, \mathrm{dcov}_{n-L}(X', Y') > c_\alpha^*$} \State $\texttt{Decision} \gets$ `Reject $H_0$'
\Else
\State $\texttt{Decision} \gets$ `Do not reject $H_0$'
\EndIf
\Ensure \texttt{Decision}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2112.14091" | "2112.14091.tar.gz" | "2024-02-05" | {
"title": "a bootstrap test for independence of time series based on the distance covariance",
"id": "2112.14091",
"abstract": "we present a test for independence of two strictly stationary time series based on a bootstrap procedure for the distance covariance. our test detects any kind of dependence between the two time series within an arbitrary maximum lag $l$. in simulation studies, our test outperforms alternative testing procedures. in proving the validity of the underlying bootstrap procedure, we generalise bounds for the wasserstein distance between an empirical measure and its marginal distribution under the assumption of $\\alpha$-mixing. previous results of this kind only existed for i.i.d. processes.",
"categories": "math.st stat.th",
"doi": "",
"created": "2021-12-28",
"updated": "2024-02-05",
"authors": [
"annika betken",
"herold dehling",
"marius kroll"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2112.14091"
} | "2024-03-15T04:47:15.995017" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bs.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"bs.bbl": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0037737815,
"insult_score": 0.0075128763,
"profanity_score": 0.014464149,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.011087708,
"flirtation_score": 0.1947049
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.70033877425315,
"hash": "071f3f1f0f071f3f",
"most_frequent_color_ratio": 93.70033877425315
}
} | [] | "algorithm" | "10e2fd0a-92e2-4bff-86a1-32eeb5514c0a" | 877 | medium |
|
\begin{algorithmic}[1]
\Function{\texttt{Relax}}{~}
\State
Initialize $\bar \beta^{(0)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^2$ through the least squares;
\State
Initialize $\bar \delta^{(0)}\leftarrow \bar \beta^{(0)} + e^{(0)}$, where $e^{(0)}\sim N(\boldsymbol 0_{d},I_{d})$;
\State
Generate $\bar\omega^{(0)} \sim U_{S^{p-1}}(\boldsymbol e_1, 1)$, $\bar\gamma^{(0)} \sim U(l_0, u_0)$; \hfill (See Algorithm \ref{al:uniform})
\For{m=1,2,...,MAXITER}
\If{$|\bar\omega^{(m)} - \bar\omega^{(m-1)}|< THRES$}
\State EXIT;
\EndIf
\State $(\bar\omega^{(m)}, \bar\gamma^{(m)}) = {\arg\min}_{(\omega,\gamma)\in \mathcal S^{p-1} \times [l_0,u_0]} \bar Q(\omega; \bar\beta^{(m-1)}, \bar\delta^{(m-1)})$ \\
\; ~ \hfill (e.g., through Newton Raphson);
\State $\bar \beta^{(m)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^21\{X_i'\bar\omega^{(m)} - \bar\gamma^{(m)}\le 0\}$;
\State $\bar \delta^{(m)}\leftarrow {\arg\min}_{\delta}\sum_{i=1}^n(Y_i - Z_i'\delta)^21\{X_i'\bar\omega^{(m)} - \bar\gamma^{(m)} > 0\}$;
\EndFor\\
\Return $\bar\omega^{(m)}$
\EndFunction
\end{algorithmic}
| \begin{algorithmic}
[1]
\Function{\texttt{Relax}}{~}
\State
Initialize $\bar \beta^{(0)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^2$ through the least squares;
\State
Initialize $\bar \delta^{(0)}\leftarrow \bar \beta^{(0)} + e^{(0)}$, where $e^{(0)}\sim N(\boldsymbol 0_{d},I_{d})$;
\State
Generate $\bar\omega^{(0)} \sim U_{S^{p-1}}(\boldsymbol e_1, 1)$, $\bar\gamma^{(0)} \sim U(l_0, u_0)$; \hfill (See Algorithm \ref{al:uniform})
\For{m=1,2,...,MAXITER}
\If{$|\bar\omega^{(m)} - \bar\omega^{(m-1)}|< THRES$}
\State EXIT;
\EndIf
\State $(\bar\omega^{(m)}, \bar\gamma^{(m)}) = {\arg\min}_{(\omega,\gamma)\in \mathcal S^{p-1} \times [l_0,u_0]} \bar Q(\omega; \bar\beta^{(m-1)}, \bar\delta^{(m-1)})$ \\
\; ~ \hfill (e.g., through Newton Raphson);
\State $\bar \beta^{(m)} \leftarrow {\arg\min}_{\beta}\sum_{i=1}^n(Y_i - Z_i'\beta)^21\{X_i'\bar\omega^{(m)} - \bar\gamma^{(m)}\le 0\}$;
\State $\bar \delta^{(m)}\leftarrow {\arg\min}_{\delta}\sum_{i=1}^n(Y_i - Z_i'\delta)^21\{X_i'\bar\omega^{(m)} - \bar\gamma^{(m)} > 0\}$;
\EndFor\\
\Return $\bar\omega^{(m)}$
\EndFunction
\end{algorithmic} | "https://arxiv.org/src/2206.06140" | "2206.06140.tar.gz" | "2024-01-13" | {
"title": "inference for change-plane regression",
"id": "2206.06140",
"abstract": "a key challenge in analyzing the behavior of change-plane estimators is that the objective function has multiple minimizers. two estimators are proposed to deal with this non-uniqueness. for each estimator, an n-rate of convergence is established, and the limiting distribution is derived. based on these results, we provide a parametric bootstrap procedure for inference. the validity of our theoretical results and the finite sample performance of the bootstrap are demonstrated through simulation experiments. we illustrate the proposed methods to latent subgroup identification in precision medicine using the actg175 aids study data.",
"categories": "math.st stat.th",
"doi": "",
"created": "2022-06-13",
"updated": "2024-01-13",
"authors": [
"chaeryon kang",
"hunyong cho",
"rui song",
"moulinath banerjee",
"eric b. laber",
"michael r. kosorok"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2206.06140"
} | "2024-03-15T06:15:14.775372" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.004180758,
"insult_score": 0.007607877,
"profanity_score": 0.015044857,
"threat_score": 0.0077154995,
"sexually_explicit_score": 0.012385205,
"flirtation_score": 0.20258527
},
"Bernoulli_2023_Oct/cp_Sec8_main.tex": {
"toxicity_score": 0.008984734,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0024418586,
"insult_score": 0.0067433715,
"profanity_score": 0.01265371,
"threat_score": 0.0070034824,
"sexually_explicit_score": 0.0101440735,
"flirtation_score": 0.19608681
},
"Bernoulli_2023_Oct/cp_Sec5_main.tex": {
"toxicity_score": 0.022728346,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0032743104,
"insult_score": 0.011996903,
"profanity_score": 0.015898837,
"threat_score": 0.006686311,
"sexually_explicit_score": 0.0087286215,
"flirtation_score": 0.25330183
},
"main.tex": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0037552824,
"insult_score": 0.0076838774,
"profanity_score": 0.016342908,
"threat_score": 0.0071588317,
"sexually_explicit_score": 0.014154521,
"flirtation_score": 0.4824789
},
"Bernoulli_2023_Oct/LICENSE": {
"toxicity_score": 0.009361716,
"severe_toxicity_score": 0.0007343292,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0072088744,
"profanity_score": 0.011577694,
"threat_score": 0.006763986,
"sexually_explicit_score": 0.0075195893,
"flirtation_score": 0.19520952
},
"Bernoulli_2023_Oct/cp_Sec2_main.tex": {
"toxicity_score": 0.008293601,
"severe_toxicity_score": 0.0008010864,
"identity_attack_score": 0.0026638457,
"insult_score": 0.0067623714,
"profanity_score": 0.013200258,
"threat_score": 0.007081157,
"sexually_explicit_score": 0.010792822,
"flirtation_score": 0.3555619
},
"Bernoulli_2023_Oct/cp_Sec4_rate_app.tex": {
"toxicity_score": 0.011246625,
"severe_toxicity_score": 0.0008678436,
"identity_attack_score": 0.0035702933,
"insult_score": 0.0073798755,
"profanity_score": 0.013302735,
"threat_score": 0.007262398,
"sexually_explicit_score": 0.009259416,
"flirtation_score": 0.25091854
},
"Bernoulli_2023_Oct/cp_Sec9_main.tex": {
"toxicity_score": 0.034984488,
"severe_toxicity_score": 0.0020885468,
"identity_attack_score": 0.009693438,
"insult_score": 0.009317887,
"profanity_score": 0.024677763,
"threat_score": 0.008453408,
"sexually_explicit_score": 0.03420676,
"flirtation_score": 0.309675
},
"Bernoulli_2023_Oct/cp_Sec6_app.tex": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0010871887,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0075508766,
"profanity_score": 0.015386449,
"threat_score": 0.0071199946,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.28519526
},
"Bernoulli_2023_Oct/cp_Sec5_app.tex": {
"toxicity_score": 0.019728716,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0048837173,
"insult_score": 0.009488888,
"profanity_score": 0.015454767,
"threat_score": 0.00756015,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.31199437
},
"Bernoulli_2023_Oct/PartCircle1a_old.eps": {
"toxicity_score": 0.007885204,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0067908717,
"profanity_score": 0.013268576,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.45258012
},
"imsart.sty": {
"toxicity_score": 0.09525062,
"severe_toxicity_score": 0.0038528442,
"identity_attack_score": 0.0071035884,
"insult_score": 0.02708937,
"profanity_score": 0.0569429,
"threat_score": 0.009204263,
"sexually_explicit_score": 0.03774539,
"flirtation_score": 0.3281287
},
"Bernoulli_2023_Oct/cp_Sec6_main.tex": {
"toxicity_score": 0.0055604824,
"severe_toxicity_score": 0.000603199,
"identity_attack_score": 0.0014799143,
"insult_score": 0.006201868,
"profanity_score": 0.011543535,
"threat_score": 0.0061814263,
"sexually_explicit_score": 0.007726009,
"flirtation_score": 0.350033
},
"Bernoulli_2023_Oct/cp_Sec4_main.tex": {
"toxicity_score": 0.009801528,
"severe_toxicity_score": 0.00076293945,
"identity_attack_score": 0.0030523231,
"insult_score": 0.007170874,
"profanity_score": 0.0127561875,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.0094363475,
"flirtation_score": 0.2546901
},
"Bernoulli_2023_Oct/imsart.cls": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0034408006,
"insult_score": 0.008234881,
"profanity_score": 0.016479544,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.33692676
},
"main.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
},
"Bernoulli_2023_Oct/graph/PartCircle1a.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Bernoulli_2023_Oct/mcp2.bib": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0044767405,
"insult_score": 0.008709883,
"profanity_score": 0.0166845,
"threat_score": 0.008336896,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.3168642
},
"Bernoulli_2023_Oct/cp_Sec4_cons_main_x_.tex": {
"toxicity_score": 0.0062202006,
"severe_toxicity_score": 0.0006341934,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064108693,
"profanity_score": 0.012482914,
"threat_score": 0.0062267366,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.30278447
},
"Bernoulli_2023_Oct/cp_main_v3.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0046247323,
"insult_score": 0.008443882,
"profanity_score": 0.01805087,
"threat_score": 0.009592636,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.46824917
},
"Bernoulli_2023_Oct/cp_Sec7_mip_main.tex": {
"toxicity_score": 0.008105109,
"severe_toxicity_score": 0.0007343292,
"identity_attack_score": 0.0029043318,
"insult_score": 0.0066103707,
"profanity_score": 0.012073003,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.00937737,
"flirtation_score": 0.2378531
},
"Bernoulli_2023_Oct/cp_app_v3.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0046247323,
"insult_score": 0.008443882,
"profanity_score": 0.01805087,
"threat_score": 0.009592636,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.46824935
},
"Bernoulli_2023_Oct/cp_Sec9_app.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0029413297,
"insult_score": 0.006828872,
"profanity_score": 0.012892825,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.17300741
},
"Bernoulli_2023_Oct/Response_to_reviewers.tex": {
"toxicity_score": 0.013382856,
"severe_toxicity_score": 0.0011587143,
"identity_attack_score": 0.0035517942,
"insult_score": 0.0077598775,
"profanity_score": 0.014976538,
"threat_score": 0.007935578,
"sexually_explicit_score": 0.01332884,
"flirtation_score": 0.27367425
},
"acmtrans-ims.bst": {
"toxicity_score": 0.014011159,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.0044767405,
"insult_score": 0.00808288,
"profanity_score": 0.013917602,
"threat_score": 0.008181547,
"sexually_explicit_score": 0.009967142,
"flirtation_score": 0.22246784
},
"Bernoulli_2023_Oct/cp_Sec3_app.tex": {
"toxicity_score": 0.02035702,
"severe_toxicity_score": 0.001335144,
"identity_attack_score": 0.002830336,
"insult_score": 0.009488888,
"profanity_score": 0.019485557,
"threat_score": 0.006712203,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.26591355
},
"Bernoulli_2023_Oct/cp_Sec4_cons_app.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0029598286,
"insult_score": 0.0069333725,
"profanity_score": 0.012995303,
"threat_score": 0.006822242,
"sexually_explicit_score": 0.008787598,
"flirtation_score": 0.23272796
},
"Bernoulli_2023_Oct/imsart.sty": {
"toxicity_score": 0.09525062,
"severe_toxicity_score": 0.0038528442,
"identity_attack_score": 0.0071035884,
"insult_score": 0.02708937,
"profanity_score": 0.0569429,
"threat_score": 0.009204263,
"sexually_explicit_score": 0.03774539,
"flirtation_score": 0.3281287
},
"Bernoulli_2023_Oct/cp_app_v3.synctex_busy_": {
"toxicity_score": 0.004429537,
"severe_toxicity_score": 0.00046253204,
"identity_attack_score": 0.0010729379,
"insult_score": 0.0059738667,
"profanity_score": 0.0103821205,
"threat_score": 0.0059840037,
"sexually_explicit_score": 0.005868228,
"flirtation_score": 0.27914122
},
"Bernoulli_2023_Oct/cp_Sec7_uniform_main.tex": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.0005507469,
"identity_attack_score": 0.0016186562,
"insult_score": 0.0062588686,
"profanity_score": 0.011031146,
"threat_score": 0.0063432488,
"sexually_explicit_score": 0.0069298176,
"flirtation_score": 0.25291386
},
"Bernoulli_2023_Oct/cp_Sec10_main.tex": {
"toxicity_score": 0.032863233,
"severe_toxicity_score": 0.0011301041,
"identity_attack_score": 0.006178642,
"insult_score": 0.017088935,
"profanity_score": 0.013849284,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.0077849864,
"flirtation_score": 0.21227238
},
"Bernoulli_2023_Oct/cp_Sec1_main.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0062526376,
"insult_score": 0.008709883,
"profanity_score": 0.016752819,
"threat_score": 0.008634649,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.29165205
},
"Bernoulli_2023_Oct/README.md": {
"toxicity_score": 0.017215505,
"severe_toxicity_score": 0.0013446808,
"identity_attack_score": 0.0045507364,
"insult_score": 0.009032886,
"profanity_score": 0.018802373,
"threat_score": 0.0071199946,
"sexually_explicit_score": 0.020642009,
"flirtation_score": 0.41113853
},
"Bernoulli_2023_Oct/cp_Sec8_app_balance.tex": {
"toxicity_score": 0.012943043,
"severe_toxicity_score": 0.0009965897,
"identity_attack_score": 0.004088263,
"insult_score": 0.0074558756,
"profanity_score": 0.014225035,
"threat_score": 0.007754337,
"sexually_explicit_score": 0.012031343,
"flirtation_score": 0.3018022
},
"Bernoulli_2023_Oct/cp_sec2_app.tex": {
"toxicity_score": 0.008419261,
"severe_toxicity_score": 0.0007581711,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0069143726,
"profanity_score": 0.012926984,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.009141461,
"flirtation_score": 0.24008897
},
"Bernoulli_2023_Oct/cp_Sec7_uniform_app.tex": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.000910759,
"identity_attack_score": 0.004069764,
"insult_score": 0.007607877,
"profanity_score": 0.013610168,
"threat_score": 0.008052089,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.25537682
},
"Bernoulli_2023_Oct/cp_Sec7_mip_app.tex": {
"toxicity_score": 0.011435116,
"severe_toxicity_score": 0.0009822845,
"identity_attack_score": 0.004125261,
"insult_score": 0.0072658747,
"profanity_score": 0.013644327,
"threat_score": 0.0078449575,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.35193428
},
"imsart.cls": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0034408006,
"insult_score": 0.008234881,
"profanity_score": 0.016479544,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.33692676
},
"Bernoulli_2023_Oct/cp_Sec8_app_uniform.tex": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.00086307526,
"identity_attack_score": 0.004162259,
"insult_score": 0.0075888764,
"profanity_score": 0.013678487,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010085096,
"flirtation_score": 0.2745943
},
"Bernoulli_2023_Oct/imsart-nameyear.bst": {
"toxicity_score": 0.0201057,
"severe_toxicity_score": 0.0013446808,
"identity_attack_score": 0.0036997858,
"insult_score": 0.010172892,
"profanity_score": 0.018119188,
"threat_score": 0.007340072,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.32274416
},
"Bernoulli_2023_Oct/cp_Sec3_main.tex": {
"toxicity_score": 0.005089255,
"severe_toxicity_score": 0.00064373016,
"identity_attack_score": 0.0011099357,
"insult_score": 0.006135368,
"profanity_score": 0.011714331,
"threat_score": 0.006084333,
"sexually_explicit_score": 0.0087286215,
"flirtation_score": 0.38523167
},
"Bernoulli_2023_Oct/cp_Sec8_app.tex": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.00093460083,
"identity_attack_score": 0.0035147965,
"insult_score": 0.007189874,
"profanity_score": 0.014464149,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.19535722
},
"Bernoulli_2023_Oct/imsart-number.bst": {
"toxicity_score": 0.019477395,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0037552824,
"insult_score": 0.00979289,
"profanity_score": 0.018119188,
"threat_score": 0.00756015,
"sexually_explicit_score": 0.01374168,
"flirtation_score": 0.37246096
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.89463350785341,
"hash": "0f8d0f3f80073c3f",
"most_frequent_color_ratio": 91.89463350785341
}
} | [] | "algorithm" | "24b257f0-16d2-4883-8b72-8fe92b5f312a" | 1107 | medium |
|
\begin{algorithmic}[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\State Let $S_1(\alpha) = S_2(\alpha)$, solve for solution $\alpha_i'$.
\If{$\alpha_i' \geq \alpha^a*$}
\State $ \boldsymbol{J} = \boldsymbol{J}\cup \{i\}$
\EndIf
\EndFor
\State $\alpha' = min\{\alpha_i\}_{i \in \boldsymbol{J}}$.
\State The upper bound of $\alpha$ is $\alpha'$, $\alpha \in (0,\alpha')$.
\end{algorithmic}
| \begin{algorithmic}
[1]
\State $\boldsymbol{J}$ = $\emptyset$
\For{$i$ in range(0,$n-1$)}
\State $S_1(\alpha) = F_i^{-1}(1-\alpha/2)$,
\State $S_2(\alpha) = F_{i+1}^{-1}(\alpha/2)$,
\State Let $S_1(\alpha) = S_2(\alpha)$, solve for solution $\alpha_i'$.
\If{$\alpha_i' \geq \alpha^a*$}
\State $ \boldsymbol{J} = \boldsymbol{J}\cup \{i\}$
\EndIf
\EndFor
\State $\alpha' = min\{\alpha_i\}_{i \in \boldsymbol{J}}$.
\State The upper bound of $\alpha$ is $\alpha'$, $\alpha \in (0,\alpha')$.
\end{algorithmic} | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole dataset. this graph is often regarded as an approximation of a reeb graph of data. the classic mapper algorithm uses fixed interval lengths and overlapping ratios, which might fail to reveal subtle features of data, especially when the underlying structure is complex. results: in this work, we introduce a distribution guided mapper algorithm named d-mapper, that utilizes the property of the probability model and data intrinsic characteristics to generate density guided covers and provides enhanced topological features. our proposed algorithm is a probabilistic model-based approach, which could serve as an alternative to non-prababilistic ones. moreover, we introduce a metric accounting for both the quality of overlap clustering and extended persistence homology to measure the performance of mapper type algorithm. our numerical experiments indicate that the d-mapper outperforms the classical mapper algorithm in various scenarios. we also apply the d-mapper to a sars-cov-2 coronavirus rna sequences dataset to explore the topological structure of different virus variants. the results indicate that the d-mapper algorithm can reveal both vertical and horizontal evolution processes of the viruses. availability: our package is available at https://github.com/shufeige/d-mapper.",
"categories": "math.at cs.lg q-bio.qm",
"doi": "",
"created": "2024-01-19",
"updated": "",
"authors": [
"yuyang tao",
"shufei ge"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.12237"
} | "2024-03-15T07:04:57.607207" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"main.tex": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0026453468,
"insult_score": 0.0070473733,
"profanity_score": 0.012277958,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.0068413517,
"flirtation_score": 0.3889268
},
"oup-authoring-template.cls": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0026453468,
"insult_score": 0.0070473733,
"profanity_score": 0.012277958,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.0068413517,
"flirtation_score": 0.3889268
},
"cat.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.23601882881661,
"hash": "3f078780cfbf3f05",
"most_frequent_color_ratio": 92.23601882881661
}
} | [] | "algorithm" | "c587d0a2-287c-4c09-80a2-b3a423534d5a" | 504 | easy |
|
\begin{algorithmic}[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align}\label{eq1}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align}
s.t.
\begin{align}
\sum_{i=1}^n x_{i,I_k}&
= 1 && \forall k\in [m] \label{eq:2} \\
x_{i,k} &\geq 0&& \forall i\in N, \forall k\in [m] \label{eq:3}\\
\sum_{k=1}^m \sum_{j=1}^n x_{j,I_k}V_{i,j}(I_k)&\leq \frac{1}{n} && \forall i\in N \label{eq:4}
\end{align}
\begin{equation} \label{eq:5}
\begin{split}
\sum_{k=1}^m x_{i,I_k}V_{i,i}(I_k)+x_{j,I_k} V_{i,j}(I_k) & \leq\sum_{k=1}^m x_{j,I_k}V_{i,i}(I_k)+x_{i,I_k} V_{i,j}(I_k)\\ & \forall i,j \in N
\end{split}
\end{equation}
\State Return an allocation which for all $i\in N$ and $I_k\in \mathcal{J}$ allocates an $x_{i,k}$ fraction of subinterval $I_k$ to agent $i$.
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Let $\mathcal{J}=\{I_1,I_2,\ldots,I_m\}$ be the set of subintervals formed the chore $[0,1]$.
\State Solve the following linear program:
\begin{align*}
\min \quad
& \sum_{i,j =1}^{n} \sum_{k=1}^m x_{j,I_k} V_{i,j}(I_k)
\end{align*}
s.t.
\begin{align*}
\sum_{i=1}^n x_{i,I_k}&
= 1 && \forall k\in [m] \\
x_{i,k} &\geq 0&& \forall i\in N, \forall k\in [m] \\
\sum_{k=1}^m \sum_{j=1}^n x_{j,I_k}V_{i,j}(I_k)&\leq \frac{1}{n} && \forall i\in N \end{align*}
\begin{equation*}
\begin{split}
\sum_{k=1}^m x_{i,I_k}V_{i,i}(I_k)+x_{j,I_k} V_{i,j}(I_k) & \leq\sum_{k=1}^m x_{j,I_k}V_{i,i}(I_k)+x_{i,I_k} V_{i,j}(I_k)\\ & \forall i,j \in N
\end{split}
\end{equation*}
\State Return an allocation which for all $i\in N$ and $I_k\in \mathcal{J}$ allocates an $x_{i,k}$ fraction of subinterval $I_k$ to agent $i$.
\end{algorithmic} | "https://arxiv.org/src/2303.12446" | "2303.12446.tar.gz" | "2024-02-24" | {
"title": "externalities in chore division",
"id": "2303.12446",
"abstract": "the chore division problem simulates the fair division of a heterogeneous, undesirable resource among several agents. in the fair division of chores, each agent only gets the disutility from its own piece. agents may, however, also be concerned with the pieces given to other agents; these externalities naturally appear in fair division situations. we first demonstrate the generalization of the classical concepts of proportionality and envy-freeness while extending the classical model by taking externalities into account.",
"categories": "cs.gt cs.ai cs.ma",
"doi": "",
"created": "2023-03-22",
"updated": "2024-02-24",
"authors": [
"mohammad azharuddin sanpui"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.12446"
} | "2024-03-15T03:42:06.657255" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"samplepaper.tex": {
"toxicity_score": 0.012880214,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0040512653,
"insult_score": 0.007892879,
"profanity_score": 0.014976538,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.012149297,
"flirtation_score": 0.23857248
},
"llncs.cls": {
"toxicity_score": 0.0032200534,
"severe_toxicity_score": 0.0004005432,
"identity_attack_score": 0.0009619443,
"insult_score": 0.005869366,
"profanity_score": 0.009750174,
"threat_score": 0.005838364,
"sexually_explicit_score": 0.0049540824,
"flirtation_score": 0.60371745
},
"samplepaper.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"mybibliography.bib": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.00089645386,
"identity_attack_score": 0.0015169121,
"insult_score": 0.006344369,
"profanity_score": 0.012038844,
"threat_score": 0.006084333,
"sexually_explicit_score": 0.0077849864,
"flirtation_score": 0.35563844
},
"readme.txt": {
"toxicity_score": 0.015770407,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.004125261,
"insult_score": 0.010058892,
"profanity_score": 0.013268576,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.0073426575,
"flirtation_score": 0.40115044
},
"splncs04.bst": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0029598286,
"insult_score": 0.007797878,
"profanity_score": 0.015454767,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.39611804
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.58268671877204,
"hash": "ffffbdbd00ff1878",
"most_frequent_color_ratio": 89.58268671877204
}
} | [] | "algorithm" | "9d382333-1f31-427c-bc83-b3b6df5f7d1d" | 848 | medium |
|
\begin{algorithm}[htb]
\caption{GreedyMatch (Greedy metric bipartite matching)}\label{alg1}
\begin{algorithmic}[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirstSearch}(r,B)$
\State $M\gets M\cup \{r\to b\}$
\EndFor
\State \textbf{return} $M$\Comment{M is the matching}
\EndProcedure
\Procedure{BreadthFirstSearch}{$r,B$}
\For{$i=1,...,d$}
\For{$v\in Q_d$, $\|v-r\|_1=i$}
\If{$v\in B$}
\State $B\gets B\setminus{v}$
\State \textbf{return} $v$\Comment{r matches to v}
\EndIf
\EndFor
\EndFor
\EndProcedure
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[htb]
\caption{GreedyMatch (Greedy metric bipartite matching)}\begin{algorithmic}
[1]
\State Input: Two multi-sets of $n$ points $R,B$ in $Q_d$.
\State Output: A matching from $R$ to $B$.
\State$\triangleright$ The set B is shared across all threads
\Procedure{WeightedMatch}{$R,B$}
\For {$r \in R$}\Comment{All for loop statements run in parallel}
\State $b\gets\mathrm{BreadthFirstSearch}(r,B)$
\State $M\gets M\cup \{r\to b\}$
\EndFor
\State \textbf{return} $M$\Comment{M is the matching}
\EndProcedure
\Procedure{BreadthFirstSearch}{$r,B$}
\For{$i=1,...,d$}
\For{$v\in Q_d$, $\|v-r\|_1=i$}
\If{$v\in B$}
\State $B\gets B\setminus{v}$
\State \textbf{return} $v$\Comment{r matches to v}
\EndIf
\EndFor
\EndFor
\EndProcedure
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\\mathcal{s}$ approaches the limiting distribution that would have resulted by training on $\\mathcal{t}$. on the theoretical side, we prove that when the metric entropy of the input data-sets is bounded, our greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance. moreover, the algorithm is simple and scalable, and we prove bounds on the efficiency of the algorithm as well. our algorithm can deliberately introduce distribution shift to perform (soft) multi-criteria optimization. as a motivating application, we train a neural net to recognize small molecule binders to mnk2 (a map kinase, responsible for cell signaling) which are non-binders to mnk1 (a highly similar protein). we tune the algorithm's parameter so that overall change in holdout loss is negligible, but the selectivity, i.e., the fraction of top 100 mnk2 binders that are mnk1 non-binders, increases from 54\\% to 95\\%, as a result of our reweighing. of the 43 distinct small molecules predicted to be most selective from the enamine catalog, 2 small molecules were experimentally verified to be selective, i.e., they reduced the enzyme activity of mnk2 below 50\\% but not mnk1, at 10$\\mu$m -- a 5\\% success rate.",
"categories": "stat.ml cs.lg q-bio.qm",
"doi": "",
"created": "2024-01-21",
"updated": "",
"authors": [
"pratik worah"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.11562"
} | "2024-03-15T07:03:20.799704" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.00719407,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0014706649,
"insult_score": 0.0070948736,
"profanity_score": 0.014122557,
"threat_score": 0.006168481,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3015664
},
"main.tex": {
"toxicity_score": 0.012000589,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0036812867,
"insult_score": 0.0077028773,
"profanity_score": 0.015386449,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.0122672515,
"flirtation_score": 0.23052543
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.2762957
},
"neurips_2023.sty": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.003644289,
"insult_score": 0.007341875,
"profanity_score": 0.013849284,
"threat_score": 0.007637825,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.36732212
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.60778608434285,
"hash": "07079f3e0f9f9f3f",
"most_frequent_color_ratio": 93.60778608434285
}
} | [] | "algorithm" | "baca73da-9f2d-48a1-8b4a-cac54c03ef32" | 777 | medium |
|
\begin{algorithmic}[1]
\For {$k=0,1, 2, \ldots$ }
\State $\bar z^k = (1 - \rho) z^k + \rho u^k$
\State $z^{k+1/2} = \bar z^k - \eta (B(u^k) + \nabla \Psi (u^k))$,
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\label{alg_sum_vi:step5}
\Statex \ \ If $\xi^k = 0$: \label{alg3_sum_vi:step6}
\State \ \ \ $G^k = \frac{1}{p}\left(\nabla \Psi\left(z^{k+1/2}\right) - \nabla \Psi(u^k)\right)$
\Statex \ \ If $\xi^k = 1$: \label{alg_sum_vi:step9}
\Statex \ \ \ Generate an vector of indexes $\hat{\xi}_k$ according to $Q$
\State \ \ $G^k = \frac{1}{1-p}\left(B_{\hat{\xi}_k}(z^{k+1/2}) - B_{\hat{\xi}_k}(u^{k})\right)$
\State $z^{k+1} = \bar z^k - \eta \left( G^k + B(u^k)+ \nabla \Psi( u^k)\right)$
\Statex Generate $\xi^{k+1/2}= \begin{cases}
1,& \text{with prob.} ~~ 1 - \rho \\
0 ,& \text{with prob.} ~~ \rho
\end{cases},$
\State $u^{k+1} = \xi^{k+1/2} u^k + (1 - \xi^{k+1/2}) z^{k+1}$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\For {$k=0,1, 2, \ldots$ }
\State $\bar z^k = (1 - \rho) z^k + \rho u^k$
\State $z^{k+1/2} = \bar z^k - \eta (B(u^k) + \nabla \Psi (u^k))$,
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\Statex \ \ If $\xi^k = 0$: \State \ \ \ $G^k = \frac{1}{p}\left(\nabla \Psi\left(z^{k+1/2}\right) - \nabla \Psi(u^k)\right)$
\Statex \ \ If $\xi^k = 1$: \Statex \ \ \ Generate an vector of indexes $\hat{\xi}_k$ according to $Q$
\State \ \ $G^k = \frac{1}{1-p}\left(B_{\hat{\xi}_k}(z^{k+1/2}) - B_{\hat{\xi}_k}(u^{k})\right)$
\State $z^{k+1} = \bar z^k - \eta \left( G^k + B(u^k)+ \nabla \Psi( u^k)\right)$
\Statex Generate $\xi^{k+1/2}= \begin{cases}
1,& \text{with prob.} ~~ 1 - \rho \\
0 ,& \text{with prob.} ~~ \rho
\end{cases},$
\State $u^{k+1} = \xi^{k+1/2} u^k + (1 - \xi^{k+1/2}) z^{k+1}$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2106.07289" | "2106.07289.tar.gz" | "2024-01-24" | {
"title": "decentralized personalized federated learning for min-max problems",
"id": "2106.07289",
"abstract": "personalized federated learning (pfl) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data. however, existing theoretical research in this field has primarily focused on distributed optimization for minimization problems. this paper is the first to study pfl for saddle point problems encompassing a broader range of optimization problems, that require more than just solving minimization problems. in this work, we consider a recently proposed pfl setting with the mixing objective function, an approach combining the learning of a global model together with locally distributed learners. unlike most previous work, which considered only the centralized setting, we work in a more general and decentralized setup that allows us to design and analyze more practical and federated ways to connect devices to the network. we proposed new algorithms to address this problem and provide a theoretical analysis of the smooth (strongly) convex-(strongly) concave saddle point problems in stochastic and deterministic cases. numerical experiments for bilinear problems and neural networks with adversarial noise demonstrate the effectiveness of the proposed methods.",
"categories": "cs.lg cs.dc math.oc",
"doi": "",
"created": "2021-06-14",
"updated": "2024-01-24",
"authors": [
"ekaterina borodich",
"aleksandr beznosikov",
"abdurakhmon sadiev",
"vadim sushko",
"nikolay savelyev",
"martin tak\u00e1\u010d",
"alexander gasnikov"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2106.07289"
} | "2024-03-15T09:00:25.016199" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"bst/sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
},
"CMS.bbl": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0024603575,
"insult_score": 0.00654387,
"profanity_score": 0.012585391,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.5493274
},
"bst/sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"profanity_score": 0.015932998,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.39879754
},
"CMS.tex": {
"toxicity_score": 0.008607753,
"severe_toxicity_score": 0.0011777878,
"identity_attack_score": 0.0026453468,
"insult_score": 0.006819372,
"profanity_score": 0.01535229,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.25891307
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"bst/sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
},
"bst/sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"bst/sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"bst/sn-apacite.bst": {
"toxicity_score": 0.019351736,
"severe_toxicity_score": 0.0014781952,
"identity_attack_score": 0.004236255,
"insult_score": 0.009564889,
"profanity_score": 0.018187506,
"threat_score": 0.008103873,
"sexually_explicit_score": 0.015569973,
"flirtation_score": 0.39166427
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.08804600541373,
"hash": "0fc3a39f80c1a33b",
"most_frequent_color_ratio": 92.08804600541373
}
} | [] | "algorithm" | "a9da996f-33bd-423b-a12b-67b1ad1b5d86" | 914 | medium |
|
\begin{algorithmic}[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B_{Y,k} \gets (Y_{(k-1)d + 1}, \ldots, Y_{kd})$
\EndFor
\For{$k = 1, \ldots, N$}
\State $B_{X,k}^* \gets$ random element from $\{B_{X,1}, \ldots, B_{X,N}\}$ drawn with replacement
\State $B_{Y,k}^* \gets$ random element from $\{B_{Y,1}, \ldots, B_{Y,N}\}$ drawn with replacement
\EndFor
\State $\left(X_1^*, \ldots, X_{Nd}^*\right) \gets \left(B_{X,1}^*, \ldots, B_{X,N}^*\right)$
\State $\left(Y_1^*, \ldots, Y_{Nd}^*\right) \gets \left(B_{Y,1}^*, \ldots, B_{Y,N}^*\right)$
\Ensure $\left(X_1^*, \ldots, X_{Nd}^*\right)$, $\left(Y_1^*, \ldots, Y_{Nd}^*\right)$
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require $(X_1, \ldots, X_n)$, $(Y_1, \ldots, Y_n)$, $d$
\State $N \gets \lfloor n/d \rfloor$
\For{$k = 1, \ldots, N$}
\State $B_{X,k} \gets (X_{(k-1)d + 1}, \ldots, X_{kd})$
\State $B_{Y,k} \gets (Y_{(k-1)d + 1}, \ldots, Y_{kd})$
\EndFor
\For{$k = 1, \ldots, N$}
\State $B_{X,k}^* \gets$ random element from $\{B_{X,1}, \ldots, B_{X,N}\}$ drawn with replacement
\State $B_{Y,k}^* \gets$ random element from $\{B_{Y,1}, \ldots, B_{Y,N}\}$ drawn with replacement
\EndFor
\State $\left(X_1^*, \ldots, X_{Nd}^*\right) \gets \left(B_{X,1}^*, \ldots, B_{X,N}^*\right)$
\State $\left(Y_1^*, \ldots, Y_{Nd}^*\right) \gets \left(B_{Y,1}^*, \ldots, B_{Y,N}^*\right)$
\Ensure $\left(X_1^*, \ldots, X_{Nd}^*\right)$, $\left(Y_1^*, \ldots, Y_{Nd}^*\right)$
\end{algorithmic} | "https://arxiv.org/src/2112.14091" | "2112.14091.tar.gz" | "2024-02-05" | {
"title": "a bootstrap test for independence of time series based on the distance covariance",
"id": "2112.14091",
"abstract": "we present a test for independence of two strictly stationary time series based on a bootstrap procedure for the distance covariance. our test detects any kind of dependence between the two time series within an arbitrary maximum lag $l$. in simulation studies, our test outperforms alternative testing procedures. in proving the validity of the underlying bootstrap procedure, we generalise bounds for the wasserstein distance between an empirical measure and its marginal distribution under the assumption of $\\alpha$-mixing. previous results of this kind only existed for i.i.d. processes.",
"categories": "math.st stat.th",
"doi": "",
"created": "2021-12-28",
"updated": "2024-02-05",
"authors": [
"annika betken",
"herold dehling",
"marius kroll"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2112.14091"
} | "2024-03-15T04:47:15.995017" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bs.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"bs.bbl": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0037737815,
"insult_score": 0.0075128763,
"profanity_score": 0.014464149,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.011087708,
"flirtation_score": 0.1947049
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.70033877425315,
"hash": "071f3f1f0f071f3f",
"most_frequent_color_ratio": 93.70033877425315
}
} | [] | "algorithm" | "d5e20090-f057-4f3e-a8b7-1a200c18eeec" | 786 | medium |
|
\begin{algorithm}[H]
\caption{Fast and General MC for OU processes}\label{alg:simul1}
\begin{algorithmic}
\Require $X_{r}$ value of the process at time $r$, $\Delta t$ simulation horizon.
\State 1. Compute the characteristic function $\phi(\cdot)$ of the integral process.
\State 2. Retrieve the CDF $P(x)$ on the $x$-grid by FFT inversion of
$\phi(\cdot)$.
\State 3. Interpolate the obtained CDF.
\State 4. Draw a uniform random variable $U\sim\mathcal{U}(0,1)$.
\State 5. Compute $X_{t} = X_r e^{-b \Delta t} + P^{-1}(U)$.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{Fast and General MC for OU processes}\begin{algorithmic}
\Require $X_{r}$ value of the process at time $r$, $\Delta t$ simulation horizon.
\State 1. Compute the characteristic function $\phi(\cdot)$ of the integral process.
\State 2. Retrieve the CDF $P(x)$ on the $x$-grid by FFT inversion of
$\phi(\cdot)$.
\State 3. Interpolate the obtained CDF.
\State 4. Draw a uniform random variable $U\sim\mathcal{U}(0,1)$.
\State 5. Compute $X_{t} = X_r e^{-b \Delta t} + P^{-1}(U)$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.15483" | "2401.15483.tar.gz" | "2024-01-27" | {
"title": "fast and general simulation of l\\'evy-driven ou processes for energy derivatives",
"id": "2401.15483",
"abstract": "l\\'evy-driven ornstein-uhlenbeck (ou) processes represent an intriguing class of stochastic processes that have garnered interest in the energy sector for their ability to capture typical features of market dynamics. however, in the current state-of-the-art, monte carlo simulations of these processes are not straightforward for two main reasons: i) algorithms are available only for some particular processes within this class; ii) they are often computationally expensive. in this paper, we introduce a new simulation technique designed to address both challenges. it relies on the numerical inversion of the characteristic function, offering a general methodology applicable to all l\\'evy-driven ou processes. moreover, leveraging fft, the proposed methodology ensures fast and accurate simulations, providing a solid basis for the widespread adoption of these processes in the energy sector. lastly, the algorithm allows an optimal control of the numerical error. we apply the technique to the pricing of energy derivatives, comparing the results with existing benchmarks. our findings indicate that the proposed methodology is at least one order of magnitude faster than existing algorithms, all while maintaining an equivalent level of accuracy.",
"categories": "q-fin.cp q-fin.mf q-fin.pr",
"doi": "",
"created": "2024-01-27",
"updated": "",
"authors": [
"roberto baviera",
"pietro manzoni"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.15483"
} | "2024-03-15T05:20:32.272792" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
},
"Sections/section4_algorithm.tex": {
"toxicity_score": 0.010806813,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007303875,
"profanity_score": 0.014190876,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.01209032,
"flirtation_score": 0.2660793
},
"Sections/section3_models.tex": {
"toxicity_score": 0.011058134,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0036627878,
"insult_score": 0.007151874,
"profanity_score": 0.013439372,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.012031343,
"flirtation_score": 0.28067464
},
"Sections/Proofs/proofXX_fwd_dynamics.tex": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0017547607,
"identity_attack_score": 0.006030651,
"insult_score": 0.009640889,
"profanity_score": 0.01764096,
"threat_score": 0.009204263,
"sexually_explicit_score": 0.017811105,
"flirtation_score": 0.15197064
},
"Sections/Proofs/proof10_OUTS_asymptotics_finite.tex": {
"toxicity_score": 0.02072494,
"severe_toxicity_score": 0.001745224,
"identity_attack_score": 0.0037552824,
"insult_score": 0.008690883,
"profanity_score": 0.01764096,
"threat_score": 0.008686432,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.33803415
},
"Sections/Proofs/proof09_OUTS_asymptotics.tex": {
"toxicity_score": 0.043399423,
"severe_toxicity_score": 0.0023460388,
"identity_attack_score": 0.013948337,
"insult_score": 0.013668913,
"profanity_score": 0.025634222,
"threat_score": 0.012647836,
"sexually_explicit_score": 0.029842447,
"flirtation_score": 0.2370132
},
"Sections/section6_conclusion.tex": {
"toxicity_score": 0.0068799187,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.001933138,
"insult_score": 0.0064013693,
"profanity_score": 0.012141321,
"threat_score": 0.0066215824,
"sexually_explicit_score": 0.0087286215,
"flirtation_score": 0.22920927
},
"Sections/Proofs/proof13_TSClassification.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0006246567,
"identity_attack_score": 0.0032743104,
"insult_score": 0.009070885,
"profanity_score": 0.011236101,
"threat_score": 0.0068416605,
"sexually_explicit_score": 0.005189991,
"flirtation_score": 0.19382104
},
"Sections/Proofs/proof03_TSStrip.tex": {
"toxicity_score": 0.049831573,
"severe_toxicity_score": 0.003452301,
"identity_attack_score": 0.010507392,
"insult_score": 0.017163089,
"profanity_score": 0.041912504,
"threat_score": 0.011793415,
"sexually_explicit_score": 0.04115297,
"flirtation_score": 0.3451637
},
"Sections/Proofs/proof06_NTScumulants.tex": {
"toxicity_score": 0.029799197,
"severe_toxicity_score": 0.002412796,
"identity_attack_score": 0.0050317086,
"insult_score": 0.013212911,
"profanity_score": 0.02392626,
"threat_score": 0.008841781,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.40780064
},
"main.bbl": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00362579,
"insult_score": 0.007474876,
"profanity_score": 0.014327513,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.21315338
},
"Sections/Proofs/proof05_NTSStrip.tex": {
"toxicity_score": 0.04290464,
"severe_toxicity_score": 0.0029945374,
"identity_attack_score": 0.0068076057,
"insult_score": 0.015948927,
"profanity_score": 0.029596692,
"threat_score": 0.010291707,
"sexually_explicit_score": 0.029016767,
"flirtation_score": 0.35799807
},
"Sections/Proofs/proof07_NTSselfdecomposable.tex": {
"toxicity_score": 0.012503231,
"severe_toxicity_score": 0.0007581711,
"identity_attack_score": 0.0032003147,
"insult_score": 0.0077598775,
"profanity_score": 0.011970525,
"threat_score": 0.006757513,
"sexually_explicit_score": 0.0075490777,
"flirtation_score": 0.2689456
},
"my_bibliography.bib": {
"toxicity_score": 0.0071626552,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0012116798,
"insult_score": 0.0067528714,
"profanity_score": 0.013985921,
"threat_score": 0.0059613483,
"sexually_explicit_score": 0.011972365,
"flirtation_score": 0.21929571
},
"Sections/section7_appendixA.tex": {
"toxicity_score": 0.015079274,
"severe_toxicity_score": 0.0015735626,
"identity_attack_score": 0.004439743,
"insult_score": 0.008690883,
"profanity_score": 0.019007329,
"threat_score": 0.008336896,
"sexually_explicit_score": 0.01769315,
"flirtation_score": 0.2779886
},
"Sections/section3_OUNTS.tex": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0008201599,
"identity_attack_score": 0.0034777985,
"insult_score": 0.0077408776,
"profanity_score": 0.013063621,
"threat_score": 0.0068999166,
"sexually_explicit_score": 0.008905552,
"flirtation_score": 0.32246694
},
"Sections/Proofs/proof08_errorBound.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0042732526,
"insult_score": 0.008994885,
"profanity_score": 0.015318131,
"threat_score": 0.0072106146,
"sexually_explicit_score": 0.013446795,
"flirtation_score": 0.27644122
},
"Sections/section2_overview.tex": {
"toxicity_score": 0.0056233127,
"severe_toxicity_score": 0.0009870529,
"identity_attack_score": 0.0012116798,
"insult_score": 0.006182868,
"profanity_score": 0.013507691,
"threat_score": 0.006000186,
"sexually_explicit_score": 0.011205662,
"flirtation_score": 0.2955835
},
"Sections/section5_application.tex": {
"toxicity_score": 0.00713124,
"severe_toxicity_score": 0.000667572,
"identity_attack_score": 0.0023863618,
"insult_score": 0.0064108693,
"profanity_score": 0.011526455,
"threat_score": 0.0067834044,
"sexually_explicit_score": 0.008905552,
"flirtation_score": 0.33962485
},
"Sections/Proofs/proof01_OULevyStrip.tex": {
"toxicity_score": 0.10870059,
"severe_toxicity_score": 0.0051116943,
"identity_attack_score": 0.009545447,
"insult_score": 0.032468732,
"profanity_score": 0.079025,
"threat_score": 0.011819307,
"sexually_explicit_score": 0.040976297,
"flirtation_score": 0.5484948
},
"Sections/section7_appendixB.tex": {
"toxicity_score": 0.006722843,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.0022013725,
"insult_score": 0.0062588686,
"profanity_score": 0.011970525,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.008787598,
"flirtation_score": 0.22375809
},
"Sections/Proofs/proof14_NTSClassification.tex": {
"toxicity_score": 0.023317585,
"severe_toxicity_score": 0.0010871887,
"identity_attack_score": 0.005512681,
"insult_score": 0.009944891,
"profanity_score": 0.012175481,
"threat_score": 0.008505191,
"sexually_explicit_score": 0.0062810685,
"flirtation_score": 0.3284234
},
"Sections/Proofs/proof12_OUNTS_asymptotics_finite.tex": {
"toxicity_score": 0.0414203,
"severe_toxicity_score": 0.0022888184,
"identity_attack_score": 0.009915425,
"insult_score": 0.013250912,
"profanity_score": 0.024404489,
"threat_score": 0.011689849,
"sexually_explicit_score": 0.03208358,
"flirtation_score": 0.25244164
},
"Sections/section1_introduction.tex": {
"toxicity_score": 0.005874634,
"severe_toxicity_score": 0.00062942505,
"identity_attack_score": 0.0018961402,
"insult_score": 0.0062873685,
"profanity_score": 0.011782649,
"threat_score": 0.006382086,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.2959039
},
"Sections/Proofs/proof02_LevyOUStrip.tex": {
"toxicity_score": 0.024849601,
"severe_toxicity_score": 0.001783371,
"identity_attack_score": 0.005438685,
"insult_score": 0.009944891,
"profanity_score": 0.021466793,
"threat_score": 0.008505191,
"sexually_explicit_score": 0.021113826,
"flirtation_score": 0.2820806
},
"Sections/Proofs/proof11_OUNTS_asymptotics.tex": {
"toxicity_score": 0.016838523,
"severe_toxicity_score": 0.0012874603,
"identity_attack_score": 0.00466173,
"insult_score": 0.008519882,
"profanity_score": 0.017094411,
"threat_score": 0.0077284453,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.39740944
},
"Sections/section3_OUTS.tex": {
"toxicity_score": 0.009550206,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0027563404,
"insult_score": 0.0072278744,
"profanity_score": 0.012414595,
"threat_score": 0.006770459,
"sexually_explicit_score": 0.007843964,
"flirtation_score": 0.22372155
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.69055551778065,
"hash": "00071f3f0f7f7f7f",
"most_frequent_color_ratio": 92.69055551778065
}
} | [] | "algorithm" | "5c81863e-90dc-41ce-95fa-829da8dcf008" | 540 | easy |
|
\begin{algorithm}[H]
\caption{KSC MCMC Algorithm}\label{alg:ksc}
\begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and Smoother): } $\boldsymbol{h}_b \sim h|y^{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta,b-1}, \mu_{b-1}$
\State \text{Sample mixture indicators: } $s_b \sim s|y^{\ast}, \boldsymbol{h}_{b-1}$
\State \text{Sample from conjugate density $\mu$: } $\mu_b \sim \mu|y_{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta, b-1}, \boldsymbol{h}_{b-1}$
\State \text{Sample from conjugate density $\sigma^2_{\eta}$: } $\mu_b \sim \mu|y^{\ast}, s_{b-1}, \phi_{b-1}, \mu_{b-1}, \boldsymbol{h}_{b-1}$
\State \text{Sample via Metropolis-Hastings $\phi$: } $\phi_b \sim \phi|y^{\ast}, s_{b-1}, \mu_{b-1}, \sigma^{2}_{\eta, b-1}, \boldsymbol{h}_{b-1}$
\EndFor
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{KSC MCMC Algorithm} \begin{algorithmic}
\Require $s_0 = 4$, $\mu_0 = 0$, $\phi_0 = 0.95$, $\sigma^{2}_{\eta,0} = 0.02$
\For{\texttt{b in} $1:B_{draws}$}
\State \text{Sample states (Kalman Filter and Smoother): } $\boldsymbol{h}_b \sim h|y^{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta,b-1}, \mu_{b-1}$
\State \text{Sample mixture indicators: } $s_b \sim s|y^{\ast}, \boldsymbol{h}_{b-1}$
\State \text{Sample from conjugate density $\mu$: } $\mu_b \sim \mu|y_{\ast}, s_{b-1}, \phi_{b-1}, \sigma^{2}_{\eta, b-1}, \boldsymbol{h}_{b-1}$
\State \text{Sample from conjugate density $\sigma^2_{\eta}$: } $\mu_b \sim \mu|y^{\ast}, s_{b-1}, \phi_{b-1}, \mu_{b-1}, \boldsymbol{h}_{b-1}$
\State \text{Sample via Metropolis-Hastings $\phi$: } $\phi_b \sim \phi|y^{\ast}, s_{b-1}, \mu_{b-1}, \sigma^{2}_{\eta, b-1}, \boldsymbol{h}_{b-1}$
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of a stochastic volatility model. in particular, the bespoke 'off-set mixture approximation' algorithm proposed by kim, shephard, and chib (1998) is explored together with a hamiltonian monte carlo algorithm implemented through stan. the sbc analysis involves a simulation study to assess whether each sampling algorithm has the capacity to produce valid inference for the correctly specified model, while also characterising statistical efficiency through the effective sample size. results show that stan's no-u-turn sampler, an implementation of hamiltonian monte carlo, produces a well-calibrated posterior estimate while the celebrated off-set mixture approach is less efficient and poorly calibrated, though model parameterisation also plays a role. limitations and restrictions of generality are discussed.",
"categories": "stat.ap econ.em",
"doi": "",
"created": "2024-01-27",
"updated": "",
"authors": [
"benjamin wee"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.12384"
} | "2024-03-15T03:46:35.266317" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"main.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
"profanity_score": 0.015147334,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.21162485
}
},
"reason": null
}
} | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.45868688692303,
"hash": "077f1f9f9f81817f",
"most_frequent_color_ratio": 89.45868688692303
}
} | [] | "algorithm" | "c155d9ac-c686-4346-9b1a-ee4b907a3fde" | 897 | medium |
|
\begin{algorithm}
\label{fig: har_st_sh}
A hybrid slice sampling transition of hit-and-run, stepping-out and shrinkage procedure
from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure on $L_t(x,\theta)$ (line of hit-and-run on level set) has inputs $x$,
$w>0$ (step size parameter from $\mathcal{R}_{d,w}$) and outputs an interval $[L,R]$.
The shrinkage procedure has input $[L,R]$ and output $y=x+s\theta$:
\begin{enumerate}
\item Choose a level $t \sim \mathcal{U}(0,\rho(x))$;
\item Choose a direction $\theta \in S_{d-1}$ uniformly distributed;
\item Stepping-out on $L_t(x,\theta)$ with $w>0$ outputs an interval $[L,R]$:
\begin{enumerate}
\item Choose $u \sim \mathcal{U}[0,1]$. Set $L= u w$ and $R=L+w$;
\item Repeat until $t \geq \rho(x+L\theta)$, i.e. $L \not \in L_t(x,\theta)$:
\quad Set $L=L-w$;
\item Repeat until $t \geq \rho(x+R\theta)$, i.e. $R \not \in L_t(x,\theta)$:
\quad Set $R=R+w$;
\end{enumerate}
\item Shrinkage procedure with input $[L,R]$ outputs $y$:
\begin{enumerate}
\item Set $\bar{L}=L$ and $\bar{R}=R$;
\item Repeat:
\begin{enumerate}
\item Choose $v\sim \mathcal{U}[0,1]$ and set $s=\bar{L}+ v (\bar{R}-\bar{L})$;
\item If $s \in L_t(x,\theta)$ return $y=x+s\theta$ and exit the loop;
\item If $s<0$ then set $\bar{L}=s$, else $\bar{R}=s$.
\end{enumerate}
\end{enumerate}
\end{enumerate}
\end{algorithm}
| \begin{algorithm}
A hybrid slice sampling transition of hit-and-run, stepping-out and shrinkage procedure
from $x$ to $y$, i.e. input $x$ and output $y$.
The stepping-out procedure on $L_t(x,\theta)$ (line of hit-and-run on level set) has inputs $x$,
$w>0$ (step size parameter from $\mathcal{R}_{d,w}$) and outputs an interval $[L,R]$.
The shrinkage procedure has input $[L,R]$ and output $y=x+s\theta$:
\begin{enumerate}
\item Choose a level $t \sim \mathcal{U}(0,\rho(x))$;
\item Choose a direction $\theta \in S_{d-1}$ uniformly distributed;
\item Stepping-out on $L_t(x,\theta)$ with $w>0$ outputs an interval $[L,R]$:
\begin{enumerate}
\item Choose $u \sim \mathcal{U}[0,1]$. Set $L= u w$ and $R=L+w$;
\item Repeat until $t \geq \rho(x+L\theta)$, i.e. $L \not \in L_t(x,\theta)$:
\quad Set $L=L-w$;
\item Repeat until $t \geq \rho(x+R\theta)$, i.e. $R \not \in L_t(x,\theta)$:
\quad Set $R=R+w$;
\end{enumerate}
\item Shrinkage procedure with input $[L,R]$ outputs $y$:
\begin{enumerate}
\item Set $\bar{L}=L$ and $\bar{R}=R$;
\item Repeat:
\begin{enumerate}
\item Choose $v\sim \mathcal{U}[0,1]$ and set $s=\bar{L}+ v (\bar{R}-\bar{L})$;
\item If $s \in L_t(x,\theta)$ return $y=x+s\theta$ and exit the loop;
\item If $s<0$ then set $\bar{L}=s$, else $\bar{R}=s$.
\end{enumerate}
\end{enumerate}
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/1409.2709" | "1409.2709.tar.gz" | "2024-02-09" | {
"title": "convergence of hybrid slice sampling via spectral gap",
"id": "1409.2709",
"abstract": "it is known that the simple slice sampler has robust convergence properties, however the class of problems where it can be implemented is limited. in contrast, we consider hybrid slice samplers which are easily implementable and where another markov chain approximately samples the uniform distribution on each slice. under appropriate assumptions on the markov chain on the slice we show a lower bound and an upper bound of the spectral gap of the hybrid slice sampler in terms of the spectral gap of the simple slice sampler. an immediate consequence of this is that spectral gap and geometric ergodicity of the hybrid slice sampler can be concluded from spectral gap and geometric ergodicity of its simple version which is very well understood. these results indicate that robustness properties of the simple slice sampler are inherited by (appropriately designed) easily implementable hybrid versions. we apply the developed theory and analyse a number of specific algorithms such as the stepping-out shrinkage slice sampling, hit-and-run slice sampling on a class of multivariate targets and an easily implementable combination of both procedures on multidimensional bimodal densities.",
"categories": "stat.me",
"doi": "",
"created": "2014-09-09",
"updated": "2024-02-09",
"authors": [
"krzysztof \u0142atuszy\u0144ski",
"daniel rudolf"
],
"affiliation": [],
"url": "https://arxiv.org/abs/1409.2709"
} | "2024-03-15T06:20:58.276849" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Lat_Ru_hybrid.tex": {
"toxicity_score": 0.010618322,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.0034408006,
"insult_score": 0.007360875,
"profanity_score": 0.0147374235,
"threat_score": 0.0072753434,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.25413388
},
"aptpub.cls": {
"toxicity_score": 0.010869644,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0030338243,
"insult_score": 0.0072658747,
"profanity_score": 0.015488927,
"threat_score": 0.007340072,
"sexually_explicit_score": 0.013918612,
"flirtation_score": 0.35688722
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.25828970331588,
"hash": "000f9f83839fbfc3",
"most_frequent_color_ratio": 91.25828970331588
}
} | [] | "algorithm" | "5a9e83a1-357e-412a-898b-4adb7ba6e1f2" | 1334 | hard |
|
\begin{algorithm}[H]
\caption{APO Spread Trading Strategy Algorithm}\label{algo2}
\begin{algorithmic}[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$
\EndProcedure
\State
\Procedure{Initialize}{fast, slow}
\State hedge$\_$ratio $\gets$ \Call{Compute Hedge Ratio}{$S_1$, $S_2$}
\State Calculate spread $\gets$ $S_1$$-$hedge$\_$ratio $\times$ $S_2$
\State fast$\_$ema $\gets$ \Call{EMA}{spread, fast}
\State slow$\_$ema $\gets$ \Call{EMA}{spread, slow}
\State apo$\_$spread $\gets$ fast$\_$ema$-$slow$\_$ema
\State position $\gets 0$
\EndProcedure
\State
\Procedure{Next}{buy threshold, sell threshold}
\State \textbf{if}{~apo$\_$spread $<$ buy$\_$threshold} \textbf{then}
\State \hspace{1cm} Execute Buy for $S_1$ and Sell Short for $S_2$
\State \textbf{else if}{apo$\_$spread $>$ sell$\_$threshold} \textbf{then}
\State \hspace{1cm} Execute Sell Short for $S_1$ and Buy for $S_2$
\State \textbf{end if}
\EndProcedure
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{APO Spread Trading Strategy Algorithm}\begin{algorithmic}
[1]
\State \textbf{Input:} Stock data for two assets $S_1$ and $S_2$, buy threshold, sell threshold
\State \textbf{Output:} Trade signals for pairs trading
\State
\Procedure{Compute Hedge Ratio}{data1, data2}
\State model $\gets$ perform OLS regression (data1, data2)
\State \Return model.params$[1]$
\EndProcedure
\State
\Procedure{Initialize}{fast, slow}
\State hedge$\_$ratio $\gets$ \Call{Compute Hedge Ratio}{$S_1$, $S_2$}
\State Calculate spread $\gets$ $S_1$$-$hedge$\_$ratio $\times$ $S_2$
\State fast$\_$ema $\gets$ \Call{EMA}{spread, fast}
\State slow$\_$ema $\gets$ \Call{EMA}{spread, slow}
\State apo$\_$spread $\gets$ fast$\_$ema$-$slow$\_$ema
\State position $\gets 0$
\EndProcedure
\State
\Procedure{Next}{buy threshold, sell threshold}
\State \textbf{if}{~apo$\_$spread $<$ buy$\_$threshold} \textbf{then}
\State \hspace{1cm} Execute Buy for $S_1$ and Sell Short for $S_2$
\State \textbf{else if}{apo$\_$spread $>$ sell$\_$threshold} \textbf{then}
\State \hspace{1cm} Execute Sell Short for $S_1$ and Buy for $S_2$
\State \textbf{end if}
\EndProcedure
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.14761" | "2401.14761.tar.gz" | "2024-01-26" | {
"title": "esg driven pairs algorithm for sustainable trading: analysis from the indian market",
"id": "2401.14761",
"abstract": "this paper proposes an algorithmic trading framework integrating environmental, social, and governance (esg) ratings with a pairs trading strategy. it addresses the demand for socially responsible investment solutions by developing a unique algorithm blending esg data with methods for identifying co-integrated stocks. this allows selecting profitable pairs adhering to esg principles. further, it incorporates technical indicators for optimal trade execution within this sustainability framework. extensive back-testing provides evidence of the model's effectiveness, consistently generating positive returns exceeding conventional pairs trading strategies, while upholding esg principles. this paves the way for a transformative approach to algorithmic trading, offering insights for investors, policymakers, and academics.",
"categories": "q-fin.tr",
"doi": "",
"created": "2024-01-26",
"updated": "",
"authors": [
"eeshaan dutta",
"sarthak diwan",
"siddhartha p. chakrabarty"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.14761"
} | "2024-03-15T05:30:05.430403" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"bib.bib": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0023401144,
"insult_score": 0.008519882,
"profanity_score": 0.016172111,
"threat_score": 0.006822242,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.2017521
},
"ED_SD_SPC_260124.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
},
"ED_SD_SPC_260124.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 87.3581322268664,
"hash": "078f8f8f9f9f813f",
"most_frequent_color_ratio": 87.3581322268664
}
} | [] | "algorithm" | "0eaefaf6-fac6-4a93-ac15-cfe451a80c42" | 1190 | hard |
|
\begin{algorithm}[H]
\caption{Min-Max Projected Gradient Descent}\label{algo:minmax}
\begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}}\)
\State \(\hat{J} \gets J(\mathbf{w}, \sigma_{\text{test}})\)
\EndIf
\EndFor
\State Compute gradient \(\nabla_\mathbf{w} J(\mathbf{w},\sigma)\)
\State \(\mathbf{w} \gets \text{Proj}_{L_2}(\mathbf{w} - \alpha \nabla_\mathbf{w} J(\mathbf{w},\sigma))\)
\If{\(\left\|\nabla_\mathbf{w} J(\mathbf{w},\sigma) - \frac{\nabla_\mathbf{w} J(\mathbf{w},\sigma)^\top \mathbf{w}}{\|\mathbf{w}\|^2} \mathbf{w}\right\| < \beta\)}
\State terminate with \(\mathbf{w}\).
\EndIf
\State \(k\gets k+1\)
\EndWhile
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{Min-Max Projected Gradient Descent} \begin{algorithmic}
\State Initialize \(k=1, \mathbf{w}\sim Uniform(|\Phi|)\)
\While{\(k<k_{max}\)}
\State \(\hat{J}=-\infty\)
\For{\(\sigma_{\text{test}} \in [0,1]\)}
\If{\(J(\mathbf{w}, \sigma_{\text{test}}) > \hat{J}\)}
\State \(\sigma \gets \sigma_{\text{test}}\)
\State \(\hat{J} \gets J(\mathbf{w}, \sigma_{\text{test}})\)
\EndIf
\EndFor
\State Compute gradient \(\nabla_\mathbf{w} J(\mathbf{w},\sigma)\)
\State \(\mathbf{w} \gets \text{Proj}_{L_2}(\mathbf{w} - \alpha \nabla_\mathbf{w} J(\mathbf{w},\sigma))\)
\If{\(\left\|\nabla_\mathbf{w} J(\mathbf{w},\sigma) - \frac{\nabla_\mathbf{w} J(\mathbf{w},\sigma)^\top \mathbf{w}}{\|\mathbf{w}\|^2} \mathbf{w}\right\| < \beta\)}
\State terminate with \(\mathbf{w}\).
\EndIf
\State \(k\gets k+1\)
\EndWhile
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2207.06392" | "2207.06392.tar.gz" | "2024-01-25" | {
"title": "relationship design for socially-aware behavior in static games",
"id": "2207.06392",
"abstract": "autonomous agents can adopt socially-aware behaviors to reduce social costs, mimicking the way animals interact in nature and humans in society. we present a new approach to model socially-aware decision-making that includes two key elements: bounded rationality and inter-agent relationships. we capture the interagent relationships by introducing a novel model called a relationship game and encode agents' bounded rationality using quantal response equilibria. for each relationship game, we define a social cost function and formulate a mechanism design problem to optimize weights for relationships that minimize social cost at the equilibrium. we address the multiplicity of equilibria by presenting the problem in two forms: min-max and min-min, aimed respectively at minimization of the highest and lowest social costs in the equilibria. we compute the quantal response equilibrium by solving a least-squares problem defined with its karush-kuhn-tucker conditions, and propose two projected gradient descent algorithms to solve the mechanism design problems. numerical results, including two-lane congestion and congestion with an ambulance, confirm that these algorithms consistently reach the equilibrium with the intended social costs.",
"categories": "cs.ma cs.sy eess.sy",
"doi": "",
"created": "2022-07-13",
"updated": "2024-01-25",
"authors": [
"shenghui chen",
"yigit e. bayiz",
"david fridovich-keil",
"ufuk topcu"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.06392"
} | "2024-03-15T08:38:27.674079" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sections/3_preliminaries.tex": {
"toxicity_score": 0.007382561,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020071338,
"insult_score": 0.0063918694,
"profanity_score": 0.012551232,
"threat_score": 0.0067057298,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.3320499
},
"eqs_data/eqs.dat": {
"toxicity_score": 0.010806813,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007778878,
"profanity_score": 0.013985921,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.010733845,
"flirtation_score": 0.17414871
},
"sections/appendix_inverse.tex": {
"toxicity_score": 0.003644158,
"severe_toxicity_score": 0.0003695488,
"identity_attack_score": 0.00086020015,
"insult_score": 0.0059453663,
"profanity_score": 0.009562299,
"threat_score": 0.005647414,
"sexually_explicit_score": 0.0049245935,
"flirtation_score": 0.24922791
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"convergence_data/bee_queen_0.3.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"sections/2_related.tex": {
"toxicity_score": 0.005937464,
"severe_toxicity_score": 0.0005841255,
"identity_attack_score": 0.0017851467,
"insult_score": 0.0062208683,
"profanity_score": 0.011697251,
"threat_score": 0.00636914,
"sexually_explicit_score": 0.008315781,
"flirtation_score": 0.2784857
},
"convergence_data/congestion_0.3.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"sections/5_method.tex": {
"toxicity_score": 0.008482091,
"severe_toxicity_score": 0.00066280365,
"identity_attack_score": 0.0029043318,
"insult_score": 0.006667371,
"profanity_score": 0.012551232,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.18856962
},
"convergence_data/congestion_0.5.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"sections/1_introduction.tex": {
"toxicity_score": 0.006754258,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0064393696,
"profanity_score": 0.012243799,
"threat_score": 0.0065180166,
"sexually_explicit_score": 0.009318393,
"flirtation_score": 0.27036667
},
"sections/4_problem.tex": {
"toxicity_score": 0.014890783,
"severe_toxicity_score": 0.0007915497,
"identity_attack_score": 0.005105704,
"insult_score": 0.007778878,
"profanity_score": 0.013234417,
"threat_score": 0.007081157,
"sexually_explicit_score": 0.013210886,
"flirtation_score": 0.24914093
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"sn-article.bbl": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0024603575,
"insult_score": 0.00654387,
"profanity_score": 0.012585391,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.5493274
},
"convergence_data/bee_queen_0.7.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"eqs_data/eqs_max.dat": {
"toxicity_score": 0.010806813,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007778878,
"profanity_score": 0.013985921,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.010733845,
"flirtation_score": 0.17414871
},
"ref.bib": {
"toxicity_score": 0.00719407,
"severe_toxicity_score": 0.001206398,
"identity_attack_score": 0.0013874196,
"insult_score": 0.0066863713,
"profanity_score": 0.013780965,
"threat_score": 0.006045496,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.33316135
},
"convergence_data/congestion_0.7.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"sn-article.tex": {
"toxicity_score": 0.0046494426,
"severe_toxicity_score": 0.00053167343,
"identity_attack_score": 0.001415168,
"insult_score": 0.006011867,
"profanity_score": 0.010569996,
"threat_score": 0.0062396824,
"sexually_explicit_score": 0.006605443,
"flirtation_score": 0.2874154
},
"eqs_data/eqs_equals.dat": {
"toxicity_score": 0.010806813,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007778878,
"profanity_score": 0.013985921,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.010733845,
"flirtation_score": 0.17414871
},
"convergence_data/bee_queen_0.5.dat": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0019608864,
"insult_score": 0.00808288,
"profanity_score": 0.013063621,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.30249768
},
"sections/6_numerical.tex": {
"toxicity_score": 0.0062516155,
"severe_toxicity_score": 0.0005722046,
"identity_attack_score": 0.0020256327,
"insult_score": 0.006296869,
"profanity_score": 0.011389818,
"threat_score": 0.006356194,
"sexually_explicit_score": 0.007961918,
"flirtation_score": 0.32268935
},
"eqs_data/eqs_min.dat": {
"toxicity_score": 0.010806813,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007778878,
"profanity_score": 0.013985921,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.010733845,
"flirtation_score": 0.17414871
},
"sections/7_conclusion.tex": {
"toxicity_score": 0.006440107,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0016186562,
"insult_score": 0.006315869,
"profanity_score": 0.011816809,
"threat_score": 0.006427396,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.22920927
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.73401572466993,
"hash": "0707821f07bf9f3f",
"most_frequent_color_ratio": 92.73401572466993
}
} | [] | "algorithm" | "d36e874d-03df-4d5a-9231-43b5899e94c2" | 858 | medium |
|
\begin{algorithmic}
\Require First stage iteration number $n,K \geq 0$, Second stage iteration number $T \geq 0$, Starting point $x_1 \in \mathcal{X}$, algorithm $\mathcal{A}$
\State Consider initial start point: $x_{1}^{0}= x_1$
\For{$ 1 \leq k \leq n$}
\State Run Algorithm $\mathcal{A}$ with $K$ iterations, obtain $(x_{K+1}^{k},y_{K+1}^{k} )$ as output.
\State Set the new restarting point $x_{1}^{k+1} \xleftarrow{} x_{K+1}^{k}$.
\EndFor
\State Run algorithm $\mathcal{A}$ with $T$ iterations using the last starting point $x_{1}^{n+1} = x_{K+1}^{n}$, obtain $(X_{T+1},Y_{T+1})$ and output.
\end{algorithmic}
| \begin{algorithmic}
\Require First stage iteration number $n,K \geq 0$, Second stage iteration number $T \geq 0$, Starting point $x_1 \in \mathcal{X}$, algorithm $\mathcal{A}$
\State Consider initial start point: $x_{1}^{0}= x_1$
\For{$ 1 \leq k \leq n$}
\State Run Algorithm $\mathcal{A}$ with $K$ iterations, obtain $(x_{K+1}^{k},y_{K+1}^{k} )$ as output.
\State Set the new restarting point $x_{1}^{k+1} \xleftarrow{} x_{K+1}^{k}$.
\EndFor
\State Run algorithm $\mathcal{A}$ with $T$ iterations using the last starting point $x_{1}^{n+1} = x_{K+1}^{n}$, obtain $(X_{T+1},Y_{T+1})$ and output.
\end{algorithmic} | "https://arxiv.org/src/2211.01758" | "2211.01758.tar.gz" | "2024-01-23" | {
"title": "optimal algorithms for stochastic complementary composite minimization",
"id": "2211.01758",
"abstract": "inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. this problem corresponds to the minimization of the sum of a (weakly) smooth function endowed with a stochastic first-order oracle, and a structured uniformly convex (possibly nonsmooth and non-lipschitz) regularization term. despite intensive work on closely related settings, prior to our work no complexity bounds for this problem were known. we close this gap by providing novel excess risk bounds, both in expectation and with high probability. our algorithms are nearly optimal, which we prove via novel lower complexity bounds for this class of problems. we conclude by providing numerical results comparing our methods to the state of the art.",
"categories": "cs.lg math.oc",
"doi": "",
"created": "2022-11-03",
"updated": "2024-01-23",
"authors": [
"alexandre d'aspremont",
"crist\u00f3bal guzm\u00e1n",
"cl\u00e9ment lezane"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2211.01758"
} | "2024-03-15T05:49:42.262116" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"data.dat": {
"toxicity_score": 0.031331215,
"severe_toxicity_score": 0.004119873,
"identity_attack_score": 0.0039587705,
"insult_score": 0.012642908,
"profanity_score": 0.031140719,
"threat_score": 0.008815889,
"sexually_explicit_score": 0.032555398,
"flirtation_score": 0.34966603
},
"references.bib": {
"toxicity_score": 0.016210219,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.001951637,
"insult_score": 0.010286893,
"profanity_score": 0.017777596,
"threat_score": 0.0066733654,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.22761779
},
"siamplain.bst": {
"toxicity_score": 0.102626406,
"severe_toxicity_score": 0.005607605,
"identity_attack_score": 0.030949092,
"insult_score": 0.025284592,
"profanity_score": 0.070942834,
"threat_score": 0.02181799,
"sexually_explicit_score": 0.055640165,
"flirtation_score": 0.2839845
},
"siamart220329.cls": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0010681152,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007645877,
"profanity_score": 0.014361672,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.010026119,
"flirtation_score": 0.4152329
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"main.tex": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0037737815,
"insult_score": 0.0075698765,
"profanity_score": 0.015079016,
"threat_score": 0.0074436385,
"sexually_explicit_score": 0.012031343,
"flirtation_score": 0.24179913
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.13502673796792,
"hash": "013f0f00000f7f3f",
"most_frequent_color_ratio": 92.13502673796792
}
} | [] | "algorithm" | "3caacf00-1183-4dd6-a7a4-0b46022efd04" | 613 | easy |
|
\begin{algorithmic}[1]\label{alg}
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=Est(u,w)$
\EndFor
\State $x_u,x_v \gets $ middle vertices of $Int'(u,v)$.
\For{$w \not\in Int'(u,v)$}
\If{$Est(x_u,w)>Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)+Est(x_u,w)$
\EndIf
\If{$Est(x_u,w)<Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)-Est(x_u,w)$
\EndIf
\EndFor
\Return
\EndIf
\State Return $Emb$
\EndFor
\State Return FALSE
\end{algorithmic}
| \begin{algorithmic}[1]
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=Est(u,w)$
\EndFor
\State $x_u,x_v \gets $ middle vertices of $Int'(u,v)$.
\For{$w \not\in Int'(u,v)$}
\If{$Est(x_u,w)>Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)+Est(x_u,w)$
\EndIf
\If{$Est(x_u,w)<Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)-Est(x_u,w)$
\EndIf
\EndFor
\Return
\EndIf
\State Return $Emb$
\EndFor
\State Return FALSE
\end{algorithmic} | "https://arxiv.org/src/2208.13855" | "2208.13855.tar.gz" | "2024-01-27" | {
"title": "determining a points configuration on the line from a subset of the pairwise distances",
"id": "2208.13855",
"abstract": "we investigate rigidity-type problems on the real line and the circle in the non-generic setting. specifically, we consider the problem of uniquely determining the positions of $n$ distinct points $v = {v_1, \\ldots, v_n}$ given a set of mutual distances $\\mathcal{p} \\subseteq {v \\choose 2}$. we establish an extremal result: if $|\\mathcal{p}| = \\omega(n^{3/2})$, then the positions of a large subset $v' \\subseteq v$, where large means $|v'| = \\omega(\\frac{|\\mathcal{p}|}{n})$, can be uniquely determined up to isometry. as a main ingredient in the proof, which may be of independent interest, we show that dense graphs $g=(v,e)$ for which every two non-adjacent vertices have only a few common neighbours must have large cliques. furthermore, we examine the problem of reconstructing $v$ from a random distance set $\\mathcal{p}$. we establish that if the distance between each pair of points is known independently with probability $p = \\frac{c \\ln(n)}{n}$ for some universal constant $c > 0$, then $v$ can be reconstructed from the distances with high probability. we provide a randomized algorithm with linear expected running time that returns the correct embedding of $v$ to the line with high probability. since we posted a preliminary version of the paper on arxiv, follow-up works have improved upon our results in the random setting. gir\\~ao, illingworth, michel, powierski, and scott proved a hitting time result for the first moment at which an time at which one can reconstruct $v$ when $\\mathcal{p}$ is revealed using the erd\\\"os--r\\'enyi evolution, our extremal result lies in the heart of their argument. montgomery, nenadov and szab\\'o resolved a conjecture we posed in a previous version and proved that w.h.p a graph sampled from the erd\\\"os--r\\'enyi evolution becomes globally rigid in $\\mathbb{r}$ at the moment it's minimum degree is $2$.",
"categories": "math.mg math.co math.pr",
"doi": "",
"created": "2022-08-29",
"updated": "2024-01-27",
"authors": [
"itai benjamini",
"elad tzalik"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.13855"
} | "2024-03-15T05:18:52.114805" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"ref.bib": {
"toxicity_score": 0.009298885,
"severe_toxicity_score": 0.0009775162,
"identity_attack_score": 0.0027378413,
"insult_score": 0.007493876,
"profanity_score": 0.014361672,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.20229049
},
"main.tex": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0033298072,
"insult_score": 0.0072658747,
"profanity_score": 0.014839902,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.25617662
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.80473599164607,
"hash": "0387c7c1c0c08f1f",
"most_frequent_color_ratio": 91.80473599164607
}
} | [] | "algorithm" | "80d5754c-08fa-427e-bebe-7c8baa170c0e" | 594 | easy |
|
\begin{algorithm}[H]
\centering
\caption{Top-two Thompson sampling (TTTS) with cost-aware selection rule}\label{alg:ttts}
\begin{algorithmic}[1]
\State {\bf Input:} History $\mathcal{H}_t$
\State Sample $I_t^{(1)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Repeat
\State Sample $I_t^{(2)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Until{$I_t^{(2)} \neq I_t^{(1)}$}
\State Determine coin bias $h_t$ via~\eqref{eq:cost-aware-IDS}.
\State \Return $I_t^{(1)}$ w/ prob $h_t$, $I_{t}^{(2)}$ otherwise.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}[H]
\centering
\caption{Top-two Thompson sampling (TTTS) with cost-aware selection rule} \begin{algorithmic}
[1]
\State {\bf Input:} History $\mathcal{H}_t$
\State Sample $I_t^{(1)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Repeat
\State Sample $I_t^{(2)} \sim \mathrm{TS}(\mathcal{H}_t)$ using Algorithm \ref{alg:ts}
\Until{$I_t^{(2)} \neq I_t^{(1)}$}
\State Determine coin bias $h_t$ via~\eqref{eq:cost-aware-IDS}.
\State \Return $I_t^{(1)}$ w/ prob $h_t$, $I_{t}^{(2)}$ otherwise.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.10592" | "2402.10592.tar.gz" | "2024-02-16" | {
"title": "optimizing adaptive experiments: a unified approach to regret minimization and best-arm identification",
"id": "2402.10592",
"abstract": "practitioners conducting adaptive experiments often encounter two competing priorities: reducing the cost of experimentation by effectively assigning treatments during the experiment itself, and gathering information swiftly to conclude the experiment and implement a treatment across the population. currently, the literature is divided, with studies on regret minimization addressing the former priority in isolation, and research on best-arm identification focusing solely on the latter. this paper proposes a unified model that accounts for both within-experiment performance and post-experiment outcomes. we then provide a sharp theory of optimal performance in large populations that unifies canonical results in the literature. this unification also uncovers novel insights. for example, the theory reveals that familiar algorithms, like the recently proposed top-two thompson sampling algorithm, can be adapted to optimize a broad class of objectives by simply adjusting a single scalar parameter. in addition, the theory reveals that enormous reductions in experiment duration can sometimes be achieved with minimal impact on both within-experiment and post-experiment regret.",
"categories": "cs.lg econ.em stat.ml",
"doi": "",
"created": "2024-02-16",
"updated": "",
"authors": [
"chao qin",
"daniel russo"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.10592"
} | "2024-03-15T03:58:07.605156" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"references.bib": {
"toxicity_score": 0.008544922,
"severe_toxicity_score": 0.0015830994,
"identity_attack_score": 0.0014706649,
"insult_score": 0.007303875,
"profanity_score": 0.016752819,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.31677276
},
"app_games_and_equilibrium_structures.tex": {
"toxicity_score": 0.012817383,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.00466173,
"insult_score": 0.007531876,
"profanity_score": 0.015147334,
"threat_score": 0.007689608,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.18803239
},
"app_onlyif_allocation.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.003348306,
"insult_score": 0.0070378734,
"profanity_score": 0.013131939,
"threat_score": 0.007081157,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.31366482
},
"app_Pareto_frontier.tex": {
"toxicity_score": 0.009613037,
"severe_toxicity_score": 0.00069618225,
"identity_attack_score": 0.003385304,
"insult_score": 0.0069048726,
"profanity_score": 0.012141321,
"threat_score": 0.006822242,
"sexually_explicit_score": 0.007726009,
"flirtation_score": 0.33392444
},
"main.tex": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0027008436,
"insult_score": 0.0073988754,
"profanity_score": 0.016752819,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.016395653,
"flirtation_score": 0.27829376
},
"app_TTTS.tex": {
"toxicity_score": 0.012943043,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0028673338,
"insult_score": 0.007721877,
"profanity_score": 0.015318131,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.20562427
},
"app_lower_bound.tex": {
"toxicity_score": 0.05255287,
"severe_toxicity_score": 0.0031471252,
"identity_attack_score": 0.014752249,
"insult_score": 0.015036922,
"profanity_score": 0.031391226,
"threat_score": 0.014048163,
"sexually_explicit_score": 0.03750948,
"flirtation_score": 0.17070149
},
"app_outline.tex": {
"toxicity_score": 0.015958898,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.00310782,
"insult_score": 0.007778878,
"profanity_score": 0.018255826,
"threat_score": 0.0078449575,
"sexually_explicit_score": 0.022175416,
"flirtation_score": 0.1978898
},
"main.bbl": {
"toxicity_score": 0.011749268,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0037182847,
"insult_score": 0.007531876,
"profanity_score": 0.014464149,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.20209788
},
"app_p_properties.tex": {
"toxicity_score": 0.0926474,
"severe_toxicity_score": 0.0049972534,
"identity_attack_score": 0.007732552,
"insult_score": 0.021546122,
"profanity_score": 0.13961408,
"threat_score": 0.008349842,
"sexually_explicit_score": 0.063413784,
"flirtation_score": 0.3840796
},
"app_iff_LaiRobbins.tex": {
"toxicity_score": 0.009110395,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0072278744,
"profanity_score": 0.012995303,
"threat_score": 0.0066733654,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.28804323
},
"app_if_allocation.tex": {
"toxicity_score": 0.009927189,
"severe_toxicity_score": 0.0007867813,
"identity_attack_score": 0.0033298072,
"insult_score": 0.0069713728,
"profanity_score": 0.012892825,
"threat_score": 0.0070034824,
"sexually_explicit_score": 0.008315781,
"flirtation_score": 0.33419365
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.64276961759417,
"hash": "003f0f7f073f0f0f",
"most_frequent_color_ratio": 91.64276961759417
}
} | [] | "algorithm" | "6413df63-72fc-4899-ac12-113004a2253b" | 557 | easy |
|
\begin{algorithmic}[1]
\Require A batch of data $\mathcal{D}$; budget $b_t$; hyper-parameters $\eta, \lambda, \beta_1, \beta_2$; final timesteps $T_\text{final}$; timesteps $T$ and $\Delta T$ for low rank approximation
\Ensure $\Delta W_k$
\For{$t = 1,...,T_{\text{final}}$}
\State Compute the binary cross-entropy loss $\mathcal{L}^t$ for a batch of data $\mathcal{D}$
\If{$t \, \% \, \Delta T = 0$ and $t<T$}
\State Compute the stabilized sensitivity $\bar{I}^t(w_{ij})$ via Equation~\eqref{eq:I} and uncertainty $\bar{U}^t(w_{ij})$ via Equation~\eqref{eq:U}
\State Compute $S_{k_i}$ for all $k$ and $i$ via Equation~\eqref{eq:S}
\State Update $ P_k^{t} = P_k^{t-1} - \eta \nabla_{P_k}\mathcal{L}^t - \lambda P_k^{t-1} $
\State Update $ Q_k^{t} = Q_k^{t-1} - \eta \nabla_{Q_k}\mathcal{L}^t - \lambda Q_k^{t-1} $
\State Update $\Lambda_{k,ii}^t = \Lambda_k^{t-1} - \eta \nabla_{\Lambda_k}\mathcal{L}^t - \lambda \Lambda_k^{t-1}$
\State Update $\hat{\Lambda}_{k,ii}^{t}$ via Equation~\eqref{eq:rs}
\EndIf
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require A batch of data $\mathcal{D}$; budget $b_t$; hyper-parameters $\eta, \lambda, \beta_1, \beta_2$; final timesteps $T_\text{final}$; timesteps $T$ and $\Delta T$ for low rank approximation
\Ensure $\Delta W_k$
\For{$t = 1,...,T_{\text{final}}$}
\State Compute the binary cross-entropy loss $\mathcal{L}^t$ for a batch of data $\mathcal{D}$
\If{$t \, \% \, \Delta T = 0$ and $t<T$}
\State Compute the stabilized sensitivity $\bar{I}^t(w_{ij})$ via Equation~\eqref{eq:I} and uncertainty $\bar{U}^t(w_{ij})$ via Equation~\eqref{eq:U}
\State Compute $S_{k_i}$ for all $k$ and $i$ via Equation~\eqref{eq:S}
\State Update $ P_k^{t} = P_k^{t-1} - \eta \nabla_{P_k}\mathcal{L}^t - \lambda P_k^{t-1} $
\State Update $ Q_k^{t} = Q_k^{t-1} - \eta \nabla_{Q_k}\mathcal{L}^t - \lambda Q_k^{t-1} $
\State Update $\Lambda_{k,ii}^t = \Lambda_k^{t-1} - \eta \nabla_{\Lambda_k}\mathcal{L}^t - \lambda \Lambda_k^{t-1}$
\State Update $\hat{\Lambda}_{k,ii}^{t}$ via Equation~\eqref{eq:rs}
\EndIf
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2402.08075" | "2402.08075.tar.gz" | "2024-02-12" | {
"title": "efficient and scalable fine-tune of language models for genome understanding",
"id": "2402.08075",
"abstract": "although dna foundation models have advanced the understanding of genomes, they still face significant challenges in the limited scale and diversity of genomic data. this limitation starkly contrasts with the success of natural language foundation models, which thrive on substantially larger scales. furthermore, genome understanding involves numerous downstream genome annotation tasks with inherent data heterogeneity, thereby necessitating more efficient and robust fine-tuning methods tailored for genomics. here, we present \\textsc{lingo}: \\textsc{l}anguage prefix f\\textsc{in}e-tuning for \\textsc{g}en\\textsc{o}mes. unlike dna foundation models, \\textsc{lingo} strategically leverages natural language foundation models' contextual cues, recalibrating their linguistic knowledge to genomic sequences. \\textsc{lingo} further accommodates numerous, heterogeneous downstream fine-tune tasks by an adaptive rank sampling method that prunes and stochastically reintroduces pruned singular vectors within small computational budgets. adaptive rank sampling outperformed existing fine-tuning methods on all benchmarked 14 genome understanding tasks, while requiring fewer than 2\\% of trainable parameters as genomic-specific adapters. impressively, applying these adapters on natural language foundation models matched or even exceeded the performance of dna foundation models. \\textsc{lingo} presents a new paradigm of efficient and scalable genome understanding via genomic-specific adapters on language models.",
"categories": "q-bio.gn cs.ai cs.lg",
"doi": "",
"created": "2024-02-12",
"updated": "",
"authors": [
"huixin zhan",
"ying nian wu",
"zijun zhang"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.08075"
} | "2024-03-15T04:39:36.010097" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"profanity_score": 0.015932998,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.39879754
},
"sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
},
"sn-article.bbl": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0024603575,
"insult_score": 0.00654387,
"profanity_score": 0.012585391,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.5493274
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"sn-apacite.bst": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0035332954,
"insult_score": 0.0105148945,
"profanity_score": 0.019143965,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.39166427
},
"sn-article.tex": {
"toxicity_score": 0.0046494426,
"severe_toxicity_score": 0.00053167343,
"identity_attack_score": 0.001415168,
"insult_score": 0.006011867,
"profanity_score": 0.010569996,
"threat_score": 0.0062396824,
"sexually_explicit_score": 0.006605443,
"flirtation_score": 0.2874154
},
"fig.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"sn-bibliography.bib": {
"toxicity_score": 0.008105109,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0015354111,
"insult_score": 0.007009373,
"profanity_score": 0.0138151245,
"threat_score": 0.006136116,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.27073145
},
"sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"empty.eps": {
"toxicity_score": 0.007885204,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0067908717,
"profanity_score": 0.013268576,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.45258012
}
},
"reason": null
}
} | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.2913975618145,
"hash": "003b1f8d87878f3f",
"most_frequent_color_ratio": 90.2913975618145
}
} | [] | "algorithm" | "bd9ea06c-0162-4902-a4a3-c71195e18bab" | 1031 | medium |
|
\begin{algorithmic}[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Set $\mathcal{D}_{\mbox{\scriptsize{train}}} = ({\boldsymbol{\theta}}_n, \mathbf{x}_n)^N_{n=1} $
\State Train $\mathcal{F}_{\phi}(\mathbf{x})$ on $\mathcal{D}_{\mbox{\scriptsize{train}}} \bigcup \mathcal{D}$ and obtain $\hat{\boldsymbol{\theta}}_0$ from $\mathcal{F}_{\hat{\phi}}(\mathbf{x}_0)$
\State Simulate $\mathbf{x}_b \sim p(;\hat{\boldsymbol{\theta}}_0)$ and obtain $\hat{\boldsymbol{\theta}}_{b}$ from $\mathcal{F}_{\hat{\phi}}(\mathbf{x}_b), b = 1, \ldots, B$
\State $a_{1,p} = \hat{\theta}_{0,p} + \mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{p}) - \mathcal{Q}^{0.05}_p(\hat{\theta}_{0,p} - \theta^1_{p}, \ldots, \hat{\theta}_{0,p} -\theta^B_p)$
\State $a_{2,p} = \hat{\theta}_{0,p} + \mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{p}) + \mathcal{Q}^{0.975}_p(\hat{\theta}_{0,p} - \theta^1_{p}, \ldots, \hat{\theta}_{0,p} -\theta^B_p)$
\State Randomly select a subset of $\mathcal{D}_{\mbox{\scriptsize{train}}}$ such that $\mathcal{D}_{\mbox{\scriptsize{train}}} \cap \mathcal{D} = \emptyset$ and add those into $\mathcal{D}$
\EndWhile
\end{algorithmic}
| \begin{algorithmic}
[1]
\While{$\mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{b,p}) > 0.3 \times S_{p}$, for all $p$}
\State Sample $\theta_{n,p} \sim \mbox{Unif}(a_{1,p}, a_{2,p}), n = 1, \ldots, N$
\State Simulate $\mathbf{x}_n \sim p(;\boldsymbol{\theta}_n), n = 1, \ldots, N$
\State Set $\mathcal{D}_{\mbox{\scriptsize{train}}} = ({\boldsymbol{\theta}}_n, \mathbf{x}_n)^N_{n=1} $
\State Train $\mathcal{F}_{\phi}(\mathbf{x})$ on $\mathcal{D}_{\mbox{\scriptsize{train}}} \bigcup \mathcal{D}$ and obtain $\hat{\boldsymbol{\theta}}_0$ from $\mathcal{F}_{\hat{\phi}}(\mathbf{x}_0)$
\State Simulate $\mathbf{x}_b \sim p(;\hat{\boldsymbol{\theta}}_0)$ and obtain $\hat{\boldsymbol{\theta}}_{b}$ from $\mathcal{F}_{\hat{\phi}}(\mathbf{x}_b), b = 1, \ldots, B$
\State $a_{1,p} = \hat{\theta}_{0,p} + \mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{p}) - \mathcal{Q}^{0.05}_p(\hat{\theta}_{0,p} - \theta^1_{p}, \ldots, \hat{\theta}_{0,p} -\theta^B_p)$
\State $a_{2,p} = \hat{\theta}_{0,p} + \mbox{bias}(\hat{\theta}_{0,p}, \tilde{\theta}_{p}) + \mathcal{Q}^{0.975}_p(\hat{\theta}_{0,p} - \theta^1_{p}, \ldots, \hat{\theta}_{0,p} -\theta^B_p)$
\State Randomly select a subset of $\mathcal{D}_{\mbox{\scriptsize{train}}}$ such that $\mathcal{D}_{\mbox{\scriptsize{train}}} \cap \mathcal{D} = \emptyset$ and add those into $\mathcal{D}$
\EndWhile
\end{algorithmic} | "https://arxiv.org/src/2303.15041" | "2303.15041.tar.gz" | "2024-02-19" | {
"title": "towards black-box parameter estimation",
"id": "2303.15041",
"abstract": "deep learning algorithms have recently shown to be a successful tool in estimating parameters of statistical models for which simulation is easy, but likelihood computation is challenging. but the success of these approaches depends on simulating parameters that sufficiently reproduce the observed data, and, at present, there is a lack of efficient methods to produce these simulations. we develop new black-box procedures to estimate parameters of statistical models based only on weak parameter structure assumptions. for well-structured likelihoods with frequent occurrences, such as in time series, this is achieved by pre-training a deep neural network on an extensive simulated database that covers a wide range of data sizes. for other types of complex dependencies, an iterative algorithm guides simulations to the correct parameter region in multiple rounds. these approaches can successfully estimate and quantify the uncertainty of parameters from non-gaussian models with complex spatial and temporal dependencies. the success of our methods is a first step towards a fully flexible automatic black-box estimation framework.",
"categories": "stat.ml cs.lg",
"doi": "",
"created": "2023-03-27",
"updated": "2024-02-19",
"authors": [
"amanda lenzi",
"haavard rue"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.15041"
} | "2024-03-15T05:01:49.289931" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main_LenziRue_Feb2024.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"main_LenziRue_Feb2024.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.7515299877601,
"hash": "070f9f838381003f",
"most_frequent_color_ratio": 90.7515299877601
}
} | [] | "algorithm" | "d6cf30dd-83ae-48eb-85fc-7d754139c906" | 1358 | hard |
|
\begin{algorithm} [h] \caption{Dynamic covariate balancing (DCB): two periods}\label{alg:alg1}
\begin{algorithmic}[1]
\Require Observations $(D_1, X_1, Y_{1},D_2, X_2, Y_2)$, treatment history $(d_{1}, d_2)$, finite parameters $K$, constraints $\delta_1(n,p), \delta_2(n,p)$.
\State Estimate $\beta_{d_{1:2}}^1,\beta_{d_{1:2}}^2$ as in Algorithm \ref{alg:coefficients1}.
\State $\hat{\gamma}_{i,1} = 0, \text{ if } D_{i,1} \neq d_{1}, \hat{\gamma}_{i,2} = 0$ if $(D_{i,1}, D_{i,2}) \neq (d_1, d_2)$
\State Estimate
\begin{equation} \label{eqn:constraint_set1}
\small
\begin{aligned}
\hat{\gamma}_1 = \arg\min_{\gamma_1} ||\gamma_1||^2, \quad \text{s.t. } &\Big\|\bar{X}_1 - \frac{1}{n} \sum_{i=1}^n \gamma_{i,1}X_{i,1} \Big\|_{\infty} \le \delta_1(n,p),
\\
& \quad 1^\top \gamma_{1} = 1, \gamma_1 \ge 0, \| \gamma_1\| _{\infty} \le \log(n) n^{-2/3}. \\
\hat{\gamma}_2 = \arg\min_{\gamma_2} ||\gamma_2||^2, \quad \text{s.t. } & \Big\|\frac{1}{n} \sum_{i=1}^n \hat{\gamma}_{i,1}H_{i,2} - \frac{1}{n} \sum_{i=1}^n \gamma_{i,2} H_{i,2} \Big\|_{\infty} \le \delta_2(n,p),
\\
& \quad 1^\top \gamma_{2} = 1, \gamma_2 \ge 0, \| \gamma_{2}\| _{\infty} \le K \log(n) n^{-2/3}.
\end{aligned}
\end{equation}
\Return $\hat{\mu}(d_1, d_2)$ as in Equation \eqref{eqn:myestimator}.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[h] \caption{Dynamic covariate balancing (DCB): two periods} \begin{algorithmic}
[1]
\Require Observations $(D_1, X_1, Y_{1},D_2, X_2, Y_2)$, treatment history $(d_{1}, d_2)$, finite parameters $K$, constraints $\delta_1(n,p), \delta_2(n,p)$.
\State Estimate $\beta_{d_{1:2}}^1,\beta_{d_{1:2}}^2$ as in Algorithm \ref{alg:coefficients1}.
\State $\hat{\gamma}_{i,1} = 0, \text{ if } D_{i,1} \neq d_{1}, \hat{\gamma}_{i,2} = 0$ if $(D_{i,1}, D_{i,2}) \neq (d_1, d_2)$
\State Estimate
\begin{equation*}
\small
\begin{aligned}
\hat{\gamma}_1 = \arg\min_{\gamma_1} ||\gamma_1||^2, \quad \text{s.t. } &\Big\|\bar{X}_1 - \frac{1}{n} \sum_{i=1}^n \gamma_{i,1}X_{i,1} \Big\|_{\infty} \le \delta_1(n,p),
\\
& \quad 1^\top \gamma_{1} = 1, \gamma_1 \ge 0, \| \gamma_1\| _{\infty} \le \log(n) n^{-2/3}. \\
\hat{\gamma}_2 = \arg\min_{\gamma_2} ||\gamma_2||^2, \quad \text{s.t. } & \Big\|\frac{1}{n} \sum_{i=1}^n \hat{\gamma}_{i,1}H_{i,2} - \frac{1}{n} \sum_{i=1}^n \gamma_{i,2} H_{i,2} \Big\|_{\infty} \le \delta_2(n,p),
\\
& \quad 1^\top \gamma_{2} = 1, \gamma_2 \ge 0, \| \gamma_{2}\| _{\infty} \le K \log(n) n^{-2/3}.
\end{aligned}
\end{equation*}
\Return $\hat{\mu}(d_1, d_2)$ as in Equation \eqref{eqn:myestimator}.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2103.01280" | "2103.01280.tar.gz" | "2024-01-26" | {
"title": "dynamic covariate balancing: estimating treatment effects over time with potential local projections",
"id": "2103.01280",
"abstract": "this paper studies the estimation and inference of treatment histories in panel data settings when treatments change dynamically over time. we propose a method that allows for (i) treatments to be assigned dynamically over time based on high-dimensional covariates, past outcomes and treatments; (ii) outcomes and time-varying covariates to depend on treatment trajectories; (iii) heterogeneity of treatment effects. our approach recursively projects potential outcomes' expectations on past histories. it then controls the bias by balancing dynamically observable characteristics. we study the asymptotic and numerical properties of the estimator and illustrate the benefits of the procedure in an empirical application.",
"categories": "econ.em math.st stat.me stat.ml stat.th",
"doi": "",
"created": "2021-03-01",
"updated": "2024-01-26",
"authors": [
"davide viviano",
"jelena bradic"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2103.01280"
} | "2024-03-15T05:21:52.236887" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"figures/plot_lowdim1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/signal1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/plot_lowdim2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/treatment_status.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/all_T_main_text_log.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/combined_plot.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/comp_time.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main_and_appendix.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"mybibliography.bib": {
"toxicity_score": 0.016021729,
"severe_toxicity_score": 0.0028800964,
"identity_attack_score": 0.0031818156,
"insult_score": 0.008310881,
"profanity_score": 0.022149978,
"threat_score": 0.0066345283,
"sexually_explicit_score": 0.023708822,
"flirtation_score": 0.24819614
},
"figures/signal2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/overlap_acemoglu.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main_and_appendix.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
},
"figures/all_T.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/acemoglu_final2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main_and_appendix.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
},
"figures/fiscal_stimulus_1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.06014628602513,
"hash": "00072ffff7ebf987",
"most_frequent_color_ratio": 92.06014628602513
}
} | [] | "algorithm" | "620fbb6e-ea98-4de2-9c21-ea3aa40af803" | 1258 | hard |
|
\begin{algorithmic}
\State initialization: $x_i^0 \in \mathbb{R}^n$ and $z_{i}^0 = 0$
\For{$t=0, 1, \dots$}
\vspace{-3ex}
\State
\begin{subequations}\label{eq:GTA}
\begin{align}\label{eq:GTAw}
\hspace{-0.3mm} w_i^{t+1} &= w_i^t -\gamma\sum_{j\in \mathcal{N}_i}\!\! {\ell}_{ij} (w_j^{t} - \delta d_j^t) - \gamma s_i^t + \delta(d_i^{t+1} - d_i^t)
\\[.5em]%
s_i^{t+1} &= s_i^t - \! \gamma\!\sum_{j\in \mathcal{N}_i} \ell_{ij} s_j^{t} + \frac{2}{\delta}(f_i(w_i^{t+1})d_i^{t+1} -f_i(w_i^t )d_i^t)\!
\end{align}
\end{subequations}
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\State initialization: $x_i^0 \in \mathbb{R}^n$ and $z_{i}^0 = 0$
\For{$t=0, 1, \dots$}
\vspace{-3ex}
\State
\begin{subequations}
\begin{align*}
\hspace{-0.3mm} w_i^{t+1} &= w_i^t -\gamma\sum_{j\in \mathcal{N}_i}\!\! {\ell}_{ij} (w_j^{t} - \delta d_j^t) - \gamma s_i^t + \delta(d_i^{t+1} - d_i^t)
\\[.5em]%
s_i^{t+1} &= s_i^t - \! \gamma\!\sum_{j\in \mathcal{N}_i} \ell_{ij} s_j^{t} + \frac{2}{\delta}(f_i(w_i^{t+1})d_i^{t+1} -f_i(w_i^t )d_i^t)\!
\end{align*}
\end{subequations}
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2110.04234" | "2110.04234.tar.gz" | "2024-02-06" | {
"title": "extremum seeking tracking for derivative-free distributed optimization",
"id": "2110.04234",
"abstract": "in this paper, we deal with a network of agents that want to cooperatively minimize the sum of local cost functions depending on a common decision variable. we consider the challenging scenario in which objective functions are unknown and agents have only access to local measurements of their local functions. we propose a novel distributed algorithm that combines a recent gradient tracking policy with an extremum-seeking technique to estimate the global descent direction. the joint use of these two techniques results in a distributed optimization scheme that provides arbitrarily accurate solution estimates through the combination of lyapunov and averaging analysis approaches with consensus theory. we perform numerical simulations in a personalized optimization framework to corroborate the theoretical results.",
"categories": "math.oc cs.sy eess.sy",
"doi": "",
"created": "2021-10-08",
"updated": "2024-02-06",
"authors": [
"nicola mimmo",
"guido carnevale",
"andrea testa",
"giuseppe notarstefano"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2110.04234"
} | "2024-03-15T04:37:47.010213" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"third_sbm_arxiv_dexs.tex": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.00089645386,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0062683686,
"profanity_score": 0.013268576,
"threat_score": 0.006259101,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.29007968
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.06787330316742,
"hash": "0f3f80e7ff80ef7f",
"most_frequent_color_ratio": 95.06787330316742
}
} | [] | "algorithm" | "087b327c-6174-4efe-90e7-c18f00613878" | 524 | easy |
|
\begin{algorithm}[!ht]\caption{Dynamic KDE, query part}\label{alg:dynamic_KDE_query}
\begin{algorithmic}[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \epsilon \in (0,1),f_{\mathsf{KDE}} \in [0,1]$}
\For{$a=1,2,\cdots,K_1$}\label{lin:first_loop}
\For{$r=1,2,\cdots,R$}\label{lin:second_loop}
\State $\mathcal{H}_{a,r}.\textsc{Recover}(q)$\label{lin:evaluate_recover}
\State $\mathcal{S} \leftarrow \mathcal{S} \cup (\mathcal{H}_{a,r}.\mathcal{R}\cap L_r) $\label{lin:choose_L_j_point}
\EndFor\label{lin:end_second_loop}
\State $\mathcal{R}_{R+1}\leftarrow$ recover points in $L_{R+1}\cap\tilde{P}_{a}$\label{lin:recover_point_J+1} \Comment{Recover by calculating $w$ directly.}
\State $\mathcal{S} \leftarrow \mathcal{S}\cup\mathcal{R}_{R+1}$\label{lin:add_point_to_S}
\For{$x_{i}\in \mathcal{S}$} \label{lin:third_loop}
\State $w_{i}\leftarrow f(x_{i},q)$
\If{$x_{i}\in L_{r}$ for some $r\in[R]$}
\State $p_{i}\leftarrow\min\{ \frac{1}{2^{r} n f_{\mathsf{KDE}} },1\}$
\ElsIf{$x_{i} \in X \setminus \bigcup_{ r \in [R] } L_{r}$}
\State $p_{i}\leftarrow\frac{1}{n}$
\EndIf
\EndFor\label{lin:end_third_loop}
\State $T_{a}\leftarrow\sum_{x_{i}\in\mathcal{S}}\frac{w_i}{p_i}$ \label{lin:output_Z_a}
\EndFor
\State \Return $\mathrm{Median}\{T_{a}\}$
\EndProcedure
\State {\bf end data structure}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}[!ht]
\caption{Dynamic KDE, query part}\begin{algorithmic}
[1]
\State {\bf data structure} \textsc{DynamicKDE} \Comment{Theorem~\ref{thm:main_result}}
\State
\Procedure{\textsc{Query}}{$q\in \mathbb{R}^d, \epsilon \in (0,1),f_{\mathsf{KDE}} \in [0,1]$}
\For{$a=1,2,\cdots,K_1$} \For{$r=1,2,\cdots,R$} \State $\mathcal{H}_{a,r}.\textsc{Recover}(q)$ \State $\mathcal{S} \leftarrow \mathcal{S} \cup (\mathcal{H}_{a,r}.\mathcal{R}\cap L_r) $ \EndFor \State $\mathcal{R}_{R+1}\leftarrow$ recover points in $L_{R+1}\cap\tilde{P}_{a}$\Comment{Recover by calculating $w$ directly.}
\State $\mathcal{S} \leftarrow \mathcal{S}\cup\mathcal{R}_{R+1}$ \For{$x_{i}\in \mathcal{S}$} \State $w_{i}\leftarrow f(x_{i},q)$
\If{$x_{i}\in L_{r}$ for some $r\in[R]$}
\State $p_{i}\leftarrow\min\{ \frac{1}{2^{r} n f_{\mathsf{KDE}} },1\}$
\ElsIf{$x_{i} \in X \setminus \bigcup_{ r \in [R] } L_{r}$}
\State $p_{i}\leftarrow\frac{1}{n}$
\EndIf
\EndFor \State $T_{a}\leftarrow\sum_{x_{i}\in\mathcal{S}}\frac{w_i}{p_i}$ \EndFor
\State \Return $\mathrm{Median}\{T_{a}\}$
\EndProcedure
\State {\bf end data structure}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2208.03915" | "2208.03915.tar.gz" | "2024-02-13" | {
"title": "dynamic maintenance of kernel density estimation data structure: from practice to theory",
"id": "2208.03915",
"abstract": "kernel density estimation (kde) stands out as a challenging task in machine learning. the problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\\{x_1, x_2, \\cdots, x_n \\} \\subset \\mathbb{r}^d$, we would like to compute $\\frac{1}{n}\\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \\in \\mathbb{r}^d$. recently, there has been a growing trend of using data structures for efficient kde. however, the proposed kde data structures focus on static settings. the robustness of kde data structures over dynamic changing data distributions is not addressed. in this work, we focus on the dynamic maintenance of kde data structures with robustness to adversarial queries. especially, we provide a theoretical framework of kde data structures. in our framework, the kde data structures only require subquadratic spaces. moreover, our data structure supports the dynamic update of the dataset in sublinear time. furthermore, we can perform adaptive queries with the potential adversary in sublinear time.",
"categories": "cs.lg stat.ml",
"doi": "",
"created": "2022-08-08",
"updated": "2024-02-13",
"authors": [
"jiehao liang",
"zhao song",
"zhaozhuo xu",
"junze yin",
"danyang zhuo"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.03915"
} | "2024-03-15T05:48:56.340651" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"preli.tex": {
"toxicity_score": 0.00609454,
"severe_toxicity_score": 0.0007247925,
"identity_attack_score": 0.0016834025,
"insult_score": 0.0062493687,
"profanity_score": 0.012073003,
"threat_score": 0.0064532873,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.29065508
},
"app_adversary.tex": {
"toxicity_score": 0.017718147,
"severe_toxicity_score": 0.001821518,
"identity_attack_score": 0.003311308,
"insult_score": 0.00806388,
"profanity_score": 0.016616182,
"threat_score": 0.008323951,
"sexually_explicit_score": 0.016159745,
"flirtation_score": 0.24439204
},
"app_correctness.tex": {
"toxicity_score": 0.0066600125,
"severe_toxicity_score": 0.0005531311,
"identity_attack_score": 0.0016464046,
"insult_score": 0.00647737,
"profanity_score": 0.010962827,
"threat_score": 0.0063238298,
"sexually_explicit_score": 0.0064285113,
"flirtation_score": 0.25106943
},
"app_data.tex": {
"toxicity_score": 0.01017851,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.003884775,
"insult_score": 0.007009373,
"profanity_score": 0.0138151245,
"threat_score": 0.007573096,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.18818803
},
"conclusion.tex": {
"toxicity_score": 0.007979449,
"severe_toxicity_score": 0.0006580353,
"identity_attack_score": 0.001655654,
"insult_score": 0.0067623714,
"profanity_score": 0.012517073,
"threat_score": 0.006382086,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.26385543
},
"abstract.tex": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.00089645386,
"identity_attack_score": 0.00414376,
"insult_score": 0.008367881,
"profanity_score": 0.013371054,
"threat_score": 0.0074565844,
"sexually_explicit_score": 0.008197826,
"flirtation_score": 0.14363346
},
"app_preli.tex": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0030523231,
"insult_score": 0.0069048726,
"profanity_score": 0.014088398,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.010792822,
"flirtation_score": 0.38172203
},
"adversary.tex": {
"toxicity_score": 0.033806015,
"severe_toxicity_score": 0.0017356873,
"identity_attack_score": 0.0065856185,
"insult_score": 0.010476895,
"profanity_score": 0.018187506,
"threat_score": 0.011249693,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.30483255
},
"app_lipschitz.tex": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.0018596649,
"identity_attack_score": 0.0013226734,
"insult_score": 0.007341875,
"profanity_score": 0.02071529,
"threat_score": 0.0060325502,
"sexually_explicit_score": 0.020759964,
"flirtation_score": 0.67361206
},
"main.tex": {
"toxicity_score": 0.022256956,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0055496786,
"insult_score": 0.010172892,
"profanity_score": 0.01982715,
"threat_score": 0.008738215,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.25408843
},
"correctness.tex": {
"toxicity_score": 0.009738698,
"severe_toxicity_score": 0.0007009506,
"identity_attack_score": 0.0026083488,
"insult_score": 0.0072848746,
"profanity_score": 0.012141321,
"threat_score": 0.006757513,
"sexually_explicit_score": 0.007961918,
"flirtation_score": 0.33721492
},
"intro.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"main.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
},
"technical.tex": {
"toxicity_score": 0.0033614214,
"severe_toxicity_score": 0.0004029274,
"identity_attack_score": 0.0008786991,
"insult_score": 0.005726865,
"profanity_score": 0.00997221,
"threat_score": 0.0057347976,
"sexually_explicit_score": 0.005337434,
"flirtation_score": 0.30908814
},
"app_technical.tex": {
"toxicity_score": 0.009864358,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0027933381,
"insult_score": 0.0072658747,
"profanity_score": 0.014532468,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.22984806
},
"ref.bib": {
"toxicity_score": 0.013445686,
"severe_toxicity_score": 0.0014781952,
"identity_attack_score": 0.0024048607,
"insult_score": 0.0081208795,
"profanity_score": 0.016616182,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.012857023,
"flirtation_score": 0.40251273
},
"data.tex": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011491776,
"identity_attack_score": 0.0044582416,
"insult_score": 0.0074368757,
"profanity_score": 0.014669105,
"threat_score": 0.007883795,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.23941755
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.26365480793542,
"hash": "041f8f1f878fbf1f",
"most_frequent_color_ratio": 94.26365480793542
}
} | [] | "algorithm" | "b3999e9e-309f-4ede-83a5-13ea8cf3e707" | 1139 | medium |
|
\begin{algorithmic}[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\State Add $(x_{i_j}, r_{i_j})$ to $C_i^*$.
\EndIf
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Sort the elements of $C_i$ by order of decreasing radius.
\ForAll{$(x_{i_j},r_{i_j}) \in C_i$}
\If{there does not exist $(x_{i_k}, r_{i_k}) \in C_i^*$ that covers $(x_{i_j}, r_{i_j})$}
\State Add $(x_{i_j}, r_{i_j})$ to $C_i^*$.
\EndIf
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2301.09734" | "2301.09734.tar.gz" | "2024-02-08" | {
"title": "topological learning in multi-class data sets",
"id": "2301.09734",
"abstract": "we specialize techniques from topological data analysis to the problem of characterizing the topological complexity (as defined in the body of the paper) of a multi-class data set. as a by-product, a topological classifier is defined that uses an open sub-covering of the data set. this sub-covering can be used to construct a simplicial complex whose topological features (e.g., betti numbers) provide information about the classification problem. we use these topological constructs to study the impact of topological complexity on learning in feedforward deep neural networks (dnns). we hypothesize that topological complexity is negatively correlated with the ability of a fully connected feedforward deep neural network to learn to classify data correctly. we evaluate our topological classification algorithm on multiple constructed and open source data sets. we also validate our hypothesis regarding the relationship between topological complexity and learning in dnn's on multiple data sets.",
"categories": "cs.lg physics.data-an",
"doi": "",
"created": "2023-01-23",
"updated": "2024-02-08",
"authors": [
"christopher griffin",
"trevor karn",
"benjamin apple"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2301.09734"
} | "2024-03-15T07:16:13.009881" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"TopologyPaper.bib": {
"toxicity_score": 0.015707577,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0058826595,
"insult_score": 0.008975885,
"profanity_score": 0.01620627,
"threat_score": 0.0077154995,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.45469284
},
"main.tex": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0013446808,
"identity_attack_score": 0.0022198714,
"insult_score": 0.008633883,
"profanity_score": 0.014532468,
"threat_score": 0.006763986,
"sexually_explicit_score": 0.008610667,
"flirtation_score": 0.30276403
},
"main.bbl": {
"toxicity_score": 0.010555492,
"severe_toxicity_score": 0.0010204315,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0073988754,
"profanity_score": 0.013849284,
"threat_score": 0.007870848,
"sexually_explicit_score": 0.010910776,
"flirtation_score": 0.512737
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.67934565487079,
"hash": "073f3f07800f8f3f",
"most_frequent_color_ratio": 89.67934565487079
}
} | [] | "algorithm" | "58f0140f-6bd1-41fb-ad1d-119bbb8b5df0" | 292 | easy |
|
\begin{algorithmic}
\If{$o \in co, co \in CO^{PC}$}
\If{$ p_o \geq p_{o^{parent}_{co}}$}
$p^{obvious}_g = p_o$\;
\Else
\For{$o' \in co$}
\If{$q_{o'} > 0$}
$p^{*}_{co} = p_{o'} + p^{*}_{co}$\
$q^{*}_{co} = q_{o'} + q^{*}_{co}$\
\EndIf
\EndFor
$p^{obvious}_g = \frac{p^{*}_{co}}{q^{*}_{co}}$\
\EndIf
\EndIf
\State $p^{obvious}_g = p_o$\
\end{algorithmic}
| \begin{algorithmic}
\If{$o \in co, co \in CO^{PC}$}
\If{$ p_o \geq p_{o^{parent}_{co}}$}
$p^{obvious}_g = p_o$\;
\Else
\For{$o' \in co$}
\If{$q_{o'} > 0$}
$p^{*}_{co} = p_{o'} + p^{*}_{co}$\
$q^{*}_{co} = q_{o'} + q^{*}_{co}$\
\EndIf
\EndFor
$p^{obvious}_g = \frac{p^{*}_{co}}{q^{*}_{co}}$\
\EndIf
\EndIf
\State $p^{obvious}_g = p_o$\
\end{algorithmic} | "https://arxiv.org/src/2402.12848" | "2402.12848.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty",
"id": "2402.12848",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, the coupling of european markets, strategic optimization of production portfolios and, finally, real-time system balancing processes. atlas was designed to simulate the various electricity markets and processes that occur from the day ahead timeframe to real-time with a high level of detail. its main aim is to capture impacts from imperfect actor coordination, evolving forecast errors and a high-level of technical constraints--both regarding different production units and the different market constraints.",
"categories": "econ.gn math.oc q-fin.ec",
"doi": "",
"created": "2024-02-20",
"updated": "",
"authors": [
"emily little",
"florent cogen",
"quentin bustarret",
"virginie dussartre",
"maxime l\u00e2asri",
"gabriel kasmi",
"marie girod",
"frederic bienvenu",
"maxime fortin",
"jean-yves bourmaud"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.12848"
} | "2024-03-15T03:29:36.738175" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Sections/OrderCreation.tex": {
"toxicity_score": 0.0062516155,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0062303683,
"profanity_score": 0.012107162,
"threat_score": 0.0064079775,
"sexually_explicit_score": 0.009141461,
"flirtation_score": 0.22277433
},
"header.tex": {
"toxicity_score": 0.019980038,
"severe_toxicity_score": 0.0017261505,
"identity_attack_score": 0.0046987277,
"insult_score": 0.009450888,
"profanity_score": 0.021603432,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.49595475
},
"Sections/OptimalDispatch.tex": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.0027378413,
"insult_score": 0.0068098716,
"profanity_score": 0.013302735,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.009908165,
"flirtation_score": 0.24522157
},
"Sections/MarketClearing.tex": {
"toxicity_score": 0.005277746,
"severe_toxicity_score": 0.00066280365,
"identity_attack_score": 0.0011746819,
"insult_score": 0.0059833666,
"profanity_score": 0.0118680475,
"threat_score": 0.005818945,
"sexually_explicit_score": 0.007726009,
"flirtation_score": 0.20037636
},
"style.tex": {
"toxicity_score": 0.0038326487,
"severe_toxicity_score": 0.00069618225,
"identity_attack_score": 0.00085557543,
"insult_score": 0.0055986145,
"profanity_score": 0.012346277,
"threat_score": 0.005754216,
"sexually_explicit_score": 0.010262027,
"flirtation_score": 0.20146786
},
"bibliography.bib": {
"toxicity_score": 0.017969469,
"severe_toxicity_score": 0.002040863,
"identity_attack_score": 0.0037922803,
"insult_score": 0.008766884,
"profanity_score": 0.022559889,
"threat_score": 0.008336896,
"sexually_explicit_score": 0.02205746,
"flirtation_score": 0.20944923
},
"Sections/FurtherWork.tex": {
"toxicity_score": 0.0073197307,
"severe_toxicity_score": 0.00074386597,
"identity_attack_score": 0.0024603575,
"insult_score": 0.006325369,
"profanity_score": 0.012517073,
"threat_score": 0.0068999166,
"sexually_explicit_score": 0.0087286215,
"flirtation_score": 0.23059084
},
"Sections/Modules/5_IDO.tex": {
"toxicity_score": 0.0055918973,
"severe_toxicity_score": 0.0006866455,
"identity_attack_score": 0.0015076627,
"insult_score": 0.0060498673,
"profanity_score": 0.011475217,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.25870383
},
"Sections/Appendices/EUPHEMIA.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0024788564,
"insult_score": 0.0069048726,
"profanity_score": 0.014669105,
"threat_score": 0.00666042,
"sexually_explicit_score": 0.012385205,
"flirtation_score": 0.29516837
},
"Sections/ExampleResults.tex": {
"toxicity_score": 0.0058432184,
"severe_toxicity_score": 0.00061035156,
"identity_attack_score": 0.0017851467,
"insult_score": 0.006163868,
"profanity_score": 0.0118680475,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.008197826,
"flirtation_score": 0.24409175
},
"Sections/Appendices/CapacityCalc.tex": {
"toxicity_score": 0.004900764,
"severe_toxicity_score": 0.0006198883,
"identity_attack_score": 0.0012209293,
"insult_score": 0.0058361157,
"profanity_score": 0.011475217,
"threat_score": 0.0059678215,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.3013494
},
"Sections/ForecastModel.tex": {
"toxicity_score": 0.005686143,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0016464046,
"insult_score": 0.0060498673,
"profanity_score": 0.012448754,
"threat_score": 0.0061166976,
"sexually_explicit_score": 0.009082484,
"flirtation_score": 0.24446987
},
"Sections/Introduction.tex": {
"toxicity_score": 0.006188785,
"severe_toxicity_score": 0.00061511993,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0062303683,
"profanity_score": 0.012073003,
"threat_score": 0.0064015044,
"sexually_explicit_score": 0.008669644,
"flirtation_score": 0.25973216
},
"main.tex": {
"toxicity_score": 0.009047564,
"severe_toxicity_score": 0.0009059906,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0069238725,
"profanity_score": 0.013780965,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.2085626
},
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
}
},
"reason": null
}
} | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.38618005222442,
"hash": "063f3f0f3f3f1f5b",
"most_frequent_color_ratio": 94.38618005222442
}
} | [] | "algorithm" | "94524db3-ed16-43b5-8abd-5b179c258c5f" | 352 | easy |
|
\begin{algorithm}[t]
\caption{Sequential Covering}
\label{algo:sc}
\begin{algorithmic}[1]
\Procedure{SequentialCovering}{$\mathcal{D}$, $n$, $len$, $\beta$}
\State $\mathcal{R} \leftarrow \emptyset$
\State $\mathcal{D}' \leftarrow \mathcal{D}$
\For{$i = 1$ to $n$}
\State $r \leftarrow \textsc{RuleInduction}(len, \beta, \mathcal{D}')$
\State $\mathcal{D}' \leftarrow \mathcal{D}' \setminus \{\boldsymbol x~|~r~\mbox{covers}~\boldsymbol x, \boldsymbol x\in\mathcal{D}'\}$
\State $\mathcal{R} \leftarrow \mathcal{R} \cup \{r\}$
\EndFor
\Return $\mathcal{R}$
\EndProcedure
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[t]
\caption{Sequential Covering}
\begin{algorithmic}
[1]
\Procedure{SequentialCovering}{$\mathcal{D}$, $n$, $len$, $\beta$}
\State $\mathcal{R} \leftarrow \emptyset$
\State $\mathcal{D}' \leftarrow \mathcal{D}$
\For{$i = 1$ to $n$}
\State $r \leftarrow \textsc{RuleInduction}(len, \beta, \mathcal{D}')$
\State $\mathcal{D}' \leftarrow \mathcal{D}' \setminus \{\boldsymbol x~|~r~\mbox{covers}~\boldsymbol x, \boldsymbol x\in\mathcal{D}'\}$
\State $\mathcal{R} \leftarrow \mathcal{R} \cup \{r\}$
\EndFor
\Return $\mathcal{R}$
\EndProcedure
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2311.00964" | "2311.00964.tar.gz" | "2024-01-17" | {
"title": "on finding bi-objective pareto-optimal fraud prevention rule sets for fintech applications",
"id": "2311.00964",
"abstract": "rules are widely used in fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. in practice, a two-stage framework of fraud prevention decision rule set mining is usually employed in large fintech institutions. this paper is concerned with finding high-quality rule subsets in a bi-objective space (such as precision and recall) from an initial pool of rules. to this end, we adopt the concept of pareto optimality and aim to find a set of non-dominated rule subsets, which constitutes a pareto front. we propose a heuristic-based framework called pors and we identify that the core of pors is the problem of solution selection on the front (ssf). we provide a systematic categorization of the ssf problem and a thorough empirical evaluation of various ssf methods on both public and proprietary datasets. we also introduce a novel variant of sequential covering algorithm called spectralrules to encourage the diversity of the initial rule set and we empirically find that spectralrules further improves the quality of the found pareto front. on two real application scenarios within alipay, we demonstrate the advantages of our proposed methodology compared to existing work.",
"categories": "cs.lg q-fin.st",
"doi": "",
"created": "2023-11-01",
"updated": "2024-01-17",
"authors": [
"chengyao wen",
"yin lou"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2311.00964"
} | "2024-03-15T05:59:22.584765" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"acmdatamodel.dbx": {
"toxicity_score": 0.057748068,
"severe_toxicity_score": 0.0022602081,
"identity_attack_score": 0.007917541,
"insult_score": 0.02212623,
"profanity_score": 0.024404489,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.023472913,
"flirtation_score": 0.3318337
},
"acmnumeric.cbx": {
"toxicity_score": 0.004335291,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0009203217,
"insult_score": 0.005688865,
"profanity_score": 0.012926984,
"threat_score": 0.0057639256,
"sexually_explicit_score": 0.0101440735,
"flirtation_score": 0.3723703
},
"paper.out": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0019550323,
"identity_attack_score": 0.00155391,
"insult_score": 0.007303875,
"profanity_score": 0.021876704,
"threat_score": 0.006246155,
"sexually_explicit_score": 0.019934284,
"flirtation_score": 0.2803414
},
"related_work.tex": {
"toxicity_score": 0.0074453913,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.0024973555,
"insult_score": 0.0065818704,
"profanity_score": 0.01265371,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.2352925
},
"intro.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"paper.bib": {
"toxicity_score": 0.0058432184,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0012301788,
"insult_score": 0.0063918694,
"profanity_score": 0.013439372,
"threat_score": 0.006149062,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.24617259
},
"acmauthoryear.bbx": {
"toxicity_score": 0.005748973,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.001572409,
"insult_score": 0.0061448677,
"profanity_score": 0.013371054,
"threat_score": 0.0060131317,
"sexually_explicit_score": 0.010615891,
"flirtation_score": 0.46901152
},
"conclusion.tex": {
"toxicity_score": 0.015519086,
"severe_toxicity_score": 0.0013828278,
"identity_attack_score": 0.0038107792,
"insult_score": 0.008614883,
"profanity_score": 0.016342908,
"threat_score": 0.007909686,
"sexually_explicit_score": 0.014626338,
"flirtation_score": 0.15356764
},
"ACM-Reference-Format.bst": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0023863618,
"insult_score": 0.00815888,
"profanity_score": 0.016479544,
"threat_score": 0.0060325502,
"sexually_explicit_score": 0.013387818,
"flirtation_score": 0.5040354
},
"approach.tex": {
"toxicity_score": 0.008796243,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0025158543,
"insult_score": 0.0067623714,
"profanity_score": 0.012995303,
"threat_score": 0.007029374,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.34695542
},
"acmnumeric.bbx": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.000667572,
"identity_attack_score": 0.0017019014,
"insult_score": 0.006163868,
"profanity_score": 0.012926984,
"threat_score": 0.006304411,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.4000424
},
"acmauthoryear.cbx": {
"toxicity_score": 0.003267176,
"severe_toxicity_score": 0.0004720688,
"identity_attack_score": 0.000776955,
"insult_score": 0.005513114,
"profanity_score": 0.0113727385,
"threat_score": 0.0056959605,
"sexually_explicit_score": 0.007460612,
"flirtation_score": 0.39135352
},
"acmart.cls": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0010442734,
"identity_attack_score": 0.0030523231,
"insult_score": 0.0075128763,
"profanity_score": 0.013712646,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.38476974
},
"paper.bbl": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0023863618,
"insult_score": 0.00815888,
"profanity_score": 0.016479544,
"threat_score": 0.0060325502,
"sexually_explicit_score": 0.013387818,
"flirtation_score": 0.5040354
},
"expirement.tex": {
"toxicity_score": 0.011497946,
"severe_toxicity_score": 0.0009536743,
"identity_attack_score": 0.0027748393,
"insult_score": 0.007132874,
"profanity_score": 0.013849284,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.01209032,
"flirtation_score": 0.1782523
},
"paper.tex": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0010299683,
"identity_attack_score": 0.0032003147,
"insult_score": 0.007474876,
"profanity_score": 0.014225035,
"threat_score": 0.0072365063,
"sexually_explicit_score": 0.010792822,
"flirtation_score": 0.22060966
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.16821044185114,
"hash": "3f0fcf4fe3f3f900",
"most_frequent_color_ratio": 91.16821044185114
}
} | [] | "algorithm" | "ff31089c-c750-4c5a-86b2-4b0f875ac086" | 590 | easy |
|
\begin{algorithmic}
\Require $n=2$, $M\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are iid and continuous with pdf $f$ and cdf $F$ for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r\geq0$ for $i=0,1,...,k-1$
\Require $G_1=\{.1,.2,...,.9\}$
\State $i\gets k$ \Comment{initialize i}
\While{$i>0$}
\State $i\gets i-1$
\For{$a\in\{1,2,...,a_0\}$}
\State $D_{a,i}\gets\Big\{\frac{jm_{a,i}}{M}:j=1,2,...,M-1\Big\}$
\For{$x\in D_{a,i}$}
\State $q_i^*(x,a)\gets 0$ \Comment{initial proposal for $q_i^*(x,a)$}
\State $v_i(x,a)\gets \eqref{eq:gi0}$\Comment{proposal for $v_i(x,a)$, see \ref{s:valg}}
\State $q_1\gets\underset{q\in G_1}{\arg\max}\ \eqref{eq:maxqh}$\Comment{see \ref{s:valg}}
\State $G_2\gets\{q_1\pm .01j: j=-9,-8,...,10\}$
\State $q_2\gets\underset{q\in G_2}{\arg\max}\ \eqref{eq:maxqh}$
\State $V\gets \eqref{eq:maxqh}\vert_{q=q_2}$
\If{$v_{i}(x,a)<V$}
\State $q_i^*(x,a)\gets q_2$
\State $v_i(x,a)\gets V$
\EndIf
\EndFor
\EndFor
\EndWhile\\
\Return{$v_i(x,a),\ \boldsymbol\pi_i=(q_i^*(x,a),1-q_i^*(x,a))$ for $a\in\{1,2,...,a_0\}$, $x\in D_{a,i}$ and $i=0,1,...,k-1$}
\end{algorithmic}
| \begin{algorithmic}
\Require $n=2$, $M\in\mathbb{N}$ sufficiently large
\Require $X_{1i}$ are iid and continuous with pdf $f$ and cdf $F$ for $i=0,1,...,k-1$
\Require $X_{2i}=1+r$, $r\geq0$ for $i=0,1,...,k-1$
\Require $G_1=\{.1,.2,...,.9\}$
\State $i\gets k$ \Comment{initialize i}
\While{$i>0$}
\State $i\gets i-1$
\For{$a\in\{1,2,...,a_0\}$}
\State $D_{a,i}\gets\Big\{\frac{jm_{a,i}}{M}:j=1,2,...,M-1\Big\}$
\For{$x\in D_{a,i}$}
\State $q_i^*(x,a)\gets 0$ \Comment{initial proposal for $q_i^*(x,a)$}
\State $v_i(x,a)\gets \eqref{eq:gi0}$\Comment{proposal for $v_i(x,a)$, see \ref{s:valg}}
\State $q_1\gets\underset{q\in G_1}{\arg\max}\ \eqref{eq:maxqh}$\Comment{see \ref{s:valg}}
\State $G_2\gets\{q_1\pm .01j: j=-9,-8,...,10\}$
\State $q_2\gets\underset{q\in G_2}{\arg\max}\ \eqref{eq:maxqh}$
\State $V\gets \eqref{eq:maxqh}\vert_{q=q_2}$
\If{$v_{i}(x,a)<V$}
\State $q_i^*(x,a)\gets q_2$
\State $v_i(x,a)\gets V$
\EndIf
\EndFor
\EndFor
\EndWhile\\
\Return{$v_i(x,a),\ \boldsymbol\pi_i=(q_i^*(x,a),1-q_i^*(x,a))$ for $a\in\{1,2,...,a_0\}$, $x\in D_{a,i}$ and $i=0,1,...,k-1$}
\end{algorithmic} | "https://arxiv.org/src/2402.17164" | "2402.17164.tar.gz" | "2024-02-26" | {
"title": "withdrawal success optimization in a pooled annuity fund",
"id": "2402.17164",
"abstract": "consider a closed pooled annuity fund investing in n assets with discrete-time rebalancing. at time 0, each annuitant makes an initial contribution to the fund, committing to a predetermined schedule of withdrawals. require annuitants to be homogeneous in the sense that their initial contributions and predetermined withdrawal schedules are identical, and their mortality distributions are identical and independent. under the forementioned setup, the probability for a particular annuitant to complete the prescribed withdrawals until death is maximized over progressively measurable portfolio weight functions. applications consider fund portfolios that mix two assets: the s&p composite index and an inflation-protected bond. the maximum probability is computed for annually rebalanced schedules consisting of an initial investment and then equal annual withdrawals until death. a considerable increase in the maximum probability is achieved by increasing the number of annuitants initially in the pool. for example, when the per-annuitant initial contribution and annual withdrawal amount are held constant, starting with 20 annuitants instead of just 1 can increase the maximum probability (measured on a scale from 0 to 1) by as much as .15.",
"categories": "q-fin.mf",
"doi": "",
"created": "2024-02-26",
"updated": "",
"authors": [
"hayden brown"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.17164"
} | "2024-03-15T02:40:56.763732" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-bibliography.bib": {
"toxicity_score": 0.0070055793,
"severe_toxicity_score": 0.00071048737,
"identity_attack_score": 0.001692652,
"insult_score": 0.0068573724,
"profanity_score": 0.012141321,
"threat_score": 0.0064144502,
"sexually_explicit_score": 0.0070772604,
"flirtation_score": 0.3025875
},
"sn-article.tex": {
"toxicity_score": 0.019226074,
"severe_toxicity_score": 0.0013637543,
"identity_attack_score": 0.0025343532,
"insult_score": 0.0096028885,
"profanity_score": 0.01442999,
"threat_score": 0.006686311,
"sexually_explicit_score": 0.008138849,
"flirtation_score": 0.30276403
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"sn-article.out": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0013923645,
"identity_attack_score": 0.0016186562,
"insult_score": 0.00653437,
"profanity_score": 0.017367685,
"threat_score": 0.006149062,
"sexually_explicit_score": 0.01686747,
"flirtation_score": 0.35803026
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"sn-article.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.004088263,
"insult_score": 0.0076268767,
"profanity_score": 0.0147374235,
"threat_score": 0.0076119336,
"sexually_explicit_score": 0.0115595255,
"flirtation_score": 0.2714431
}
},
"reason": null
}
} | {
"num_done": {
"equation": 3,
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.29144663799954,
"hash": "030ffe3e879f3f1f",
"most_frequent_color_ratio": 94.29144663799954
}
} | [] | "algorithm" | "adab2d01-b315-47e7-b57d-71e692da3add" | 1096 | medium |
|
\begin{algorithm}
\caption{Greedy algorithm for the menu selection problem}\label{alg:greedy}
\begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}
\State By definition there exists $j\in O$ such that $|j \succ O \setminus \{ j \} | <t$.
\State Let $j$ be a minimal\newcounter{minimalfootnote}\setcounter{minimalfootnote}{\thefootnote}\footnote{This can be replaced with any other consistent tie-breaking with no change to the analysis below.} such $j$, and update $O\gets O\setminus\{j\}$.
\ElsIf{$O$ is not $u$-uncontestable\footnote{This item can be consistently swapped with the preceding one with no change to the analysis below.}}
\State By definition there exists $j\in G\setminus O$ such that $|j\succ O|\ge u$.
\State Let $j$ be a minimal\newcounter{savedcurrentfootnote}\setcounter{savedcurrentfootnote}{\thefootnote}\setcounter{footnote}{\theminimalfootnote}\footnotemark\setcounter{footnote}{\thesavedcurrentfootnote} such $j$, and update $O\gets O\cup\{j\}$.
\EndIf
\EndWhile
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Greedy algorithm for the menu selection problem}\begin{algorithmic}
\State Initialize $O\gets \emptyset$.\footnote{This can be replaced with any other menu of public goods with no change to the analysis below.}
\While{$O$ is not $(t,u)$-stable}
\If{$O$ is not $t$-feasible}
\State By definition there exists $j\in O$ such that $|j \succ O \setminus \{ j \} | <t$.
\State Let $j$ be a minimal\newcounter{minimalfootnote}\setcounter{minimalfootnote}{\thefootnote}\footnote{This can be replaced with any other consistent tie-breaking with no change to the analysis below.} such $j$, and update $O\gets O\setminus\{j\}$.
\ElsIf{$O$ is not $u$-uncontestable\footnote{This item can be consistently swapped with the preceding one with no change to the analysis below.}}
\State By definition there exists $j\in G\setminus O$ such that $|j\succ O|\ge u$.
\State Let $j$ be a minimal\newcounter{savedcurrentfootnote}\setcounter{savedcurrentfootnote}{\thefootnote}\setcounter{footnote}{\theminimalfootnote}\footnotemark\setcounter{footnote}{\thesavedcurrentfootnote} such $j$, and update $O\gets O\cup\{j\}$.
\EndIf
\EndWhile
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.11370" | "2402.11370.tar.gz" | "2024-02-17" | {
"title": "stable menus of public goods: a matching problem",
"id": "2402.11370",
"abstract": "we study a matching problem between agents and public goods, in settings without monetary transfers. since goods are public, they have no capacity constraints. there is no exogenously defined budget of goods to be provided. rather, each provided good must justify its cost, leading to strong complementarities in the \"preferences\" of goods. furthermore, goods that are in high demand given other already-provided goods must also be provided. the question of the existence of a stable solution (a menu of public goods to be provided) exhibits a rich combinatorial structure. we uncover sufficient conditions and necessary conditions for guaranteeing the existence of a stable solution, and derive both positive and negative results for strategyproof stable matching.",
"categories": "cs.gt econ.th math.co",
"doi": "",
"created": "2024-02-17",
"updated": "",
"authors": [
"sara fish",
"yannai a. gonczarowski",
"sergiu hart"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.11370"
} | "2024-03-15T03:42:28.124433" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.0143253105,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0042177555,
"insult_score": 0.008291881,
"profanity_score": 0.016137952,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.013092931,
"flirtation_score": 0.22479805
},
"main.bbl": {
"toxicity_score": 0.017843807,
"severe_toxicity_score": 0.0013160706,
"identity_attack_score": 0.00362579,
"insult_score": 0.009374888,
"profanity_score": 0.017299367,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.47433925
}
},
"reason": null
}
} | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 97.67418874178337,
"hash": "0101ffffffffff00",
"most_frequent_color_ratio": 97.67418874178337
}
} | [] | "algorithm" | "f228aa68-67de-4a62-83c8-a48cf1b77d0a" | 1175 | hard |
|
\begin{algorithm}
\caption{Definitive Node}
\begin{algorithmic}
\While{running}
\State object position $\gets$ detection algorithm
\If{object is detected}
\If{no predictive action in progress}
\State carry out \textit{definitive action}
\ElsIf{previous goal not within tolerance}
\State preempt and carry out \textit{definitive action}
\EndIf
\EndIf
\EndWhile
\end{algorithmic}
\label{alg:definitive_alg}
\end{algorithm}
| \begin{algorithm}
\caption{Definitive Node}
\begin{algorithmic}
\While{running}
\State object position $\gets$ detection algorithm
\If{object is detected}
\If{no predictive action in progress}
\State carry out \textit{definitive action}
\ElsIf{previous goal not within tolerance}
\State preempt and carry out \textit{definitive action}
\EndIf
\EndIf
\EndWhile
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2203.00156" | "2203.00156.tar.gz" | "2024-02-19" | {
"title": "preemptive motion planning for human-to-robot indirect placement handovers",
"id": "2203.00156",
"abstract": "as technology advances, the need for safe, efficient, and collaborative human-robot-teams has become increasingly important. one of the most fundamental collaborative tasks in any setting is the object handover. human-to-robot handovers can take either of two approaches: (1) direct hand-to-hand or (2) indirect hand-to-placement-to-pick-up. the latter approach ensures minimal contact between the human and robot but can also result in increased idle time due to having to wait for the object to first be placed down on a surface. to minimize such idle time, the robot must preemptively predict the human intent of where the object will be placed. furthermore, for the robot to preemptively act in any sort of productive manner, predictions and motion planning must occur in real-time. we introduce a novel prediction-planning pipeline that allows the robot to preemptively move towards the human agent's intended placement location using gaze and gestures as model inputs. in this paper, we investigate the performance and drawbacks of our early intent predictor-planner as well as the practical benefits of using such a pipeline through a human-robot case study.",
"categories": "cs.ro cs.ai cs.cv cs.lg",
"doi": "10.1109/icra46639.2022.9811558",
"created": "2022-02-28",
"updated": "2024-02-19",
"authors": [
"andrew choi",
"mohammad khalid jawed",
"jungseock joo"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2203.00156"
} | "2024-03-15T04:29:43.247605" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"paper.tex": {
"toxicity_score": 0.020607091,
"severe_toxicity_score": 0.0013256073,
"identity_attack_score": 0.0026453468,
"insult_score": 0.00983089,
"profanity_score": 0.014190876,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.007902941,
"flirtation_score": 0.30276403
},
"ieeeconf.cls": {
"toxicity_score": 0.0058432184,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0014799143,
"insult_score": 0.0062588686,
"profanity_score": 0.012722028,
"threat_score": 0.0059613483,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.43877357
},
"paper.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"paper.bib": {
"toxicity_score": 0.00804228,
"severe_toxicity_score": 0.001707077,
"identity_attack_score": 0.0013504218,
"insult_score": 0.006990373,
"profanity_score": 0.016035475,
"threat_score": 0.005948403,
"sexually_explicit_score": 0.019108603,
"flirtation_score": 0.56967556
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.41957611221548,
"hash": "1f2f0f878387bf3f",
"most_frequent_color_ratio": 92.41957611221548
}
} | [] | "algorithm" | "5eea9701-8fc0-4e67-9adb-bda16d1c0676" | 393 | easy |
|
\begin{algorithm}[H]
\scriptsize
\caption{The Newton's Optimization Method Modified with Adding a Multiple of the Identity}\label{Newton method}
\begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the initial value of parameters; $\boldsymbol{\phi}$ represents sufficient statistics. }
\State $converged \gets $False$;\ n \gets 0;\ tol \gets 1\times 10^{-8};\ maxI \gets 400$; \ $\Delta\boldsymbol{\beta}\gets \bold{0}$;
\While{$converged$ is $False$ and $n\le maxI$ }
\State $\alpha \gets \Call{BackTrackingLineSearch}{\boldsymbol{\beta}, \Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}}$ \Comment{$\alpha$ is the step size}
\State $\boldsymbol{\beta} \gets \boldsymbol{\beta} + \alpha \Delta\boldsymbol{\beta}$ \Comment{Update the parameters}
\State $H \gets \nabla^2_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi}$);\ $G \gets \nabla_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi})$ \Comment{Compute the Hessian and gradient of the objective $L$ \eqref{MLE optimization goal}}
\State $\lambda \gets 1\times 10^{-3}$
\State $L_H,fail \gets \Call{CholeskyDecomposition}{H}$ \Comment{Standard Cholesky Decomposition Algorithm}
\While{$fail$} \Comment{$fail$ is $True$ if Cholesky decomposition failed}
\State $L_H,fail \gets \Call{CholeskyDecomposition}{H + \lambda I}$ \Comment{Adding a multiple of the identity}
\State $\lambda \gets 10\times\lambda$
\EndWhile
\State $\bold{w} = \Call{TriangularSolve}{L_H, -G}$ \Comment{Solve $L_H \bold{w} = -G$ in which $L_H$ is lower triangular matrix}
\State $\Delta\boldsymbol{\beta} = \Call{TriangularSolve}{L_H^T, \bold{w} }$ \Comment{Compute the update direction of the Newton's method}
\State $res \gets 0.5 \times \Delta\boldsymbol{\beta} \cdot \nabla L(\boldsymbol{\beta}) $
\State $converged \gets res \le tol$ or $\alpha \le 1\times10^{-6}$ \Comment{Check Convergence}
\State $n\gets n +1$
\EndWhile
\State \textbf{return} $\boldsymbol{\beta}$
\EndFunction
\Function{BackTrackingLineSearch}{$\boldsymbol{\beta}$, $\Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}$} \Comment{Backtracking line search to determine the step size}
\State $\alpha \gets 2$; $s \gets 0$;\ $maxT \gets 25$;\ $satisfied \gets False$
\While{$satisfied$ is $False$ and $s \le maxT$}
\If{not $satisfied$}
\State $\alpha \gets 0.5 \alpha$
\EndIf
\State $satisfied \gets \Call{ArmijoCondition}{\boldsymbol{\beta},\alpha \Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}}$
\State $s\gets s + 1$
\EndWhile
\State \textbf{return} $\alpha$
\EndFunction
\Function{ArmijoCondition} {$\boldsymbol{\beta}$, $\Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}$} \Comment{The Armijo's condition as stopping criterion of line search}
\State $c\gets 5\times 10^{-4}$;\ $atol \gets 5\times 10^{-6}$;\ $rtol \gets 5\times 10^{-5}$
\State $gradD = -c \nabla_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi})\cdot\Delta\boldsymbol{\beta}$
\State $LD = L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi} ) - L(\boldsymbol{\beta} + \Delta\boldsymbol{\beta};\bold{M}, \boldsymbol{\phi})$
\State \textbf{return} $(LD - gradD) \ge -( atol + rtol\times |LD| ) $ \Comment{The RHS is $0$ with floating-point error tolerance}
\EndFunction
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\scriptsize
\caption{The Newton's Optimization Method Modified with Adding a Multiple of the Identity}\begin{algorithmic}
\Function{NewtonOptimization}{$\bold{M}$, $\boldsymbol{\beta}$, $\boldsymbol{\phi}$}\Comment{$\bold{M}$ represents moments; $\boldsymbol{\beta}$ is the initial value of parameters; $\boldsymbol{\phi}$ represents sufficient statistics. }
\State $converged \gets $False$;\ n \gets 0;\ tol \gets 1\times 10^{-8};\ maxI \gets 400$; \ $\Delta\boldsymbol{\beta}\gets \bold{0}$;
\While{$converged$ is $False$ and $n\le maxI$ }
\State $\alpha \gets \Call{BackTrackingLineSearch}{\boldsymbol{\beta}, \Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}}$ \Comment{$\alpha$ is the step size}
\State $\boldsymbol{\beta} \gets \boldsymbol{\beta} + \alpha \Delta\boldsymbol{\beta}$ \Comment{Update the parameters}
\State $H \gets \nabla^2_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi}$);\ $G \gets \nabla_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi})$ \Comment{Compute the Hessian and gradient of the objective $L$ \eqref{MLE optimization goal}}
\State $\lambda \gets 1\times 10^{-3}$
\State $L_H,fail \gets \Call{CholeskyDecomposition}{H}$ \Comment{Standard Cholesky Decomposition Algorithm}
\While{$fail$} \Comment{$fail$ is $True$ if Cholesky decomposition failed}
\State $L_H,fail \gets \Call{CholeskyDecomposition}{H + \lambda I}$ \Comment{Adding a multiple of the identity}
\State $\lambda \gets 10\times\lambda$
\EndWhile
\State $\bold{w} = \Call{TriangularSolve}{L_H, -G}$ \Comment{Solve $L_H \bold{w} = -G$ in which $L_H$ is lower triangular matrix}
\State $\Delta\boldsymbol{\beta} = \Call{TriangularSolve}{L_H^T, \bold{w} }$ \Comment{Compute the update direction of the Newton's method}
\State $res \gets 0.5 \times \Delta\boldsymbol{\beta} \cdot \nabla L(\boldsymbol{\beta}) $
\State $converged \gets res \le tol$ or $\alpha \le 1\times10^{-6}$ \Comment{Check Convergence}
\State $n\gets n +1$
\EndWhile
\State \textbf{return} $\boldsymbol{\beta}$
\EndFunction
\Function{BackTrackingLineSearch}{$\boldsymbol{\beta}$, $\Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}$} \Comment{Backtracking line search to determine the step size}
\State $\alpha \gets 2$; $s \gets 0$;\ $maxT \gets 25$;\ $satisfied \gets False$
\While{$satisfied$ is $False$ and $s \le maxT$}
\If{not $satisfied$}
\State $\alpha \gets 0.5 \alpha$
\EndIf
\State $satisfied \gets \Call{ArmijoCondition}{\boldsymbol{\beta},\alpha \Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}}$
\State $s\gets s + 1$
\EndWhile
\State \textbf{return} $\alpha$
\EndFunction
\Function{ArmijoCondition} {$\boldsymbol{\beta}$, $\Delta\boldsymbol{\beta}, \bold{M}, \boldsymbol{\phi}$} \Comment{The Armijo's condition as stopping criterion of line search}
\State $c\gets 5\times 10^{-4}$;\ $atol \gets 5\times 10^{-6}$;\ $rtol \gets 5\times 10^{-5}$
\State $gradD = -c \nabla_{\boldsymbol{\beta}} L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi})\cdot\Delta\boldsymbol{\beta}$
\State $LD = L(\boldsymbol{\beta}; \bold{M}, \boldsymbol{\phi} ) - L(\boldsymbol{\beta} + \Delta\boldsymbol{\beta};\bold{M}, \boldsymbol{\phi})$
\State \textbf{return} $(LD - gradD) \ge -( atol + rtol\times |LD| ) $ \Comment{The RHS is $0$ with floating-point error tolerance}
\EndFunction
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2303.02898" | "2303.02898.tar.gz" | "2024-02-19" | {
"title": "stabilizing the maximal entropy moment method for rarefied gas dynamics at single-precision",
"id": "2303.02898",
"abstract": "the maximal entropy moment method (mem) is systematic solution of the challenging problem: generating extended hydrodynamic equations valid for both dense and rarefied gases. however, simulating mem suffers from a computational expensive and ill-conditioned maximal entropy problem. it causes numerical overflow and breakdown when the numerical precision is insufficient, especially for flows like high-speed shock waves. it also prevents modern gpus from accelerating mem with their enormous single floating-point precision computation power. this paper aims to stabilize mem, making it possible to simulating very strong normal shock waves on modern gpus at single precision. we improve the condition number of the maximal entropy problem by proposing gauge transformations, which moves not only flow fields but also hydrodynamic equations into a more optimal coordinate system. we addressed numerical overflow and breakdown in the maximal entropy problem by employing the canonical form of distribution and a modified newton optimization method. moreover, we discovered a counter-intuitive phenomenon that over-refined spatial mesh beyond mean free path degrades the stability of mem. with these techniques, we accomplished single-precision gpu simulations of high speed shock wave up to mach 10 utilizing 35 moments mem, while previous methods only achieved mach 4 on double-precision.",
"categories": "physics.flu-dyn cs.lg",
"doi": "",
"created": "2023-03-06",
"updated": "2024-02-19",
"authors": [
"candi zheng",
"wang yang",
"shiyi chen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.02898"
} | "2024-03-15T03:56:31.323537" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.0070369947,
"severe_toxicity_score": 0.00088214874,
"identity_attack_score": 0.0019053896,
"insult_score": 0.0065628705,
"profanity_score": 0.013951761,
"threat_score": 0.0061814263,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.39370233
},
"elsarticle-num.bst": {
"toxicity_score": 0.016712861,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0036627878,
"insult_score": 0.008937885,
"profanity_score": 0.017504321,
"threat_score": 0.0074306927,
"sexually_explicit_score": 0.012857023,
"flirtation_score": 0.44020516
},
"cas-refs.bib": {
"toxicity_score": 0.004712273,
"severe_toxicity_score": 0.0003862381,
"identity_attack_score": 0.0008047034,
"insult_score": 0.0062303683,
"profanity_score": 0.010774951,
"threat_score": 0.0056506502,
"sexually_explicit_score": 0.0047181738,
"flirtation_score": 0.23901649
},
"refer.bib": {
"toxicity_score": 0.008984734,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0020441315,
"insult_score": 0.0070758737,
"profanity_score": 0.014498309,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.011205662,
"flirtation_score": 0.2895855
},
"elsarticle-harv.bst": {
"toxicity_score": 0.016838523,
"severe_toxicity_score": 0.0013065338,
"identity_attack_score": 0.004439743,
"insult_score": 0.0088618845,
"profanity_score": 0.016889455,
"threat_score": 0.0077284453,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.44743943
},
"elsarticle.cls": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.0031263188,
"insult_score": 0.007493876,
"profanity_score": 0.0138151245,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.44272235
},
"elsarticle-num-names.bst": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.00093460083,
"identity_attack_score": 0.0035332954,
"insult_score": 0.0073798755,
"profanity_score": 0.013405213,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.009141461,
"flirtation_score": 0.39693123
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.78463929199559,
"hash": "008eb89f3fbf3f0f",
"most_frequent_color_ratio": 90.78463929199559
}
} | [] | "algorithm" | "0d877c03-1645-43f8-8c1f-0e3c03858739" | 3349 | hard |
|
\begin{algorithm}
\caption{TE2Rules}\label{alg:te2rules}
\begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
\\
\For{$r \gets candidates$}
\State $p \gets getPrecision(r \implies positiveLabel)$
\If{$p > 1 - \delta$}
\State $candidates.remove(r)$
\State $solutions.append(r)$
\EndIf
\EndFor
\EndFor
\\ \Comment{Rule Simplification}
\State $solutions \gets greedySetCover(solutions)$
\\
\Return $solutions$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{TE2Rules}\begin{algorithmic}
\State $solutions \gets []$
\\ \Comment{Rule Generation}
\For{$k \gets 1, 2, 3, \ldots n$}
\If{$k = 1$}
\State $candidates \gets getNodeRules(model)$
\Else
\State $candidates \gets getNextStage(candidates, k)$
\EndIf
\\
\For{$r \gets candidates$}
\State $p \gets getPrecision(r \implies positiveLabel)$
\If{$p > 1 - \delta$}
\State $candidates.remove(r)$
\State $solutions.append(r)$
\EndIf
\EndFor
\EndFor
\\ \Comment{Rule Simplification}
\State $solutions \gets greedySetCover(solutions)$
\\
\Return $solutions$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2206.14359" | "2206.14359.tar.gz" | "2024-01-23" | {
"title": "te2rules: explaining tree ensembles using rules",
"id": "2206.14359",
"abstract": "tree ensemble (te) models, such as gradient boosted trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. this paper introduces te2rules (tree ensemble to rules), a novel approach for explaining binary classification tree ensemble models through a list of rules, particularly focusing on explaining the minority class. many state-of-the-art explainers struggle with minority class explanations, making te2rules valuable in such cases. the rules generated by te2rules closely approximate the original model, ensuring high fidelity, providing an accurate and interpretable means to understand decision-making. experimental results demonstrate that te2rules scales effectively to tree ensembles with hundreds of trees, achieving higher fidelity within runtimes comparable to baselines. te2rules allows for a trade-off between runtime and fidelity, enhancing its practical applicability. the implementation is available here: https://github.com/linkedin/te2rules.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2022-06-28",
"updated": "2024-01-23",
"authors": [
"g roshan lal",
"xiaotong chen",
"varun mithal"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2206.14359"
} | "2024-03-15T09:04:28.850184" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"checklist.tex": {
"toxicity_score": 0.007288316,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0020811295,
"insult_score": 0.00654387,
"profanity_score": 0.012995303,
"threat_score": 0.006712203,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.18074825
},
"main.tex": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.00362579,
"insult_score": 0.0075888764,
"profanity_score": 0.015591404,
"threat_score": 0.0076507707,
"sexually_explicit_score": 0.014449406,
"flirtation_score": 0.4688245
},
"method.tex": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.00414376,
"insult_score": 0.0077408776,
"profanity_score": 0.015932998,
"threat_score": 0.008065036,
"sexually_explicit_score": 0.014685315,
"flirtation_score": 0.17910686
},
"introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"main.bbl": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003921773,
"insult_score": 0.007607877,
"profanity_score": 0.014634945,
"threat_score": 0.0074824756,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.2033491
},
"related.tex": {
"toxicity_score": 0.0074453913,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.0024973555,
"insult_score": 0.0065818704,
"profanity_score": 0.01265371,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.2352925
},
"results.tex": {
"toxicity_score": 0.021432023,
"severe_toxicity_score": 0.0019454956,
"identity_attack_score": 0.005105704,
"insult_score": 0.0096028885,
"profanity_score": 0.018529098,
"threat_score": 0.008686432,
"sexually_explicit_score": 0.017457243,
"flirtation_score": 0.19775106
},
"problem.tex": {
"toxicity_score": 0.009927189,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0028673338,
"insult_score": 0.007132874,
"profanity_score": 0.012961143,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.007961918,
"flirtation_score": 0.19516823
},
"appendix.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.001077652,
"identity_attack_score": 0.002626848,
"insult_score": 0.0067433715,
"profanity_score": 0.014669105,
"threat_score": 0.0071070488,
"sexually_explicit_score": 0.012680092,
"flirtation_score": 0.21909615
},
"conclusion.tex": {
"toxicity_score": 0.008921904,
"severe_toxicity_score": 0.000705719,
"identity_attack_score": 0.0020811295,
"insult_score": 0.006885872,
"profanity_score": 0.012687869,
"threat_score": 0.0067186756,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.26385543
},
"aaai24.bst": {
"toxicity_score": 0.016712861,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0039402717,
"insult_score": 0.009165886,
"profanity_score": 0.018324144,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.40847883
},
"main.bib": {
"toxicity_score": 0.0070369947,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0013504218,
"insult_score": 0.0066388706,
"profanity_score": 0.012961143,
"threat_score": 0.006051969,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.21000691
},
"abstract.tex": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012874603,
"identity_attack_score": 0.0036812867,
"insult_score": 0.007892879,
"profanity_score": 0.01535229,
"threat_score": 0.007689608,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.19561295
},
"aaai24.sty": {
"toxicity_score": 0.004837934,
"severe_toxicity_score": 0.0011110306,
"identity_attack_score": 0.00069833454,
"insult_score": 0.0057743657,
"profanity_score": 0.015079016,
"threat_score": 0.0055859215,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.38160232
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.70896211887897,
"hash": "001f0f8f8f8f3f1f",
"most_frequent_color_ratio": 94.70896211887897
}
} | [] | "algorithm" | "17e9cc5a-ac03-4730-8407-36fb5edfb838" | 603 | easy |
|
\begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Lack-of-fit Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\label{alg:LOFT}
\begin{algorithmic}[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-corrected estimators $T_n(t) = \check{\Lambda}_{\mathbf{C}}(t) - f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)$, and construct the corresponding bootstrap statistics $\{\check{L}^{(r)}_n(t) = \tilde{\boldsymbol{\Phi}}^{(r)}_n (t) - \sum_{j =1}^k\partial_j f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)\tilde{\boldsymbol{\Phi}}^{(r)}_n (v_j)\}_{r = 1}^B$.
\State Calculate $M_r =\max_{i_* \le j \le i^*} |\check{L}^{(r)}_n(t_j)|_\infty$, $r=1,2,\cdots,B$.
\State For a given $\alpha \in (0,1)$, find the $(1-\alpha)$-th sample quantile of $\{M_r\}_{r=1}^B$, $\hat{q}_{n,1-\alpha}$.
\State For the Lack-of-fit Test $H_0: \Lambda_{\mathbf{C}}(t) - f(t, \{\Lambda_{\mathbf{C}}(t_i)\}_{i =1}^k) = \mathbf{0}, \quad t \in (0,1)$, reject the null hypothesis if $ \max_{i_* \le j \le i^*}|T_n(t_j)|_\infty > \hat{q}_{n,1-\alpha}/\sqrt{n}$.
\end{algorithmic}
\vspace{4pt}
\hrule
\end{algorithm}
| \begin{algorithm}
\floatname{algorithm}{\bf Algorithm}
\caption{Lack-of-fit Test}
\vspace{4pt}
\hrule
\vspace{4pt}
\begin{algorithmic}
[1]
\State Perform Step 1 - 3 proposed in \textbf{Algorithm} \ref{alg:EFT}. Based on $\{\check{\Lambda}_{\mathbf{C}}(t_j)\}_{ j =1}^n$, obtain the Jackknife bias-corrected estimators $T_n(t) = \check{\Lambda}_{\mathbf{C}}(t) - f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)$, and construct the corresponding bootstrap statistics $\{\check{L}^{(r)}_n(t) = \tilde{\boldsymbol{\Phi}}^{(r)}_n (t) - \sum_{j =1}^k\partial_j f(t, \{\check{\Lambda}_{\mathbf{C}}(v_i)\}_{i =1}^k)\tilde{\boldsymbol{\Phi}}^{(r)}_n (v_j)\}_{r = 1}^B$.
\State Calculate $M_r =\max_{i_* \le j \le i^*} |\check{L}^{(r)}_n(t_j)|_\infty$, $r=1,2,\cdots,B$.
\State For a given $\alpha \in (0,1)$, find the $(1-\alpha)$-th sample quantile of $\{M_r\}_{r=1}^B$, $\hat{q}_{n,1-\alpha}$.
\State For the Lack-of-fit Test $H_0: \Lambda_{\mathbf{C}}(t) - f(t, \{\Lambda_{\mathbf{C}}(t_i)\}_{i =1}^k) = \mathbf{0}, \quad t \in (0,1)$, reject the null hypothesis if $ \max_{i_* \le j \le i^*}|T_n(t_j)|_\infty > \hat{q}_{n,1-\alpha}/\sqrt{n}$.
\end{algorithmic}
\vspace{4pt}
\hrule
\end{algorithm} | "https://arxiv.org/src/2310.11724" | "2310.11724.tar.gz" | "2024-02-26" | {
"title": "simultaneous nonparametric inference of m-regression under complex temporal dynamics",
"id": "2310.11724",
"abstract": "the paper considers simultaneous nonparametric inference for a wide class of m-regression models with time-varying coefficients. the covariates and errors of the regression model are tackled as a general class of nonstationary time series and are allowed to be cross-dependent. we construct $\\sqrt{n}$-consistent inference for the cumulative regression function, whose limiting properties are disclosed using bahadur representation and gaussian approximation theory. a simple and unified self-convolved bootstrap procedure is proposed. with only one tuning parameter, the bootstrap consistently simulates the desired limiting behavior of the m-estimators under complex temporal dynamics, even under the possible presence of breakpoints in time series. our methodology leads to a unified framework to conduct general classes of exact function tests, lack-of-fit tests, and qualitative tests for the time-varying coefficients under complex temporal dynamics. these tests enable one to, among many others, conduct variable selection procedures, check for constancy and linearity, as well as verify shape assumptions, including monotonicity and convexity. as applications, our method is utilized to study the time-varying properties of global climate data and microsoft stock return, respectively.",
"categories": "stat.me math.st stat.th",
"doi": "",
"created": "2023-10-18",
"updated": "2024-02-26",
"authors": [
"miaoshiqi liu",
"zhou zhou"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2310.11724"
} | "2024-03-15T03:19:25.303660" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Mregression.tex": {
"toxicity_score": 0.10175867,
"severe_toxicity_score": 0.005378723,
"identity_attack_score": 0.026408968,
"insult_score": 0.02173949,
"profanity_score": 0.06295506,
"threat_score": 0.037530303,
"sexually_explicit_score": 0.062000398,
"flirtation_score": 0.20748816
},
"style.sty": {
"toxicity_score": 0.005780388,
"severe_toxicity_score": 0.00077724457,
"identity_attack_score": 0.0013966691,
"insult_score": 0.006296869,
"profanity_score": 0.013849284,
"threat_score": 0.0059807673,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.18532352
},
"tmp_for_main.tex": {
"toxicity_score": 0.0051520853,
"severe_toxicity_score": 0.00059604645,
"identity_attack_score": 0.0011423088,
"insult_score": 0.006705371,
"profanity_score": 0.0118680475,
"threat_score": 0.005773635,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.39044937
},
"SuppPub_abbre.bib": {
"toxicity_score": 0.013697007,
"severe_toxicity_score": 0.001168251,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00817788,
"profanity_score": 0.01490822,
"threat_score": 0.0077802283,
"sexually_explicit_score": 0.010910776,
"flirtation_score": 0.24290006
},
"SuppPub.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"Mregression.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"tmp.tex": {
"toxicity_score": 0.004712273,
"severe_toxicity_score": 0.0005698204,
"identity_attack_score": 0.0009896926,
"insult_score": 0.00654387,
"profanity_score": 0.011663092,
"threat_score": 0.0057412707,
"sexually_explicit_score": 0.007667032,
"flirtation_score": 0.4112136
},
"SuppPub.tex": {
"toxicity_score": 0.09481675,
"severe_toxicity_score": 0.0051879883,
"identity_attack_score": 0.02439919,
"insult_score": 0.02193286,
"profanity_score": 0.056441884,
"threat_score": 0.032695748,
"sexually_explicit_score": 0.05882028,
"flirtation_score": 0.23722197
},
"latexmkrc": {
"toxicity_score": 0.0055918973,
"severe_toxicity_score": 0.00056505203,
"identity_attack_score": 0.0007630808,
"insult_score": 0.0066958712,
"profanity_score": 0.0108774295,
"threat_score": 0.006000186,
"sexually_explicit_score": 0.0075490777,
"flirtation_score": 0.31496802
},
"Mregression_abbre.bib": {
"toxicity_score": 0.050573748,
"severe_toxicity_score": 0.0034713745,
"identity_attack_score": 0.012441003,
"insult_score": 0.016632931,
"profanity_score": 0.03489832,
"threat_score": 0.014393489,
"sexually_explicit_score": 0.040976297,
"flirtation_score": 0.24290006
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.16367265469061,
"hash": "0184838f0ffb7f03",
"most_frequent_color_ratio": 88.16367265469061
}
} | [] | "algorithm" | "3975bd04-8bec-48cd-bdc5-1b9c14cc3c68" | 1196 | hard |
|
\begin{algorithm}
\caption{List Viterbi Algorithm \cite{seshadri1994list}}\label{alg:cap_lva}
\hspace*{\algorithmicindent} \textbf{Input: $L, \tau, \left\{\mathbf{\pi}_i\right\}_{i=1}^N, \{\mathbf{\Phi}_i \}_{i=1}^N$} \\
\hspace*{\algorithmicindent} \textbf{Output: ${\mathbf{Z}}$}
\begin{algorithmic}[1]
\Statex \textbf{\underline{Initialization}}:
\For{$s \gets 1$ to $2^{\tau}$}
\State $\Psi(1,s,1) \gets \left\{\mathbf{\pi}_i\right\}_{i=1}^N\left(s\right)$ \Comment{initial probability for each \newline \hspace*{12em} state} \label{alg:lva:line:ini1}
\State $\xi\left(n,s,l\right) \gets s$ \Comment{previous state of each state}\label{alg:lva:line:ini2}
\For{$l \gets 2$ to $L$}
\State $\Psi(1,s,l) \gets 0$
\State $\xi\left(n,s,l\right) \gets s$
\EndFor
\EndFor
\Statex \textbf{\underline{Recursion}}:
\For{$n \gets 2$ to $N$}
\For{$s_2 \gets 1$ to $2^{\tau}$}
\For{$l\gets 1$ to $L$}
\State \Comment{find the probability of the $l$-most likely \newline \hspace*{6em} previous state and its rank}
\State $\Psi(n,s_2,l) = \max^{(l)}_{{\substack{s_1\in\{1,...,2^{\tau}\} \\ k\in\{1,..,L\}}}}
\newline \hspace*{11.5em}
\left\{ \Psi(n-1,s_1,k) \Phi_n \left( s_1,s_2\right)\right\}$ \label{alg:lva:line:rec1}
\State $\left({s_1}^*, k^*\right) = \arg\max^{(l)}_{{\substack{s_1\in\{1,...,2^{\tau}\} \\ k\in\{1,..,L\}}}} \newline \hspace*{11.5em}
\left\{ \Psi(n-1, s_1,k) \Phi_n \left( s_1,s_2\right)\right\}$
\State $\xi\left(n,s_2,l\right) \gets {s_1}^*$ \label{alg:lva:line:rec2}
\State $\chi\left(n,l\right) \gets k^*$ \label{alg:lva:line:rec3}
\EndFor
\EndFor
\EndFor
\Statex \textbf{\underline{Backtracking}}:
\For{$l \gets 1$ to $L$}
\State $\mathbf{Z}_{l,N} \gets \arg\max_{s\in\{1,...,2^{\tau}\}}\left\{ \Psi(N,s,l)\right\}$ \label{alg:lva:line:back1}
\State $l_N \gets \chi\left(n,l\right)$
\For{$n \gets N-1$ to $1$}
\State $\mathbf{Z}_{l,n} \gets \xi\left(n+1,\mathbf{Z}_{l_{n+1},n+1},l_{n+1}\right)$ \label{alg:lva:line:back2}
\State $l_{n} \gets \chi\left(n+1,l_{n+1}\right) $
\EndFor
\EndFor
\State \Return $\mathbf{Z}$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{List Viterbi Algorithm \cite{seshadri1994list}}\hspace*{\algorithmicindent} \textbf{Input: $L, \tau, \left\{\mathbf{\pi}_i\right\}_{i=1}^N, \{\mathbf{\Phi}_i \}_{i=1}^N$} \\
\hspace*{\algorithmicindent} \textbf{Output: ${\mathbf{Z}}$}
\begin{algorithmic}
[1]
\Statex \textbf{\underline{Initialization}}:
\For{$s \gets 1$ to $2^{\tau}$}
\State $\Psi(1,s,1) \gets \left\{\mathbf{\pi}_i\right\}_{i=1}^N\left(s\right)$ \Comment{initial probability for each \newline \hspace*{12em} state} \State $\xi\left(n,s,l\right) \gets s$ \Comment{previous state of each state} \For{$l \gets 2$ to $L$}
\State $\Psi(1,s,l) \gets 0$
\State $\xi\left(n,s,l\right) \gets s$
\EndFor
\EndFor
\Statex \textbf{\underline{Recursion}}:
\For{$n \gets 2$ to $N$}
\For{$s_2 \gets 1$ to $2^{\tau}$}
\For{$l\gets 1$ to $L$}
\State \Comment{find the probability of the $l$-most likely \newline \hspace*{6em} previous state and its rank}
\State $\Psi(n,s_2,l) = \max^{(l)}_{{\substack{s_1\in\{1,...,2^{\tau}\} \\ k\in\{1,..,L\}}}}
\newline \hspace*{11.5em}
\left\{ \Psi(n-1,s_1,k) \Phi_n \left( s_1,s_2\right)\right\}$ \State $\left({s_1}^*, k^*\right) = \arg\max^{(l)}_{{\substack{s_1\in\{1,...,2^{\tau}\} \\ k\in\{1,..,L\}}}} \newline \hspace*{11.5em}
\left\{ \Psi(n-1, s_1,k) \Phi_n \left( s_1,s_2\right)\right\}$
\State $\xi\left(n,s_2,l\right) \gets {s_1}^*$ \State $\chi\left(n,l\right) \gets k^*$ \EndFor
\EndFor
\EndFor
\Statex \textbf{\underline{Backtracking}}:
\For{$l \gets 1$ to $L$}
\State $\mathbf{Z}_{l,N} \gets \arg\max_{s\in\{1,...,2^{\tau}\}}\left\{ \Psi(N,s,l)\right\}$ \State $l_N \gets \chi\left(n,l\right)$
\For{$n \gets N-1$ to $1$}
\State $\mathbf{Z}_{l,n} \gets \xi\left(n+1,\mathbf{Z}_{l_{n+1},n+1},l_{n+1}\right)$ \State $l_{n} \gets \chi\left(n+1,l_{n+1}\right) $
\EndFor
\EndFor
\State \Return $\mathbf{Z}$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated statistical prior represented in trellis, e.g., finite state machines and markov processes. we introduce a variation of list viterbi algorithm (lva) to enable accurate recovery using much fewer tests than objectives, which efficiently gains from the correlated prior statistics structure. our numerical results demonstrate that the proposed multi-stage gt (msgt) algorithm can obtain the optimal maximum a posteriori (map) performance with feasible complexity in practical regimes, such as with covid-19 and sparse signal recovery applications, and reduce in the scenarios tested the number of pooled tests by at least $25\\%$ compared to existing classical low complexity gt algorithms. moreover, we analytically characterize the complexity of the proposed msgt algorithm that guarantees its efficiency.",
"categories": "cs.it math.it q-bio.qm stat.ap",
"doi": "",
"created": "2024-02-15",
"updated": "",
"authors": [
"ayelet c. portnoy",
"alejandro cohen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.10018"
} | "2024-03-15T04:20:09.862331" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"GT_with_prior_statistics_for_ISIT.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"GT_with_prior_statistics_for_ISIT.tex": {
"toxicity_score": 0.0074139764,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.002192123,
"insult_score": 0.00654387,
"profanity_score": 0.013644327,
"threat_score": 0.0063238298,
"sexually_explicit_score": 0.01209032,
"flirtation_score": 0.31215188
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.05211947265502,
"hash": "0f3c3f17c73f1f1f",
"most_frequent_color_ratio": 95.05211947265502
}
} | [] | "algorithm" | "9741bf89-a3a7-404d-a0c2-61fe903d957a" | 1864 | hard |
|
\begin{algorithm}[htbp]
\caption{Noisy Nodes algorithm}\label{alg:nn}
\begin{algorithmic}[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-level noise of each atom
\Statex ${\rm LabelHead}_{\theta_{l}}$: Network module with parameter $\theta_{l}$ for prediction of graph-level label of $x_{i}$
\Statex$X$: Training dataset
\Statex$x_i$: Input conformation
\Statex$y_i$: Label of $x_i$
\Statex$T$: Training steps
\Statex$\mathcal N$: Gaussian distribution
\Statex$\lambda_{p}$: Loss weight of property prediction loss
\Statex$\lambda_{n}$: Loss weight of Noisy Nodes loss
\While{$T \neq 0$}
\State $x_i, y_i$ = dataloader($X$) \Comment{random sample $x_i$ and corresponding label $y_i$ from $X$}
\State $\tilde{x} = x_{i} + \Delta{x_i}$ , where $\Delta{x_i} \sim \mathcal{N}(0, {\tau}^2I_{3N})$, $N$ is atom number of $x_i$
\State $y_{i}^{pred}={\rm LabelHead}_{\theta_{l}}(GNN_{\theta}(\tilde{x}))$
\State $\Delta{x_i}^{pred}={\rm NoiseHead}_{\theta_{n}}(GNN_{\theta}(\tilde{x}))$
\State Loss = $\lambda_{p}$PropertyPredictionLoss$(y_{i}^{pred}, y_i)$+$\lambda_{n}||\Delta{x_i}^{pred} - \Delta{x_i}||_{2}^{2}$
\State Optimise(Loss)
\State $T = T - 1$
\EndWhile
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[htbp]
\caption{Noisy Nodes algorithm}\begin{algorithmic}
[1]
\Require
\Statex$\tau$: Scale of coordinate noise
\Statex$GNN_{\theta}$: Graph Neural Network with parameter $\theta$
\Statex ${\rm NoiseHead}_{\theta_{n}}$: Network module with parameter $\theta_{n}$ for prediction of node-level noise of each atom
\Statex ${\rm LabelHead}_{\theta_{l}}$: Network module with parameter $\theta_{l}$ for prediction of graph-level label of $x_{i}$
\Statex$X$: Training dataset
\Statex$x_i$: Input conformation
\Statex$y_i$: Label of $x_i$
\Statex$T$: Training steps
\Statex$\mathcal N$: Gaussian distribution
\Statex$\lambda_{p}$: Loss weight of property prediction loss
\Statex$\lambda_{n}$: Loss weight of Noisy Nodes loss
\While{$T \neq 0$}
\State $x_i, y_i$ = dataloader($X$) \Comment{random sample $x_i$ and corresponding label $y_i$ from $X$}
\State $\tilde{x} = x_{i} + \Delta{x_i}$ , where $\Delta{x_i} \sim \mathcal{N}(0, {\tau}^2I_{3N})$, $N$ is atom number of $x_i$
\State $y_{i}^{pred}={\rm LabelHead}_{\theta_{l}}(GNN_{\theta}(\tilde{x}))$
\State $\Delta{x_i}^{pred}={\rm NoiseHead}_{\theta_{n}}(GNN_{\theta}(\tilde{x}))$
\State Loss = $\lambda_{p}$PropertyPredictionLoss$(y_{i}^{pred}, y_i)$+$\lambda_{n}||\Delta{x_i}^{pred} - \Delta{x_i}||_{2}^{2}$
\State Optimise(Loss)
\State $T = T - 1$
\EndWhile
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2307.10683" | "2307.10683.tar.gz" | "2024-02-26" | {
"title": "fractional denoising for 3d molecular pre-training",
"id": "2307.10683",
"abstract": "coordinate denoising is a promising 3d molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. the underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. to tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. however, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. to this end, we propose to decouple the two types of noise and design a novel fractional denoising method (frad), which only denoises the latter coordinate part. in this way, frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. extensive experiments show the effectiveness of frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of qm9 and on 7 out of 8 targets of md17.",
"categories": "q-bio.qm cs.lg physics.chem-ph",
"doi": "",
"created": "2023-07-20",
"updated": "2024-02-26",
"authors": [
"shikun feng",
"yuyan ni",
"yanyan lan",
"zhi-ming ma",
"wei-ying ma"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2307.10683"
} | "2024-03-15T02:31:31.211598" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.0060317097,
"severe_toxicity_score": 0.00049829483,
"identity_attack_score": 0.0013781702,
"insult_score": 0.0068003717,
"profanity_score": 0.012141321,
"threat_score": 0.005935457,
"sexually_explicit_score": 0.005691297,
"flirtation_score": 0.36217216
},
"icml2023.sty": {
"toxicity_score": 0.007979449,
"severe_toxicity_score": 0.0008583069,
"identity_attack_score": 0.003089321,
"insult_score": 0.006847872,
"profanity_score": 0.012858666,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.010497936,
"flirtation_score": 0.4737403
},
"algorithm.sty": {
"toxicity_score": 0.009110395,
"severe_toxicity_score": 0.0008392334,
"identity_attack_score": 0.002885833,
"insult_score": 0.0071043735,
"profanity_score": 0.012277958,
"threat_score": 0.0072106146,
"sexually_explicit_score": 0.009259416,
"flirtation_score": 0.37593517
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"main.bbl": {
"toxicity_score": 0.012817383,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.003903274,
"insult_score": 0.007721877,
"profanity_score": 0.015113175,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.012326228,
"flirtation_score": 0.21668419
},
"algorithmic.sty": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.00096797943,
"identity_attack_score": 0.003311308,
"insult_score": 0.0072278744,
"profanity_score": 0.012585391,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.010321004,
"flirtation_score": 0.3980075
},
"main.tex": {
"toxicity_score": 0.0074139764,
"severe_toxicity_score": 0.00064373016,
"identity_attack_score": 0.0017573982,
"insult_score": 0.0070378734,
"profanity_score": 0.011116544,
"threat_score": 0.006220264,
"sexually_explicit_score": 0.005956694,
"flirtation_score": 0.38869497
},
"icml2023.bst": {
"toxicity_score": 0.015330595,
"severe_toxicity_score": 0.0015735626,
"identity_attack_score": 0.0034777985,
"insult_score": 0.008690883,
"profanity_score": 0.020032106,
"threat_score": 0.0077802283,
"sexually_explicit_score": 0.018990649,
"flirtation_score": 0.5348196
}
},
"reason": null
}
} | {
"num_done": {
"equation": 3,
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.80857252484745,
"hash": "0f033f3f0f1d8f3f",
"most_frequent_color_ratio": 91.80857252484745
}
} | [] | "algorithm" | "6c26f623-77f2-4dce-88a6-0f8337a35300" | 1359 | hard |
|
\begin{algorithm}[h!]
\caption{Distance matrix computation with Gzip}
\label{algo_gzip}
\scriptsize
\begin{algorithmic}[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hspace{0.2cm} s_{1}$}
\State \texttt{ $Cs_{1} \gets Gzip\hspace{0.2cm} compressed \hspace{0.2cm} Es_{1}$}
\State \texttt{ $Ls_{1} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{1}$}
\State \texttt{ $D \_ local \gets [\ ]$}
\For{\texttt{ $s_{2}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{2} \gets encoded \hspace{0.2cm} s_{2}$}
\State \texttt{ $Cs_{2} \gets Gzip \hspace{0.2cm} compressed \hspace{0.2cm} Es_{2}$}
\State \texttt{ $Ls_{2} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{2}$}
\State \texttt{ $s_{1}s_{2} \gets Concatenate(s_{1},s_{2})$}
\State \texttt{ $Es_{1}s_{2} \gets encoded \hspace{0.2cm} s_{1}s_{2}$}
\State \texttt{ $Cs_{1}s_{2} \gets Gzip \hspace{0.2cm} compressed \hspace{0.2cm} Es_{1}s_{2}$}
\State \texttt{ $Ls_{1}s_{2} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{1}s_{2}$}
\State NCD $\gets$ $\dfrac{L s_1 s_2 - Min (Ls_1, Ls_2)}{Max(Ls_1, Ls_2)}$
\State \texttt{ $D\_local.append(NCD)$}
\EndFor
\State $D.append(D\_local)$
\EndFor
\State return $D$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[h!]
\caption{Distance matrix computation with Gzip}
\scriptsize
\begin{algorithmic}
[1]
\Statex \textbf{Input:}\texttt{ Set of sequences(S)}
\Statex \textbf{Output:}\texttt{ Distance Matrix(D)}
\For{\texttt{ $s_{1}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{1} \gets encoded \hspace{0.2cm} s_{1}$}
\State \texttt{ $Cs_{1} \gets Gzip\hspace{0.2cm} compressed \hspace{0.2cm} Es_{1}$}
\State \texttt{ $Ls_{1} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{1}$}
\State \texttt{ $D \_ local \gets [\ ]$}
\For{\texttt{ $s_{2}$ in S\hspace{0.2cm}}}
\State \texttt{ $Es_{2} \gets encoded \hspace{0.2cm} s_{2}$}
\State \texttt{ $Cs_{2} \gets Gzip \hspace{0.2cm} compressed \hspace{0.2cm} Es_{2}$}
\State \texttt{ $Ls_{2} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{2}$}
\State \texttt{ $s_{1}s_{2} \gets Concatenate(s_{1},s_{2})$}
\State \texttt{ $Es_{1}s_{2} \gets encoded \hspace{0.2cm} s_{1}s_{2}$}
\State \texttt{ $Cs_{1}s_{2} \gets Gzip \hspace{0.2cm} compressed \hspace{0.2cm} Es_{1}s_{2}$}
\State \texttt{ $Ls_{1}s_{2} \gets length \hspace{0.2cm} of \hspace{0.2cm} Cs_{1}s_{2}$}
\State NCD $\gets$ $\dfrac{L s_1 s_2 - Min (Ls_1, Ls_2)}{Max(Ls_1, Ls_2)}$
\State \texttt{ $D\_local.append(NCD)$}
\EndFor
\State $D.append(D\_local)$
\EndFor
\State return $D$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.08117" | "2402.08117.tar.gz" | "2024-02-12" | {
"title": "a universal non-parametric approach for improved molecular sequence analysis",
"id": "2402.08117",
"abstract": "in the field of biological research, it is essential to comprehend the characteristics and functions of molecular sequences. the classification of molecular sequences has seen widespread use of neural network-based techniques. despite their astounding accuracy, these models often require a substantial number of parameters and more data collection. in this work, we present a novel approach based on the compression-based model, motivated from \\cite{jiang2023low}, which combines the simplicity of basic compression algorithms like gzip and bz2, with normalized compression distance (ncd) algorithm to achieve better performance on classification tasks without relying on handcrafted features or pre-trained models. firstly, we compress the molecular sequence using well-known compression algorithms, such as gzip and bz2. by leveraging the latent structure encoded in compressed files, we compute the normalized compression distance between each pair of molecular sequences, which is derived from the kolmogorov complexity. this gives us a distance matrix, which is the input for generating a kernel matrix using a gaussian kernel. next, we employ kernel principal component analysis (pca) to get the vector representations for the corresponding molecular sequence, capturing important structural and functional information. the resulting vector representations provide an efficient yet effective solution for molecular sequence analysis and can be used in ml-based downstream tasks. the proposed approach eliminates the need for computationally intensive deep neural networks (dnns), with their large parameter counts and data requirements. instead, it leverages a lightweight and universally accessible compression-based model.",
"categories": "cs.lg q-bio.qm",
"doi": "",
"created": "2024-02-12",
"updated": "",
"authors": [
"sarwan ali",
"tamkanat e ali",
"prakash chourasia",
"murray patterson"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.08117"
} | "2024-03-15T04:40:32.096340" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"llncs.tex": {
"toxicity_score": 0.0032200534,
"severe_toxicity_score": 0.0004005432,
"identity_attack_score": 0.0009619443,
"insult_score": 0.005869366,
"profanity_score": 0.009750174,
"threat_score": 0.005838364,
"sexually_explicit_score": 0.0049540824,
"flirtation_score": 0.60371745
},
"source_files/Basic_flow_diagram.graphml": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0010442734,
"identity_attack_score": 0.004402745,
"insult_score": 0.007778878,
"profanity_score": 0.015318131,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.3600297
},
"splncs04.bst": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0029598286,
"insult_score": 0.007797878,
"profanity_score": 0.015454767,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.39611804
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"source_files/Flow_diagram_gzip.graphml": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0010442734,
"identity_attack_score": 0.004402745,
"insult_score": 0.007778878,
"profanity_score": 0.015318131,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.3600297
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"main.tex": {
"toxicity_score": 0.034041706,
"severe_toxicity_score": 0.0018119812,
"identity_attack_score": 0.0048097214,
"insult_score": 0.015948927,
"profanity_score": 0.022833163,
"threat_score": 0.00866054,
"sexually_explicit_score": 0.016749516,
"flirtation_score": 0.26077706
},
"references.bib": {
"toxicity_score": 0.0060002944,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0011561831,
"insult_score": 0.006724371,
"profanity_score": 0.011680172,
"threat_score": 0.0058448366,
"sexually_explicit_score": 0.006752886,
"flirtation_score": 0.23795502
},
"main.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.66365870479558,
"hash": "001f1f1f9f8f1f3f",
"most_frequent_color_ratio": 93.66365870479558
}
} | [] | "algorithm" | "b663ba9a-2624-4817-8f27-d393d80853d6" | 1315 | hard |
|
\begin{algorithm}[h]
\caption{Coupled MCMC}
\label{alg:cmcmc}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$, iteration number $M\in\mathbb{N}$ and proposal $q_l$.}
\item{Initialize: Sample $\theta_0^{l}$ from the prior and then run Algorithm \ref{alg:dpf} with parameter $\theta_0^{l}$ to give $(\overline{w}_{0,\Delta_l:T}^l(l),\overline{w}_{0,\Delta_{l-1}:T}^{l-1}(l))$ , denoting the normalizing constant estimate $\hat{\tilde{p}}_{\theta_0^{l}}^N(y_{1:T})$. Set $k=1$.}
\item{Iterate:
Sample $\theta'|\theta_{k-1}^{l}$ from the proposal $q_l(\cdot|\theta_{k-1}^{l})$ and then run Algorithm \ref{alg:dpf} with parameter $\theta'$, denoting the normalizing constant estimate $\hat{\tilde{p}}_{\theta'}^N(y_{1:T})$ and proposed paths $({\overline{w}_{\theta',\Delta_l:T}^l},\overline{w}_{\theta',\Delta_{l-1}:T}^{l-1})$.
Set $\theta_k^{l}=\theta'$, $(\overline{w}_{k,\Delta_l:T}^l(l),\overline{w}_{k,\Delta_{l-1}:T}^{l-1}(l))=({\overline{w}_{\theta',\Delta_l:T}^l},\overline{w}_{\theta',\Delta_{l-1}:T}^{l-1})$ and $\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})=\hat{\tilde{p}}_{\theta'}^N(y_{1:T})$ with probability
$$
\min\left\{1,\frac{\hat{\tilde{p}}_{\theta'}^N(y_{1:T})\pi(\theta')q_l(\theta_k^{l}|\theta')}{\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})\pi(\theta_k^{l})q_l(\theta'|\theta_k^{l})}\right\}
$$
otherwise set $\theta_k^{l}=\theta_{k-1}^l$, $(\overline{w}_{k,\Delta_l:T}^l(l),\overline{w}_{k,\Delta_{l-1}:T}^{l-1}(l))=(\overline{w}_{k-1,\Delta_l:T}^l(l),\overline{w}_{k-1,\Delta_{l-1}:T}^{l-1}(l))$ and $\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})=\hat{\tilde{p}}_{\theta_{k-1}^l}^N(y_{1:T})$. Set $k=k+1$ and if $k=M+1$ go to step 4, otherwise go to the start to step 3.}
\item{Output: $(\theta_{0:M}^{l},\overline{w}_{0:M,\Delta_l:T}^l(l),\overline{w}_{0:M,\Delta_{l-1}:T}^{l-1}(l))$.}
\end{enumerate}
\end{algorithm}
| \begin{algorithm}
[h]
\caption{Coupled MCMC}
\begin{enumerate}
\item{Input: data $y_{1:T}$, level $l\in\mathbb{N}$, particle number $N\in\mathbb{N}$, iteration number $M\in\mathbb{N}$ and proposal $q_l$.}
\item{Initialize: Sample $\theta_0^{l}$ from the prior and then run Algorithm \ref{alg:dpf} with parameter $\theta_0^{l}$ to give $(\overline{w}_{0,\Delta_l:T}^l(l),\overline{w}_{0,\Delta_{l-1}:T}^{l-1}(l))$ , denoting the normalizing constant estimate $\hat{\tilde{p}}_{\theta_0^{l}}^N(y_{1:T})$. Set $k=1$.}
\item{Iterate:
Sample $\theta'|\theta_{k-1}^{l}$ from the proposal $q_l(\cdot|\theta_{k-1}^{l})$ and then run Algorithm \ref{alg:dpf} with parameter $\theta'$, denoting the normalizing constant estimate $\hat{\tilde{p}}_{\theta'}^N(y_{1:T})$ and proposed paths $({\overline{w}_{\theta',\Delta_l:T}^l},\overline{w}_{\theta',\Delta_{l-1}:T}^{l-1})$.
Set $\theta_k^{l}=\theta'$, $(\overline{w}_{k,\Delta_l:T}^l(l),\overline{w}_{k,\Delta_{l-1}:T}^{l-1}(l))=({\overline{w}_{\theta',\Delta_l:T}^l},\overline{w}_{\theta',\Delta_{l-1}:T}^{l-1})$ and $\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})=\hat{\tilde{p}}_{\theta'}^N(y_{1:T})$ with probability
$$
\min\left\{1,\frac{\hat{\tilde{p}}_{\theta'}^N(y_{1:T})\pi(\theta')q_l(\theta_k^{l}|\theta')}{\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})\pi(\theta_k^{l})q_l(\theta'|\theta_k^{l})}\right\}
$$
otherwise set $\theta_k^{l}=\theta_{k-1}^l$, $(\overline{w}_{k,\Delta_l:T}^l(l),\overline{w}_{k,\Delta_{l-1}:T}^{l-1}(l))=(\overline{w}_{k-1,\Delta_l:T}^l(l),\overline{w}_{k-1,\Delta_{l-1}:T}^{l-1}(l))$ and $\hat{\tilde{p}}_{\theta_k^{l}}^N(y_{1:T})=\hat{\tilde{p}}_{\theta_{k-1}^l}^N(y_{1:T})$. Set $k=k+1$ and if $k=M+1$ go to step 4, otherwise go to the start to step 3.}
\item{Output: $(\theta_{0:M}^{l},\overline{w}_{0:M,\Delta_l:T}^l(l),\overline{w}_{0:M,\Delta_{l-1}:T}^{l-1}(l))$.}
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/2310.03114" | "2310.03114.tar.gz" | "2024-02-19" | {
"title": "bayesian parameter inference for partially observed stochastic volterra equations",
"id": "2310.03114",
"abstract": "in this article we consider bayesian parameter inference for a type of partially observed stochastic volterra equation (sve). sves are found in many areas such as physics and mathematical finance. in the latter field they can be used to represent long memory in unobserved volatility processes. in many cases of practical interest, sves must be time-discretized and then parameter inference is based upon the posterior associated to this time-discretized process. based upon recent studies on time-discretization of sves (e.g. richard et al. 2021), we use euler-maruyama methods for the afore-mentioned discretization. we then show how multilevel markov chain monte carlo (mcmc) methods (jasra et al. 2018) can be applied in this context. in the examples we study, we give a proof that shows that the cost to achieve a mean square error (mse) of $\\mathcal{o}(\\epsilon^2)$, $\\epsilon>0$, is {$\\mathcal{o}(\\epsilon^{-\\tfrac{4}{2h+1}})$, where $h$ is the hurst parameter. if one uses a single level mcmc method then the cost is $\\mathcal{o}(\\epsilon^{-\\tfrac{2(2h+3)}{2h+1}})$} to achieve the same mse. we illustrate these results in the context of state-space and stochastic volatility models, with the latter applied to real data.",
"categories": "stat.co stat.me",
"doi": "",
"created": "2023-10-04",
"updated": "2024-02-19",
"authors": [
"ajay jasra",
"hamza ruzayqat",
"amin wu"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2310.03114"
} | "2024-03-15T05:09:03.161347" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"rs_bpe_final.tex": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.004236255,
"insult_score": 0.0075128763,
"profanity_score": 0.015454767,
"threat_score": 0.007573096,
"sexually_explicit_score": 0.013033954,
"flirtation_score": 0.22880295
},
"rs_bpe_final.out": {
"toxicity_score": 0.006440107,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0012024303,
"insult_score": 0.0060783676,
"profanity_score": 0.015591404,
"threat_score": 0.005935457,
"sexually_explicit_score": 0.014449406,
"flirtation_score": 0.34677386
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.10584912969048,
"hash": "048c8700ffe3b187",
"most_frequent_color_ratio": 91.10584912969048
}
} | [] | "algorithm" | "6f9cb0c5-bf27-416d-86c5-f27e90eca495" | 1869 | hard |
|
\begin{algorithmic}[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s on diagonal of $\mathbf I$}.
\For{$i = n,n-1,...,2$}
\State $v_{i} \gets v_{i-1}$
\EndFor
\State $v_{1} \gets 0$
\EndIf
\State Identify the set of groups $G$ of elements of $v$ corresponding to equal entries in $x$.
\State $\tilde v \gets v$
\For{$g$ in $G$}
\State Calculate sum $\mathbf s$ of $v$'s elements in group $g$.
\State Set $0$ in all $\tilde v$'s elements in group $g$.
\If{$\tilde \omega = 0$}
\State Set the $\tilde v$'s first element in group $g$ to $\mathbf s$.
\Else
\State Set the $\tilde v$'s last element in group $g$ to $\mathbf s$.
\EndIf
\EndFor
\State $c_{\tilde \omega} \gets$ cumulative sum of $\tilde v$
\EndFor
\State $c \gets \omega c_1 + (1-\omega)c_0$
\Comment Multiplication and addition element-wise.
\State Find the inverse of $\sigma$.
\State Apply the $\sigma^{-1}$ to $c$.
\State \Return $c$
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s on diagonal of $\mathbf I$}.
\For{$i = n,n-1,...,2$}
\State $v_{i} \gets v_{i-1}$
\EndFor
\State $v_{1} \gets 0$
\EndIf
\State Identify the set of groups $G$ of elements of $v$ corresponding to equal entries in $x$.
\State $\tilde v \gets v$
\For{$g$ in $G$}
\State Calculate sum $\mathbf s$ of $v$'s elements in group $g$.
\State Set $0$ in all $\tilde v$'s elements in group $g$.
\If{$\tilde \omega = 0$}
\State Set the $\tilde v$'s first element in group $g$ to $\mathbf s$.
\Else
\State Set the $\tilde v$'s last element in group $g$ to $\mathbf s$.
\EndIf
\EndFor
\State $c_{\tilde \omega} \gets$ cumulative sum of $\tilde v$
\EndFor
\State $c \gets \omega c_1 + (1-\omega)c_0$
\Comment Multiplication and addition element-wise.
\State Find the inverse of $\sigma$.
\State Apply the $\sigma^{-1}$ to $c$.
\State \Return $c$
\end{algorithmic} | "https://arxiv.org/src/2401.15205" | "2401.15205.tar.gz" | "2024-01-26" | {
"title": "csranks: an r package for estimation and inference involving ranks",
"id": "2401.15205",
"abstract": "this article introduces the r package csranks for estimation and inference involving ranks. first, we review methods for the construction of confidence sets for ranks, namely marginal and simultaneous confidence sets as well as confidence sets for the identities of the tau-best. second, we review methods for estimation and inference in regressions involving ranks. third, we describe the implementation of these methods in csranks and illustrate their usefulness in two examples: one about the quantification of uncertainty in the pisa ranking of countries and one about the measurement of intergenerational mobility using rank-rank regressions.",
"categories": "econ.em",
"doi": "",
"created": "2024-01-26",
"updated": "",
"authors": [
"denis chetverikov",
"magne mogstad",
"pawel morgen",
"joseph romano",
"azeem shaikh",
"daniel wilhelm"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.15205"
} | "2024-03-15T05:34:18.919334" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"jss.bst": {
"toxicity_score": 0.020960633,
"severe_toxicity_score": 0.0016307831,
"identity_attack_score": 0.004236255,
"insult_score": 0.010818896,
"profanity_score": 0.020168742,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.016513607,
"flirtation_score": 0.3519259
},
"article.tex": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0039587705,
"insult_score": 0.007797878,
"profanity_score": 0.016479544,
"threat_score": 0.0076637166,
"sexually_explicit_score": 0.014036566,
"flirtation_score": 0.19115013
},
"article.bbl": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0011014938,
"identity_attack_score": 0.0037367835,
"insult_score": 0.007493876,
"profanity_score": 0.014464149,
"threat_score": 0.0074306927,
"sexually_explicit_score": 0.01102873,
"flirtation_score": 0.2327938
},
"jss.cls": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.00362579,
"insult_score": 0.00808288,
"profanity_score": 0.015010698,
"threat_score": 0.0071070488,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.41704082
},
"Sweave.sty": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0016117096,
"identity_attack_score": 0.0010498142,
"insult_score": 0.0062208683,
"profanity_score": 0.018665737,
"threat_score": 0.0057865805,
"sexually_explicit_score": 0.026067909,
"flirtation_score": 0.31151596
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.27648368185095,
"hash": "070fbf3f87873f3f",
"most_frequent_color_ratio": 93.27648368185095
}
} | [] | "algorithm" | "9d127570-44a1-4dbc-b561-5220d71eed5d" | 1162 | hard |
|
\begin{algorithm}
\caption{Multiplication of indicator matrix of vector $x$ with arbitrary vector $v$}
\label{alg:ind-mat-mult}
\begin{algorithmic}[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s on diagonal of $\mathbf I$}.
\For{$i = n,n-1,...,2$}
\State $v_{i} \gets v_{i-1}$
\EndFor
\State $v_{1} \gets 0$
\EndIf
\State Identify the set of groups $G$ of elements of $v$ corresponding to equal entries in $x$.
\State $\tilde v \gets v$
\For{$g$ in $G$}
\State Calculate sum $\mathbf s$ of $v$'s elements in group $g$.
\State Set $0$ in all $\tilde v$'s elements in group $g$.
\If{$\tilde \omega = 0$}
\State Set the $\tilde v$'s first element in group $g$ to $\mathbf s$.
\Else
\State Set the $\tilde v$'s last element in group $g$ to $\mathbf s$.
\EndIf
\EndFor
\State $c_{\tilde \omega} \gets$ cumulative sum of $\tilde v$
\EndFor
\State $c \gets \omega c_1 + (1-\omega)c_0$
\Comment Multiplication and addition element-wise.
\State Find the inverse of $\sigma$.
\State Apply the $\sigma^{-1}$ to $c$.
\State \Return $c$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Multiplication of indicator matrix of vector $x$ with arbitrary vector $v$}
\begin{algorithmic}
[1]
\Require $n,p\in\mathbb N, \omega \in [0,1], x\in\mathbb R^n, v \in\mathbb R^{n}$.
\State Find a sorting permutation $\sigma$ of vector $x$.
\State Apply the $\sigma$ to $x$ and $v$, in place.
\For{$\tilde \omega = 1,0$}
\If{$\tilde \omega = 0$}
\Comment{Shift $v$ by one place because of 0s on diagonal of $\mathbf I$}.
\For{$i = n,n-1,...,2$}
\State $v_{i} \gets v_{i-1}$
\EndFor
\State $v_{1} \gets 0$
\EndIf
\State Identify the set of groups $G$ of elements of $v$ corresponding to equal entries in $x$.
\State $\tilde v \gets v$
\For{$g$ in $G$}
\State Calculate sum $\mathbf s$ of $v$'s elements in group $g$.
\State Set $0$ in all $\tilde v$'s elements in group $g$.
\If{$\tilde \omega = 0$}
\State Set the $\tilde v$'s first element in group $g$ to $\mathbf s$.
\Else
\State Set the $\tilde v$'s last element in group $g$ to $\mathbf s$.
\EndIf
\EndFor
\State $c_{\tilde \omega} \gets$ cumulative sum of $\tilde v$
\EndFor
\State $c \gets \omega c_1 + (1-\omega)c_0$
\Comment Multiplication and addition element-wise.
\State Find the inverse of $\sigma$.
\State Apply the $\sigma^{-1}$ to $c$.
\State \Return $c$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.15205" | "2401.15205.tar.gz" | "2024-01-26" | {
"title": "csranks: an r package for estimation and inference involving ranks",
"id": "2401.15205",
"abstract": "this article introduces the r package csranks for estimation and inference involving ranks. first, we review methods for the construction of confidence sets for ranks, namely marginal and simultaneous confidence sets as well as confidence sets for the identities of the tau-best. second, we review methods for estimation and inference in regressions involving ranks. third, we describe the implementation of these methods in csranks and illustrate their usefulness in two examples: one about the quantification of uncertainty in the pisa ranking of countries and one about the measurement of intergenerational mobility using rank-rank regressions.",
"categories": "econ.em",
"doi": "",
"created": "2024-01-26",
"updated": "",
"authors": [
"denis chetverikov",
"magne mogstad",
"pawel morgen",
"joseph romano",
"azeem shaikh",
"daniel wilhelm"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.15205"
} | "2024-03-15T05:34:18.919334" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"jss.bst": {
"toxicity_score": 0.020960633,
"severe_toxicity_score": 0.0016307831,
"identity_attack_score": 0.004236255,
"insult_score": 0.010818896,
"profanity_score": 0.020168742,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.016513607,
"flirtation_score": 0.3519259
},
"article.tex": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0039587705,
"insult_score": 0.007797878,
"profanity_score": 0.016479544,
"threat_score": 0.0076637166,
"sexually_explicit_score": 0.014036566,
"flirtation_score": 0.19115013
},
"article.bbl": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0011014938,
"identity_attack_score": 0.0037367835,
"insult_score": 0.007493876,
"profanity_score": 0.014464149,
"threat_score": 0.0074306927,
"sexually_explicit_score": 0.01102873,
"flirtation_score": 0.2327938
},
"jss.cls": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.00362579,
"insult_score": 0.00808288,
"profanity_score": 0.015010698,
"threat_score": 0.0071070488,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.41704082
},
"Sweave.sty": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0016117096,
"identity_attack_score": 0.0010498142,
"insult_score": 0.0062208683,
"profanity_score": 0.018665737,
"threat_score": 0.0057865805,
"sexually_explicit_score": 0.026067909,
"flirtation_score": 0.31151596
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.27648368185095,
"hash": "070fbf3f87873f3f",
"most_frequent_color_ratio": 93.27648368185095
}
} | [] | "algorithm" | "86deadbe-3f00-473d-85a1-1eebf50a7ec8" | 1281 | hard |
|
\begin{algorithm}[H]
\caption{Reconstruction($\mathcal{P}$)}
\begin{algorithmic}[1]\label{alg}
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=Est(u,w)$
\EndFor
\State $x_u,x_v \gets $ middle vertices of $Int'(u,v)$.
\For{$w \not\in Int'(u,v)$}
\If{$Est(x_u,w)>Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)+Est(x_u,w)$
\EndIf
\If{$Est(x_u,w)<Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)-Est(x_u,w)$
\EndIf
\EndFor
\Return
\EndIf
\State Return $Emb$
\EndFor
\State Return FALSE
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{Reconstruction($\mathcal{P}$)}
\begin{algorithmic}[1]
\For{$i=1...\ln(n)$}
\State Sample $u,v $ from $V$ uniformly.
\State Compute $Est$ on $\{u,v\} \times V$.
\State Compute $Int(u,v)$.
\If{ $|Int(u,v)|\geq \frac{n}{2}$}
\State Compute $Int'(u,v)$.
\For{$w \in Int'(u,v)$}
\State $Emb(w)=Est(u,w)$
\EndFor
\State $x_u,x_v \gets $ middle vertices of $Int'(u,v)$.
\For{$w \not\in Int'(u,v)$}
\If{$Est(x_u,w)>Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)+Est(x_u,w)$
\EndIf
\If{$Est(x_u,w)<Est(x_v,w)$}
\State $Emb(w)= Emb(x_u)-Est(x_u,w)$
\EndIf
\EndFor
\Return
\EndIf
\State Return $Emb$
\EndFor
\State Return FALSE
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2208.13855" | "2208.13855.tar.gz" | "2024-01-27" | {
"title": "determining a points configuration on the line from a subset of the pairwise distances",
"id": "2208.13855",
"abstract": "we investigate rigidity-type problems on the real line and the circle in the non-generic setting. specifically, we consider the problem of uniquely determining the positions of $n$ distinct points $v = {v_1, \\ldots, v_n}$ given a set of mutual distances $\\mathcal{p} \\subseteq {v \\choose 2}$. we establish an extremal result: if $|\\mathcal{p}| = \\omega(n^{3/2})$, then the positions of a large subset $v' \\subseteq v$, where large means $|v'| = \\omega(\\frac{|\\mathcal{p}|}{n})$, can be uniquely determined up to isometry. as a main ingredient in the proof, which may be of independent interest, we show that dense graphs $g=(v,e)$ for which every two non-adjacent vertices have only a few common neighbours must have large cliques. furthermore, we examine the problem of reconstructing $v$ from a random distance set $\\mathcal{p}$. we establish that if the distance between each pair of points is known independently with probability $p = \\frac{c \\ln(n)}{n}$ for some universal constant $c > 0$, then $v$ can be reconstructed from the distances with high probability. we provide a randomized algorithm with linear expected running time that returns the correct embedding of $v$ to the line with high probability. since we posted a preliminary version of the paper on arxiv, follow-up works have improved upon our results in the random setting. gir\\~ao, illingworth, michel, powierski, and scott proved a hitting time result for the first moment at which an time at which one can reconstruct $v$ when $\\mathcal{p}$ is revealed using the erd\\\"os--r\\'enyi evolution, our extremal result lies in the heart of their argument. montgomery, nenadov and szab\\'o resolved a conjecture we posed in a previous version and proved that w.h.p a graph sampled from the erd\\\"os--r\\'enyi evolution becomes globally rigid in $\\mathbb{r}$ at the moment it's minimum degree is $2$.",
"categories": "math.mg math.co math.pr",
"doi": "",
"created": "2022-08-29",
"updated": "2024-01-27",
"authors": [
"itai benjamini",
"elad tzalik"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.13855"
} | "2024-03-15T05:18:52.114805" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"ref.bib": {
"toxicity_score": 0.009298885,
"severe_toxicity_score": 0.0009775162,
"identity_attack_score": 0.0027378413,
"insult_score": 0.007493876,
"profanity_score": 0.014361672,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.20229049
},
"main.tex": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0033298072,
"insult_score": 0.0072658747,
"profanity_score": 0.014839902,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.25617662
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.80473599164607,
"hash": "0387c7c1c0c08f1f",
"most_frequent_color_ratio": 91.80473599164607
}
} | [] | "algorithm" | "3a7a9213-9913-4cde-9483-1ca68be7bab2" | 672 | easy |
|
\begin{algorithm}[h]
\caption{Randomized for Decentralized Min-Max (RDMM)}
\label{alg_sum}
\hspace*{\algorithmicindent} {\bf Parameters:} stepsize $\gamma$, probability $p$, probability $\rho$\\
\hspace*{\algorithmicindent} {\bf Initialization:} choose $ x^0,y^0$, $x^0_m = x^0$, $y^0_m = y^0$ for all $m$
\begin{algorithmic}[1]
\For {$k=0,1, 2, \ldots$ }
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $\bar x_m^k = (1 - \rho) x_m^k + \rho u^k_{x_m}$, \ \ $\bar y_m^k = (1 - \rho) y_m^k + \rho u^k_{y_m}$
\Statex All devices \textcolor{red}{\textbf{communicate}} to locally compute:
\State $\bar u_{x_m}^k = \lambda\sum_{i=1}^M w_{m,i} u_{x_m}^k$, \ \ $\bar u_{y_m}^k = \lambda\sum_{i=1}^M w_{m,i} u_{x_m}^k$
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $x_m^{k+\frac{1}{2}} = \bar x_m^k - \eta (\nabla_x f(u^k_{x_m}, u^k_{y_m}) + \bar u_{x_m}^k)$,
\State $y_m^{k+\frac{1}{2}} = \bar y_m^k - \eta ( - \nabla_y f(u^k_{x_m}, u^k_{y_m}) + \bar u_{y_m}^k) $
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\label{alg_sum:step5}
\Statex If $\xi^k = 0$ all devices \textcolor{red}{\textbf{communicate}} and compute: \label{alg_sum:step6}
\State \ \ \ $g_{x_m}^k = \frac{\lambda}{p}\sum_{i=1}^M w_{m,i} \left(x^{k+\frac{1}{2}}_{m} - u_{x_m}^k\right)$, \ \ \ $g_{y_m}^k = \frac{\lambda}{p}\sum_{i=1}^M w_{m,i} \left(y^{k+\frac{1}{2}}_{m} - u_{y_m}^k\right)$
\Statex If $\xi^k = 1$ all devices make \textcolor{blue}{\textbf{local computations}}: \label{alg_sum:step9}
\Statex \ \ \ Generate an vector of indexes $\hat{\xi}^k_m$ according to distribution $Q$
\State \ \ \ $g_{x_m}^k = \frac{1}{1-p}\left(\nabla_x f_{\hat{\xi}_m^k}\left(x_m^{k+\frac{1}{2}}, y_m^{k+\frac{1}{2}}\right) - \nabla_x f_{\hat{\xi}_m^k}(u_{x_m}^{k}, u_{y_m}^{k})\right)$
\State \ \ \ $g_{y_m}^k = - \frac{1}{1-p}\left(\nabla_
y f_{\hat{\xi}_m^k}\left(x_m^{k+\frac{1}{2}}, y_m^{k+\frac{1}{2}}\right) - \nabla_y f_{\hat{\xi}_m^k}(u_{x_m}^{k}, u_{y_m}^{k})\right)$
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $x_m^{k+1} = \bar x_m^k - \eta \left( g_{x_m}^k + \nabla_{x} f(u^k_{x_m}, u^k_{y_m})+ \bar u^k_{x_m}\right)$,
\State $y_m^{k+1} = \bar y_m^k - \eta \left(g
_{y_m}^k - \nabla_y f(u^k_{x_m}, u^k_{y_m}) + \bar u^k_{y_m}\right)$
\Statex Generate $\delta^{k}= \begin{cases}
1,& \text{with probability} ~~ 1 - \rho \\
0 ,& \text{with probability} ~~ \rho
\end{cases},$
\State $u^{k+1}_{x_m} = \delta^{k} u^k_{x_m} + (1 - \delta^{k}) x_m^{k+1}$, \ \ \ $u^{k+1}_{y_m} = \delta^{k} u^k_{y_m} + (1 - \delta^{k}) y_m^{k+1}$
\EndFor
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[h]
\caption{Randomized for Decentralized Min-Max (RDMM)}
\hspace*{\algorithmicindent} {\bf Parameters:} stepsize $\gamma$, probability $p$, probability $\rho$\\
\hspace*{\algorithmicindent} {\bf Initialization:} choose $ x^0,y^0$, $x^0_m = x^0$, $y^0_m = y^0$ for all $m$
\begin{algorithmic}
[1]
\For {$k=0,1, 2, \ldots$ }
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $\bar x_m^k = (1 - \rho) x_m^k + \rho u^k_{x_m}$, \ \ $\bar y_m^k = (1 - \rho) y_m^k + \rho u^k_{y_m}$
\Statex All devices \textcolor{red}{\textbf{communicate}} to locally compute:
\State $\bar u_{x_m}^k = \lambda\sum_{i=1}^M w_{m,i} u_{x_m}^k$, \ \ $\bar u_{y_m}^k = \lambda\sum_{i=1}^M w_{m,i} u_{x_m}^k$
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $x_m^{k+\frac{1}{2}} = \bar x_m^k - \eta (\nabla_x f(u^k_{x_m}, u^k_{y_m}) + \bar u_{x_m}^k)$,
\State $y_m^{k+\frac{1}{2}} = \bar y_m^k - \eta ( - \nabla_y f(u^k_{x_m}, u^k_{y_m}) + \bar u_{y_m}^k) $
\Statex Generate $\xi^k = \begin{cases}
1,& \text{with probability} ~~ 1 - p \\
0 ,& \text{with probability} ~~ p
\end{cases},$
\Statex If $\xi^k = 0$ all devices \textcolor{red}{\textbf{communicate}} and compute: \State \ \ \ $g_{x_m}^k = \frac{\lambda}{p}\sum_{i=1}^M w_{m,i} \left(x^{k+\frac{1}{2}}_{m} - u_{x_m}^k\right)$, \ \ \ $g_{y_m}^k = \frac{\lambda}{p}\sum_{i=1}^M w_{m,i} \left(y^{k+\frac{1}{2}}_{m} - u_{y_m}^k\right)$
\Statex If $\xi^k = 1$ all devices make \textcolor{blue}{\textbf{local computations}}: \Statex \ \ \ Generate an vector of indexes $\hat{\xi}^k_m$ according to distribution $Q$
\State \ \ \ $g_{x_m}^k = \frac{1}{1-p}\left(\nabla_x f_{\hat{\xi}_m^k}\left(x_m^{k+\frac{1}{2}}, y_m^{k+\frac{1}{2}}\right) - \nabla_x f_{\hat{\xi}_m^k}(u_{x_m}^{k}, u_{y_m}^{k})\right)$
\State \ \ \ $g_{y_m}^k = - \frac{1}{1-p}\left(\nabla_
y f_{\hat{\xi}_m^k}\left(x_m^{k+\frac{1}{2}}, y_m^{k+\frac{1}{2}}\right) - \nabla_y f_{\hat{\xi}_m^k}(u_{x_m}^{k}, u_{y_m}^{k})\right)$
\Statex \textcolor{blue}{\textbf{Local updates}} for all clients:
\State $x_m^{k+1} = \bar x_m^k - \eta \left( g_{x_m}^k + \nabla_{x} f(u^k_{x_m}, u^k_{y_m})+ \bar u^k_{x_m}\right)$,
\State $y_m^{k+1} = \bar y_m^k - \eta \left(g
_{y_m}^k - \nabla_y f(u^k_{x_m}, u^k_{y_m}) + \bar u^k_{y_m}\right)$
\Statex Generate $\delta^{k}= \begin{cases}
1,& \text{with probability} ~~ 1 - \rho \\
0 ,& \text{with probability} ~~ \rho
\end{cases},$
\State $u^{k+1}_{x_m} = \delta^{k} u^k_{x_m} + (1 - \delta^{k}) x_m^{k+1}$, \ \ \ $u^{k+1}_{y_m} = \delta^{k} u^k_{y_m} + (1 - \delta^{k}) y_m^{k+1}$
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2106.07289" | "2106.07289.tar.gz" | "2024-01-24" | {
"title": "decentralized personalized federated learning for min-max problems",
"id": "2106.07289",
"abstract": "personalized federated learning (pfl) has witnessed remarkable advancements, enabling the development of innovative machine learning applications that preserve the privacy of training data. however, existing theoretical research in this field has primarily focused on distributed optimization for minimization problems. this paper is the first to study pfl for saddle point problems encompassing a broader range of optimization problems, that require more than just solving minimization problems. in this work, we consider a recently proposed pfl setting with the mixing objective function, an approach combining the learning of a global model together with locally distributed learners. unlike most previous work, which considered only the centralized setting, we work in a more general and decentralized setup that allows us to design and analyze more practical and federated ways to connect devices to the network. we proposed new algorithms to address this problem and provide a theoretical analysis of the smooth (strongly) convex-(strongly) concave saddle point problems in stochastic and deterministic cases. numerical experiments for bilinear problems and neural networks with adversarial noise demonstrate the effectiveness of the proposed methods.",
"categories": "cs.lg cs.dc math.oc",
"doi": "",
"created": "2021-06-14",
"updated": "2024-01-24",
"authors": [
"ekaterina borodich",
"aleksandr beznosikov",
"abdurakhmon sadiev",
"vadim sushko",
"nikolay savelyev",
"martin tak\u00e1\u010d",
"alexander gasnikov"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2106.07289"
} | "2024-03-15T09:00:25.016199" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"bst/sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
},
"CMS.bbl": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0024603575,
"insult_score": 0.00654387,
"profanity_score": 0.012585391,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.5493274
},
"bst/sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"profanity_score": 0.015932998,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.39879754
},
"CMS.tex": {
"toxicity_score": 0.008607753,
"severe_toxicity_score": 0.0011777878,
"identity_attack_score": 0.0026453468,
"insult_score": 0.006819372,
"profanity_score": 0.01535229,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.25891307
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"bst/sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
},
"bst/sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"bst/sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"bst/sn-apacite.bst": {
"toxicity_score": 0.019351736,
"severe_toxicity_score": 0.0014781952,
"identity_attack_score": 0.004236255,
"insult_score": 0.009564889,
"profanity_score": 0.018187506,
"threat_score": 0.008103873,
"sexually_explicit_score": 0.015569973,
"flirtation_score": 0.39166427
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.08804600541373,
"hash": "0fc3a39f80c1a33b",
"most_frequent_color_ratio": 92.08804600541373
}
} | [] | "algorithm" | "ad3c6e42-e72f-4f1e-99cd-7a2b8eb41b3e" | 2610 | hard |
|
\begin{algorithmic}
\Require $\eta>0$\\
\State $x_{k+1}=x_k-\eta \nabla f(x_k)$
\end{algorithmic}
| \begin{algorithmic}
\Require $\eta>0$\\
\State $x_{k+1}=x_k-\eta \nabla f(x_k)$
\end{algorithmic} | "https://arxiv.org/src/2309.04877" | "2309.04877.tar.gz" | "2024-02-26" | {
"title": "a gentle introduction to gradient-based optimization and variational inequalities for machine learning",
"id": "2309.04877",
"abstract": "the rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. in these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. gradient-based methods remain essential -- given the high dimensionality and large scale of machine-learning problems -- but simple gradient descent is no longer the point of departure for algorithm design. we provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. while we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.",
"categories": "cs.lg stat.ml",
"doi": "",
"created": "2023-09-09",
"updated": "2024-02-26",
"authors": [
"neha s. wadia",
"yatin dandi",
"michael i. jordan"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.04877"
} | "2024-03-15T03:14:09.276985" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.0009775162,
"identity_attack_score": 0.003311308,
"insult_score": 0.007132874,
"profanity_score": 0.014327513,
"threat_score": 0.007340072,
"sexually_explicit_score": 0.011500548,
"flirtation_score": 0.2104973
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 96.1849061012296,
"hash": "f1dfdfccefe4e1e1",
"most_frequent_color_ratio": 96.1849061012296
}
} | [] | "algorithm" | "075ca4b9-ef3b-4779-9e2b-06af9f6e4597" | 97 | easy |
|
\begin{algorithm}[htb]
\caption{Recover Pathways}\label{path:alg}
\begin{algorithmic}[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of pathways of a given length $L$ that are prominently different between the two phenotypes.
\Statex $\triangleright$ Algorithm starts:
\State Compute and sort the list of genes in descending order in terms of the absolute value of their correlation coefficient with the pathological phenotype, denote this list as $\vec{g}$
\State Compute $C:= \mathrm{Diag}(\vec{g})(\tilde{B'}-\tilde{B})$
\State Fix a positive threshold $\theta$ and set $C_{ij}=0$ if $C_{ij}<\theta$, denote the resulting matrix as $\Pi_\theta$
\State Compute the set of paths of length $L$ in the graph with adjacency matrix $\Pi_\theta$ and return them.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[htb]
\caption{Recover Pathways}\begin{algorithmic}
[1]
\Statex Input: The underlying linear dynamical systems matrices $\tilde{B}$ and $\tilde{B'}$ and the correlations between each of the coordinates (corresponding to genes) and the two phenotypes of interest.
\Statex Output: A set of pathways of a given length $L$ that are prominently different between the two phenotypes.
\Statex $\triangleright$ Algorithm starts:
\State Compute and sort the list of genes in descending order in terms of the absolute value of their correlation coefficient with the pathological phenotype, denote this list as $\vec{g}$
\State Compute $C:= \mathrm{Diag}(\vec{g})(\tilde{B'}-\tilde{B})$
\State Fix a positive threshold $\theta$ and set $C_{ij}=0$ if $C_{ij}<\theta$, denote the resulting matrix as $\Pi_\theta$
\State Compute the set of paths of length $L$ in the graph with adjacency matrix $\Pi_\theta$ and return them.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.11858" | "2401.11858.tar.gz" | "2024-01-22" | {
"title": "approximating a linear dynamical system from non-sequential data",
"id": "2401.11858",
"abstract": "given non-sequential snapshots from instances of a dynamical system, we design a compressed sensing based algorithm that reconstructs the dynamical system. we formally prove that successful reconstruction is possible under the assumption that we can construct an approximate clock from a subset of the coordinates of the underlying system. as an application, we argue that our assumption is likely true for genomic datasets, and we recover the underlying nuclear receptor networks and predict pathways, as opposed to genes, that may differentiate phenotypes in some publicly available datasets.",
"categories": "q-bio.gn",
"doi": "",
"created": "2024-01-22",
"updated": "",
"authors": [
"cliff stein",
"pratik worah"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.11858"
} | "2024-03-15T07:04:04.342410" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"recomb_pathways.ipynb": {
"toxicity_score": 0.026145924,
"severe_toxicity_score": 0.0021266937,
"identity_attack_score": 0.00828752,
"insult_score": 0.011996903,
"profanity_score": 0.020373698,
"threat_score": 0.009929225,
"sexually_explicit_score": 0.021585643,
"flirtation_score": 0.35266018
},
"main.tex": {
"toxicity_score": 0.034041706,
"severe_toxicity_score": 0.0018119812,
"identity_attack_score": 0.0048097214,
"insult_score": 0.015948927,
"profanity_score": 0.022833163,
"threat_score": 0.00866054,
"sexually_explicit_score": 0.016749516,
"flirtation_score": 0.26077706
},
"main.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
},
"main.bib": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.002885833,
"insult_score": 0.00819688,
"profanity_score": 0.014771583,
"threat_score": 0.0074048014,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.17821212
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.1675392670157,
"hash": "00801f1f001f3f3f",
"most_frequent_color_ratio": 88.1675392670157
}
} | [] | "algorithm" | "e80af0ad-8e6d-41f9-add9-9d8d2a426ab3" | 962 | medium |
|
\begin{algorithm}[H]
\caption{Model-based deterministic policy REINFORCE} \label{alg: det reinforce}
\begin{algorithmic} [1]
\State Initialize deterministic policy $\mu_\theta$.
\State Choose a batch size $K$, a gradient based optimization algorithm, a corresponding learning rate $\lambda > 0$, a time step size $\Delta t$ and a stopping criterion.
\Repeat
\State Simulate $K$ trajectories by running the policy in the environment's dynamics.
\State Estimate the policy gradient $\nabla_\theta J(\mu_\theta)$ by
\begin{equation*}
\frac{1}{K} \sum\limits_{k=1}^K \sum\limits_{t=0}^{T^{(k)} -1} \Bigl(- \Delta t \, \mu_\theta(s_t^{(k)}) \cdot \nabla_\theta \mu_\theta(s_t^{(k)}) + G_0(\tau; \theta) \eta_{t+1} \cdot \nabla_\theta \mu_\theta(s_t^{(k)}) \Bigr).
\end{equation*}
\State Update the parameters $\theta$ based on the optimization algorithm.
\Until{stopping criterion is fulfilled.}
\end{algorithmic}
\label{alg: reinforce deterministic policy}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{Model-based deterministic policy REINFORCE} \begin{algorithmic}
[1]
\State Initialize deterministic policy $\mu_\theta$.
\State Choose a batch size $K$, a gradient based optimization algorithm, a corresponding learning rate $\lambda > 0$, a time step size $\Delta t$ and a stopping criterion.
\Repeat
\State Simulate $K$ trajectories by running the policy in the environment's dynamics.
\State Estimate the policy gradient $\nabla_\theta J(\mu_\theta)$ by
\begin{equation*}
\frac{1}{K} \sum\limits_{k=1}^K \sum\limits_{t=0}^{T^{(k)} -1} \Bigl(- \Delta t \, \mu_\theta(s_t^{(k)}) \cdot \nabla_\theta \mu_\theta(s_t^{(k)}) + G_0(\tau; \theta) \eta_{t+1} \cdot \nabla_\theta \mu_\theta(s_t^{(k)}) \Bigr).
\end{equation*}
\State Update the parameters $\theta$ based on the optimization algorithm.
\Until{stopping criterion is fulfilled.}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2211.02474" | "2211.02474.tar.gz" | "2024-02-15" | {
"title": "connecting stochastic optimal control and reinforcement learning",
"id": "2211.02474",
"abstract": "in this paper the connection between stochastic optimal control and reinforcement learning is investigated. our main motivation is to apply importance sampling to sampling rare events which can be reformulated as an optimal control problem. by using a parameterised approach the optimal control problem becomes a stochastic optimization problem which still raises some open questions regarding how to tackle the scalability to high-dimensional problems and how to deal with the intrinsic metastability of the system. to explore new methods we link the optimal control problem to reinforcement learning since both share the same underlying framework, namely a markov decision process (mdp). for the optimal control problem we show how the mdp can be formulated. in addition we discuss how the stochastic optimal control problem can be interpreted in the framework of reinforcement learning. at the end of the article we present the application of two different reinforcement learning algorithms to the optimal control problem and a comparison of the advantages and disadvantages of the two algorithms.",
"categories": "math.oc",
"doi": "",
"created": "2022-11-04",
"updated": "2024-02-15",
"authors": [
"jannes quer",
"enric ribera borrell"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2211.02474"
} | "2024-03-15T04:03:02.445897" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"references.bib": {
"toxicity_score": 0.006377276,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0011284347,
"insult_score": 0.006714871,
"profanity_score": 0.013610168,
"threat_score": 0.0058513097,
"sexually_explicit_score": 0.011972365,
"flirtation_score": 0.18740572
},
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.46838996998741,
"hash": "1f0f80003f3f1f7f",
"most_frequent_color_ratio": 93.46838996998741
}
} | [] | "algorithm" | "4d5cb538-dc46-4f72-8994-0e813f48c45a" | 898 | medium |
|
\begin{algorithm}[h!]
\caption{zCDP-NFL}
\label{alg_zCDP_NFL}
\begin{algorithmic}[1]
\item[] \textbf{Initialization:} ${\bf w}_k^{(0)}=\mathbf{0}$, $\boldsymbol{\gamma}_k^{(0)}=\mathbf{0}$, $\forall k \in \mathcal{K}$
\item[] \textit{-- Procedure at client $k$ --}
\item[] \textbf{For} iteration $n = 1, 2, \hdots$:
\begin{align}
\label{PrimalUpdate}
{\bf w}_k^{(n)} &= \arg\min_{{\bf w}_k} \hat{f_k}({\bf w}_k;\widetilde{\mathcal{V}}^{(n-1)}) + {\bf w}_k^\mathsf{T}\boldsymbol{\gamma}_k^{(n-1)} + \rho\sum_{l\in\mathcal{N}_k}\bigg\|{\bf w}_k-\frac{\widetilde{{\bf w}}_k^{(n-1)}+\widetilde{{\bf w}}_l^{(n-1)}}{2}\bigg\|^2 \\
\label{NoisePerturbation}
\widetilde{{\bf w}}_k^{(n)} &= {\bf w}_k^{(n)} {+} \boldsymbol{\xi}_k^{(n)} \\
\label{DualUpdate}
\boldsymbol{\gamma}_k^{(n)} & = \boldsymbol{\gamma}_k^{(n-1)} {+} \rho\sum_{l\in\mathcal{N}_k}\left(\widetilde{{\bf w}}_k^{(n)} {-} \widetilde{{\bf w}}_l^{(n)}\right)
\end{align}
\item[] \textbf{End For}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[h!]
\caption{zCDP-NFL}
\begin{algorithmic}
[1]
\item[] \textbf{Initialization:} ${\bf w}_k^{(0)}=\mathbf{0}$, $\boldsymbol{\gamma}_k^{(0)}=\mathbf{0}$, $\forall k \in \mathcal{K}$
\item[] \textit{-- Procedure at client $k$ --}
\item[] \textbf{For} iteration $n = 1, 2, \hdots$:
\begin{align*}
{\bf w}_k^{(n)} &= \arg\min_{{\bf w}_k} \hat{f_k}({\bf w}_k;\widetilde{\mathcal{V}}^{(n-1)}) + {\bf w}_k^\mathsf{T}\boldsymbol{\gamma}_k^{(n-1)} + \rho\sum_{l\in\mathcal{N}_k}\bigg\|{\bf w}_k-\frac{\widetilde{{\bf w}}_k^{(n-1)}+\widetilde{{\bf w}}_l^{(n-1)}}{2}\bigg\|^2 \\
\widetilde{{\bf w}}_k^{(n)} &= {\bf w}_k^{(n)} {+} \boldsymbol{\xi}_k^{(n)} \\
\boldsymbol{\gamma}_k^{(n)} & = \boldsymbol{\gamma}_k^{(n-1)} {+} \rho\sum_{l\in\mathcal{N}_k}\left(\widetilde{{\bf w}}_k^{(n)} {-} \widetilde{{\bf w}}_l^{(n)}\right)
\end{align*}
\item[] \textbf{End For}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2306.14012" | "2306.14012.tar.gz" | "2024-02-21" | {
"title": "private networked federated learning for nonsmooth objectives",
"id": "2306.14012",
"abstract": "this paper develops a networked federated learning algorithm to solve nonsmooth objective functions. to guarantee the confidentiality of the participants with respect to each other and potential eavesdroppers, we use the zero-concentrated differential privacy notion (zcdp). privacy is achieved by perturbing the outcome of the computation at each client with a variance-decreasing gaussian noise. zcdp allows for better accuracy than the conventional $(\\epsilon, \\delta)$-dp and stronger guarantees than the more recent r\\'enyi-dp by assuming adversaries aggregate all the exchanged messages. the proposed algorithm relies on the distributed alternating direction method of multipliers (admm) and uses the approximation of the augmented lagrangian to handle nonsmooth objective functions. the developed private networked federated learning algorithm has a competitive privacy accuracy trade-off and handles nonsmooth and non-strongly convex problems. we provide complete theoretical proof for the privacy guarantees and the algorithm's convergence to the exact solution. we also prove under additional assumptions that the algorithm converges in $o(1/n)$ admm iterations. finally, we observe the performance of the algorithm in a series of numerical simulations.",
"categories": "math.oc stat.ml",
"doi": "",
"created": "2023-06-24",
"updated": "2024-02-21",
"authors": [
"fran\u00e7ois gauthier",
"cristiano gratton",
"naveen k. d. venkategowda",
"stefan werner"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2306.14012"
} | "2024-03-15T04:36:37.987005" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"References.bib": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.001335144,
"identity_attack_score": 0.00088332384,
"insult_score": 0.0072278744,
"profanity_score": 0.013302735,
"threat_score": 0.005825418,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.24294384
},
"Plots/TradeOff_ELA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"elsarticle.cls": {
"toxicity_score": 0.011560776,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.0031263188,
"insult_score": 0.007493876,
"profanity_score": 0.0138151245,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.44272235
},
"Manuscript.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0031633168,
"insult_score": 0.0072658747,
"profanity_score": 0.015318131,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.24283215
},
"Plots/TradeOff_RID.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Plots/CV_LAD.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Manuscript.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.2762957
},
"Plots/CV_RID.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Plots/CV_ELA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"elsarticle-num.bst": {
"toxicity_score": 0.016712861,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0036627878,
"insult_score": 0.008937885,
"profanity_score": 0.017504321,
"threat_score": 0.0074306927,
"sexually_explicit_score": 0.012857023,
"flirtation_score": 0.44020516
},
"command_input.tex": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0034592997,
"insult_score": 0.00819688,
"profanity_score": 0.016001316,
"threat_score": 0.007857903,
"sexually_explicit_score": 0.016749516,
"flirtation_score": 0.34442067
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.6173851714714,
"hash": "000f3f80bb1fdf3f",
"most_frequent_color_ratio": 94.6173851714714
}
} | [] | "algorithm" | "f9526157-cf01-4965-9515-4ceda777786c" | 903 | medium |
|
\begin{algorithm}[t]
\caption{Procedure of the component-specific aggregation for Micro-disentanglement}
\footnotesize
\label{algorithm1}
\begin{algorithmic}[1]
\Require $\left \{ \mathbf{x}_i \in \mathbb{R}^f\right \} $: the set of node feature vectors, $i \in \tilde{\mathbf{N}}\left ( u \right )$;
\Ensure $\mathbf{h}_u$, $\mathbf{c}_u$;
\renewcommand{\algorithmicensure}{\textbf{trainable paramters:}}
\Ensure \textcolor{black}{$\mathbf{W}$: projection matrices.}
\textcolor{black}{$\alpha, \beta$: coefficients of component-specific aggregation.}
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure $K$: number of component.
$\tilde{T}$: iterations of dynamic assignment.
\For{$i\in \tilde{\mathbf{N}}\left( u \right)$}
\For{$k=1$ to $K$}
\State $\mathbf{c}^{k}_i \leftarrow \sigma \left ( \mathbf{W}_k \mathbf{x}_i \right )$
\State $\mathbf{c}^{k}_i \leftarrow \frac{\mathbf{c}^k_i}{\left \| \mathbf{c}^k_i \right \|_{2}} $
\EndFor
\EndFor
\State initialize $\tilde{\mathbf{h}}^{k}_{u} = \mathbf{c}^k_u$
\For{iteration $\tilde{t}=1$ to $\tilde{T}$}
\For{$v \in \mathbf{N} \left( u \right)$}
\State $s^k_v(u) \leftarrow \cos(\mathbf{h}^{k}_{u}, \mathbf{c}^{k}_{v})$, $\forall k = 1, \cdots, K$.
\State Calculate $p^k_{u\to v}$ and $q^k_{v \to u}$ by Eq.$\left( \ref{p} \right)$
\EndFor
\For{$k=1$ to $K$ do}
\State $ {\mathbf{h}^{k}_{u}}= \tilde{\mathbf{h}}^{k}_{u} + \alpha \sum_{v \in \mathbf{N}(u)}q^{k}_{v\to u}\mathbf{c}^{k}_{v} + \beta \sum_{v \in \mathbf{N}(u)}p^{k}_{u \to v}\mathbf{c}^{k}_{v}$
\State $\mathbf{h}^{k}_{u} =\frac{{\mathbf{h}}^{k}_{u}}{\left \| {\mathbf{h}}^{k}_{u} \right \|_{2}} $
\State $\tilde{\mathbf{h}}^{k}_{u} \leftarrow \mathbf{h}^{k}_{u}$
\EndFor
\EndFor
\State $\mathbf{c}_{u} \leftarrow $ $\textrm{concat}(\mathbf{c}^{1}_{u},\mathbf{c}^{2}_{u},\cdots,\mathbf{c}^{K}_{u} )$
\State \textcolor{black}{$\mathbf{h}_{u} \leftarrow \textrm{concat}(\mathbf{h}^{1}_{u},\mathbf{h}^{2}_{u},\cdots,\mathbf{h}^{K}_{u} )$}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[t]
\caption{Procedure of the component-specific aggregation for Micro-disentanglement}
\footnotesize
\begin{algorithmic}[1]
\Require $\left \{ \mathbf{x}_i \in \mathbb{R}^f\right \} $: the set of node feature vectors, $i \in \tilde{\mathbf{N}}\left ( u \right )$;
\Ensure $\mathbf{h}_u$, $\mathbf{c}_u$;
\renewcommand{\algorithmicensure}{\textbf{trainable paramters:}}
\Ensure \textcolor{black}{$\mathbf{W}$: projection matrices.}
\textcolor{black}{$\alpha, \beta$: coefficients of component-specific aggregation.}
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure $K$: number of component.
$\tilde{T}$: iterations of dynamic assignment.
\For{$i\in \tilde{\mathbf{N}}\left( u \right)$}
\For{$k=1$ to $K$}
\State $\mathbf{c}^{k}_i \leftarrow \sigma \left ( \mathbf{W}_k \mathbf{x}_i \right )$
\State $\mathbf{c}^{k}_i \leftarrow \frac{\mathbf{c}^k_i}{\left \| \mathbf{c}^k_i \right \|_{2}} $
\EndFor
\EndFor
\State initialize $\tilde{\mathbf{h}}^{k}_{u} = \mathbf{c}^k_u$
\For{iteration $\tilde{t}=1$ to $\tilde{T}$}
\For{$v \in \mathbf{N} \left( u \right)$}
\State $s^k_v(u) \leftarrow \cos(\mathbf{h}^{k}_{u}, \mathbf{c}^{k}_{v})$, $\forall k = 1, \cdots, K$.
\State Calculate $p^k_{u\to v}$ and $q^k_{v \to u}$ by Eq.$\left( \ref{p} \right)$
\EndFor
\For{$k=1$ to $K$ do}
\State $ {\mathbf{h}^{k}_{u}}= \tilde{\mathbf{h}}^{k}_{u} + \alpha \sum_{v \in \mathbf{N}(u)}q^{k}_{v\to u}\mathbf{c}^{k}_{v} + \beta \sum_{v \in \mathbf{N}(u)}p^{k}_{u \to v}\mathbf{c}^{k}_{v}$
\State $\mathbf{h}^{k}_{u} =\frac{{\mathbf{h}}^{k}_{u}}{\left \| {\mathbf{h}}^{k}_{u} \right \|_{2}} $
\State $\tilde{\mathbf{h}}^{k}_{u} \leftarrow \mathbf{h}^{k}_{u}$
\EndFor
\EndFor
\State $\mathbf{c}_{u} \leftarrow $ $\textrm{concat}(\mathbf{c}^{1}_{u},\mathbf{c}^{2}_{u},\cdots,\mathbf{c}^{K}_{u} )$
\State \textcolor{black}{$\mathbf{h}_{u} \leftarrow \textrm{concat}(\mathbf{h}^{1}_{u},\mathbf{h}^{2}_{u},\cdots,\mathbf{h}^{K}_{u} )$}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between nodes caused by factor entanglement. in this paper, we propose an \\underline{\\textbf{a}}dversarial \\underline{\\textbf{d}}isentangled \\underline{\\textbf{g}}raph \\underline{\\textbf{c}}onvolutional \\underline{\\textbf{n}}etwork (adgcn) for disentangled graph representation learning. to begin with, we point out two aspects of graph disentanglement that need to be considered, i.e., micro-disentanglement and macro-disentanglement. for them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes. on the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer to improve the separability among component distributions, thus restricting the interdependence among components. additionally, to reveal the topological graph structure, a diversity-preserving node sampling approach is proposed, by which the graph structure can be progressively refined in a way of local structure awareness. the experimental results on various real-world graph data verify that our adgcn obtains more favorable performance over currently available alternatives. the source codes of adgcn are available at \\textit{\\url{https://github.com/ssgood/adgcn}}.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2021-03-12",
"updated": "2024-01-24",
"authors": [
"shuai zheng",
"zhenfeng zhu",
"zhizhe liu",
"jian cheng",
"yao zhao"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2103.07295"
} | "2024-03-15T08:58:36.178534" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"TAI_template.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"IEEEtai.cls": {
"toxicity_score": 0.0031415154,
"severe_toxicity_score": 0.00054597855,
"identity_attack_score": 0.0008232023,
"insult_score": 0.0055938642,
"profanity_score": 0.011099464,
"threat_score": 0.0056894873,
"sexually_explicit_score": 0.00687084,
"flirtation_score": 0.46642172
},
"IEEEexample.bib": {
"toxicity_score": 0.04216247,
"severe_toxicity_score": 0.005683899,
"identity_attack_score": 0.0075845607,
"insult_score": 0.014276917,
"profanity_score": 0.041160982,
"threat_score": 0.009929225,
"sexually_explicit_score": 0.05369676,
"flirtation_score": 0.23710166
},
"TAI_template.tex": {
"toxicity_score": 0.008544922,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0021551251,
"insult_score": 0.006819372,
"profanity_score": 0.015591404,
"threat_score": 0.006550381,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.24663074
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.49801299438593,
"hash": "00071f3f1f0f3f1f",
"most_frequent_color_ratio": 93.49801299438593
}
} | [] | "algorithm" | "007657ac-7dc8-4e1b-bf6f-cfa73422c7d0" | 1981 | hard |
|
\begin{algorithmic}[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ inventories $(I^p, I^t)$ \textbf{do}
\State {\bfseries }\quad \quad Score $W^{p_i}_{j}$ = similarity($Emb({I^p_j}), Emb(S^p_i)$)
\State {\bfseries }\quad \quad Score $W^{t_i}_{j}$ = similarity($Emb({I^t_j}), Emb(S^t_i)$)
\State {\bfseries } \quad \textbf{end for}
\State {\bfseries } \textbf{end for}
\end{algorithmic}
| \begin{algorithmic}
[1]
\State {\bfseries }\textbf{for} i = 1,2,$\cdots$, T \textbf{do}
\State {\bfseries } \quad Automatically transcribe dialogue turn pairs $(S^p_i,S^t_i)$
\State {\bfseries }\quad \textbf{for} $(I^p_j, I^t_j) \in$ inventories $(I^p, I^t)$ \textbf{do}
\State {\bfseries }\quad \quad Score $W^{p_i}_{j}$ = similarity($Emb({I^p_j}), Emb(S^p_i)$)
\State {\bfseries }\quad \quad Score $W^{t_i}_{j}$ = similarity($Emb({I^t_j}), Emb(S^t_i)$)
\State {\bfseries } \quad \textbf{end for}
\State {\bfseries } \textbf{end for}
\end{algorithmic} | "https://arxiv.org/src/2402.14701" | "2402.14701.tar.gz" | "2024-02-22" | {
"title": "compass: computational mapping of patient-therapist alliance strategies with language modeling",
"id": "2402.14701",
"abstract": "the therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. in this paper, we present compass, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. our approach utilizes advanced large language models to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory. analyzing a dataset of over 950 sessions covering diverse psychiatric conditions, we demonstrate the effectiveness of our method in microscopically mapping patient-therapist alignment trajectories and providing interpretability for clinical psychiatry and in identifying emerging patterns related to the condition being treated. by employing various neural topic modeling techniques in combination with generative language prompting, we analyze the topical characteristics of different psychiatric conditions and incorporate temporal modeling to capture the evolution of topics at a turn-level resolution. this combined framework enhances the understanding of therapeutic interactions, enabling timely feedback for therapists regarding conversation quality and providing interpretable insights to improve the effectiveness of psychotherapy.",
"categories": "cs.cl cs.ai cs.hc cs.lg q-bio.nc",
"doi": "",
"created": "2024-02-22",
"updated": "",
"authors": [
"baihan lin",
"djallel bouneffouf",
"yulia landa",
"rachel jespersen",
"cheryl corcoran",
"guillermo cecchi"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.14701"
} | "2024-03-15T03:21:50.438155" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"main.bib": {
"toxicity_score": 0.0065657673,
"severe_toxicity_score": 0.0016498566,
"identity_attack_score": 0.0009989422,
"insult_score": 0.00649637,
"profanity_score": 0.014361672,
"threat_score": 0.005738034,
"sexually_explicit_score": 0.0150981555,
"flirtation_score": 0.1938464
},
"apalike.bst": {
"toxicity_score": 0.013948329,
"severe_toxicity_score": 0.0010251999,
"identity_attack_score": 0.005105704,
"insult_score": 0.008500882,
"profanity_score": 0.013849284,
"threat_score": 0.0074954215,
"sexually_explicit_score": 0.011146685,
"flirtation_score": 0.4208946
},
"sec_abstract.tex": {
"toxicity_score": 0.02048268,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0061046463,
"insult_score": 0.010932897,
"profanity_score": 0.012243799,
"threat_score": 0.0066151097,
"sexually_explicit_score": 0.006222091,
"flirtation_score": 0.17787476
},
"arxiv.sty": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0016117096,
"identity_attack_score": 0.0010498142,
"insult_score": 0.0062208683,
"profanity_score": 0.018665737,
"threat_score": 0.0057865805,
"sexually_explicit_score": 0.026067909,
"flirtation_score": 0.31151596
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"sec_body.tex": {
"toxicity_score": 0.014639461,
"severe_toxicity_score": 0.0014972687,
"identity_attack_score": 0.004680229,
"insult_score": 0.008253881,
"profanity_score": 0.01620627,
"threat_score": 0.008466354,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.22629106
}
},
"reason": null
}
} | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.8658828308359,
"hash": "070f83a3bf830f3e",
"most_frequent_color_ratio": 88.8658828308359
}
} | [] | "algorithm" | "cf032692-19a5-4d39-92ed-352dff0bc225" | 552 | easy |
|
\begin{algorithm}
\caption{MPS Encoding Procedure}
\hspace*{\algorithmicindent}
\begin{algorithmic}[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${\bf M}_\ell$ on domain [a,b]}
\State {Zero out ${\bf M}_\ell$ outside domain $D_\ell$}
\EndFor
\State \Return{ ${\bf M}_T \leftarrow \sum_{\ell=0}^{2^k} {\bf M}_\ell$}
\end{algorithmic}
\label{alg:mps_enc}
\end{algorithm}
| \begin{algorithm}
\caption{MPS Encoding Procedure}
\hspace*{\algorithmicindent}
\begin{algorithmic}
[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${\bf M}_\ell$ on domain [a,b]}
\State {Zero out ${\bf M}_\ell$ outside domain $D_\ell$}
\EndFor
\State \Return{ ${\bf M}_T \leftarrow \sum_{\ell=0}^{2^k} {\bf M}_\ell$}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2303.01562" | "2303.01562.tar.gz" | "2024-02-16" | {
"title": "quantum state preparation of normal distributions using matrix product states",
"id": "2303.01562",
"abstract": "state preparation is a necessary component of many quantum algorithms. in this work, we combine a method for efficiently representing smooth differentiable probability distributions using matrix product states with recently discovered techniques for initializing quantum states to approximate matrix product states. using this, we generate quantum states encoding a class of normal probability distributions in a trapped ion quantum computer for up to 20 qubits. we provide an in depth analysis of the different sources of error which contribute to the overall fidelity of this state preparation procedure. our work provides a study in quantum hardware for scalable distribution loading, which is the basis of a wide range of algorithms that provide quantum advantage.",
"categories": "quant-ph",
"doi": "10.1038/s41534-024-00805-0",
"created": "2023-03-02",
"updated": "2024-02-16",
"authors": [
"jason iaconis",
"sonika johri",
"elton yechao zhu"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.01562"
} | "2024-03-15T04:16:05.461100" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.0137598375,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.0041992567,
"insult_score": 0.007949878,
"profanity_score": 0.01846078,
"threat_score": 0.007909686,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.42432743
},
"naturemag-doi.bst": {
"toxicity_score": 0.013571346,
"severe_toxicity_score": 0.0011110306,
"identity_attack_score": 0.003921773,
"insult_score": 0.00810188,
"profanity_score": 0.01402008,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.008905552,
"flirtation_score": 0.43227512
},
"jabbrv.sty": {
"toxicity_score": 0.0059688794,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0018498929,
"insult_score": 0.0066008708,
"profanity_score": 0.011167783,
"threat_score": 0.006375613,
"sexually_explicit_score": 0.0069298176,
"flirtation_score": 0.4899393
},
"wlscirep.cls": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0009727478,
"identity_attack_score": 0.0045322375,
"insult_score": 0.00971689,
"profanity_score": 0.014566627,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.33412078
},
"jabbrv-ltwa-en.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"jabbrv-ltwa-all.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.64233317725848,
"hash": "070c0f7fbf8f3f1f",
"most_frequent_color_ratio": 91.64233317725848
}
} | [] | "algorithm" | "19e2bdac-427d-457c-9927-d88ff797cdfe" | 592 | easy |
|
\begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random variables of law $\mathcal{N}(0,1)$
\State \textbf{Step 3} : Set $V := \exp\bigg[-\theta\bigg(\hat{\zeta}\left(\frac{W(\theta v \eta^2 T)}{\theta v\eta^2 T}e^{\eta\sqrt{T}U}\right)-u - \frac{W(\theta v \eta^2 T)}{\theta \eta^2 T}e^{\eta\sqrt{T}U}\bigg) 1_{U\leq \frac{\ln(\hat{K})}{\eta\sqrt{T}}+\frac{W(\theta v \eta^2 T)}{\eta\sqrt{T}}}\bigg] \phi_{\hat{K}}( U,\theta)$ with the parameters $\hat{\zeta}$, $\hat{K}$ and the function $\phi_{\hat{K}}$ specified in Theorem \ref{prop_dec_gen} and $\theta:= \lambda\gamma (1-\rho^2)$.
\State \textbf{Step 4} : Compute the mean $m$ of $V$
\State \textbf{Step 5} : Compute the approximation of the random part $M:=-\frac{e^{-rT}}{\gamma(1-\rho^{2})}\ln\left(m\right)$
\State \textbf{Step 6} : Return the approximation of the bid reservation price $D_{\zeta,K}+M$.
\end{algorithmic}
| \begin{algorithmic}
\Require $n > 0$
\State \textbf{Step 1} : Choose $u$ and $v$ such that the hypothesis of Theorem \ref{prop_dec_gen} are satisfied
\State \textbf{Step 2} : Generate a vector $U$ of $n$ i.i.d random variables of law $\mathcal{N}(0,1)$
\State \textbf{Step 3} : Set $V := \exp\bigg[-\theta\bigg(\hat{\zeta}\left(\frac{W(\theta v \eta^2 T)}{\theta v\eta^2 T}e^{\eta\sqrt{T}U}\right)-u - \frac{W(\theta v \eta^2 T)}{\theta \eta^2 T}e^{\eta\sqrt{T}U}\bigg) 1_{U\leq \frac{\ln(\hat{K})}{\eta\sqrt{T}}+\frac{W(\theta v \eta^2 T)}{\eta\sqrt{T}}}\bigg] \phi_{\hat{K}}( U,\theta)$ with the parameters $\hat{\zeta}$, $\hat{K}$ and the function $\phi_{\hat{K}}$ specified in Theorem \ref{prop_dec_gen} and $\theta:= \lambda\gamma (1-\rho^2)$.
\State \textbf{Step 4} : Compute the mean $m$ of $V$
\State \textbf{Step 5} : Compute the approximation of the random part $M:=-\frac{e^{-rT}}{\gamma(1-\rho^{2})}\ln\left(m\right)$
\State \textbf{Step 6} : Return the approximation of the bid reservation price $D_{\zeta,K}+M$.
\end{algorithmic} | "https://arxiv.org/src/2105.08804" | "2105.08804.tar.gz" | "2024-02-20" | {
"title": "efficient approximations for utility-based pricing",
"id": "2105.08804",
"abstract": "in a context of illiquidity, the reservation price is a well-accepted alternative to the usual martingale approach which does not apply. however, this price is not available in closed form and requires numerical methods such as monte carlo or polynomial approximations to evaluate it. we show that these methods can be inaccurate and propose a deterministic decomposition of the reservation price using the lambert function. this decomposition allows us to perform an improved monte carlo method, which we name lambert monte carlo (lmc) and to give deterministic approximations of the reservation price and of the optimal strategies based on the lambert function. we also give an answer to the problem of selecting a hedging asset that minimizes the reservation price and also the cash invested. our theoretical results are illustrated by numerical simulations.",
"categories": "q-fin.cp q-fin.pr",
"doi": "",
"created": "2021-05-18",
"updated": "2024-02-20",
"authors": [
"laurence carassus",
"massinissa ferhoune"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2105.08804"
} | "2024-03-15T03:14:06.406557" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"WileyNJD-APA.bst": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0035332954,
"insult_score": 0.0105148945,
"profanity_score": 0.019143965,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.39166427
},
"vancouver-authoryear.bst": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0008010864,
"identity_attack_score": 0.0027378413,
"insult_score": 0.008348881,
"profanity_score": 0.012277958,
"threat_score": 0.0063432488,
"sexually_explicit_score": 0.0063695344,
"flirtation_score": 0.44019285
},
"WileyNJD-ACS.bst": {
"toxicity_score": 0.010555492,
"severe_toxicity_score": 0.0006723404,
"identity_attack_score": 0.0024788564,
"insult_score": 0.007607877,
"profanity_score": 0.010706633,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.0049540824,
"flirtation_score": 0.32464102
},
"Lambert_article.tex": {
"toxicity_score": 0.0032514685,
"severe_toxicity_score": 0.00044584274,
"identity_attack_score": 0.00071683346,
"insult_score": 0.0055938642,
"profanity_score": 0.010655395,
"threat_score": 0.0057347976,
"sexually_explicit_score": 0.006222091,
"flirtation_score": 0.22855924
},
"WileyNJD-VANCOUVER.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"WileyNJD-Harvard.bst": {
"toxicity_score": 0.014702292,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.00310782,
"insult_score": 0.008633883,
"profanity_score": 0.014156717,
"threat_score": 0.007094103,
"sexually_explicit_score": 0.00979021,
"flirtation_score": 0.41990307
},
"WileyNJD-AMS.bst": {
"toxicity_score": 0.021549871,
"severe_toxicity_score": 0.0016975403,
"identity_attack_score": 0.0040142676,
"insult_score": 0.010552894,
"profanity_score": 0.021056883,
"threat_score": 0.0074954215,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.35523984
},
"WileyNJD-AMA.bst": {
"toxicity_score": 0.048842013,
"severe_toxicity_score": 0.0029182434,
"identity_attack_score": 0.006363631,
"insult_score": 0.018452216,
"profanity_score": 0.02385794,
"threat_score": 0.008738215,
"sexually_explicit_score": 0.02146769,
"flirtation_score": 0.5213245
},
"rss.bst": {
"toxicity_score": 0.045378547,
"severe_toxicity_score": 0.0031471252,
"identity_attack_score": 0.021585498,
"insult_score": 0.019096779,
"profanity_score": 0.027273865,
"threat_score": 0.00866054,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.3583878
},
"wiley-article.cls": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0013065338,
"identity_attack_score": 0.0025713511,
"insult_score": 0.009203887,
"profanity_score": 0.014395831,
"threat_score": 0.0068287146,
"sexually_explicit_score": 0.007843964,
"flirtation_score": 0.30276403
}
},
"reason": null
}
} | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.00963716324964,
"hash": "7f03435c233f0f03",
"most_frequent_color_ratio": 90.00963716324964
}
} | [] | "algorithm" | "3d593311-5722-4e13-87a9-aa19e91f493d" | 1043 | medium |
|
\begin{algorithm}
\caption{SBC}\label{alg:sbc}
\begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsymbol{y}^{sim}_k \sim \pi(\boldsymbol{y}|\boldsymbol{\theta}^{sim}_k)$
\State \text{Draw 999 posterior samples post burn in:} $\{\boldsymbol{\theta}_1,\dots , \boldsymbol{\theta}_{999}\}_k \sim \pi(\boldsymbol{\theta} | \boldsymbol{y}^{sim}_k)$
\State \text{Compute rank statistics:} $\boldsymbol{r} = \mathrm{rank}(\{\boldsymbol{\theta}_1,\dots , \boldsymbol{\theta}_{999}\}_k, \boldsymbol{\theta}^{sim}_k)$
\EndFor
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{SBC} \begin{algorithmic}
\For{\texttt{k in} $1:5000$}
\State \text{Draw from joint prior: } $\boldsymbol{\theta}^{sim}_k \sim\pi (\boldsymbol{\theta})$
\State \text{Simulate data set with 1000 observations: } $\boldsymbol{y}^{sim}_k \sim \pi(\boldsymbol{y}|\boldsymbol{\theta}^{sim}_k)$
\State \text{Draw 999 posterior samples post burn in:} $\{\boldsymbol{\theta}_1,\dots , \boldsymbol{\theta}_{999}\}_k \sim \pi(\boldsymbol{\theta} | \boldsymbol{y}^{sim}_k)$
\State \text{Compute rank statistics:} $\boldsymbol{r} = \mathrm{rank}(\{\boldsymbol{\theta}_1,\dots , \boldsymbol{\theta}_{999}\}_k, \boldsymbol{\theta}^{sim}_k)$
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.12384" | "2402.12384.tar.gz" | "2024-01-27" | {
"title": "comparing mcmc algorithms in stochastic volatility models using simulation based calibration",
"id": "2402.12384",
"abstract": "simulation based calibration (sbc) is applied to analyse two commonly used, competing markov chain monte carlo algorithms for estimating the posterior distribution of a stochastic volatility model. in particular, the bespoke 'off-set mixture approximation' algorithm proposed by kim, shephard, and chib (1998) is explored together with a hamiltonian monte carlo algorithm implemented through stan. the sbc analysis involves a simulation study to assess whether each sampling algorithm has the capacity to produce valid inference for the correctly specified model, while also characterising statistical efficiency through the effective sample size. results show that stan's no-u-turn sampler, an implementation of hamiltonian monte carlo, produces a well-calibrated posterior estimate while the celebrated off-set mixture approach is less efficient and poorly calibrated, though model parameterisation also plays a role. limitations and restrictions of generality are discussed.",
"categories": "stat.ap econ.em",
"doi": "",
"created": "2024-01-27",
"updated": "",
"authors": [
"benjamin wee"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.12384"
} | "2024-03-15T03:27:09.183136" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"main.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
"profanity_score": 0.015147334,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.21162485
}
},
"reason": null
}
} | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.41802604820217,
"hash": "3f3f9f878180837f",
"most_frequent_color_ratio": 89.41802604820217
}
} | [] | "algorithm" | "90e559be-54c3-4e8c-95ae-f7dee9c1f90f" | 693 | easy |
|
\begin{algorithm}
\label{algor2}Let $C=\left( c_{ij}\right) $ be an $m\times n$ BPM with
non-null maximal equalizer $E=(e_{ij})$.
\indent For each $c_{ij}=\frac{B_{1}^{ij}|B_{2}^{ij}|\cdots|B_{r}^{ij}}%
{A_{1}^{ij}|A_{2}^{ij}|\cdots|A_{r}^{ij^{\mathstrut}}}$ in $C:$
\indent\indent Let $e$ be the first element in $e_{ij}$.
\indent \indent Let $a^{ij}_{1} | \cdots| a^{ij}_{p} := \mathcal{EP}
_{IS(c_{ij})}(A^{ij}_{e + 1} \cup\cdots\cup A^{ij}_{r})$.
\indent \indent Let $b^{ij}_{1} | \cdots| b^{ij}_{q} := \mathcal{EP}
_{OS(c_{ij})}(B^{ij}_{1} \cup\cdots\cup B^{ij}_{e})$.
\indent\indent For $k$ from $1$ to $p:$
\indent \indent \indent Let $a^{ij}_{k, e + 1} | \cdots| a^{ij}_{k, r} :=
(A^{ij}_{e + 1} \cap a^{ij}_{k}) | \cdots| (A^{ij}_{r} \cap a^{ij}_{k})$.
\indent\indent For $k$ from $1$ to $q:$
\indent\indent\indent Let $b_{k,1}^{ij}|\cdots|b_{k,e}^{ij}:=(B_{1}^{ij}\cap
b_{k}^{ij})|\cdots|(B_{e}^{ij}\cap b_{k}^{ij})$.
\indent\indent Define the formal matrix decomposition
\[
C_{1}^{ij}C_{2}^{ij}=%
\begin{pmatrix}
\frac{b_{1,1}^{ij}|\cdots|b_{1,e}^{ij}}{A_{1}^{ij}|\cdots|A_{e}%
^{ij^{\mathstrut}}}\\
\vdots\\
\frac{b_{q,1}^{ij}|\cdots|b_{q,e}^{ij}}{A_{1}^{ij}|\cdots|A_{e}%
^{ij^{\mathstrut}}}%
\end{pmatrix}%
\begin{pmatrix}
\frac{B_{e+1}^{ij}|\cdots|B_{r}^{ij}}{a_{1,e+1}^{ij}|\cdots|a_{1,r}%
^{ij^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{ij}|\cdots|B_{r}^{ij}}%
{a_{p,e+1}^{ij}|\cdots|a_{p,r}^{ij^{\mathstrut}}}%
\end{pmatrix}
.
\]
The \textbf{transverse decomposition of} $C$ \textbf{with respect to} $E$ is
the formal matrix factorization $C:=C_{1}C_{2}$ where
\[
C_{1}=\left( C_{1}^{ij}\right) =%
\begin{pmatrix}
\frac{b_{1,1}^{11}|\cdots|b_{1,e}^{11}}{A_{1}^{11}|\cdots|A_{e}%
^{11^{\mathstrut}}} & & \frac{b_{1,1}^{1n}|\cdots|b_{1,e}^{1n}}{A_{1}%
^{1n}|\cdots|A_{e}^{1n^{\mathstrut}}}\\
\vdots & \cdots & \vdots\\
\frac{b_{q,1}^{11}|\cdots|b_{q,e}^{11}}{A_{1}^{11}|\cdots|A_{e}%
^{11^{\mathstrut}}} & & \frac{b_{q,1}^{1n}|\cdots|b_{q,e}^{1n}}{A_{1}%
^{1n}|\cdots|A_{e}^{1n^{\mathstrut}}}\\
\vdots & & \vdots\\
\frac{b_{1,1}^{m1}|\cdots|b_{1,e}^{m1}}{A_{1}^{m1}|\cdots|A_{e}%
^{m1^{\mathstrut}}} & & \frac{b_{1,1}^{mn}|\cdots|b_{1,e}^{mn}}{A_{1}%
^{mn}|\cdots|A_{e}^{mn^{\mathstrut}}}\\
\vdots & \cdots & \vdots\\
\frac{b_{q,1}^{m1}|\cdots|b_{q,e}^{m1}}{A_{1}^{m1}|\cdots|A_{e}%
^{m1^{\mathstrut}}} & & \frac{b_{q,1}^{mn}|\cdots|b_{q,e}^{mn}}{A_{1}%
^{mn}|\cdots|A_{e}^{mn^{\mathstrut}}}%
\end{pmatrix}
\]
and $C_{2}=\left( C_{2}^{ij}\right) =$
\[%
\begin{pmatrix}
\frac{B_{e+1}^{11}|\cdots|B_{r}^{11}}{a_{1,e+1}^{11}|\cdots|a_{1,r}%
^{11^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{11}|\cdots|B_{r}^{11}}%
{a_{p,e+1}^{11}|\cdots|a_{p,r}^{11^{\mathstrut}}} & \cdots & \frac
{B_{e+1}^{1n}|\cdots|B_{r}^{1n}}{a_{1,e+1}^{1n}|\cdots|a_{1,r}^{1n^{\mathstrut
}}} & \cdots & \frac{B_{e+1}^{1n}|\cdots|B_{r}^{1n}}{a_{p,e+1}^{1n}%
|\cdots|a_{p,r}^{1n^{\mathstrut}}}\\
& \vdots & & & & \vdots & \\
\frac{B_{e+1}^{m1}|\cdots|B_{r}^{m1}}{a_{1,e+1}^{m1}|\cdots|a_{1,r}%
^{m1^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{m1}|\cdots|B_{r}^{m1}}%
{a_{p,e+1}^{m1}|\cdots|a_{p,r}^{m1^{\mathstrut}}} & \cdots & \frac
{B_{e+1}^{mn}|\cdots|B_{r}^{mn}}{a_{1,e+1}^{mn}|\cdots|a_{1,r}^{mn^{\mathstrut
}}} & \cdots & \frac{B_{e+1}^{mn}|\cdots|B_{r}^{mn}}{a_{p,e+1}^{mn}%
|\cdots|a_{p,r}^{mn^{\mathstrut}}}%
\end{pmatrix}
.
\]
\medskip
\noindent Using first elements of the entries in $E_{1}:=E$ compute the
transverse decomposition $C=C_{1}C_{2};$ then $C_{1}$ is indecomposable. If
$C_{2}$ is decomposable, its maximal equalizer $E_{2}$ is non-null. Using the
first elements of the entries in $E_{2},$ calculate the transverse
decomposition $C_{2}:=C_{2}^{\prime}C_{3};$ then $C_{2}^{\prime}$ is
indecomposable. Continue recursively $s-1$ steps until the maximal equalizer
is null. The \textbf{indecomposable factorization} is $C=C_{1}C_{2}^{\prime
}\cdots C_{s-1}^{\prime}C_{s}$.
\end{algorithm}
| \begin{algorithm}
Let $C=\left( c_{ij}\right) $ be an $m\times n$ BPM with
non-null maximal equalizer $E=(e_{ij})$.
\indent For each $c_{ij}=\frac{B_{1}^{ij}|B_{2}^{ij}|\cdots|B_{r}^{ij}}%
{A_{1}^{ij}|A_{2}^{ij}|\cdots|A_{r}^{ij^{\mathstrut}}}$ in $C:$
\indent\indent Let $e$ be the first element in $e_{ij}$.
\indent \indent Let $a^{ij}_{1} | \cdots| a^{ij}_{p} := \mathcal{EP}
_{IS(c_{ij})}(A^{ij}_{e + 1} \cup\cdots\cup A^{ij}_{r})$.
\indent \indent Let $b^{ij}_{1} | \cdots| b^{ij}_{q} := \mathcal{EP}
_{OS(c_{ij})}(B^{ij}_{1} \cup\cdots\cup B^{ij}_{e})$.
\indent\indent For $k$ from $1$ to $p:$
\indent \indent \indent Let $a^{ij}_{k, e + 1} | \cdots| a^{ij}_{k, r} :=
(A^{ij}_{e + 1} \cap a^{ij}_{k}) | \cdots| (A^{ij}_{r} \cap a^{ij}_{k})$.
\indent\indent For $k$ from $1$ to $q:$
\indent\indent\indent Let $b_{k,1}^{ij}|\cdots|b_{k,e}^{ij}:=(B_{1}^{ij}\cap
b_{k}^{ij})|\cdots|(B_{e}^{ij}\cap b_{k}^{ij})$.
\indent\indent Define the formal matrix decomposition
\[
C_{1}^{ij}C_{2}^{ij}=%
\begin{pmatrix}
\frac{b_{1,1}^{ij}|\cdots|b_{1,e}^{ij}}{A_{1}^{ij}|\cdots|A_{e}%
^{ij^{\mathstrut}}}\\
\vdots\\
\frac{b_{q,1}^{ij}|\cdots|b_{q,e}^{ij}}{A_{1}^{ij}|\cdots|A_{e}%
^{ij^{\mathstrut}}}%
\end{pmatrix}%
\begin{pmatrix}
\frac{B_{e+1}^{ij}|\cdots|B_{r}^{ij}}{a_{1,e+1}^{ij}|\cdots|a_{1,r}%
^{ij^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{ij}|\cdots|B_{r}^{ij}}%
{a_{p,e+1}^{ij}|\cdots|a_{p,r}^{ij^{\mathstrut}}}%
\end{pmatrix}
.
\]
The \textbf{transverse decomposition of} $C$ \textbf{with respect to} $E$ is
the formal matrix factorization $C:=C_{1}C_{2}$ where
\[
C_{1}=\left( C_{1}^{ij}\right) =%
\begin{pmatrix}
\frac{b_{1,1}^{11}|\cdots|b_{1,e}^{11}}{A_{1}^{11}|\cdots|A_{e}%
^{11^{\mathstrut}}} & & \frac{b_{1,1}^{1n}|\cdots|b_{1,e}^{1n}}{A_{1}%
^{1n}|\cdots|A_{e}^{1n^{\mathstrut}}}\\
\vdots & \cdots & \vdots\\
\frac{b_{q,1}^{11}|\cdots|b_{q,e}^{11}}{A_{1}^{11}|\cdots|A_{e}%
^{11^{\mathstrut}}} & & \frac{b_{q,1}^{1n}|\cdots|b_{q,e}^{1n}}{A_{1}%
^{1n}|\cdots|A_{e}^{1n^{\mathstrut}}}\\
\vdots & & \vdots\\
\frac{b_{1,1}^{m1}|\cdots|b_{1,e}^{m1}}{A_{1}^{m1}|\cdots|A_{e}%
^{m1^{\mathstrut}}} & & \frac{b_{1,1}^{mn}|\cdots|b_{1,e}^{mn}}{A_{1}%
^{mn}|\cdots|A_{e}^{mn^{\mathstrut}}}\\
\vdots & \cdots & \vdots\\
\frac{b_{q,1}^{m1}|\cdots|b_{q,e}^{m1}}{A_{1}^{m1}|\cdots|A_{e}%
^{m1^{\mathstrut}}} & & \frac{b_{q,1}^{mn}|\cdots|b_{q,e}^{mn}}{A_{1}%
^{mn}|\cdots|A_{e}^{mn^{\mathstrut}}}%
\end{pmatrix}
\]
and $C_{2}=\left( C_{2}^{ij}\right) =$
\[%
\begin{pmatrix}
\frac{B_{e+1}^{11}|\cdots|B_{r}^{11}}{a_{1,e+1}^{11}|\cdots|a_{1,r}%
^{11^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{11}|\cdots|B_{r}^{11}}%
{a_{p,e+1}^{11}|\cdots|a_{p,r}^{11^{\mathstrut}}} & \cdots & \frac
{B_{e+1}^{1n}|\cdots|B_{r}^{1n}}{a_{1,e+1}^{1n}|\cdots|a_{1,r}^{1n^{\mathstrut
}}} & \cdots & \frac{B_{e+1}^{1n}|\cdots|B_{r}^{1n}}{a_{p,e+1}^{1n}%
|\cdots|a_{p,r}^{1n^{\mathstrut}}}\\
& \vdots & & & & \vdots & \\
\frac{B_{e+1}^{m1}|\cdots|B_{r}^{m1}}{a_{1,e+1}^{m1}|\cdots|a_{1,r}%
^{m1^{\mathstrut}}} & \cdots & \frac{B_{e+1}^{m1}|\cdots|B_{r}^{m1}}%
{a_{p,e+1}^{m1}|\cdots|a_{p,r}^{m1^{\mathstrut}}} & \cdots & \frac
{B_{e+1}^{mn}|\cdots|B_{r}^{mn}}{a_{1,e+1}^{mn}|\cdots|a_{1,r}^{mn^{\mathstrut
}}} & \cdots & \frac{B_{e+1}^{mn}|\cdots|B_{r}^{mn}}{a_{p,e+1}^{mn}%
|\cdots|a_{p,r}^{mn^{\mathstrut}}}%
\end{pmatrix}
.
\]
\medskip
\noindent Using first elements of the entries in $E_{1}:=E$ compute the
transverse decomposition $C=C_{1}C_{2};$ then $C_{1}$ is indecomposable. If
$C_{2}$ is decomposable, its maximal equalizer $E_{2}$ is non-null. Using the
first elements of the entries in $E_{2},$ calculate the transverse
decomposition $C_{2}:=C_{2}^{\prime}C_{3};$ then $C_{2}^{\prime}$ is
indecomposable. Continue recursively $s-1$ steps until the maximal equalizer
is null. The \textbf{indecomposable factorization} is $C=C_{1}C_{2}^{\prime
}\cdots C_{s-1}^{\prime}C_{s}$.
\end{algorithm} | "https://arxiv.org/src/2111.05799" | "2111.05799.tar.gz" | "2024-01-29" | {
"title": "computing the dimension of a bipartition matrix",
"id": "2111.05799",
"abstract": "this article presents a computer program that computes the dimension of a bipartition matrix. its dimension has three independent components: row dimension, column dimension, and entry dimension. the program applies four routines of independent interest: (1) a routine that factors a bipartition as a formal product of indecomposables, (2) a routine that computes the inverse and recovers the bipartition, (3) a routine that factors a bipartition matrix as a formal product of indecomposables, and (4) a routine that calculates the \"transpose-rotation\" of a bipartition matrix (the column dimension is the row dimension of the transpose-rotation).",
"categories": "math.co math.at",
"doi": "",
"created": "2021-11-09",
"updated": "2024-01-29",
"authors": [
"dawson freeman",
"ronald umble"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2111.05799"
} | "2024-03-15T05:13:52.306224" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Bipartition-Dimension-1-29-2024.tex": {
"toxicity_score": 0.012440401,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003385304,
"insult_score": 0.007873878,
"profanity_score": 0.015625564,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0122672515,
"flirtation_score": 0.26602322
},
"P3b.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.50724943771931,
"hash": "070f8f01e9e97f56",
"most_frequent_color_ratio": 94.50724943771931
}
} | [] | "algorithm" | "f69850cb-6a6e-4abd-89f6-55a2e01f5153" | 3869 | hard |
|
\begin{algorithmic}[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation}\label{eqn:phiTL}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L)} =\nabla\cdot ae_i \, \mbox{ in }Q_{2L}, \hspace{0.3in} \phi_{i,T}^{(L)}=0 \, \mbox{ on }\partial Q_{2L}.
\end{equation}
\State Calculate the approximate homogenized coefficients via \begin{equation}\label{eqn:algahL}
a_h^{(L)}e_i=\int \omega q_{i,T}^{(L)},
\end{equation} where \begin{equation}\label{eqn:defqiTL} q_{i,T}^{(L)}:=a(e_i+\nabla\phi_{i,T}^{(L)})\end{equation} and $\omega(x)=\frac{1}{L^d}\hat{\omega}(\frac{x}{L})$ with $\hat{\omega}$ as in Theorem \ref{thm:luottooptimal}.
\State Find $\tilde{u}_h^{(L)}$ on $\partial Q_L$:
\begin{equation}\label{eqn:alguhtildeL}
\tilde{u}_h^{(L)} =\int G_h^{(L)}* (\nabla\cdot g),
\end{equation}where $G_h^{(L)}(x) := \frac{1}{4\pi\left|(a_h^{(L)})^{-1/2}x\right|}$ is the Green function for the constant-coefficient operator $-\nabla\cdot a_h^{(L)} \nabla$.
\State Solve for approximate first-order flux correctors $\sigma_{i,T}^{(L)}=\{\sigma_{ijk,T}^{(L)}\}_{j,k}$: \begin{equation}\label{eqn:algsigma}
\dfrac{1}{T}\sigma_{ijk,T}^{(L)}-\Delta \sigma_{ijk,T}^{(L)} =\partial_j q_{ik,T}^{(L)}-\partial_k q_{ij,T}^{(L)} \, \mbox{ in }Q_{\frac{7}{4}L}, \hspace{0.3in} \sigma_{ijk,T}^{(L)}=0 \, \mbox{ on }\partial Q_{\frac{7}{4}L}.
\end{equation}
\State Solve for approximate second-order correctors $\psi_{ij,T}^{(L)}$: \begin{equation}\label{eqn:2ndcorapprox}
\dfrac{1}{T}\psi_{ij,T}^{(L)} - \nabla\cdot a \nabla \psi_{ij,T}^{(L)} = \nabla\cdot (\phi_{i,T}^{(L)}a-\sigma_{i,T}^{(L)})e_j \, \mbox{ in }Q_{\frac{3}{2}L}, \hspace{0.3in} \psi_{ij,T}^{(L)}=0 \,\mbox{ on }\partial Q_{\frac{3}{2}L}.
\end{equation}
\State For the indices \begin{equation}\label{eqn:calJ}(i,j)\in \mathcal{J}=\{(1,2),(1,3),(2,3),(2,2),(3,3)\},\end{equation} calculate \begin{equation}\label{eqn:cijlt}
c_{ij,T}^{(L)}=-\int g\cdot \nabla \Bigl(\sum_{k=1}^3\phi_{k,T}^{(L)}\partial_k v_{h,ij}^{(L)}+(2-\delta_{ij})(\psi_{ij,T}^{(L)}-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}\psi_{11,T}^{(L)})\Bigr) ,
\end{equation} where $v_{h,ij}^{(L)}$ denote the $a_h^{(L)}$-harmonic polynomials \begin{equation}\label{eqn:harmpolL}
v_{h,ij}^{(L)}=(1-\dfrac{1}{2}\delta_{ij})(x_ix_j-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}x_1^2).
\end{equation}
\State Obtain $u_h^{(L)}$ as \begin{equation}\label{eqn:algapproxbdry}
u_h^{(L)}=\tilde{u}_h^{(L)}+ \sum_{i=1}^3(\int g \cdot\nabla \phi_{i,T}^{(L)})\partial_i G_h^{(L)} +\sum_{(i,j)\in\mathcal{J}}c_{ij,T}^{(L)}\partial_{ij} G_h^{(L)}.
\end{equation}
\State Solve for $u^{(L)}$ (here and for the rest of the paper we adopt Einstein's summation convention for repeated indices): \begin{equation}\label{eqn:finalapprox}
-\nabla \cdot a \nabla u^{(L)}=\nabla \cdot g\text{ in }Q_L,\hspace{0.3in} u^{(L)}=(1+\phi_{i,T}^{(L)}\partial_i+\psi_{ij,T}^{(L)}\partial_{ij}) u_h^{(L)}\text{ on }\partial Q_L.
\end{equation}
\end{algorithmic}
| \begin{algorithmic}
[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation*}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L)} =\nabla\cdot ae_i \, \mbox{ in }Q_{2L}, \hspace{0.3in} \phi_{i,T}^{(L)}=0 \, \mbox{ on }\partial Q_{2L}.
\end{equation*}
\State Calculate the approximate homogenized coefficients via \begin{equation*}
a_h^{(L)}e_i=\int \omega q_{i,T}^{(L)},
\end{equation*} where \begin{equation*}
q_{i,T}^{(L)}:=a(e_i+\nabla\phi_{i,T}^{(L)})\end{equation*} and $\omega(x)=\frac{1}{L^d}\hat{\omega}(\frac{x}{L})$ with $\hat{\omega}$ as in Theorem \ref{thm:luottooptimal}.
\State Find $\tilde{u}_h^{(L)}$ on $\partial Q_L$:
\begin{equation*}
\tilde{u}_h^{(L)} =\int G_h^{(L)}* (\nabla\cdot g),
\end{equation*}where $G_h^{(L)}(x) := \frac{1}{4\pi\left|(a_h^{(L)})^{-1/2}x\right|}$ is the Green function for the constant-coefficient operator $-\nabla\cdot a_h^{(L)} \nabla$.
\State Solve for approximate first-order flux correctors $\sigma_{i,T}^{(L)}=\{\sigma_{ijk,T}^{(L)}\}_{j,k}$: \begin{equation*}
\dfrac{1}{T}\sigma_{ijk,T}^{(L)}-\Delta \sigma_{ijk,T}^{(L)} =\partial_j q_{ik,T}^{(L)}-\partial_k q_{ij,T}^{(L)} \, \mbox{ in }Q_{\frac{7}{4}L}, \hspace{0.3in} \sigma_{ijk,T}^{(L)}=0 \, \mbox{ on }\partial Q_{\frac{7}{4}L}.
\end{equation*}
\State Solve for approximate second-order correctors $\psi_{ij,T}^{(L)}$: \begin{equation*}
\dfrac{1}{T}\psi_{ij,T}^{(L)} - \nabla\cdot a \nabla \psi_{ij,T}^{(L)} = \nabla\cdot (\phi_{i,T}^{(L)}a-\sigma_{i,T}^{(L)})e_j \, \mbox{ in }Q_{\frac{3}{2}L}, \hspace{0.3in} \psi_{ij,T}^{(L)}=0 \,\mbox{ on }\partial Q_{\frac{3}{2}L}.
\end{equation*}
\State For the indices \begin{equation*}
(i,j)\in \mathcal{J}=\{(1,2),(1,3),(2,3),(2,2),(3,3)\},\end{equation*} calculate \begin{equation*}
c_{ij,T}^{(L)}=-\int g\cdot \nabla \Bigl(\sum_{k=1}^3\phi_{k,T}^{(L)}\partial_k v_{h,ij}^{(L)}+(2-\delta_{ij})(\psi_{ij,T}^{(L)}-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}\psi_{11,T}^{(L)})\Bigr) ,
\end{equation*} where $v_{h,ij}^{(L)}$ denote the $a_h^{(L)}$-harmonic polynomials \begin{equation*}
v_{h,ij}^{(L)}=(1-\dfrac{1}{2}\delta_{ij})(x_ix_j-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}x_1^2).
\end{equation*}
\State Obtain $u_h^{(L)}$ as \begin{equation*}
u_h^{(L)}=\tilde{u}_h^{(L)}+ \sum_{i=1}^3(\int g \cdot\nabla \phi_{i,T}^{(L)})\partial_i G_h^{(L)} +\sum_{(i,j)\in\mathcal{J}}c_{ij,T}^{(L)}\partial_{ij} G_h^{(L)}.
\end{equation*}
\State Solve for $u^{(L)}$ (here and for the rest of the paper we adopt Einstein's summation convention for repeated indices): \begin{equation*}
-\nabla \cdot a \nabla u^{(L)}=\nabla \cdot g\text{ in }Q_L,\hspace{0.3in} u^{(L)}=(1+\phi_{i,T}^{(L)}\partial_i+\psi_{ij,T}^{(L)}\partial_{ij}) u_h^{(L)}\text{ on }\partial Q_L.
\end{equation*}
\end{algorithmic} | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in an infinite heterogeneous medium, in a situation where the medium is only known in a box of diameter $l\\gg\\ell$ around the support of the charge. we propose a boundary condition that with overwhelming probability is (near) optimal with respect to scaling in terms of $\\ell$ and $l$, in the setting where the medium is a sample from a stationary ensemble with a finite range of dependence (set to be unity and with the assumption that $\\ell \\gg 1$). the boundary condition is motivated by quantitative stochastic homogenization that allows for a multipole expansion [bgo20]. this work extends [lo21], the algorithm in which is optimal in two dimension, and thus we need to take quadrupoles, next to dipoles, into account. this in turn relies on stochastic estimates of second-order, next to first-order, correctors. these estimates are provided for finite range ensembles under consideration, based on an extension of the semi-group approach of [go15].",
"categories": "math.ap cs.na math.na math.pr",
"doi": "",
"created": "2021-09-03",
"updated": "2024-01-11",
"authors": [
"jianfeng lu",
"felix otto",
"lihan wang"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2109.01616"
} | "2024-03-15T06:22:17.156672" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Revised_Manuscript.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
"profanity_score": 0.015147334,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.21162485
},
"Revised_Manuscript.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.30642147521877,
"hash": "0f0f0707007f3f03",
"most_frequent_color_ratio": 89.30642147521877
}
} | [] | "algorithm" | "3b44e8b9-4d16-4467-81ba-03d224d36301" | 2900 | hard |
|
\begin{algorithm}[htb]
\caption{ScaledGreedyReweight (scale distributions and call bipartite matching)}\label{alg2}
\begin{algorithmic}[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported on $B$. $\P_B'$ is close to $\alpha\P_R+(1-\alpha)\P_B$ in $W_1$, under assumptions of Theorem~\ref{main1:thm}.
\For{$r\in R$}
\State $\mathrm{Supply}(r)\gets C\cdot\alpha\P_R(r)$
\EndFor
\For{$b\in B$}
\State $\mathrm{Demand(b)}\gets C - C\cdot(1-\alpha)\P_B(r)$
\If{$\mathrm{Demand}(b) < 0$}
\State $\mathrm{Demand(b)}\gets 0$
\EndIf
\EndFor
\State Create multi-set $B',R'$ with multiplicities of each element being equal to their Demand and Supply respectively.
\State Use $\mathrm{GreedyMatch}(R', B')$ to compute the met (matched) demands, i.e., the extent to which the demands of $B$ that are actually fulfilled by $R$.
\State Normalize the weights of met demands to obtain a probability distribution $\P_B'$ supported on $B$.
\State \textbf{return} $\P_B'$.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[htb]
\caption{ScaledGreedyReweight (scale distributions and call bipartite matching)}\begin{algorithmic}
[1]
\State Input: Two probability distributions $\P_B,\P_R$ supported on $B,R\subset Q_d$, and a tilt factor $\alpha\in(0,1)$.
\State Output: Probability distribution $\P_B'$ supported on $B$. $\P_B'$ is close to $\alpha\P_R+(1-\alpha)\P_B$ in $W_1$, under assumptions of Theorem~\ref{main1:thm}.
\For{$r\in R$}
\State $\mathrm{Supply}(r)\gets C\cdot\alpha\P_R(r)$
\EndFor
\For{$b\in B$}
\State $\mathrm{Demand(b)}\gets C - C\cdot(1-\alpha)\P_B(r)$
\If{$\mathrm{Demand}(b) < 0$}
\State $\mathrm{Demand(b)}\gets 0$
\EndIf
\EndFor
\State Create multi-set $B',R'$ with multiplicities of each element being equal to their Demand and Supply respectively.
\State Use $\mathrm{GreedyMatch}(R', B')$ to compute the met (matched) demands, i.e., the extent to which the demands of $B$ that are actually fulfilled by $R$.
\State Normalize the weights of met demands to obtain a probability distribution $\P_B'$ supported on $B$.
\State \textbf{return} $\P_B'$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.11562" | "2401.11562.tar.gz" | "2024-01-21" | {
"title": "enhancing selectivity using wasserstein distance based reweighing",
"id": "2401.11562",
"abstract": "given two labeled data-sets $\\mathcal{s}$ and $\\mathcal{t}$, we design a simple and efficient greedy algorithm to reweigh the loss function such that the limiting distribution of the neural network weights that result from training on $\\mathcal{s}$ approaches the limiting distribution that would have resulted by training on $\\mathcal{t}$. on the theoretical side, we prove that when the metric entropy of the input data-sets is bounded, our greedy algorithm outputs a close to optimal reweighing, i.e., the two invariant distributions of network weights will be provably close in total variation distance. moreover, the algorithm is simple and scalable, and we prove bounds on the efficiency of the algorithm as well. our algorithm can deliberately introduce distribution shift to perform (soft) multi-criteria optimization. as a motivating application, we train a neural net to recognize small molecule binders to mnk2 (a map kinase, responsible for cell signaling) which are non-binders to mnk1 (a highly similar protein). we tune the algorithm's parameter so that overall change in holdout loss is negligible, but the selectivity, i.e., the fraction of top 100 mnk2 binders that are mnk1 non-binders, increases from 54\\% to 95\\%, as a result of our reweighing. of the 43 distinct small molecules predicted to be most selective from the enamine catalog, 2 small molecules were experimentally verified to be selective, i.e., they reduced the enzyme activity of mnk2 below 50\\% but not mnk1, at 10$\\mu$m -- a 5\\% success rate.",
"categories": "stat.ml cs.lg q-bio.qm",
"doi": "",
"created": "2024-01-21",
"updated": "",
"authors": [
"pratik worah"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.11562"
} | "2024-03-15T07:13:16.850922" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.00719407,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0014706649,
"insult_score": 0.0070948736,
"profanity_score": 0.014122557,
"threat_score": 0.006168481,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3015664
},
"main.tex": {
"toxicity_score": 0.012000589,
"severe_toxicity_score": 0.0011539459,
"identity_attack_score": 0.0036812867,
"insult_score": 0.0077028773,
"profanity_score": 0.015386449,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.0122672515,
"flirtation_score": 0.23052543
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"neurips_2023.sty": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.003644289,
"insult_score": 0.007341875,
"profanity_score": 0.013849284,
"threat_score": 0.007637825,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.36732212
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.44048865619546,
"hash": "00033f3fbf2f013f",
"most_frequent_color_ratio": 90.44048865619546
}
} | [] | "algorithm" | "25f8bc0a-8006-4207-a351-20765eae61d3" | 1107 | medium |
|
\begin{algorithm}
\caption{Func-LiNGAM (Can be regarded as vector-based DirectLiNGAM but with FPCA preprocessing.)}
\label{algo1}
\begin{algorithmic}[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its variable subscripts $U$ and a $Wp \times n$ data
matrix as $F$, initialize an ordered list of functions $K=\emptyset$ and $m:=1$;
\State \textbf{Output:} Adjacent Matrix $\hat{T}\in\mathbb{R}^{p\times p}$
\State Use FPCA for finite approximating each random vector to make their dimensions from $Wp$ to $Mp$, where $M$ is the number of principal components.
\Repeat
\State
(a) Perform least squares regressions of the approximating random vector $\hat{f}_{i}\in\mathbb{R}^M$ on $\hat{f}_{j}\in\mathbb{R}^M$ for all $ i \in U \backslash K(i \neq j) $ and compute the residual vectors $ \mathbf{r}^{(j)} $ and the residual data matrix $ \mathbf{R}^{(j)} $ from the data matrix $ {F} $ for all $ j \in U \backslash K $. Find a variable $ \hat{f}_{m} $ that is most independent of its residuals:
$$
\hat{f}_{m}=\arg \min _{j \in U \backslash K} MI\left(\hat{f}_{j} ; U \backslash K\right),
$$
where $ MI $ is the independence measure such as mutual information or other measures.
\State (b) Append $ m $ to the end of $ K $.
\State (c) Let $\hat{\mathbf{f}}:=\mathbf{r}^{(m)}, \hat{F}:=\mathbf{R}^{(m)} $.
\Until{$p-1$ subscripts are appended to $K$}
\State Append the remaining variable to the end of $ K $.
\State Construct a strictly lower triangular matrix $\hat{T} $ by following the order in $ K $, and estimate the connection strengths $ \hat{T}_{i j} $ by using least squares regression in this paper.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Func-LiNGAM (Can be regarded as vector-based DirectLiNGAM but with FPCA preprocessing.)}
\begin{algorithmic}
[1]
\State \textbf{Input:} Each function has $W$ time points, then construct $Wp$-dimensional random vector ${f}$ ($W$: Full-time points) for $p$ functions, a set of its variable subscripts $U$ and a $Wp \times n$ data
matrix as $F$, initialize an ordered list of functions $K=\emptyset$ and $m:=1$;
\State \textbf{Output:} Adjacent Matrix $\hat{T}\in\mathbb{R}^{p\times p}$
\State Use FPCA for finite approximating each random vector to make their dimensions from $Wp$ to $Mp$, where $M$ is the number of principal components.
\Repeat
\State
(a) Perform least squares regressions of the approximating random vector $\hat{f}_{i}\in\mathbb{R}^M$ on $\hat{f}_{j}\in\mathbb{R}^M$ for all $ i \in U \backslash K(i \neq j) $ and compute the residual vectors $ \mathbf{r}^{(j)} $ and the residual data matrix $ \mathbf{R}^{(j)} $ from the data matrix $ {F} $ for all $ j \in U \backslash K $. Find a variable $ \hat{f}_{m} $ that is most independent of its residuals:
$$
\hat{f}_{m}=\arg \min _{j \in U \backslash K} MI\left(\hat{f}_{j} ; U \backslash K\right),
$$
where $ MI $ is the independence measure such as mutual information or other measures.
\State (b) Append $ m $ to the end of $ K $.
\State (c) Let $\hat{\mathbf{f}}:=\mathbf{r}^{(m)}, \hat{F}:=\mathbf{R}^{(m)} $.
\Until{$p-1$ subscripts are appended to $K$}
\State Append the remaining variable to the end of $ K $.
\State Construct a strictly lower triangular matrix $\hat{T} $ by following the order in $ K $, and estimate the connection strengths $ \hat{T}_{i j} $ by using least squares regression in this paper.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.09641" | "2401.09641.tar.gz" | "2024-01-17" | {
"title": "functional linear non-gaussian acyclic model for causal discovery",
"id": "2401.09641",
"abstract": "in causal discovery, non-gaussianity has been used to characterize the complete configuration of a linear non-gaussian acyclic model (lingam), encompassing both the causal ordering of variables and their respective connection strengths. however, lingam can only deal with the finite-dimensional case. to expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the functional linear non-gaussian acyclic model (func-lingam). our motivation stems from the desire to identify causal relationships in brain-effective connectivity tasks involving, for example, fmri and eeg datasets. we demonstrate why the original lingam fails to handle these inherently infinite-dimensional datasets and explain the availability of functional data analysis from both empirical and theoretical perspectives. {we establish theoretical guarantees of the identifiability of the causal relationship among non-gaussian random vectors and even random functions in infinite-dimensional hilbert spaces.} to address the issue of sparsity in discrete time points within intrinsic infinite-dimensional functional data, we propose optimizing the coordinates of the vectors using functional principal component analysis. experimental results on synthetic data verify the ability of the proposed framework to identify causal relationships among multivariate functions using the observed samples. for real data, we focus on analyzing the brain connectivity patterns derived from fmri data.",
"categories": "cs.lg math.st q-bio.nc stat.me stat.th",
"doi": "",
"created": "2024-01-17",
"updated": "",
"authors": [
"tian-le yang",
"kuang-yao lee",
"kun zhang",
"joe suzuki"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.09641"
} | "2024-03-15T07:19:39.274028" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sample.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.00414376,
"insult_score": 0.0076838774,
"profanity_score": 0.015591404,
"threat_score": 0.0077672824,
"sexually_explicit_score": 0.013092931,
"flirtation_score": 0.26038328
},
"tab1.tex": {
"toxicity_score": 0.014073989,
"severe_toxicity_score": 0.0014877319,
"identity_attack_score": 0.004236255,
"insult_score": 0.00808288,
"profanity_score": 0.017094411,
"threat_score": 0.008039144,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.15742163
},
"references.bib": {
"toxicity_score": 0.010869644,
"severe_toxicity_score": 0.0011587143,
"identity_attack_score": 0.0028118372,
"insult_score": 0.007531876,
"profanity_score": 0.014225035,
"threat_score": 0.0071199946,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.29760137
},
"sample.bbl": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.003884775,
"insult_score": 0.007664877,
"profanity_score": 0.015079016,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.011854411,
"flirtation_score": 0.2308639
},
"README.md": {
"toxicity_score": 0.013005874,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0040142676,
"insult_score": 0.008348881,
"profanity_score": 0.015728042,
"threat_score": 0.0074436385,
"sexually_explicit_score": 0.01480327,
"flirtation_score": 0.3544391
},
"fmri3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"jmlr2e.sty": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0053276913,
"insult_score": 0.007949878,
"profanity_score": 0.015693882,
"threat_score": 0.00814271,
"sexually_explicit_score": 0.013092931,
"flirtation_score": 0.41926858
},
"brainnet.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.37682325369897,
"hash": "87076f3c042f037f",
"most_frequent_color_ratio": 93.37682325369897
}
} | [] | "algorithm" | "a6e0c1ce-e220-4765-8133-591483e0b7ea" | 1745 | hard |
|
\begin{algorithm}
\caption{Extragradient Method}
\label{alg:lec3-extragradient}
\begin{algorithmic}
\Require $\eta > 0$\\
\State $x_{k+1} = x_k - \eta F(\Tilde{x}_k),$ where\\
\\
$\Tilde{x}_k = x_k - \eta F(x_k)$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Extragradient Method}
\begin{algorithmic}
\Require $\eta > 0$\\
\State $x_{k+1} = x_k - \eta F(\Tilde{x}_k),$ where\\
\\
$\Tilde{x}_k = x_k - \eta F(x_k)$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2309.04877" | "2309.04877.tar.gz" | "2024-02-26" | {
"title": "a gentle introduction to gradient-based optimization and variational inequalities for machine learning",
"id": "2309.04877",
"abstract": "the rapid progress in machine learning in recent years has been based on a highly productive connection to gradient-based optimization. further progress hinges in part on a shift in focus from pattern recognition to decision-making and multi-agent problems. in these broader settings, new mathematical challenges emerge that involve equilibria and game theory instead of optima. gradient-based methods remain essential -- given the high dimensionality and large scale of machine-learning problems -- but simple gradient descent is no longer the point of departure for algorithm design. we provide a gentle introduction to a broader framework for gradient-based algorithms in machine learning, beginning with saddle points and monotone games, and proceeding to general variational inequalities. while we provide convergence proofs for several of the algorithms that we present, our main focus is that of providing motivation and intuition.",
"categories": "cs.lg stat.ml",
"doi": "",
"created": "2023-09-09",
"updated": "2024-02-26",
"authors": [
"neha s. wadia",
"yatin dandi",
"michael i. jordan"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.04877"
} | "2024-03-15T03:14:09.276985" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.0009775162,
"identity_attack_score": 0.003311308,
"insult_score": 0.007132874,
"profanity_score": 0.014327513,
"threat_score": 0.007340072,
"sexually_explicit_score": 0.011500548,
"flirtation_score": 0.2104973
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 96.1849061012296,
"hash": "f1dfdfccefe4e1e1",
"most_frequent_color_ratio": 96.1849061012296
}
} | [] | "algorithm" | "d809b30b-07a6-41d0-b1cb-4a292afc86f2" | 215 | easy |
|
\begin{algorithmic}
\Require $\theta_0 \in \mathbb{R}^n, b_0 > 0$
\For{$k \in \mathbb{N}$}
\State $\theta_{k} = \theta_{k-1} - \frac{1}{b_{k-1}} \dot{F}(\theta_{k-1})$
\State $b_k = b_{k-1} + \frac{||\dot{F}(\theta_{k})||_2^2}{b_{k-1}}$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\Require $\theta_0 \in \mathbb{R}^n, b_0 > 0$
\For{$k \in \mathbb{N}$}
\State $\theta_{k} = \theta_{k-1} - \frac{1}{b_{k-1}} \dot{F}(\theta_{k-1})$
\State $b_k = b_{k-1} + \frac{||\dot{F}(\theta_{k})||_2^2}{b_{k-1}}$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in data science. focusing on data science applications with expensive objective function evaluations yet inexpensive gradient function evaluations, gradient methods that never make objective function evaluations are either being rejuvenated or actively developed. however, as we show, such gradient methods are all susceptible to catastrophic divergence under realistic conditions for data science applications. in light of this, gradient methods which make use of objective function evaluations become more appealing, yet, as we show, can result in an exponential increase in objective evaluations between accepted iterates. as a result, existing gradient methods are poorly suited to the needs of optimization problems arising from data science. in this work, we address this gap by developing a generic methodology that economically uses objective function evaluations in a problem-driven manner to prevent catastrophic divergence and avoid an explosion in objective evaluations between accepted iterates. our methodology allows for specific procedures that can make use of specific step size selection methodologies or search direction strategies, and we develop a novel step size selection methodology that is well-suited to data science applications. we show that a procedure resulting from our methodology is highly competitive with standard optimization methods on cutest test problems. we then show a procedure resulting from our methodology is highly favorable relative to standard optimization methods on optimization problems arising in our target data science applications. thus, we provide a novel gradient methodology that is better suited to optimization problems arising in data science.",
"categories": "math.oc stat.co",
"doi": "",
"created": "2023-09-19",
"updated": "2024-02-15",
"authors": [
"christian varner",
"vivak patel"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.10894"
} | "2024-03-15T05:23:50.845023" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"table/novel-step-size-param-table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558756,
"profanity_score": 0.015659723,
"threat_score": 0.007547205,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2449079
},
"section/algo_psuedocode/general_algorithm.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"section/algo_psuedocode/outer_loop.tex": {
"toxicity_score": 0.011246625,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0035147965,
"insult_score": 0.0074368757,
"profanity_score": 0.015283971,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.011913388,
"flirtation_score": 0.23316474
},
"data/cutest/all_rel_change_gradient_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
},
"data/cutest/all_rel_change_function_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
},
"data/gee/total_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"section/evaluation_explosion/preliminaries.tex": {
"toxicity_score": 0.011623607,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.0034408006,
"insult_score": 0.007360875,
"profanity_score": 0.013439372,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.010615891,
"flirtation_score": 0.33654565
},
"section/smoothness-of-ds-problems/overview.tex": {
"toxicity_score": 0.020960633,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0051797,
"insult_score": 0.0107048955,
"profanity_score": 0.016103793,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.014626338,
"flirtation_score": 0.17700504
},
"section/catastrophic_divergence/nesterov_acceleration.tex": {
"toxicity_score": 0.013131535,
"severe_toxicity_score": 0.0010538101,
"identity_attack_score": 0.0052166977,
"insult_score": 0.007721877,
"profanity_score": 0.0147374235,
"threat_score": 0.007974415,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.19002186
},
"section/algorithm.tex": {
"toxicity_score": 0.036634352,
"severe_toxicity_score": 0.002193451,
"identity_attack_score": 0.011543332,
"insult_score": 0.012110904,
"profanity_score": 0.02303812,
"threat_score": 0.014220826,
"sexually_explicit_score": 0.027365407,
"flirtation_score": 0.22199428
},
"ref.bib": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.002293867,
"insult_score": 0.007360875,
"profanity_score": 0.0137468055,
"threat_score": 0.006925808,
"sexually_explicit_score": 0.009672256,
"flirtation_score": 0.23720506
},
"section/notation.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0029043318,
"insult_score": 0.0071138735,
"profanity_score": 0.0157622,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2506604
},
"section/catastrophic_divergence/adaptive_scaled_tr.tex": {
"toxicity_score": 0.017089844,
"severe_toxicity_score": 0.001206398,
"identity_attack_score": 0.005512681,
"insult_score": 0.009336887,
"profanity_score": 0.015488927,
"threat_score": 0.00814271,
"sexually_explicit_score": 0.01480327,
"flirtation_score": 0.3277508
},
"section/evaluation_explosion/cubic_regularized_newton.tex": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.00062942505,
"identity_attack_score": 0.002830336,
"insult_score": 0.007493876,
"profanity_score": 0.011902207,
"threat_score": 0.006485652,
"sexually_explicit_score": 0.007106749,
"flirtation_score": 0.21416461
},
"section/catastrophic_divergence.tex": {
"toxicity_score": 0.0056233127,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0016649036,
"insult_score": 0.0061543677,
"profanity_score": 0.012038844,
"threat_score": 0.006330303,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.3247941
},
"section/catastrophic_divergence/wngrad-div.tex": {
"toxicity_score": 0.029917045,
"severe_toxicity_score": 0.0014877319,
"identity_attack_score": 0.0070665907,
"insult_score": 0.011958904,
"profanity_score": 0.017914234,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.39471897
},
"data/gee/fieller_categorization.csv": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0008916855,
"identity_attack_score": 0.002885833,
"insult_score": 0.007892879,
"profanity_score": 0.012722028,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.40000224
},
"figure/gee_wedderburn/evaluations_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/smoothness-of-ds-problems/poisson-reg/poisson-reg.tex": {
"toxicity_score": 0.022139108,
"severe_toxicity_score": 0.0011014938,
"identity_attack_score": 0.0034592997,
"insult_score": 0.010096892,
"profanity_score": 0.013712646,
"threat_score": 0.007832011,
"sexually_explicit_score": 0.0101440735,
"flirtation_score": 0.23723446
},
"data/gee/func_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"section/evaluation_explosion/dynamic_method.tex": {
"toxicity_score": 0.019854378,
"severe_toxicity_score": 0.0013637543,
"identity_attack_score": 0.0056236745,
"insult_score": 0.009146886,
"profanity_score": 0.016479544,
"threat_score": 0.008815889,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.23163475
},
"data/gee/func_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"section/algo_psuedocode/novel-step-size-procedure.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"data/gee/fieller_cpu_data.csv": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0029182434,
"identity_attack_score": 0.002034882,
"insult_score": 0.009640889,
"profanity_score": 0.015147334,
"threat_score": 0.008764107,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.25993884
},
"table/multiple_objective_evaluation_table.tex": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0015068054,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0077598775,
"profanity_score": 0.017367685,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.22981189
},
"figure/first_order_relative_change/total_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"figure/second_order_relative_change/total_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"data/gee/grad_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"figure/cpu_time_cutest/second_order_cpu_time.tex": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0043102503,
"insult_score": 0.008329881,
"profanity_score": 0.016889455,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.48778152
},
"figure/gee_wedderburn/cpu_comp_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/catastrophic_divergence/constant_step.tex": {
"toxicity_score": 0.0143253105,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0034223017,
"insult_score": 0.007664877,
"profanity_score": 0.014190876,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.012385205,
"flirtation_score": 0.427231
},
"figure/gee_fiellercreasy/cpu_comp_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"figure/second_order_relative_change/gradient_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"section/algo_psuedocode/local_lipschitz_est.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"section/algo_psuedocode/inner_loop.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0012683868,
"identity_attack_score": 0.0037737815,
"insult_score": 0.0074558756,
"profanity_score": 0.015488927,
"threat_score": 0.007935578,
"sexually_explicit_score": 0.012031343,
"flirtation_score": 0.1759681
},
"figure/objective_threshold_diagram.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/catastrophic_divergence/barzilai_borwein.tex": {
"toxicity_score": 0.015079274,
"severe_toxicity_score": 0.00092983246,
"identity_attack_score": 0.004125261,
"insult_score": 0.00813988,
"profanity_score": 0.0127561875,
"threat_score": 0.0074824756,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.37749934
},
"figure/catastrophic_divergence_building_function.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/evaluation_explosion/armijo.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.0019238886,
"insult_score": 0.0077028773,
"profanity_score": 0.013302735,
"threat_score": 0.006051969,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.33172962
},
"section/smoothness-of-ds-problems/correlation-via-gee/correlation-via-gee.tex": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00092983246,
"identity_attack_score": 0.003866276,
"insult_score": 0.0074368757,
"profanity_score": 0.013610168,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.010615891,
"flirtation_score": 0.24296696
},
"figure/second_order_relative_change/function_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"section/evaluation_explosion/adaptive_cubic_regularization.tex": {
"toxicity_score": 0.008796243,
"severe_toxicity_score": 0.0006246567,
"identity_attack_score": 0.0027748393,
"insult_score": 0.0068003717,
"profanity_score": 0.011184863,
"threat_score": 0.006589218,
"sexually_explicit_score": 0.006516977,
"flirtation_score": 0.1969631
},
"section/results-cutest.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0051427023,
"insult_score": 0.009127886,
"profanity_score": 0.017162729,
"threat_score": 0.008065036,
"sexually_explicit_score": 0.014095544,
"flirtation_score": 0.44243842
},
"figure/first_order_relative_change/gradient_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"GD_Triggers.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"table/objective_free_table.tex": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0015068054,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0077598775,
"profanity_score": 0.017367685,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.22981189
},
"section/algo_psuedocode/naive_algorithm.tex": {
"toxicity_score": 0.016021729,
"severe_toxicity_score": 0.0018501282,
"identity_attack_score": 0.0047727237,
"insult_score": 0.008272881,
"profanity_score": 0.020373698,
"threat_score": 0.008427517,
"sexually_explicit_score": 0.016159745,
"flirtation_score": 0.20608959
},
"data/gee/total_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"figure/gee_fiellercreasy/evaluations_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"data/cutest/all_cpu_won.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0018498929,
"insult_score": 0.008747884,
"profanity_score": 0.010569996,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.0049245935,
"flirtation_score": 0.29574385
},
"data/gee/leaf_cpu_data.csv": {
"toxicity_score": 0.024967449,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0023031165,
"insult_score": 0.013326911,
"profanity_score": 0.015113175,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.2429553
},
"section/smoothness-of-ds-problems/poisson-reg/smoothness-proof.tex": {
"toxicity_score": 0.009864358,
"severe_toxicity_score": 0.0008201599,
"identity_attack_score": 0.0020996283,
"insult_score": 0.0068763723,
"profanity_score": 0.012346277,
"threat_score": 0.006763986,
"sexually_explicit_score": 0.009082484,
"flirtation_score": 0.22990656
},
"section/catastrophic_divergence/polyak.tex": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0075888764,
"profanity_score": 0.011970525,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.20474647
},
"section/catastrophic_divergence/bregman_distance.tex": {
"toxicity_score": 0.009487377,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.0033298072,
"insult_score": 0.006895372,
"profanity_score": 0.013063621,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.010321004,
"flirtation_score": 0.41113064
},
"figure/cpu_time_cutest/first_order_cpu_time.tex": {
"toxicity_score": 0.031331215,
"severe_toxicity_score": 0.0043296814,
"identity_attack_score": 0.004439743,
"insult_score": 0.011540901,
"profanity_score": 0.054437835,
"threat_score": 0.007948523,
"sexually_explicit_score": 0.043803066,
"flirtation_score": 0.35417965
},
"section/results-gee.tex": {
"toxicity_score": 0.014136819,
"severe_toxicity_score": 0.00096797943,
"identity_attack_score": 0.0043102503,
"insult_score": 0.00813988,
"profanity_score": 0.015386449,
"threat_score": 0.0076119336,
"sexually_explicit_score": 0.011854411,
"flirtation_score": 0.545385
},
"section/catastrophic_divergence/preliminaries.tex": {
"toxicity_score": 0.013257195,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.004347248,
"insult_score": 0.007835878,
"profanity_score": 0.013678487,
"threat_score": 0.007741391,
"sexually_explicit_score": 0.010497936,
"flirtation_score": 0.27346042
},
"data/cutest/all_cpu_lost.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0018498929,
"insult_score": 0.008747884,
"profanity_score": 0.010569996,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.0049245935,
"flirtation_score": 0.29574385
},
"table/gee-terminal-iterate-cat.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0029043318,
"insult_score": 0.0071138735,
"profanity_score": 0.0157622,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2506604
},
"table/gee-experiment-table.tex": {
"toxicity_score": 0.015833238,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0050317086,
"insult_score": 0.008766884,
"profanity_score": 0.017572641,
"threat_score": 0.008077981,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.19330522
},
"section/problem_formulation.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.003348306,
"insult_score": 0.006819372,
"profanity_score": 0.011355659,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.0063695344,
"flirtation_score": 0.27690822
},
"section/conclusion.tex": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.0006055832,
"identity_attack_score": 0.0027933381,
"insult_score": 0.0075508766,
"profanity_score": 0.011082385,
"threat_score": 0.0066668927,
"sexually_explicit_score": 0.005897717,
"flirtation_score": 0.20846651
},
"section/catastrophic_divergence/negative_curvature.tex": {
"toxicity_score": 0.0154562555,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.0043102503,
"insult_score": 0.007873878,
"profanity_score": 0.015079016,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.30951333
},
"section/abstract.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.0005078316,
"identity_attack_score": 0.0026638457,
"insult_score": 0.006952373,
"profanity_score": 0.010074687,
"threat_score": 0.00636914,
"sexually_explicit_score": 0.0046002194,
"flirtation_score": 0.17139499
},
"section/smoothness-of-ds-problems/nn/nn.tex": {
"toxicity_score": 0.017969469,
"severe_toxicity_score": 0.0009965897,
"identity_attack_score": 0.006178642,
"insult_score": 0.009868891,
"profanity_score": 0.015283971,
"threat_score": 0.007689608,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.24728048
},
"GD_Triggers.bbl": {
"toxicity_score": 0.017843807,
"severe_toxicity_score": 0.0013160706,
"identity_attack_score": 0.00362579,
"insult_score": 0.009374888,
"profanity_score": 0.017299367,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.47433925
},
"section/introduction.tex": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.00096797943,
"identity_attack_score": 0.0045322375,
"insult_score": 0.007778878,
"profanity_score": 0.013576009,
"threat_score": 0.00756015,
"sexually_explicit_score": 0.009318393,
"flirtation_score": 0.21079259
},
"figure/first_order_relative_change/function_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"table/cutest-experiment-table.tex": {
"toxicity_score": 0.015833238,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0050317086,
"insult_score": 0.008766884,
"profanity_score": 0.017572641,
"threat_score": 0.008077981,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.19330522
},
"section/smoothness_canonical_ds_problems.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.0035517942,
"insult_score": 0.008291881,
"profanity_score": 0.014293353,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.013505772,
"flirtation_score": 0.30319104
},
"section/catastrophic_divergence/lipschitz_approximation.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0011301041,
"identity_attack_score": 0.0030338243,
"insult_score": 0.0076838774,
"profanity_score": 0.015386449,
"threat_score": 0.007832011,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.50405544
},
"section/evaluation_explosion.tex": {
"toxicity_score": 0.019351736,
"severe_toxicity_score": 0.0015830994,
"identity_attack_score": 0.0039772694,
"insult_score": 0.008367881,
"profanity_score": 0.017845914,
"threat_score": 0.009307829,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.35859555
},
"data/gee/grad_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"section/novel-step-size.tex": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0009441376,
"identity_attack_score": 0.0042732526,
"insult_score": 0.007930879,
"profanity_score": 0.014805742,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.012798046,
"flirtation_score": 0.41932905
},
"table/data_science_problems_table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558756,
"profanity_score": 0.015659723,
"threat_score": 0.007547205,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2449079
},
"section/smoothness-of-ds-problems/factor-analysis/factor-analysis.tex": {
"toxicity_score": 0.008984734,
"severe_toxicity_score": 0.0005698204,
"identity_attack_score": 0.002330865,
"insult_score": 0.006885872,
"profanity_score": 0.011082385,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.006222091,
"flirtation_score": 0.16150479
},
"data/cutest/all_rel_change_total_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.98582783127969,
"hash": "038788939f93879f",
"most_frequent_color_ratio": 92.98582783127969
}
} | [] | "algorithm" | "b8b6e914-0741-4441-8e4d-98dd2fb4c052" | 262 | easy |
|
\begin{algorithmic}[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Initialize: $A_0\gets \Phi$
\For {$i \in [m]$}
\State Let $u_i$ be the element $u\in P_i$ maximizing $f(u~|~A_{i-1}) := f(A_{i-1}\cup \{u\}) - f(A_{i-1})$.
\State $A_i\gets A_{i-1}\cup \{u_i\}$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2208.03367" | "2208.03367.tar.gz" | "2024-02-12" | {
"title": "sublinear time algorithm for online weighted bipartite matching",
"id": "2208.03367",
"abstract": "online bipartite matching is a fundamental problem in online algorithms. the goal is to match two sets of vertices to maximize the sum of the edge weights, where for one set of vertices, each vertex and its corresponding edge weights appear in a sequence. currently, in the practical recommendation system or search engine, the weights are decided by the inner product between the deep representation of a user and the deep representation of an item. the standard online matching needs to pay $nd$ time to linear scan all the $n$ items, computing weight (assuming each representation vector has length $d$), and then deciding the matching based on the weights. however, in reality, the $n$ could be very large, e.g. in online e-commerce platforms. thus, improving the time of computing weights is a problem of practical significance. in this work, we provide the theoretical foundation for computing the weights approximately. we show that, with our proposed randomized data structures, the weights can be computed in sublinear time while still preserving the competitive ratio of the matching algorithm.",
"categories": "cs.ds cs.lg",
"doi": "",
"created": "2022-08-05",
"updated": "2024-02-12",
"authors": [
"hang hu",
"zhao song",
"runzhou tao",
"zhaozhuo xu",
"junze yin",
"danyang zhuo"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.03367"
} | "2024-03-15T06:18:53.303533" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"conclusion.tex": {
"toxicity_score": 0.006440107,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0016186562,
"insult_score": 0.006315869,
"profanity_score": 0.011816809,
"threat_score": 0.006427396,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.22920927
},
"ref.bib": {
"toxicity_score": 0.010743983,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0025713511,
"insult_score": 0.008044879,
"profanity_score": 0.014361672,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.34625474
},
"proof_match.tex": {
"toxicity_score": 0.011246625,
"severe_toxicity_score": 0.0007534027,
"identity_attack_score": 0.0028118372,
"insult_score": 0.0075888764,
"profanity_score": 0.01265371,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.009495324,
"flirtation_score": 0.21742633
},
"proof_ds.tex": {
"toxicity_score": 0.021432023,
"severe_toxicity_score": 0.0019454956,
"identity_attack_score": 0.005105704,
"insult_score": 0.0096028885,
"profanity_score": 0.018529098,
"threat_score": 0.008686432,
"sexually_explicit_score": 0.017457243,
"flirtation_score": 0.19775106
},
"main.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
},
"abstract.tex": {
"toxicity_score": 0.017718147,
"severe_toxicity_score": 0.0008916855,
"identity_attack_score": 0.0048837173,
"insult_score": 0.008937885,
"profanity_score": 0.013439372,
"threat_score": 0.007094103,
"sexually_explicit_score": 0.0074901003,
"flirtation_score": 0.21914779
},
"combine.tex": {
"toxicity_score": 0.0060631246,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0014706649,
"insult_score": 0.0061068675,
"profanity_score": 0.011577694,
"threat_score": 0.0065050707,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.32542008
},
"app_ip.tex": {
"toxicity_score": 0.015707577,
"severe_toxicity_score": 0.0007247925,
"identity_attack_score": 0.0036812867,
"insult_score": 0.008253881,
"profanity_score": 0.012038844,
"threat_score": 0.006485652,
"sexually_explicit_score": 0.011795434,
"flirtation_score": 0.21994334
},
"appendix.tex": {
"toxicity_score": 0.11347008,
"severe_toxicity_score": 0.0038719177,
"identity_attack_score": 0.0038107792,
"insult_score": 0.061801232,
"profanity_score": 0.10116278,
"threat_score": 0.005928984,
"sexually_explicit_score": 0.012444182,
"flirtation_score": 0.19755182
},
"intro.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"match.tex": {
"toxicity_score": 0.017215505,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0052906936,
"insult_score": 0.008348881,
"profanity_score": 0.013473531,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.017221333,
"flirtation_score": 0.31504887
},
"app_preli.tex": {
"toxicity_score": 0.009110395,
"severe_toxicity_score": 0.001077652,
"identity_attack_score": 0.0029968263,
"insult_score": 0.006952373,
"profanity_score": 0.014259194,
"threat_score": 0.007353018,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.31201267
},
"main.tex": {
"toxicity_score": 0.022256956,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0055496786,
"insult_score": 0.010172892,
"profanity_score": 0.01982715,
"threat_score": 0.008738215,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.25408843
},
"other.tex": {
"toxicity_score": 0.0074139764,
"severe_toxicity_score": 0.00072956085,
"identity_attack_score": 0.0025713511,
"insult_score": 0.00651537,
"profanity_score": 0.0127561875,
"threat_score": 0.0067963502,
"sexually_explicit_score": 0.0094363475,
"flirtation_score": 0.22207785
},
"sample.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007341875,
"profanity_score": 0.013439372,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.21946533
},
"ds.tex": {
"toxicity_score": 0.0052463305,
"severe_toxicity_score": 0.0005578995,
"identity_attack_score": 0.0016371552,
"insult_score": 0.0059548668,
"profanity_score": 0.011475217,
"threat_score": 0.0061878995,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.22045304
},
"concl.tex": {
"toxicity_score": 0.007979449,
"severe_toxicity_score": 0.0006580353,
"identity_attack_score": 0.001655654,
"insult_score": 0.0067623714,
"profanity_score": 0.012517073,
"threat_score": 0.006382086,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.26385543
},
"preli.tex": {
"toxicity_score": 0.0070684096,
"severe_toxicity_score": 0.00081062317,
"identity_attack_score": 0.0019423875,
"insult_score": 0.0064488696,
"profanity_score": 0.01261955,
"threat_score": 0.0065956907,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.33757907
},
"app_algorithm.tex": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0013256073,
"identity_attack_score": 0.004347248,
"insult_score": 0.007645877,
"profanity_score": 0.015864678,
"threat_score": 0.007883795,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.18720119
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.547506926787,
"hash": "3f3f3f007f3f9f7f",
"most_frequent_color_ratio": 94.547506926787
}
} | [] | "algorithm" | "61951fcc-e041-412f-90c5-01c349f7d42b" | 250 | easy |
|
\begin{algorithm}
\caption{Bootstrap particle filter algorithm}\label{euclidBF1}
\begin{algorithmic}[1]
\For {$k = 1,...,\textit{N}$}
\State $t=1$, \text{draw sample} $X^{k}_{(1)} \sim p(X_{(1)})$;
\EndFor
\For {$t = 2,...,\textit{T}$}
\For {$k = 1,...,\textit{N}$}
\State Draw sample $X_{(t)}^k \sim p(X_{(t)} \vert X^{*k}_{(t-1)})$;
\State Calculate weights $w_{(t)}^k = p(Y_{(t)} \vert X_{(t)}^k)$;
\EndFor
\State Estimate the log-likelihood component for the $t^{th}$ observation, $\hat{l}_{(t)} = \log \left(\dfrac{\sum_j w_{(t)}^j}{N}\right)$;
\State Normalise weights $W_{(t)}^k = \dfrac{w_{(t)}^k}{\sum_j w_{(t)}^j}$ for $k \in \{1, 2, \dots, N \}$;
\State Resample with replacement $N$ particles $X_{(t)}^k$ based on the normalised importance weights;
\State Estimate the overall log-likelihood $L^* = \sum_t \hat{l}_{(t)}$.
\EndFor
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Bootstrap particle filter algorithm}\begin{algorithmic}
[1]
\For {$k = 1,...,\textit{N}$}
\State $t=1$, \text{draw sample} $X^{k}_{(1)} \sim p(X_{(1)})$;
\EndFor
\For {$t = 2,...,\textit{T}$}
\For {$k = 1,...,\textit{N}$}
\State Draw sample $X_{(t)}^k \sim p(X_{(t)} \vert X^{*k}_{(t-1)})$;
\State Calculate weights $w_{(t)}^k = p(Y_{(t)} \vert X_{(t)}^k)$;
\EndFor
\State Estimate the log-likelihood component for the $t^{th}$ observation, $\hat{l}_{(t)} = \log \left(\dfrac{\sum_j w_{(t)}^j}{N}\right)$;
\State Normalise weights $W_{(t)}^k = \dfrac{w_{(t)}^k}{\sum_j w_{(t)}^j}$ for $k \in \{1, 2, \dots, N \}$;
\State Resample with replacement $N$ particles $X_{(t)}^k$ based on the normalised importance weights;
\State Estimate the overall log-likelihood $L^* = \sum_t \hat{l}_{(t)}$.
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2105.04789" | "2105.04789.tar.gz" | "2024-02-10" | {
"title": "innovative approaches in soil carbon sequestration modelling for better prediction with limited data",
"id": "2105.04789",
"abstract": "soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the paris and kyoto protocol agreements. land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. the model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (soc) components. in this study, we introduce two novel soc models and a new rothc-like model and investigate how the soc components and complexity of the soc models affect the soc prediction in the presence of small and sparse time series data. we develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions. the published version of this study is available in scientific reports (https://www.nature.com/articles/s41598-024-53516-z/<10.1038/s41598-024-53516-z>)",
"categories": "stat.co stat.ap",
"doi": "10.1038/s41598-024-53516-z",
"created": "2021-05-11",
"updated": "2024-02-10",
"authors": [
"mohammad javad davoudabadi",
"daniel pagendam",
"christopher drovandi",
"jeff baldock",
"gentry white"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2105.04789"
} | "2024-03-15T06:18:35.682065" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"references.bib": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003348306,
"insult_score": 0.007664877,
"profanity_score": 0.015932998,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.014685315,
"flirtation_score": 0.41820723
},
"Rplot_02.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"wlscirep.cls": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0009727478,
"identity_attack_score": 0.0045322375,
"insult_score": 0.00971689,
"profanity_score": 0.014566627,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.33412078
},
"Rplot.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"jabbrv.sty": {
"toxicity_score": 0.0059688794,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0018498929,
"insult_score": 0.0066008708,
"profanity_score": 0.011167783,
"threat_score": 0.006375613,
"sexually_explicit_score": 0.0069298176,
"flirtation_score": 0.4899393
},
"jabbrv-ltwa-all.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"jabbrv-ltwa-en.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"main.tex": {
"toxicity_score": 0.0137598375,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.0041992567,
"insult_score": 0.007949878,
"profanity_score": 0.01846078,
"threat_score": 0.007909686,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.42432743
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.02737925251014,
"hash": "033f0b0be7c3bf3f",
"most_frequent_color_ratio": 92.02737925251014
}
} | [] | "algorithm" | "ae1d167d-3320-4830-87f9-279934cddf17" | 858 | medium |
|
\begin{algorithmic}
\Require Time limit of each subproblem $g_i(x,\lambda)$ (e.g., 10 sec.)
\State
$S \gets \{1,\cdots, N\}$
\While{\texttt{!cut\_added}}
\For{$i$ in $S$}
\State Solve $g_i(x,\lambda)$; $\texttt{TS}_i$ $\gets$ termination status of $g_i(x,\lambda)$
\If{$\texttt{TS}_i = \texttt{OPTIMAL}$}
\State Lines 19-23 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\ElsIf{$\texttt{TS}_i = \texttt{INFEASIBLE}$}
\State $S \gets S \setminus \{i\}$
\ElsIf{$\texttt{TS}_i = \texttt{UNBOUNDED}$}
\State Lines 25-31 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\ElsIf{$\texttt{TS}_i$ = \texttt{TIME LIMIT}}
\If{$g(x,\lambda)$ has a solution}
\State Lines 19-23 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\EndIf
\EndIf
\EndFor
\If{$S=\emptyset$}
\State $\texttt{UB} \gets v^\texttt{k}$; $\texttt{progressed} \gets \texttt{true}$
\EndIf
\EndWhile
\end{algorithmic}
| \begin{algorithmic}
\Require Time limit of each subproblem $g_i(x,\lambda)$ (e.g., 10 sec.)
\State
$S \gets \{1,\cdots, N\}$
\While{\texttt{!cut\_added}}
\For{$i$ in $S$}
\State Solve $g_i(x,\lambda)$; $\texttt{TS}_i$ $\gets$ termination status of $g_i(x,\lambda)$
\If{$\texttt{TS}_i = \texttt{OPTIMAL}$}
\State Lines 19-23 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\ElsIf{$\texttt{TS}_i = \texttt{INFEASIBLE}$}
\State $S \gets S \setminus \{i\}$
\ElsIf{$\texttt{TS}_i = \texttt{UNBOUNDED}$}
\State Lines 25-31 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\ElsIf{$\texttt{TS}_i$ = \texttt{TIME LIMIT}}
\If{$g(x,\lambda)$ has a solution}
\State Lines 19-23 of Algorithm \ref{algo}; %$\texttt{progressed} \gets \texttt{true}$
\EndIf
\EndIf
\EndFor
\If{$S=\emptyset$}
\State $\texttt{UB} \gets v^\texttt{k}$; $\texttt{progressed} \gets \texttt{true}$
\EndIf
\EndWhile
\end{algorithmic} | "https://arxiv.org/src/2211.05903" | "2211.05903.tar.gz" | "2024-02-02" | {
"title": "two-stage distributionally robust conic linear programming over 1-wasserstein balls",
"id": "2211.05903",
"abstract": "this paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 wasserstein balls. we present optimality conditions for the dual of the worst-case expectation problem, which characterizes worst-case uncertain parameters for its inner maximization problem. this condition offers an alternative proof, a counter-example, and an extension to previous works. additionally, the condition highlights the potential advantage of a specific distance metric for out-of-sample performance, as exemplified in a numerical study on a facility location problem with demand uncertainty. a cutting-plane-based algorithm and a variety of algorithmic enhancements are proposed with a finite convergence proof under less stringent assumptions.",
"categories": "math.oc",
"doi": "",
"created": "2022-11-10",
"updated": "2024-02-02",
"authors": [
"geunyeong byeon",
"kaiwen fang",
"kibaek kim"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2211.05903"
} | "2024-03-15T04:52:56.783142" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"ex_shared.tex": {
"toxicity_score": 0.009298885,
"severe_toxicity_score": 0.000872612,
"identity_attack_score": 0.0034962974,
"insult_score": 0.007132874,
"profanity_score": 0.012926984,
"threat_score": 0.006925808,
"sexually_explicit_score": 0.008787598,
"flirtation_score": 0.26775497
},
"main.tex": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.004180758,
"insult_score": 0.0075698765,
"profanity_score": 0.0147374235,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.012385205,
"flirtation_score": 0.25282642
},
"siamplain.bst": {
"toxicity_score": 0.102626406,
"severe_toxicity_score": 0.005607605,
"identity_attack_score": 0.030949092,
"insult_score": 0.025284592,
"profanity_score": 0.070942834,
"threat_score": 0.02181799,
"sexually_explicit_score": 0.055640165,
"flirtation_score": 0.2839845
},
"siamart220329.cls": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0010681152,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007645877,
"profanity_score": 0.014361672,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.010026119,
"flirtation_score": 0.4152329
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.8174729713314,
"hash": "1f9f87878797bf0f",
"most_frequent_color_ratio": 92.8174729713314
}
} | [] | "algorithm" | "1c9f8775-cd50-4d9c-9ae6-c8d056ad740a" | 931 | medium |
|
\begin{algorithm}
\caption{Idealized algorithm \label{alg:fake}}
\begin{algorithmic}[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \int G_h*(\nabla\cdot g)$.
\State Solve \eqref{eqn:intrsig} for first-order flux correctors $\sigma_{ijk}$ and \eqref{eqn:2ndcordef} for second-order correctors $\psi_{ij}$.
\State Obtain $u_h$ via \eqref{eqn:effectivequadp}.
\State Solve \eqref{eqn:coruhat} for $\hat{u}$, which is the approximation we desire.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Idealized algorithm }
\begin{algorithmic}
[1]
\State Solve \eqref{intrphi} for first-order correctors $\phi_i$.
\State Determine the homogenized coefficients $a_h$ via \eqref{intrhomcoeff}.
\State Solve \eqref{intruhtilde} for $\tilde{u}_h$ on $\partial Q_L$ by $\tilde{u}_h = \int G_h*(\nabla\cdot g)$.
\State Solve \eqref{eqn:intrsig} for first-order flux correctors $\sigma_{ijk}$ and \eqref{eqn:2ndcordef} for second-order correctors $\psi_{ij}$.
\State Obtain $u_h$ via \eqref{eqn:effectivequadp}.
\State Solve \eqref{eqn:coruhat} for $\hat{u}$, which is the approximation we desire.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in an infinite heterogeneous medium, in a situation where the medium is only known in a box of diameter $l\\gg\\ell$ around the support of the charge. we propose a boundary condition that with overwhelming probability is (near) optimal with respect to scaling in terms of $\\ell$ and $l$, in the setting where the medium is a sample from a stationary ensemble with a finite range of dependence (set to be unity and with the assumption that $\\ell \\gg 1$). the boundary condition is motivated by quantitative stochastic homogenization that allows for a multipole expansion [bgo20]. this work extends [lo21], the algorithm in which is optimal in two dimension, and thus we need to take quadrupoles, next to dipoles, into account. this in turn relies on stochastic estimates of second-order, next to first-order, correctors. these estimates are provided for finite range ensembles under consideration, based on an extension of the semi-group approach of [go15].",
"categories": "math.ap cs.na math.na math.pr",
"doi": "",
"created": "2021-09-03",
"updated": "2024-01-11",
"authors": [
"jianfeng lu",
"felix otto",
"lihan wang"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2109.01616"
} | "2024-03-15T06:22:17.156672" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Revised_Manuscript.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
"profanity_score": 0.015147334,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.21162485
},
"Revised_Manuscript.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.30642147521877,
"hash": "0f0f0707007f3f03",
"most_frequent_color_ratio": 89.30642147521877
}
} | [] | "algorithm" | "80e5fb75-33cd-4630-a116-698b32bf731d" | 649 | easy |
|
\begin{algorithmic}
\Require $\theta_0, m > 0, m' > 0$
\For{$k = 0,...$}
\If{$\ddot{F}(\theta_k) \succcurlyeq 0$}
\State $s_k' = 0$
\Else
\State $s_k' = $ \Call{SelectDirection()}{}
\EndIf
\If{$\dot{F}(\theta_k) = 0$}
\State $s_k = 0$
\Else
\State $s_k = -\dot{F}(\theta_k)$
\EndIf
\If{$s_k = s_k' = 0$}
\State \Return{$\theta_k$}
\EndIf
\State $\theta_{k+1} = \theta_k + m s_k + m' s_k'$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\Require $\theta_0, m > 0, m' > 0$
\For{$k = 0,...$}
\If{$\ddot{F}(\theta_k) \succcurlyeq 0$}
\State $s_k' = 0$
\Else
\State $s_k' = $ \Call{SelectDirection()}{}
\EndIf
\If{$\dot{F}(\theta_k) = 0$}
\State $s_k = 0$
\Else
\State $s_k = -\dot{F}(\theta_k)$
\EndIf
\If{$s_k = s_k' = 0$}
\State \Return{$\theta_k$}
\EndIf
\State $\theta_{k+1} = \theta_k + m s_k + m' s_k'$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2309.10894" | "2309.10894.tar.gz" | "2024-02-15" | {
"title": "a novel gradient methodology with economical objective function evaluations for data science applications",
"id": "2309.10894",
"abstract": "gradient methods are experiencing a growth in methodological and theoretical developments owing to the challenges of optimization problems arising in data science. focusing on data science applications with expensive objective function evaluations yet inexpensive gradient function evaluations, gradient methods that never make objective function evaluations are either being rejuvenated or actively developed. however, as we show, such gradient methods are all susceptible to catastrophic divergence under realistic conditions for data science applications. in light of this, gradient methods which make use of objective function evaluations become more appealing, yet, as we show, can result in an exponential increase in objective evaluations between accepted iterates. as a result, existing gradient methods are poorly suited to the needs of optimization problems arising from data science. in this work, we address this gap by developing a generic methodology that economically uses objective function evaluations in a problem-driven manner to prevent catastrophic divergence and avoid an explosion in objective evaluations between accepted iterates. our methodology allows for specific procedures that can make use of specific step size selection methodologies or search direction strategies, and we develop a novel step size selection methodology that is well-suited to data science applications. we show that a procedure resulting from our methodology is highly competitive with standard optimization methods on cutest test problems. we then show a procedure resulting from our methodology is highly favorable relative to standard optimization methods on optimization problems arising in our target data science applications. thus, we provide a novel gradient methodology that is better suited to optimization problems arising in data science.",
"categories": "math.oc stat.co",
"doi": "",
"created": "2023-09-19",
"updated": "2024-02-15",
"authors": [
"christian varner",
"vivak patel"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.10894"
} | "2024-03-15T05:23:50.845023" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"table/novel-step-size-param-table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558756,
"profanity_score": 0.015659723,
"threat_score": 0.007547205,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2449079
},
"section/algo_psuedocode/general_algorithm.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"section/algo_psuedocode/outer_loop.tex": {
"toxicity_score": 0.011246625,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0035147965,
"insult_score": 0.0074368757,
"profanity_score": 0.015283971,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.011913388,
"flirtation_score": 0.23316474
},
"data/cutest/all_rel_change_gradient_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
},
"data/cutest/all_rel_change_function_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
},
"data/gee/total_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"section/evaluation_explosion/preliminaries.tex": {
"toxicity_score": 0.011623607,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.0034408006,
"insult_score": 0.007360875,
"profanity_score": 0.013439372,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.010615891,
"flirtation_score": 0.33654565
},
"section/smoothness-of-ds-problems/overview.tex": {
"toxicity_score": 0.020960633,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0051797,
"insult_score": 0.0107048955,
"profanity_score": 0.016103793,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.014626338,
"flirtation_score": 0.17700504
},
"section/catastrophic_divergence/nesterov_acceleration.tex": {
"toxicity_score": 0.013131535,
"severe_toxicity_score": 0.0010538101,
"identity_attack_score": 0.0052166977,
"insult_score": 0.007721877,
"profanity_score": 0.0147374235,
"threat_score": 0.007974415,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.19002186
},
"section/algorithm.tex": {
"toxicity_score": 0.036634352,
"severe_toxicity_score": 0.002193451,
"identity_attack_score": 0.011543332,
"insult_score": 0.012110904,
"profanity_score": 0.02303812,
"threat_score": 0.014220826,
"sexually_explicit_score": 0.027365407,
"flirtation_score": 0.22199428
},
"ref.bib": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.002293867,
"insult_score": 0.007360875,
"profanity_score": 0.0137468055,
"threat_score": 0.006925808,
"sexually_explicit_score": 0.009672256,
"flirtation_score": 0.23720506
},
"section/notation.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0029043318,
"insult_score": 0.0071138735,
"profanity_score": 0.0157622,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2506604
},
"section/catastrophic_divergence/adaptive_scaled_tr.tex": {
"toxicity_score": 0.017089844,
"severe_toxicity_score": 0.001206398,
"identity_attack_score": 0.005512681,
"insult_score": 0.009336887,
"profanity_score": 0.015488927,
"threat_score": 0.00814271,
"sexually_explicit_score": 0.01480327,
"flirtation_score": 0.3277508
},
"section/evaluation_explosion/cubic_regularized_newton.tex": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.00062942505,
"identity_attack_score": 0.002830336,
"insult_score": 0.007493876,
"profanity_score": 0.011902207,
"threat_score": 0.006485652,
"sexually_explicit_score": 0.007106749,
"flirtation_score": 0.21416461
},
"section/catastrophic_divergence.tex": {
"toxicity_score": 0.0056233127,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0016649036,
"insult_score": 0.0061543677,
"profanity_score": 0.012038844,
"threat_score": 0.006330303,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.3247941
},
"section/catastrophic_divergence/wngrad-div.tex": {
"toxicity_score": 0.029917045,
"severe_toxicity_score": 0.0014877319,
"identity_attack_score": 0.0070665907,
"insult_score": 0.011958904,
"profanity_score": 0.017914234,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.39471897
},
"data/gee/fieller_categorization.csv": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0008916855,
"identity_attack_score": 0.002885833,
"insult_score": 0.007892879,
"profanity_score": 0.012722028,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.40000224
},
"figure/gee_wedderburn/evaluations_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/smoothness-of-ds-problems/poisson-reg/poisson-reg.tex": {
"toxicity_score": 0.022139108,
"severe_toxicity_score": 0.0011014938,
"identity_attack_score": 0.0034592997,
"insult_score": 0.010096892,
"profanity_score": 0.013712646,
"threat_score": 0.007832011,
"sexually_explicit_score": 0.0101440735,
"flirtation_score": 0.23723446
},
"data/gee/func_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"section/evaluation_explosion/dynamic_method.tex": {
"toxicity_score": 0.019854378,
"severe_toxicity_score": 0.0013637543,
"identity_attack_score": 0.0056236745,
"insult_score": 0.009146886,
"profanity_score": 0.016479544,
"threat_score": 0.008815889,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.23163475
},
"data/gee/func_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"section/algo_psuedocode/novel-step-size-procedure.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"data/gee/fieller_cpu_data.csv": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0029182434,
"identity_attack_score": 0.002034882,
"insult_score": 0.009640889,
"profanity_score": 0.015147334,
"threat_score": 0.008764107,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.25993884
},
"table/multiple_objective_evaluation_table.tex": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0015068054,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0077598775,
"profanity_score": 0.017367685,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.22981189
},
"figure/first_order_relative_change/total_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"figure/second_order_relative_change/total_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"data/gee/grad_evals_leaf.csv": {
"toxicity_score": 0.014450971,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008557882,
"profanity_score": 0.013405213,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.35794142
},
"figure/cpu_time_cutest/second_order_cpu_time.tex": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0043102503,
"insult_score": 0.008329881,
"profanity_score": 0.016889455,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.48778152
},
"figure/gee_wedderburn/cpu_comp_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/catastrophic_divergence/constant_step.tex": {
"toxicity_score": 0.0143253105,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0034223017,
"insult_score": 0.007664877,
"profanity_score": 0.014190876,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.012385205,
"flirtation_score": 0.427231
},
"figure/gee_fiellercreasy/cpu_comp_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"figure/second_order_relative_change/gradient_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"section/algo_psuedocode/local_lipschitz_est.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"section/algo_psuedocode/inner_loop.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0012683868,
"identity_attack_score": 0.0037737815,
"insult_score": 0.0074558756,
"profanity_score": 0.015488927,
"threat_score": 0.007935578,
"sexually_explicit_score": 0.012031343,
"flirtation_score": 0.1759681
},
"figure/objective_threshold_diagram.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/catastrophic_divergence/barzilai_borwein.tex": {
"toxicity_score": 0.015079274,
"severe_toxicity_score": 0.00092983246,
"identity_attack_score": 0.004125261,
"insult_score": 0.00813988,
"profanity_score": 0.0127561875,
"threat_score": 0.0074824756,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.37749934
},
"figure/catastrophic_divergence_building_function.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"section/evaluation_explosion/armijo.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.00096321106,
"identity_attack_score": 0.0019238886,
"insult_score": 0.0077028773,
"profanity_score": 0.013302735,
"threat_score": 0.006051969,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.33172962
},
"section/smoothness-of-ds-problems/correlation-via-gee/correlation-via-gee.tex": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00092983246,
"identity_attack_score": 0.003866276,
"insult_score": 0.0074368757,
"profanity_score": 0.013610168,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.010615891,
"flirtation_score": 0.24296696
},
"figure/second_order_relative_change/function_calls.tex": {
"toxicity_score": 0.033334624,
"severe_toxicity_score": 0.004043579,
"identity_attack_score": 0.0062526376,
"insult_score": 0.011122898,
"profanity_score": 0.03715288,
"threat_score": 0.011146127,
"sexually_explicit_score": 0.040446278,
"flirtation_score": 0.3976111
},
"section/evaluation_explosion/adaptive_cubic_regularization.tex": {
"toxicity_score": 0.008796243,
"severe_toxicity_score": 0.0006246567,
"identity_attack_score": 0.0027748393,
"insult_score": 0.0068003717,
"profanity_score": 0.011184863,
"threat_score": 0.006589218,
"sexually_explicit_score": 0.006516977,
"flirtation_score": 0.1969631
},
"section/results-cutest.tex": {
"toxicity_score": 0.016964182,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0051427023,
"insult_score": 0.009127886,
"profanity_score": 0.017162729,
"threat_score": 0.008065036,
"sexually_explicit_score": 0.014095544,
"flirtation_score": 0.44243842
},
"figure/first_order_relative_change/gradient_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"GD_Triggers.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"table/objective_free_table.tex": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0015068054,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0077598775,
"profanity_score": 0.017367685,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.22981189
},
"section/algo_psuedocode/naive_algorithm.tex": {
"toxicity_score": 0.016021729,
"severe_toxicity_score": 0.0018501282,
"identity_attack_score": 0.0047727237,
"insult_score": 0.008272881,
"profanity_score": 0.020373698,
"threat_score": 0.008427517,
"sexually_explicit_score": 0.016159745,
"flirtation_score": 0.20608959
},
"data/gee/total_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"figure/gee_fiellercreasy/evaluations_boxplots.tex": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"data/cutest/all_cpu_won.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0018498929,
"insult_score": 0.008747884,
"profanity_score": 0.010569996,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.0049245935,
"flirtation_score": 0.29574385
},
"data/gee/leaf_cpu_data.csv": {
"toxicity_score": 0.024967449,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0023031165,
"insult_score": 0.013326911,
"profanity_score": 0.015113175,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.2429553
},
"section/smoothness-of-ds-problems/poisson-reg/smoothness-proof.tex": {
"toxicity_score": 0.009864358,
"severe_toxicity_score": 0.0008201599,
"identity_attack_score": 0.0020996283,
"insult_score": 0.0068763723,
"profanity_score": 0.012346277,
"threat_score": 0.006763986,
"sexually_explicit_score": 0.009082484,
"flirtation_score": 0.22990656
},
"section/catastrophic_divergence/polyak.tex": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0075888764,
"profanity_score": 0.011970525,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.20474647
},
"section/catastrophic_divergence/bregman_distance.tex": {
"toxicity_score": 0.009487377,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.0033298072,
"insult_score": 0.006895372,
"profanity_score": 0.013063621,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.010321004,
"flirtation_score": 0.41113064
},
"figure/cpu_time_cutest/first_order_cpu_time.tex": {
"toxicity_score": 0.031331215,
"severe_toxicity_score": 0.0043296814,
"identity_attack_score": 0.004439743,
"insult_score": 0.011540901,
"profanity_score": 0.054437835,
"threat_score": 0.007948523,
"sexually_explicit_score": 0.043803066,
"flirtation_score": 0.35417965
},
"section/results-gee.tex": {
"toxicity_score": 0.014136819,
"severe_toxicity_score": 0.00096797943,
"identity_attack_score": 0.0043102503,
"insult_score": 0.00813988,
"profanity_score": 0.015386449,
"threat_score": 0.0076119336,
"sexually_explicit_score": 0.011854411,
"flirtation_score": 0.545385
},
"section/catastrophic_divergence/preliminaries.tex": {
"toxicity_score": 0.013257195,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.004347248,
"insult_score": 0.007835878,
"profanity_score": 0.013678487,
"threat_score": 0.007741391,
"sexually_explicit_score": 0.010497936,
"flirtation_score": 0.27346042
},
"data/cutest/all_cpu_lost.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0018498929,
"insult_score": 0.008747884,
"profanity_score": 0.010569996,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.0049245935,
"flirtation_score": 0.29574385
},
"table/gee-terminal-iterate-cat.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0029043318,
"insult_score": 0.0071138735,
"profanity_score": 0.0157622,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2506604
},
"table/gee-experiment-table.tex": {
"toxicity_score": 0.015833238,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0050317086,
"insult_score": 0.008766884,
"profanity_score": 0.017572641,
"threat_score": 0.008077981,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.19330522
},
"section/problem_formulation.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.003348306,
"insult_score": 0.006819372,
"profanity_score": 0.011355659,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.0063695344,
"flirtation_score": 0.27690822
},
"section/conclusion.tex": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.0006055832,
"identity_attack_score": 0.0027933381,
"insult_score": 0.0075508766,
"profanity_score": 0.011082385,
"threat_score": 0.0066668927,
"sexually_explicit_score": 0.005897717,
"flirtation_score": 0.20846651
},
"section/catastrophic_divergence/negative_curvature.tex": {
"toxicity_score": 0.0154562555,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.0043102503,
"insult_score": 0.007873878,
"profanity_score": 0.015079016,
"threat_score": 0.008401625,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.30951333
},
"section/abstract.tex": {
"toxicity_score": 0.008859074,
"severe_toxicity_score": 0.0005078316,
"identity_attack_score": 0.0026638457,
"insult_score": 0.006952373,
"profanity_score": 0.010074687,
"threat_score": 0.00636914,
"sexually_explicit_score": 0.0046002194,
"flirtation_score": 0.17139499
},
"section/smoothness-of-ds-problems/nn/nn.tex": {
"toxicity_score": 0.017969469,
"severe_toxicity_score": 0.0009965897,
"identity_attack_score": 0.006178642,
"insult_score": 0.009868891,
"profanity_score": 0.015283971,
"threat_score": 0.007689608,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.24728048
},
"GD_Triggers.bbl": {
"toxicity_score": 0.017843807,
"severe_toxicity_score": 0.0013160706,
"identity_attack_score": 0.00362579,
"insult_score": 0.009374888,
"profanity_score": 0.017299367,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.47433925
},
"section/introduction.tex": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.00096797943,
"identity_attack_score": 0.0045322375,
"insult_score": 0.007778878,
"profanity_score": 0.013576009,
"threat_score": 0.00756015,
"sexually_explicit_score": 0.009318393,
"flirtation_score": 0.21079259
},
"figure/first_order_relative_change/function_calls.tex": {
"toxicity_score": 0.028856417,
"severe_toxicity_score": 0.0032615662,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011160898,
"profanity_score": 0.033144772,
"threat_score": 0.009230154,
"sexually_explicit_score": 0.034442667,
"flirtation_score": 0.36386168
},
"table/cutest-experiment-table.tex": {
"toxicity_score": 0.015833238,
"severe_toxicity_score": 0.0015163422,
"identity_attack_score": 0.0050317086,
"insult_score": 0.008766884,
"profanity_score": 0.017572641,
"threat_score": 0.008077981,
"sexually_explicit_score": 0.016277699,
"flirtation_score": 0.19330522
},
"section/smoothness_canonical_ds_problems.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.0035517942,
"insult_score": 0.008291881,
"profanity_score": 0.014293353,
"threat_score": 0.007314181,
"sexually_explicit_score": 0.013505772,
"flirtation_score": 0.30319104
},
"section/catastrophic_divergence/lipschitz_approximation.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0011301041,
"identity_attack_score": 0.0030338243,
"insult_score": 0.0076838774,
"profanity_score": 0.015386449,
"threat_score": 0.007832011,
"sexually_explicit_score": 0.013800657,
"flirtation_score": 0.50405544
},
"section/evaluation_explosion.tex": {
"toxicity_score": 0.019351736,
"severe_toxicity_score": 0.0015830994,
"identity_attack_score": 0.0039772694,
"insult_score": 0.008367881,
"profanity_score": 0.017845914,
"threat_score": 0.009307829,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.35859555
},
"data/gee/grad_evals_fieller.csv": {
"toxicity_score": 0.014576632,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.003144818,
"insult_score": 0.008519882,
"profanity_score": 0.013849284,
"threat_score": 0.0083886795,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3583591
},
"section/novel-step-size.tex": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0009441376,
"identity_attack_score": 0.0042732526,
"insult_score": 0.007930879,
"profanity_score": 0.014805742,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.012798046,
"flirtation_score": 0.41932905
},
"table/data_science_problems_table.tex": {
"toxicity_score": 0.011309455,
"severe_toxicity_score": 0.0012969971,
"identity_attack_score": 0.0034592997,
"insult_score": 0.0074558756,
"profanity_score": 0.015659723,
"threat_score": 0.007547205,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2449079
},
"section/smoothness-of-ds-problems/factor-analysis/factor-analysis.tex": {
"toxicity_score": 0.008984734,
"severe_toxicity_score": 0.0005698204,
"identity_attack_score": 0.002330865,
"insult_score": 0.006885872,
"profanity_score": 0.011082385,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.006222091,
"flirtation_score": 0.16150479
},
"data/cutest/all_rel_change_total_calls.csv": {
"toxicity_score": 0.0048065186,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.00087407435,
"insult_score": 0.006021367,
"profanity_score": 0.012380436,
"threat_score": 0.005809236,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.3112504
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.98582783127969,
"hash": "038788939f93879f",
"most_frequent_color_ratio": 92.98582783127969
}
} | [] | "algorithm" | "b6d8b1b3-9db6-4497-93b6-55f66fadd2dd" | 414 | easy |
|
\begin{algorithmic}[1]
\For{$variant$ in variants}
\State dates $\gets$ unique dates in which $variant$ exists
\State dates.sort()
\State model\_initial\_weights $\gets$ random
\For{$d$ in dates}
\State retro\_data $\gets$ all data before $d$
\State processed\_data $\gets$ preprocess $retro\_data$
\State dataset $\gets$ structured $processed\_data$ into graphs
\State train\_dataset, val\_dataset $\gets$ temporally split $dataset$ into 80\% training and the most recent 20\% for validation
\State model $\gets$ initialize desired model with $model\_initial\_weights$
\State epochs $\gets$ 100
\State optimizer $\gets$ Adam(lr=0.05)
\State early\_stopper $\gets$ EarlyStopper(patience=3)
\State $model$.train($epochs$, $optimizer$, $early\_stopper$, $train\_dataset$, $val\_dataset$)
\State best\_model\_weights $\gets early\_stopper$
\State $model\_initial\_weights$ $\gets$ $best\_model\_weights$
\EndFor
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\For{$variant$ in variants}
\State dates $\gets$ unique dates in which $variant$ exists
\State dates.sort()
\State model\_initial\_weights $\gets$ random
\For{$d$ in dates}
\State retro\_data $\gets$ all data before $d$
\State processed\_data $\gets$ preprocess $retro\_data$
\State dataset $\gets$ structured $processed\_data$ into graphs
\State train\_dataset, val\_dataset $\gets$ temporally split $dataset$ into 80\% training and the most recent 20\% for validation
\State model $\gets$ initialize desired model with $model\_initial\_weights$
\State epochs $\gets$ 100
\State optimizer $\gets$ Adam(lr=0.05)
\State early\_stopper $\gets$ EarlyStopper(patience=3)
\State $model$.train($epochs$, $optimizer$, $early\_stopper$, $train\_dataset$, $val\_dataset$)
\State best\_model\_weights $\gets early\_stopper$
\State $model\_initial\_weights$ $\gets$ $best\_model\_weights$
\EndFor
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2401.03390" | "2401.03390.tar.gz" | "2024-01-07" | {
"title": "global prediction of covid-19 variant emergence using dynamics-informed graph neural networks",
"id": "2401.03390",
"abstract": "during the covid-19 pandemic, a major driver of new surges has been the emergence of new variants. when a new variant emerges in one or more countries, other nations monitor its spread in preparation for its potential arrival. the impact of the variant and the timing of epidemic peaks in a country highly depend on when the variant arrives. the current methods for predicting the spread of new variants rely on statistical modeling, however, these methods work only when the new variant has already arrived in the region of interest and has a significant prevalence. the question arises: can we predict when (and if) a variant that exists elsewhere will arrive in a given country and reach a certain prevalence? we propose a variant-dynamics-informed graph neural network (gnn) approach. first, we derive the dynamics of variant prevalence across pairs of regions (countries) that applies to a large class of epidemic models. the dynamics suggest that ratios of variant proportions lead to simpler patterns. therefore, we use ratios of variant proportions along with some parameters estimated from the dynamics as features in a gnn. we develop a benchmarking tool to evaluate variant emergence prediction over 87 countries and 36 variants. we leverage this tool to compare our gnn-based approach against our dynamics-only model and a number of machine learning models. results show that the proposed dynamics-informed gnn method retrospectively outperforms all the baselines, including the currently pervasive framework of physics-informed neural networks (pinns) that incorporates the dynamics in the loss function.",
"categories": "q-bio.pe cs.lg physics.soc-ph",
"doi": "",
"created": "2024-01-07",
"updated": "",
"authors": [
"majd al aawar",
"srikar mutnuri",
"mansooreh montazerin",
"ajitesh srivastava"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.03390"
} | "2024-03-15T07:51:28.358213" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"other/0_paper.tex": {
"toxicity_score": 0.009801528,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.0030153254,
"insult_score": 0.007132874,
"profanity_score": 0.015967157,
"threat_score": 0.006874025,
"sexually_explicit_score": 0.015687928,
"flirtation_score": 0.25975358
},
"sample.bib": {
"toxicity_score": 0.023553278,
"severe_toxicity_score": 0.0025177002,
"identity_attack_score": 0.0054756827,
"insult_score": 0.011692902,
"profanity_score": 0.025497584,
"threat_score": 0.006815769,
"sexually_explicit_score": 0.015923835,
"flirtation_score": 0.2914525
},
"other/1_introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"IEEEtran.cls": {
"toxicity_score": 0.0049635945,
"severe_toxicity_score": 0.0007343292,
"identity_attack_score": 0.0012024303,
"insult_score": 0.006011867,
"profanity_score": 0.012312118,
"threat_score": 0.005861019,
"sexually_explicit_score": 0.008374758,
"flirtation_score": 0.4981806
},
"images/graph_creation.svg": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0040142676,
"insult_score": 0.0076838774,
"profanity_score": 0.015625564,
"threat_score": 0.0072235605,
"sexually_explicit_score": 0.012149297,
"flirtation_score": 0.3154877
},
"other/6_conclusion.tex": {
"toxicity_score": 0.010115679,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.0029968263,
"insult_score": 0.007132874,
"profanity_score": 0.014122557,
"threat_score": 0.007353018,
"sexually_explicit_score": 0.010262027,
"flirtation_score": 0.30380663
},
"other/3_methodology.tex": {
"toxicity_score": 0.0064715217,
"severe_toxicity_score": 0.0007724762,
"identity_attack_score": 0.001933138,
"insult_score": 0.006344369,
"profanity_score": 0.012346277,
"threat_score": 0.0065180166,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.1947523
},
"other/4_experiments.tex": {
"toxicity_score": 0.009990019,
"severe_toxicity_score": 0.00094890594,
"identity_attack_score": 0.0024418586,
"insult_score": 0.006895372,
"profanity_score": 0.01354185,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.23574468
},
"other/2_relatedwork.tex": {
"toxicity_score": 0.0069741644,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0024788564,
"insult_score": 0.006363369,
"profanity_score": 0.01220964,
"threat_score": 0.0067898775,
"sexually_explicit_score": 0.008315781,
"flirtation_score": 0.20454182
},
"main-v1.tex": {
"toxicity_score": 0.009801528,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.0030153254,
"insult_score": 0.007132874,
"profanity_score": 0.015967157,
"threat_score": 0.006874025,
"sexually_explicit_score": 0.015687928,
"flirtation_score": 0.25975358
},
"main-v1.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"other/5_discussion.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0009393692,
"identity_attack_score": 0.0029783275,
"insult_score": 0.007170874,
"profanity_score": 0.014874061,
"threat_score": 0.0072235605,
"sexually_explicit_score": 0.012562137,
"flirtation_score": 0.22211537
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.23877662220748,
"hash": "038c7f3f6f3f0304",
"most_frequent_color_ratio": 91.23877662220748
}
} | [] | "algorithm" | "7e531cdb-879f-49b5-9344-6bf1711b98d9" | 935 | medium |
|
\begin{algorithm} [tb!]
\footnotesize
\caption{\small {\sf HuGE-D} walking procedure}
\label{HuGE-D_walk}
\begin{algorithmic}[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdot \max \left\{\frac{deg(u)}{deg(v)}, \frac{deg(v)}{deg(u)} \right\} \right)$} // Eq.~\ref{accept_CNHRW}
{\flushleft{{\bf getStateQueryResult($W, P(u,v)$)}}} %// {return results to querying walkers for retrieving the state and decide sampling outcome}
\State{generate a random number $\eta \in\left[0,1\right]$}
\If{$P(u,v)> \eta $}
\State{$W.path$.append($v$), $W.cur$ = $v$, $W.steps$ ++}
\State{$L$ = $W.steps$}
\State compute $H(W)$ and $R\left(H(W),L\right)$ // Eq.~\ref{path_entropy}, \ref{path_corr}
\If{$R^2(H(W),L) < \mu$}
\State terminate the walk
\Else
\State{generate another candidate node $t$ of $v$}
\State{sendStateQuery($v$, $t$, $W$)}
\EndIf
\Else
\State{backtrack to $u$ and generate another candidate node $v'$ of $u$}
\State{sendStateQuery($u$, $v'$, $W$)}
\EndIf
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[tb!]
\footnotesize
\caption{\small {\sf HuGE-D} walking procedure}
\begin{algorithmic}
[1]
\Require current node $u$, candidate node $v$, Walker $W$, {\sf HuGE} parameter $\mu$
\Ensure walker state updates
{\flushleft{{\bf sendStateQuery($u$, $v$, $W$)}}} %//{submit the walker-to-vertex query messages and process the state queries}
\State{$P(u,v) = Z\left(\frac{1}{deg(u)-Cm(u, v)}\cdot \max \left\{\frac{deg(u)}{deg(v)}, \frac{deg(v)}{deg(u)} \right\} \right)$} // Eq.~\ref{accept_CNHRW}
{\flushleft{{\bf getStateQueryResult($W, P(u,v)$)}}} %// {return results to querying walkers for retrieving the state and decide sampling outcome}
\State{generate a random number $\eta \in\left[0,1\right]$}
\If{$P(u,v)> \eta $}
\State{$W.path$.append($v$), $W.cur$ = $v$, $W.steps$ ++}
\State{$L$ = $W.steps$}
\State compute $H(W)$ and $R\left(H(W),L\right)$ // Eq.~\ref{path_entropy}, \ref{path_corr}
\If{$R^2(H(W),L) < \mu$}
\State terminate the walk
\Else
\State{generate another candidate node $t$ of $v$}
\State{sendStateQuery($v$, $t$, $W$)}
\EndIf
\Else
\State{backtrack to $u$ and generate another candidate node $v'$ of $u$}
\State{sendStateQuery($u$, $v'$, $W$)}
\EndIf
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2303.15702" | "2303.15702.tar.gz" | "2024-02-25" | {
"title": "distributed graph embedding with information-oriented random walks",
"id": "2303.15702",
"abstract": "graph embedding maps graph nodes to low-dimensional vectors, and is widely adopted in machine learning tasks. the increasing availability of billion-edge graphs underscores the importance of learning efficient and effective embeddings on large graphs, such as link prediction on twitter with over one billion edges. most existing graph embedding methods fall short of reaching high data scalability. in this paper, we present a general-purpose, distributed, information-centric random walk-based graph embedding framework, distger, which can scale to embed billion-edge graphs. distger incrementally computes information-centric random walks. it further leverages a multi-proximity-aware, streaming, parallel graph partitioning strategy, simultaneously achieving high local partition quality and excellent workload balancing across machines. distger also improves the distributed skip-gram learning model to generate node embeddings by optimizing the access locality, cpu throughput, and synchronization efficiency. experiments on real-world graphs demonstrate that compared to state-of-the-art distributed graph embedding frameworks, including knightking, distdgl, and pytorch-biggraph, distger exhibits 2.33x-129x acceleration, 45% reduction in cross-machines communication, and > 10% effectiveness improvement in downstream tasks.",
"categories": "cs.dc cs.lg",
"doi": "",
"created": "2023-03-27",
"updated": "2024-02-25",
"authors": [
"peng fang",
"arijit khan",
"siqiang luo",
"fang wang",
"dan feng",
"zhenli li",
"wei yin",
"yuchao cao"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.15702"
} | "2024-03-15T03:43:03.810720" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Figures/SG_Pw2v_DSGL_pSGNScc_1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"Figures/Dist_generality_table_HuGE+.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"macros.tex": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0014972687,
"identity_attack_score": 0.002034882,
"insult_score": 0.008405882,
"profanity_score": 0.01805087,
"threat_score": 0.0062785195,
"sexually_explicit_score": 0.014213498,
"flirtation_score": 0.26119846
},
"Figures/DSGL_framework_v3_1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"4-experiments.tex": {
"toxicity_score": 0.017718147,
"severe_toxicity_score": 0.0019931793,
"identity_attack_score": 0.0032003147,
"insult_score": 0.008766884,
"profanity_score": 0.023584666,
"threat_score": 0.0076637166,
"sexually_explicit_score": 0.01792906,
"flirtation_score": 0.39472604
},
"Figures/Dist_sampling_training_Mpad_efficiency.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/computation_incremental_mechanism_1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"ACM-Reference-Format.bst": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0023863618,
"insult_score": 0.00815888,
"profanity_score": 0.016479544,
"threat_score": 0.0060325502,
"sexually_explicit_score": 0.013387818,
"flirtation_score": 0.5040354
},
"acmart.bib": {
"toxicity_score": 0.0055918973,
"severe_toxicity_score": 0.0010633469,
"identity_attack_score": 0.0008185776,
"insult_score": 0.006344369,
"profanity_score": 0.0137468055,
"threat_score": 0.005657123,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.24354401
},
"Figures/Dist_scalability_partition.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/SG_Pw2v_DSGL_pSGNScc-1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"0-abstract.tex": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012874603,
"identity_attack_score": 0.0036812867,
"insult_score": 0.007892879,
"profanity_score": 0.01535229,
"threat_score": 0.007689608,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.19561295
},
"5-related.tex": {
"toxicity_score": 0.014765122,
"severe_toxicity_score": 0.0017738342,
"identity_attack_score": 0.002848835,
"insult_score": 0.0081208795,
"profanity_score": 0.020851927,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.016749516,
"flirtation_score": 0.41871166
},
"main.tex": {
"toxicity_score": 0.007885204,
"severe_toxicity_score": 0.0007581711,
"identity_attack_score": 0.002848835,
"insult_score": 0.006705371,
"profanity_score": 0.011611853,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.4564441
},
"ref.bib": {
"toxicity_score": 0.007602467,
"severe_toxicity_score": 0.0010251999,
"identity_attack_score": 0.002192123,
"insult_score": 0.0065533705,
"profanity_score": 0.012926984,
"threat_score": 0.0064468146,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.21777496
},
"1-introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"Figures/Dist_time_auc.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/Dist_total_time_partition.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"2-Prelimenaries.tex": {
"toxicity_score": 0.010492661,
"severe_toxicity_score": 0.0008392334,
"identity_attack_score": 0.0030523231,
"insult_score": 0.0071138735,
"profanity_score": 0.013063621,
"threat_score": 0.007327127,
"sexually_explicit_score": 0.010262027,
"flirtation_score": 0.37547684
},
"Figures/Dist_Mpad_streaming_vertex_time.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/MPGP_gama.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"6-conclusions.tex": {
"toxicity_score": 0.008419261,
"severe_toxicity_score": 0.00081062317,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0066293706,
"profanity_score": 0.012585391,
"threat_score": 0.0068999166,
"sexually_explicit_score": 0.008610667,
"flirtation_score": 0.22679359
},
"Figures/Dist_scalability_datasize.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/SG_Pw2v_DSGL_pSGNScc.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"3-huger.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011444092,
"identity_attack_score": 0.0038292783,
"insult_score": 0.007645877,
"profanity_score": 0.015044857,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.25947285
},
"Figures/DSGL_framework_v3.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"main.bbl": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0023863618,
"insult_score": 0.00815888,
"profanity_score": 0.016479544,
"threat_score": 0.0060325502,
"sexually_explicit_score": 0.013387818,
"flirtation_score": 0.5040354
},
"Figures/DistGER__framework_1_1.eps": {
"toxicity_score": 0.0061573703,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0064013693,
"profanity_score": 0.012175481,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.007372146,
"flirtation_score": 0.47139618
},
"7-appendix.tex": {
"toxicity_score": 0.013571346,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.004162259,
"insult_score": 0.0075888764,
"profanity_score": 0.014532468,
"threat_score": 0.008039144,
"sexually_explicit_score": 0.012621114,
"flirtation_score": 0.20776823
},
"Figures/Dist_MLC_mac_mic_F1_1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"acmart.cls": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0010442734,
"identity_attack_score": 0.0030523231,
"insult_score": 0.0075128763,
"profanity_score": 0.013712646,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.38476974
}
},
"reason": null
}
} | {
"num_done": {
"table": 3,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.99172862862423,
"hash": "01170f1f1f1f3f1f",
"most_frequent_color_ratio": 91.99172862862423
}
} | [] | "algorithm" | "2a3afe1a-0255-410b-8d3d-06d52db8df53" | 1223 | hard |
|
\begin{algorithm}[t]
\caption{EM-like procedure of the proposed ADGCN}
\label{algorithm3}
\footnotesize
\begin{algorithmic}[1] %ÿÐÐÏÔʾÐкÅ
\Require
$\mathcal{G} = \{V, E\} $, $\mathbf{X}$, $A$.
\Ensure $\mathbf{H}$: the node disentangled representation matrix of the given graph
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure: $K$, $\tilde{T}$, $T$, $m$, $\gamma$, $\lambda$, $\eta$
\State Initialization:
$\mathbf{W}=\{\mathbf{W}_k\}_{k=1}^{K}$: projection matrix.
\textcolor{black}{$\alpha$, $\beta$: coefficients of component-specific aggregation.}
$\theta_{D}$: parameters of the discriminator $D$.
\For{epoch $t = 1$ to $T$}
\State \textbf{E-step:}
Obtain $\left \{ \mathbf{h}_i^{(t-1)} \right \}_{i=1}^n$ and $\left \{ \mathbf{c}_i ^{(t-1)}\right \}_{i=1}^n$ through \textbf{Alg.}~\ref{algorithm1}.
Obtain $A^{(t)}$ through \textbf{Alg.}~\ref{algorithm2} if performing graph refinement.
\State \textbf{M-step:}
Sample the "real" components from $\left \{ \mathbf{h}_i^{(t-1)} \right \}_{i=1}^n$.
Sample the "fake" components from $\left \{ \mathbf{c}_i^{(t-1)} \right \}_{i=1}^n$.
Update $\mathbf{W}_k$, $k=1,\cdots,K$ by minimizing $\mathcal{L}$ in Eq. (\ref{loss_G}).
Update $\theta_{D}$ by minimizing $\mathcal{L}_{D}$ in Eq. (\ref{loss_G}).
\EndFor
\State \textbf{return} $\mathbf{H}=\left[\mathbf{h}_1^{(T-1)},\cdots,\mathbf{h}_n^{(T-1)}\right]$.
\end{algorithmic}
\label{EM-like}
\end{algorithm}
| \begin{algorithm}
[t]
\caption{EM-like procedure of the proposed ADGCN}
\footnotesize
\begin{algorithmic}[1]
%ÿÐÐÏÔʾÐкÅ
\Require
$\mathcal{G} = \{V, E\} $, $\mathbf{X}$, $A$.
\Ensure $\mathbf{H}$: the node disentangled representation matrix of the given graph
\renewcommand{\algorithmicensure}{\textbf{Hyper-paramters:}}
\Ensure: $K$, $\tilde{T}$, $T$, $m$, $\gamma$, $\lambda$, $\eta$
\State Initialization:
$\mathbf{W}=\{\mathbf{W}_k\}_{k=1}^{K}$: projection matrix.
\textcolor{black}{$\alpha$, $\beta$: coefficients of component-specific aggregation.}
$\theta_{D}$: parameters of the discriminator $D$.
\For{epoch $t = 1$ to $T$}
\State \textbf{E-step:}
Obtain $\left \{ \mathbf{h}_i^{(t-1)} \right \}_{i=1}^n$ and $\left \{ \mathbf{c}_i ^{(t-1)}\right \}_{i=1}^n$ through \textbf{Alg.}~\ref{algorithm1}.
Obtain $A^{(t)}$ through \textbf{Alg.}~\ref{algorithm2} if performing graph refinement.
\State \textbf{M-step:}
Sample the "real" components from $\left \{ \mathbf{h}_i^{(t-1)} \right \}_{i=1}^n$.
Sample the "fake" components from $\left \{ \mathbf{c}_i^{(t-1)} \right \}_{i=1}^n$.
Update $\mathbf{W}_k$, $k=1,\cdots,K$ by minimizing $\mathcal{L}$ in Eq. (\ref{loss_G}).
Update $\theta_{D}$ by minimizing $\mathcal{L}_{D}$ in Eq. (\ref{loss_G}).
\EndFor
\State \textbf{return} $\mathbf{H}=\left[\mathbf{h}_1^{(T-1)},\cdots,\mathbf{h}_n^{(T-1)}\right]$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2103.07295" | "2103.07295.tar.gz" | "2024-01-24" | {
"title": "adversarial graph disentanglement",
"id": "2103.07295",
"abstract": "a real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. however, most existing methods lack consideration of the intrinsic differences in relations between nodes caused by factor entanglement. in this paper, we propose an \\underline{\\textbf{a}}dversarial \\underline{\\textbf{d}}isentangled \\underline{\\textbf{g}}raph \\underline{\\textbf{c}}onvolutional \\underline{\\textbf{n}}etwork (adgcn) for disentangled graph representation learning. to begin with, we point out two aspects of graph disentanglement that need to be considered, i.e., micro-disentanglement and macro-disentanglement. for them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes. on the basis of micro-disentanglement, we further propose a macro-disentanglement adversarial regularizer to improve the separability among component distributions, thus restricting the interdependence among components. additionally, to reveal the topological graph structure, a diversity-preserving node sampling approach is proposed, by which the graph structure can be progressively refined in a way of local structure awareness. the experimental results on various real-world graph data verify that our adgcn obtains more favorable performance over currently available alternatives. the source codes of adgcn are available at \\textit{\\url{https://github.com/ssgood/adgcn}}.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2021-03-12",
"updated": "2024-01-24",
"authors": [
"shuai zheng",
"zhenfeng zhu",
"zhizhe liu",
"jian cheng",
"yao zhao"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2103.07295"
} | "2024-03-15T08:52:54.851311" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"TAI_template.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"IEEEtai.cls": {
"toxicity_score": 0.0031415154,
"severe_toxicity_score": 0.00054597855,
"identity_attack_score": 0.0008232023,
"insult_score": 0.0055938642,
"profanity_score": 0.011099464,
"threat_score": 0.0056894873,
"sexually_explicit_score": 0.00687084,
"flirtation_score": 0.46642172
},
"IEEEexample.bib": {
"toxicity_score": 0.04216247,
"severe_toxicity_score": 0.005683899,
"identity_attack_score": 0.0075845607,
"insult_score": 0.014276917,
"profanity_score": 0.041160982,
"threat_score": 0.009929225,
"sexually_explicit_score": 0.05369676,
"flirtation_score": 0.23710166
},
"TAI_template.tex": {
"toxicity_score": 0.008544922,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0021551251,
"insult_score": 0.006819372,
"profanity_score": 0.015591404,
"threat_score": 0.006550381,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.24663074
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.87942177277327,
"hash": "003f3f1f1f0f0f1f",
"most_frequent_color_ratio": 92.87942177277327
}
} | [] | "algorithm" | "9f7e356c-96f5-4c30-ad81-d17283d8b305" | 1397 | hard |
|
\begin{algorithmic}[1]
\Procedure{AdaptiveEstimation}{$n$, $p_0$, $N$, $a_{LW}$, $t_{RS}$}
\State draw $\lbrace x_k \rbrace$ from $p_0(x)$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\For{$i \in 1.. N$}
\State $\hat{T}_{\chi} \gets \sum_j \omega_k \cdot x_k$
\State $\tau \gets \xi \cdot \hat{T}_{\chi}$
\For{$j \in 1.. R$}
\State $m_j \gets \mbox{EXPERIMENT} (\tau)$
\EndFor
\State $r_i \gets \sum_{j=1}^R m_j$
\State $\lbrace \omega_k \rbrace \gets \lbrace \omega_k \cdot p \left(r_i|T_{\chi}, R \right) \rbrace$
\State $\lbrace \omega_k \rbrace \gets \lbrace \omega_k/\left(\sum_k \omega_k\right) \rbrace$
\If {$1/ \sum \omega_k^2 < n \cdot t_{RS}$}
\State $\lbrace x_k \rbrace \gets \mbox{RESAMPLE} (\lbrace x_k \rbrace, \lbrace \omega_k \rbrace, a_{LW})$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\EndIf
\EndFor
\\
\Return $\hat{T}_{\chi}$
\EndProcedure
\\
\Procedure{RESAMPLE}{$\lbrace x_k \rbrace$, $\lbrace \omega_k \rbrace$, $a_{LW}$}
\State $\mu \gets \sum_k x_k \cdot \omega_k$
\State $\sigma^2 \gets \sum_k x_k^2 \cdot \omega_k - \mu^2$
\State $\mu' \gets a_{LW} \cdot x_k + (1-a_{LW})\cdot \mu$
\State $\lbrace x_k \rbrace \gets \mbox{NORMAL} (\mu', \sigma^2)$
\\
\Return $\lbrace x_k \rbrace$
\EndProcedure
\end{algorithmic}
| \begin{algorithmic}
[1]
\Procedure{AdaptiveEstimation}{$n$, $p_0$, $N$, $a_{LW}$, $t_{RS}$}
\State draw $\lbrace x_k \rbrace$ from $p_0(x)$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\For{$i \in 1.. N$}
\State $\hat{T}_{\chi} \gets \sum_j \omega_k \cdot x_k$
\State $\tau \gets \xi \cdot \hat{T}_{\chi}$
\For{$j \in 1.. R$}
\State $m_j \gets \mbox{EXPERIMENT} (\tau)$
\EndFor
\State $r_i \gets \sum_{j=1}^R m_j$
\State $\lbrace \omega_k \rbrace \gets \lbrace \omega_k \cdot p \left(r_i|T_{\chi}, R \right) \rbrace$
\State $\lbrace \omega_k \rbrace \gets \lbrace \omega_k/\left(\sum_k \omega_k\right) \rbrace$
\If {$1/ \sum \omega_k^2 < n \cdot t_{RS}$}
\State $\lbrace x_k \rbrace \gets \mbox{RESAMPLE} (\lbrace x_k \rbrace, \lbrace \omega_k \rbrace, a_{LW})$
\State $\lbrace \omega_k \rbrace \gets \lbrace 1/K \rbrace$
\EndIf
\EndFor
\\
\Return $\hat{T}_{\chi}$
\EndProcedure
\\
\Procedure{RESAMPLE}{$\lbrace x_k \rbrace$, $\lbrace \omega_k \rbrace$, $a_{LW}$}
\State $\mu \gets \sum_k x_k \cdot \omega_k$
\State $\sigma^2 \gets \sum_k x_k^2 \cdot \omega_k - \mu^2$
\State $\mu' \gets a_{LW} \cdot x_k + (1-a_{LW})\cdot \mu$
\State $\lbrace x_k \rbrace \gets \mbox{NORMAL} (\mu', \sigma^2)$
\\
\Return $\lbrace x_k \rbrace$
\EndProcedure
\end{algorithmic} | "https://arxiv.org/src/2210.06103" | "2210.06103.tar.gz" | "2024-01-24" | {
"title": "real-time adaptive estimation of decoherence timescales for a single qubit",
"id": "2210.06103",
"abstract": "characterising the time over which quantum coherence survives is critical for any implementation of quantum bits, memories and sensors. the usual method for determining a quantum system's decoherence rate involves a suite of experiments probing the entire expected range of this parameter, and extracting the resulting estimation in post-processing. here we present an adaptive multi-parameter bayesian approach, based on a simple analytical update rule, to estimate the key decoherence timescales ($t_1$, $t_2^*$ and $t_2$) and the corresponding decay exponent of a quantum system in real time, using information gained in preceding experiments. this approach reduces the time required to reach a given uncertainty by a factor up to an order of magnitude, depending on the specific experiment, compared to the standard protocol of curve fitting. a further speed-up of a factor $\\sim 2$ can be realised by performing our optimisation with respect to sensitivity as opposed to variance.",
"categories": "quant-ph",
"doi": "10.1103/physrevapplied.21.024026",
"created": "2022-10-12",
"updated": "2024-01-24",
"authors": [
"muhammad junaid arshad",
"christiaan bekker",
"ben haylock",
"krzysztof skrzypczak",
"daniel white",
"benjamin griffiths",
"joe gore",
"gavin w. morley",
"patrick salter",
"jason smith",
"inbar zohar",
"amit finkler",
"yoann altmann",
"erik m. gauger",
"cristian bonato"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2210.06103"
} | "2024-03-15T03:45:29.808933" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Final_PhysRevApplied.tex": {
"toxicity_score": 0.015644746,
"severe_toxicity_score": 0.0021839142,
"identity_attack_score": 0.002034882,
"insult_score": 0.008595883,
"profanity_score": 0.025360947,
"threat_score": 0.0063497215,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.4170283
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.47594991774477,
"hash": "0f9f87878f1f0f0f",
"most_frequent_color_ratio": 93.47594991774477
}
} | [] | "algorithm" | "41d685a5-3acd-4c17-9dcc-0f4631dcc050" | 1273 | hard |
|
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\ell=1,2,\cdots$)
\If{$\{\!\{c_v^{(\ell-1)}|v \in V\}\!\} \neq \{\!\{d_p^{(\ell-1)}|p \in P\}\!\}$}
\State \Return $\mathcal{G} \not\simeq \mathcal{H}$
\EndIf
\For{$v\in V$}
\State $c_v^{(\ell)} \leftarrow \textsc{Hash}\Bigl(c_v^{(\ell-1)}, \{\!\{c_u^{(\ell-1)}|u\in\mathcal{N}_v\}\!\},
\underline{\{\!\{\{\!\{c_{u_1}^{(\ell-1)}, c_{u_2}^{(\ell-1)}\}\!\}|u_1,u_2\in\mathcal{N}_v, (u_1,u_2)\in E\}\!\}} \Bigr)$
\EndFor
\For{$p\in P$}
\State $d_p^{(\ell)} \leftarrow \textsc{Hash}\Bigl(d_p^{(\ell-1)}, \{\!\{d_q^{(\ell-1)}|q\in\mathcal{N}_p\}\!\}, \underline{\{\!\{\{\!\{d_{q_1}^{(\ell-1)}, d_{q_2}^{(\ell-1)}\}\!\}|q_1,q_2\in\mathcal{N}_p, (q_1,q_2)\in F\}\!\}}\Bigr)$
\EndFor
\Until convergence
\State \Return $\mathcal{G} \simeq \mathcal{H}$
\end{algorithmic}
| \begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\ell=1,2,\cdots$)
\If{$\{\!\{c_v^{(\ell-1)}|v \in V\}\!\} \neq \{\!\{d_p^{(\ell-1)}|p \in P\}\!\}$}
\State \Return $\mathcal{G} \not\simeq \mathcal{H}$
\EndIf
\For{$v\in V$}
\State $c_v^{(\ell)} \leftarrow \textsc{Hash}\Bigl(c_v^{(\ell-1)}, \{\!\{c_u^{(\ell-1)}|u\in\mathcal{N}_v\}\!\},
\underline{\{\!\{\{\!\{c_{u_1}^{(\ell-1)}, c_{u_2}^{(\ell-1)}\}\!\}|u_1,u_2\in\mathcal{N}_v, (u_1,u_2)\in E\}\!\}} \Bigr)$
\EndFor
\For{$p\in P$}
\State $d_p^{(\ell)} \leftarrow \textsc{Hash}\Bigl(d_p^{(\ell-1)}, \{\!\{d_q^{(\ell-1)}|q\in\mathcal{N}_p\}\!\}, \underline{\{\!\{\{\!\{d_{q_1}^{(\ell-1)}, d_{q_2}^{(\ell-1)}\}\!\}|q_1,q_2\in\mathcal{N}_p, (q_1,q_2)\in F\}\!\}}\Bigr)$
\EndFor
\Until convergence
\State \Return $\mathcal{G} \simeq \mathcal{H}$
\end{algorithmic} | "https://arxiv.org/src/2206.02059" | "2206.02059.tar.gz" | "2024-01-23" | {
"title": "empowering gnns via edge-aware weisfeiler-leman algorithm",
"id": "2206.02059",
"abstract": "message passing graph neural networks (gnns) are known to have their expressiveness upper-bounded by 1-dimensional weisfeiler-leman (1-wl) algorithm. to achieve more powerful gnns, existing attempts either require ad hoc features, or involve operations that incur high time and space complexities. in this work, we propose a general and provably powerful gnn framework that preserves the scalability of the message passing scheme. in particular, we first propose to empower 1-wl for graph isomorphism test by considering edges among neighbors, giving rise to nc-1-wl. the expressiveness of nc-1-wl is shown to be strictly above 1-wl and below 3-wl theoretically. further, we propose the nc-gnn framework as a differentiable neural version of nc-1-wl. our simple implementation of nc-gnn is provably as powerful as nc-1-wl. experiments demonstrate that our nc-gnn performs effectively and efficiently on various benchmarks.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2022-06-04",
"updated": "2024-01-23",
"authors": [
"meng liu",
"haiyang yu",
"shuiwang ji"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2206.02059"
} | "2024-03-15T09:04:06.314342" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"math_commands.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0074368757,
"profanity_score": 0.011526455,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.0059272056,
"flirtation_score": 0.37736848
},
"tmlr.bst": {
"toxicity_score": 0.02048268,
"severe_toxicity_score": 0.0017642975,
"identity_attack_score": 0.003921773,
"insult_score": 0.00983089,
"profanity_score": 0.023379711,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.021585643,
"flirtation_score": 0.53546125
},
"fancyhdr.sty": {
"toxicity_score": 0.06065326,
"severe_toxicity_score": 0.0034332275,
"identity_attack_score": 0.0056236745,
"insult_score": 0.022255141,
"profanity_score": 0.05268429,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.036801755,
"flirtation_score": 0.3852209
},
"main.bib": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0032373124,
"insult_score": 0.007797878,
"profanity_score": 0.016752819,
"threat_score": 0.008039144,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.3190224
},
"NCGNN.bbl": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0012159348,
"identity_attack_score": 0.00414376,
"insult_score": 0.007797878,
"profanity_score": 0.01535229,
"threat_score": 0.0076507707,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.195667
},
"tmlr.sty": {
"toxicity_score": 0.006754258,
"severe_toxicity_score": 0.00047922134,
"identity_attack_score": 0.0013781702,
"insult_score": 0.006999873,
"profanity_score": 0.011458136,
"threat_score": 0.0057606893,
"sexually_explicit_score": 0.005543854,
"flirtation_score": 0.48204565
},
"LICENSE": {
"toxicity_score": 0.00823077,
"severe_toxicity_score": 0.0007581711,
"identity_attack_score": 0.001951637,
"insult_score": 0.0069713728,
"profanity_score": 0.012175481,
"threat_score": 0.0068028234,
"sexually_explicit_score": 0.0065759546,
"flirtation_score": 0.17881343
},
"NCGNN.tex": {
"toxicity_score": 0.017969469,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0061416444,
"insult_score": 0.009127886,
"profanity_score": 0.018392462,
"threat_score": 0.008764107,
"sexually_explicit_score": 0.01875474,
"flirtation_score": 0.36146027
},
"README.md": {
"toxicity_score": 0.013822667,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0036997858,
"insult_score": 0.00810188,
"profanity_score": 0.016342908,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.31661955
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.82406955040274,
"hash": "0f0f1f1f871f853f",
"most_frequent_color_ratio": 91.82406955040274
}
} | [] | "algorithm" | "2b50446c-c04b-43c2-83e3-c766cf13bf7e" | 1062 | medium |
|
\begin{algorithmic}[1]
\Require $x_0\in K_0 \in\mathcal{K}_h(\Omega)$ and $x_1$ vertices of a 1-simplex $e$.
\Ensure Number of elements $N$, elements $\{K_0,..,K_{N-1}\}\in\mathcal{K}_h(\Omega)^N$.
\State $K\gets K_0$
\State $F_{\text{old}}\gets $ NULL
\State $K_{\text{old}}\gets $ NULL
\State $N \gets 1$
\State $E \gets \{K\}$
\While{$x_1\notin K$}
\State Find the isoparametric mapping $\phi_K:K_{\text{ref}}\mapsto K$
\State Find face $F\subset\partial K$ s.t. $F\neq F_{\text{old}}$ and $\phi_K^{-1}(e) \cap \phi_K^{-1}(F)\neq \emptyset$
\State $K\gets K\in\mathcal{K}_h(\Omega)$ s.t. $F\subset\partial K$ and $K\neq K_{\text{old}}$ ($K$ on the other side of face $F$)
\State $F_{\text{old}}\gets F$
\State $N\gets N+1$
\State $E \gets E \cup \{K\}$
\EndWhile
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require $x_0\in K_0 \in\mathcal{K}_h(\Omega)$ and $x_1$ vertices of a 1-simplex $e$.
\Ensure Number of elements $N$, elements $\{K_0,..,K_{N-1}\}\in\mathcal{K}_h(\Omega)^N$.
\State $K\gets K_0$
\State $F_{\text{old}}\gets $ NULL
\State $K_{\text{old}}\gets $ NULL
\State $N \gets 1$
\State $E \gets \{K\}$
\While{$x_1\notin K$}
\State Find the isoparametric mapping $\phi_K:K_{\text{ref}}\mapsto K$
\State Find face $F\subset\partial K$ s.t. $F\neq F_{\text{old}}$ and $\phi_K^{-1}(e) \cap \phi_K^{-1}(F)\neq \emptyset$
\State $K\gets K\in\mathcal{K}_h(\Omega)$ s.t. $F\subset\partial K$ and $K\neq K_{\text{old}}$ ($K$ on the other side of face $F$)
\State $F_{\text{old}}\gets F$
\State $N\gets N+1$
\State $E \gets E \cup \{K\}$
\EndWhile
\end{algorithmic} | "https://arxiv.org/src/2301.04923" | "2301.04923.tar.gz" | "2024-02-02" | {
"title": "semi-lagrangian finite-element exterior calculus for incompressible flows",
"id": "2301.04923",
"abstract": "we develop a mesh-based semi-lagrangian discretization of the time-dependent incompressible navier-stokes equations with free boundary conditions recast as a non-linear transport problem for a momentum 1-form. a linearly implicit fully discrete version of the scheme enjoys excellent stability properties in the vanishing viscosity limit and is applicable to inviscid incompressible euler flows. conservation of energy and helicity are enforced separately.",
"categories": "math.na cs.na",
"doi": "",
"created": "2023-01-12",
"updated": "2024-02-02",
"authors": [
"wouter tonnon",
"ralf hiptmair"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2301.04923"
} | "2024-03-15T04:54:39.959037" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"empty.eps": {
"toxicity_score": 0.007885204,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0067908717,
"profanity_score": 0.013268576,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.45258012
},
"fig.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"profanity_score": 0.015932998,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.39879754
},
"cons_p_2_Euler_EnergyCons_2ndOrder_V_0_i_2_L2_Norm_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"cons_p_28_Euler_V_0_i_2_L2_Norm_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"p_2_Euler_EnergyCons_V_0_i_2_L2_Error_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"cons_p_6_Euler_V_0_i_2_Helicity_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
},
"sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"p_9_Euler_V_0_i_2_L2_Error_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"sn-article.bbl": {
"toxicity_score": 0.0126288915,
"severe_toxicity_score": 0.0011491776,
"identity_attack_score": 0.004069764,
"insult_score": 0.007607877,
"profanity_score": 0.014874061,
"threat_score": 0.007624879,
"sexually_explicit_score": 0.011618502,
"flirtation_score": 0.20600256
},
"cons_p_6_Euler_V_0_i_2_L2_Norm_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"sn-bibliography.bib": {
"toxicity_score": 0.01017851,
"severe_toxicity_score": 0.0016975403,
"identity_attack_score": 0.0014799143,
"insult_score": 0.007664877,
"profanity_score": 0.01764096,
"threat_score": 0.006039023,
"sexually_explicit_score": 0.01604179,
"flirtation_score": 0.26678315
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"p_6_Euler_V_0_i_2_L2_Error_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"sn-apacite.bst": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0035332954,
"insult_score": 0.0105148945,
"profanity_score": 0.019143965,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.39166427
},
"cons_p_3_Euler_V_0_i_2_L2_Norm_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"p_3_Euler_V_0_i_2_L2_Error_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"sn-article.tex": {
"toxicity_score": 0.0046494426,
"severe_toxicity_score": 0.00053167343,
"identity_attack_score": 0.001415168,
"insult_score": 0.006011867,
"profanity_score": 0.010569996,
"threat_score": 0.0062396824,
"sexually_explicit_score": 0.006605443,
"flirtation_score": 0.2874154
},
"cons_p_2_Euler_EnergyCons_1stOrder_V_0_i_2_L2_Norm_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
},
"p_0_Euler_EnergyCons_V_0_i_2_L2_Error_u.pgf": {
"toxicity_score": 0.005434822,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0010729379,
"insult_score": 0.00651537,
"profanity_score": 0.013371054,
"threat_score": 0.00579629,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.6397543
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.81175571290635,
"hash": "013f3f3f03003f3f",
"most_frequent_color_ratio": 93.81175571290635
}
} | [] | "algorithm" | "c04d749a-3b62-4631-9bcf-7f56ea5f76ae" | 784 | medium |
|
\begin{algorithmic}[1]
\State Set the step-size $(\alpha_k)_{k=0}^\infty$, and initialize $(\theta _0,\lambda_0 )$.
\For{$k \in \{0,\ldots\}$}
\State Observe $s_k \sim d^{\beta}$, $a_k \sim \beta(\cdot|s_k)$, and $s_k'\sim P(\cdot | s_k,a_k)$, $r_k :=r(s_k,a_k,s_k')$.
\State Update parameters according to
\begin{align*}
&\theta _{k + 1} = \theta _k + \alpha _k [(\phi_k - \gamma \rho _k \phi_{k}')(\phi_k ^T \lambda _k ) - \phi_k (\phi_k ^T \theta_k )],\\
&\lambda _{k + 1} = \lambda _k + \alpha _k \delta _k \phi_k,
\end{align*}
where $\phi_k:=\phi(s_k),\phi_{k}':=\phi(s_{k}')$, $\rho_k : = \frac{{\pi (a_k |s_k )}}{{\beta (a_k |s_k )}}$, and $\delta_k =\rho _k r_k +\gamma \rho _k (\phi_{k}')^T \theta_k -\phi_k^T \theta_k$.
\EndFor
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Set the step-size $(\alpha_k)_{k=0}^\infty$, and initialize $(\theta _0,\lambda_0 )$.
\For{$k \in \{0,\ldots\}$}
\State Observe $s_k \sim d^{\beta}$, $a_k \sim \beta(\cdot|s_k)$, and $s_k'\sim P(\cdot | s_k,a_k)$, $r_k :=r(s_k,a_k,s_k')$.
\State Update parameters according to
\begin{align*}
&\theta _{k + 1} = \theta _k + \alpha _k [(\phi_k - \gamma \rho _k \phi_{k}')(\phi_k ^T \lambda _k ) - \phi_k (\phi_k ^T \theta_k )],\\
&\lambda _{k + 1} = \lambda _k + \alpha _k \delta _k \phi_k,
\end{align*}
where $\phi_k:=\phi(s_k),\phi_{k}':=\phi(s_{k}')$, $\rho_k : = \frac{{\pi (a_k |s_k )}}{{\beta (a_k |s_k )}}$, and $\delta_k =\rho _k r_k +\gamma \rho _k (\phi_{k}')^T \theta_k -\phi_k^T \theta_k$.
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2109.04033" | "2109.04033.tar.gz" | "2024-01-22" | {
"title": "new versions of gradient temporal difference learning",
"id": "2109.04033",
"abstract": "sutton, szepesv\\'{a}ri and maei introduced the first gradient temporal-difference (gtd) learning algorithms compatible with both linear function approximation and off-policy training. the goal of this paper is (a) to propose some variants of gtds with extensive comparative analysis and (b) to establish new theoretical analysis frameworks for the gtds. these variants are based on convex-concave saddle-point interpretations of gtds, which effectively unify all the gtds into a single framework, and provide simple stability analysis based on recent results on primal-dual gradient dynamics. finally, numerical comparative analysis is given to evaluate these approaches.",
"categories": "cs.lg",
"doi": "",
"created": "2021-09-09",
"updated": "2024-01-22",
"authors": [
"donghwan lee",
"han-dong lim",
"jihoon park",
"okyong choi"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2109.04033"
} | "2024-03-15T09:08:57.627292" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"fig3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"paper.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"fig5.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"fig4.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"fig2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"paper.tex": {
"toxicity_score": 0.008105109,
"severe_toxicity_score": 0.0011301041,
"identity_attack_score": 0.0023123662,
"insult_score": 0.0067908717,
"profanity_score": 0.014600786,
"threat_score": 0.006550381,
"sexually_explicit_score": 0.012562137,
"flirtation_score": 0.3556029
},
"fig1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.15865577254752,
"hash": "07000ff1cfff007f",
"most_frequent_color_ratio": 93.15865577254752
}
} | [] | "algorithm" | "874fade3-fe36-4f8b-ab1a-ca8d4e76cafb" | 756 | medium |
|
\begin{algorithm}[t]
\caption{Transfer learning algorithm for tabular contextual multi-armed bandits}
\label{alg:UCB-TL-tabular}
\begin{algorithmic}[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\widetilde{\pi}(s)$ by Procedure~\ref{alg:EA-TL-tabular}$\big(s,\mathcal{I},\mathcal{D}^{P} \big)$.}
\State{Initialize $N(s)\gets0$.}
\Comment{initialize time for policy $\widetilde{\pi}(s)$}
\EndFor
\For{$t=1,\dots,n_{Q}$}
\State{Draw a sample $X_{t}^{Q} \sim Q_{X}$.}
\State{Denote state $s = X_{t}^{Q}$.}
\State{Set $N(s)\gets N(s)+1$.}
\Comment{update times $X_{t}^{Q} = s$}
\State{Set $\pi_{t}\gets\widetilde{\pi}_{N(s)}(s)$.}
\Comment{choose arm by policy $\widetilde{\pi}(s)$}
\EndFor
\State{\textbf{Output:} policy $\{\pi_{t}\}_{t\geq1}$.}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[t]
\caption{Transfer learning algorithm for tabular contextual multi-armed bandits}
\begin{algorithmic}
[1]
\State{\textbf{Input:} set of arms $\mathcal{I}$, horizon length $n_{Q}$, $P$-data $\mathcal{D}^{P}$.}
\For{$s \in \mathcal{S}$}
\State{Initialize the policy $\widetilde{\pi}(s)$ by Procedure~\ref{alg:EA-TL-tabular}$\big(s,\mathcal{I},\mathcal{D}^{P} \big)$.}
\State{Initialize $N(s)\gets0$.}
\Comment{initialize time for policy $\widetilde{\pi}(s)$}
\EndFor
\For{$t=1,\dots,n_{Q}$}
\State{Draw a sample $X_{t}^{Q} \sim Q_{X}$.}
\State{Denote state $s = X_{t}^{Q}$.}
\State{Set $N(s)\gets N(s)+1$.}
\Comment{update times $X_{t}^{Q} = s$}
\State{Set $\pi_{t}\gets\widetilde{\pi}_{N(s)}(s)$.}
\Comment{choose arm by policy $\widetilde{\pi}(s)$}
\EndFor
\State{\textbf{Output:} policy $\{\pi_{t}\}_{t\geq1}$.}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2211.12612" | "2211.12612.tar.gz" | "2024-01-24" | {
"title": "transfer learning for contextual multi-armed bandits",
"id": "2211.12612",
"abstract": "motivated by a range of applications, we study in this paper the problem of transfer learning for nonparametric contextual multi-armed bandits under the covariate shift model, where we have data collected on source bandits before the start of the target bandit learning. the minimax rate of convergence for the cumulative regret is established and a novel transfer learning algorithm that attains the minimax regret is proposed. the results quantify the contribution of the data from the source domains for learning in the target domain in the context of nonparametric contextual multi-armed bandits. in view of the general impossibility of adaptation to unknown smoothness, we develop a data-driven algorithm that achieves near-optimal statistical guarantees (up to a logarithmic factor) while automatically adapting to the unknown parameters over a large collection of parameter spaces under an additional self-similarity assumption. a simulation study is carried out to illustrate the benefits of utilizing the data from the auxiliary source domains for learning in the target domain.",
"categories": "stat.ml cs.lg math.st stat.th",
"doi": "",
"created": "2022-11-22",
"updated": "2024-01-24",
"authors": [
"changxiao cai",
"t. tony cai",
"hongzhe li"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2211.12612"
} | "2024-03-15T08:57:26.780784" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"adp-nP-beta6-kappa10-nQ1e5-gamma1bmin5bmax10gmax2under0.5-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"bibfileRL.bib": {
"toxicity_score": 0.010492661,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.0022383702,
"insult_score": 0.0074178753,
"profanity_score": 0.014190876,
"threat_score": 0.0066151097,
"sexually_explicit_score": 0.010910776,
"flirtation_score": 0.30652624
},
"kappa-K2-beta8-gamma1-nQ1e5-nP20-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"proof_smooth.tex": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.00074386597,
"identity_attack_score": 0.0017204003,
"insult_score": 0.008044879,
"profanity_score": 0.013507691,
"threat_score": 0.0059678215,
"sexually_explicit_score": 0.009613278,
"flirtation_score": 0.32680815
},
"problem.tex": {
"toxicity_score": 0.013005874,
"severe_toxicity_score": 0.0009584427,
"identity_attack_score": 0.003644289,
"insult_score": 0.007797878,
"profanity_score": 0.013917602,
"threat_score": 0.007547205,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.23518674
},
"Bandit_TL.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"discussion.tex": {
"toxicity_score": 0.013382856,
"severe_toxicity_score": 0.0012397766,
"identity_attack_score": 0.0039402717,
"insult_score": 0.007854878,
"profanity_score": 0.016479544,
"threat_score": 0.007935578,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.22211537
},
"nQ-K2-beta8-nQ1e5-nP0.5-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Bandit_TL.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0045507364,
"insult_score": 0.007607877,
"profanity_score": 0.015898837,
"threat_score": 0.0077154995,
"sexually_explicit_score": 0.013269863,
"flirtation_score": 0.20662601
},
"abstract.tex": {
"toxicity_score": 0.062236045,
"severe_toxicity_score": 0.002412796,
"identity_attack_score": 0.013948337,
"insult_score": 0.024124376,
"profanity_score": 0.029050145,
"threat_score": 0.011560392,
"sexually_explicit_score": 0.018990649,
"flirtation_score": 0.29612872
},
"nP-K2-beta8-nQ1e5-gamma1-kappa1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"adp-nP-beta8-kappa10-nQ1e5-gamma1bmin5bmax10gmax2under0.5-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"nQ-K4-beta8-nQ1e5-nP0.5-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"results.tex": {
"toxicity_score": 0.013697007,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.004328749,
"insult_score": 0.007721877,
"profanity_score": 0.015147334,
"threat_score": 0.0076637166,
"sexually_explicit_score": 0.01332884,
"flirtation_score": 0.20463914
},
"proof_lower_bound.tex": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.001039505,
"identity_attack_score": 0.004384246,
"insult_score": 0.0077598775,
"profanity_score": 0.014327513,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.3022332
},
"related-work.tex": {
"toxicity_score": 0.010555492,
"severe_toxicity_score": 0.0009775162,
"identity_attack_score": 0.003921773,
"insult_score": 0.0072468747,
"profanity_score": 0.0147374235,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.23073673
},
"notation.tex": {
"toxicity_score": 0.011623607,
"severe_toxicity_score": 0.0011873245,
"identity_attack_score": 0.0037182847,
"insult_score": 0.007607877,
"profanity_score": 0.015113175,
"threat_score": 0.0074306927,
"sexually_explicit_score": 0.012562137,
"flirtation_score": 0.21015726
},
"gamma-K2-beta8-kappa10-nQ1e5-nP10-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"nQ-K2-beta8-nQ1e5-nP3-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"intro.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0039402717,
"insult_score": 0.0075698765,
"profanity_score": 0.015318131,
"threat_score": 0.0077025536,
"sexually_explicit_score": 0.013564749,
"flirtation_score": 0.2300378
},
"lemma.tex": {
"toxicity_score": 0.011435116,
"severe_toxicity_score": 0.0013828278,
"identity_attack_score": 0.0034962974,
"insult_score": 0.0075508766,
"profanity_score": 0.016752819,
"threat_score": 0.007534259,
"sexually_explicit_score": 0.018047014,
"flirtation_score": 0.2945637
},
"nQ-K2-beta8-nQ1e5-nP1000000-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"proof_upper_bound.tex": {
"toxicity_score": 0.014011159,
"severe_toxicity_score": 0.0012493134,
"identity_attack_score": 0.0045137387,
"insult_score": 0.00806388,
"profanity_score": 0.014976538,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.011146685,
"flirtation_score": 0.2661005
},
"para-nP-beta8-kappa10-nQ1e5-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"adaptivity.tex": {
"toxicity_score": 0.010743983,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003144818,
"insult_score": 0.0070948736,
"profanity_score": 0.014361672,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.0122672515,
"flirtation_score": 0.19645838
},
"nQ-K4-beta8-nQ1e5-nP1000000-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"numerical.tex": {
"toxicity_score": 0.013068704,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007607877,
"profanity_score": 0.014566627,
"threat_score": 0.007832011,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.2357703
},
"pic_alg.eps": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0008058548,
"identity_attack_score": 0.0014521659,
"insult_score": 0.0066863713,
"profanity_score": 0.012858666,
"threat_score": 0.0062137907,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.43023613
},
"tabular.tex": {
"toxicity_score": 0.00804228,
"severe_toxicity_score": 0.000705719,
"identity_attack_score": 0.0023863618,
"insult_score": 0.0067338715,
"profanity_score": 0.012380436,
"threat_score": 0.006738094,
"sexually_explicit_score": 0.00855169,
"flirtation_score": 0.23074746
},
"nQ-K4-beta8-nQ1e5-nP3-kappa10-gamma1-ucb100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"proof_adaptivity.tex": {
"toxicity_score": 0.014073989,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0047727237,
"insult_score": 0.007911879,
"profanity_score": 0.015283971,
"threat_score": 0.007819066,
"sexually_explicit_score": 0.012562137,
"flirtation_score": 0.31647885
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.53523658732384,
"hash": "000f879f99873d3f",
"most_frequent_color_ratio": 90.53523658732384
}
} | [] | "algorithm" | "5a749169-3644-439f-8884-291269657cea" | 867 | medium |
|
\begin{algorithm}[ht]
\caption{Correlated pseudo-marginal algorithm}\label{CPMalgorithm}
\begin{algorithmic}[1]
\State Initialise $\boldsymbol{\theta}_0$;
\For {$m = 1,...,\textit{M}^*$}
\State Sample $\boldsymbol{\theta}^{*} \sim Q(.\vert \boldsymbol{\theta}_{m-1})$;
\State Sample $\xi \sim N(\textbf{0}, \boldsymbol{I})$ and set $U^* = \tau U_{m-1} + \sqrt{1-\tau ^2} \xi$;
\State Compute the estimator $\hat{p} (\mathbf{Y} \vert \boldsymbol{\theta} ^*, U^*)$ using Algorithm \ref{BPF_CPM}
\State Compute the acceptance ratio:
\begin{align*}
r=\frac{\hat{p} (\mathbf{Y} \vert \boldsymbol{\theta} ^*, U^*) p(\boldsymbol{\theta} ^*)Q(\boldsymbol{\theta}_{m-1} \vert \boldsymbol{\theta}^*)}{\hat{p}(\mathbf{Y} \vert \boldsymbol{\theta}_{m-1}, U_{m-1} ) p(\boldsymbol{\theta}_{m-1} )Q(\boldsymbol{\theta}^*\vert \Theta_{m-1} )};
\end{align*}
\State Accept $(\boldsymbol{\theta} ^*, U^*)$ with probability $\min (r,1)$ otherwise, output $(\boldsymbol{\theta}_{m-1}, U_{m-1})$
\EndFor
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[ht]
\caption{Correlated pseudo-marginal algorithm}\begin{algorithmic}
[1]
\State Initialise $\boldsymbol{\theta}_0$;
\For {$m = 1,...,\textit{M}^*$}
\State Sample $\boldsymbol{\theta}^{*} \sim Q(.\vert \boldsymbol{\theta}_{m-1})$;
\State Sample $\xi \sim N(\textbf{0}, \boldsymbol{I})$ and set $U^* = \tau U_{m-1} + \sqrt{1-\tau ^2} \xi$;
\State Compute the estimator $\hat{p} (\mathbf{Y} \vert \boldsymbol{\theta} ^*, U^*)$ using Algorithm \ref{BPF_CPM}
\State Compute the acceptance ratio:
\begin{align*}
r=\frac{\hat{p} (\mathbf{Y} \vert \boldsymbol{\theta} ^*, U^*) p(\boldsymbol{\theta} ^*)Q(\boldsymbol{\theta}_{m-1} \vert \boldsymbol{\theta}^*)}{\hat{p}(\mathbf{Y} \vert \boldsymbol{\theta}_{m-1}, U_{m-1} ) p(\boldsymbol{\theta}_{m-1} )Q(\boldsymbol{\theta}^*\vert \Theta_{m-1} )};
\end{align*}
\State Accept $(\boldsymbol{\theta} ^*, U^*)$ with probability $\min (r,1)$ otherwise, output $(\boldsymbol{\theta}_{m-1}, U_{m-1})$
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2105.04789" | "2105.04789.tar.gz" | "2024-02-10" | {
"title": "innovative approaches in soil carbon sequestration modelling for better prediction with limited data",
"id": "2105.04789",
"abstract": "soil carbon accounting and prediction play a key role in building decision support systems for land managers selling carbon credits, in the spirit of the paris and kyoto protocol agreements. land managers typically rely on computationally complex models fit using sparse datasets to make these accounts and predictions. the model complexity and sparsity of the data can lead to over-fitting, leading to inaccurate results when making predictions with new data. modellers address over-fitting by simplifying their models and reducing the number of parameters, and in the current context this could involve neglecting some soil organic carbon (soc) components. in this study, we introduce two novel soc models and a new rothc-like model and investigate how the soc components and complexity of the soc models affect the soc prediction in the presence of small and sparse time series data. we develop model selection methods that can identify the soil carbon model with the best predictive performance, in light of the available data. through this analysis we reveal that commonly used complex soil carbon models can over-fit in the presence of sparse time series data, and our simpler models can produce more accurate predictions. the published version of this study is available in scientific reports (https://www.nature.com/articles/s41598-024-53516-z/<10.1038/s41598-024-53516-z>)",
"categories": "stat.co stat.ap",
"doi": "10.1038/s41598-024-53516-z",
"created": "2021-05-11",
"updated": "2024-02-10",
"authors": [
"mohammad javad davoudabadi",
"daniel pagendam",
"christopher drovandi",
"jeff baldock",
"gentry white"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2105.04789"
} | "2024-03-15T06:18:35.682065" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"references.bib": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003348306,
"insult_score": 0.007664877,
"profanity_score": 0.015932998,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.014685315,
"flirtation_score": 0.41820723
},
"Rplot_02.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"wlscirep.cls": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0009727478,
"identity_attack_score": 0.0045322375,
"insult_score": 0.00971689,
"profanity_score": 0.014566627,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.33412078
},
"Rplot.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"jabbrv.sty": {
"toxicity_score": 0.0059688794,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0018498929,
"insult_score": 0.0066008708,
"profanity_score": 0.011167783,
"threat_score": 0.006375613,
"sexually_explicit_score": 0.0069298176,
"flirtation_score": 0.4899393
},
"jabbrv-ltwa-all.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"jabbrv-ltwa-en.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"main.tex": {
"toxicity_score": 0.0137598375,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.0041992567,
"insult_score": 0.007949878,
"profanity_score": 0.01846078,
"threat_score": 0.007909686,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.42432743
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.02737925251014,
"hash": "033f0b0be7c3bf3f",
"most_frequent_color_ratio": 92.02737925251014
}
} | [] | "algorithm" | "1c15adea-7ae9-43cd-805e-50ca6b7d14d4" | 996 | medium |
|
\begin{algorithm}
\caption{sPCA}
\begin{algorithmic}[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqref{factormatrix_RaDFaM} and \eqref{signal_RaDFaM} for $t\in[T]$.
\State \textbf{Output}: $\widehat{\mathbf{R}}$, $\widehat{\mathbf{C}}$, $\{\widehat{\mathbf{Z}}_t\}_{t=1}^T$,
$\{\widehat{\mathbf{F}}_t\}_{t=1}^T$, $\{\widehat{\mathbf{E}}_t\}_{t=1}^T$, and $\{\widehat{\mathbf{S}}_t\}_{t=1}^T$.
\end{algorithmic}
\label{algorithm}
\end{algorithm}
| \begin{algorithm}
\caption{sPCA}
\begin{algorithmic}
[1]
\State \textbf{Input}: matrix observations $\{\mathbf{X}_t\}_{t=1}^T$, factor numbers $k_1$ and $k_2$.
\State Estimate loading matrices by equations \eqref{estimator_R} and \eqref{estimator_C}.
\State Estimate factor matrices and the signal part by equations \eqref{factormatrix_RaDFaM} and \eqref{signal_RaDFaM} for $t\in[T]$.
\State \textbf{Output}: $\widehat{\mathbf{R}}$, $\widehat{\mathbf{C}}$, $\{\widehat{\mathbf{Z}}_t\}_{t=1}^T$,
$\{\widehat{\mathbf{F}}_t\}_{t=1}^T$, $\{\widehat{\mathbf{E}}_t\}_{t=1}^T$, and $\{\widehat{\mathbf{S}}_t\}_{t=1}^T$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2209.14846" | "2209.14846.tar.gz" | "2024-02-12" | {
"title": "modeling and learning on high-dimensional matrix-variate sequences",
"id": "2209.14846",
"abstract": "we propose a new matrix factor model, named radfam, which is strictly derived based on the general rank decomposition and assumes a structure of a high-dimensional vector factor model for each basis vector. radfam contributes a novel class of low-rank latent structure that makes tradeoff between signal intensity and dimension reduction from the perspective of tensor subspace. based on the intrinsic separable covariance structure of radfam, for a collection of matrix-valued observations, we derive a new class of pca variants for estimating loading matrices, and sequentially the latent factor matrices. the peak signal-to-noise ratio of radfam is proved to be superior in the category of pca-type estimations. we also establish the asymptotic theory including the consistency, convergence rates, and asymptotic distributions for components in the signal part. numerically, we demonstrate the performance of radfam in applications such as matrix reconstruction, supervised learning, and clustering, on uncorrelated and correlated data, respectively.",
"categories": "stat.me",
"doi": "",
"created": "2022-09-29",
"updated": "2024-02-12",
"authors": [
"xu zhang",
"catherine c. liu",
"jianhua guo",
"k. c. yuen",
"a. h. welsh"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2209.14846"
} | "2024-03-15T05:49:17.008547" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"radfam.out": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0019550323,
"identity_attack_score": 0.001812895,
"insult_score": 0.0069713728,
"profanity_score": 0.01982715,
"threat_score": 0.00636914,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.4088044
},
"Figures/difference_2w_step_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_indices_2w_QMLE.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_sPCA_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"radfam.out.bak": {
"toxicity_score": 0.006440107,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0012024303,
"insult_score": 0.0060783676,
"profanity_score": 0.015591404,
"threat_score": 0.005935457,
"sexually_explicit_score": 0.014449406,
"flirtation_score": 0.34677386
},
"Figures/hcluster_countries_2w_step.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_indices_sPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/true_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"RaDFaM.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"Figures/difference_alphaPCA_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_PCA_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_step_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/X1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_QMLE_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_countries_2w_QMLE.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_autoPCA_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"RaDFaM.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
},
"Figures/difference_2w_QMLE_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_countries_alphaPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_sPCA_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_indices_2w_step.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/R.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/TIME3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/normality.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"RaDFaM.bib": {
"toxicity_score": 0.005717558,
"severe_toxicity_score": 0.0007009506,
"identity_attack_score": 0.0025343532,
"insult_score": 0.008044879,
"profanity_score": 0.012482914,
"threat_score": 0.006330303,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.31746772
},
"Figures/X2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_PCA_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_indices_proPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/C.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_autoPCA_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/true_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_step_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_alphaPCA_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_PCA_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_countries_autoPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_countries_proPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/AUC.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_alphaPCA_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_proPCA_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_proPCA_cross.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_autoPCA_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_proPCA_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/hcluster_indices_autoPCA.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/S.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_sPCA_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/true_column.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Figures/difference_2w_QMLE_row.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 87.15488855116514,
"hash": "0103070003ffbf03",
"most_frequent_color_ratio": 87.15488855116514
}
} | [] | "algorithm" | "6179e980-9b67-4da1-bd64-c6c6aec5351c" | 646 | easy |
|
\begin{algorithm}[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford (RrDR) method \label{r-RDRK}}
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=0$, extrapolation/relaxation parameter $\alpha\in(0,1)$ and an initial $x^0\in \mathbb{R}^{n}$.
\begin{enumerate}
\item[1:] Set $z^{k}_0:=x^k$.
\item[2:] {\bf for $\ell=1,\ldots,r$ do}
\item[3:] \ \ \ Select $j_{k_{\ell}}\in\{1,\ldots,m\}$ with probability $\mbox{Pr}(\mbox{row}=j_{k_{\ell}})=\frac{\|a_{j_{k_{\ell}}}\|^2_2}{\|A\|_{F}^2}$.
\item[4:] \ \ \ Compute
$$
z_{\ell}^{k}:=z_{\ell-1}^k-2\frac{\langle a_{j_{k_{\ell}}},z_{\ell-1}^k\rangle-b_{j_{k_{\ell}}}}{\|a_{j_{k_\ell}}\|^2_2}a_{j_{k_{\ell}}}.
$$
\item[5:] {\bf end for}
\item[6:] Update
$$
x^{k+1}:=(1-\alpha) x^k+\alpha z_{r}^k.
$$
\item[7:] If the stopping rule is satisfied, stop and go to output. Otherwise, set $k=k+1$ and return to Step $1$.
\end{enumerate}
\Ensure
The approximate solution $ x^k $.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[htpb]
\caption{Randomized $r$-sets-Douglas-Rachford (RrDR) method }
\begin{algorithmic}
\Require
$A\in \mathbb{R}^{m\times n}$, $b\in \mathbb{R}^m$, $r\in\mathbb{Z}_{+}$, $k=0$, extrapolation/relaxation parameter $\alpha\in(0,1)$ and an initial $x^0\in \mathbb{R}^{n}$.
\begin{enumerate}
\item[1:] Set $z^{k}_0:=x^k$.
\item[2:] {\bf for $\ell=1,\ldots,r$ do}
\item[3:] \ \ \ Select $j_{k_{\ell}}\in\{1,\ldots,m\}$ with probability $\mbox{Pr}(\mbox{row}=j_{k_{\ell}})=\frac{\|a_{j_{k_{\ell}}}\|^2_2}{\|A\|_{F}^2}$.
\item[4:] \ \ \ Compute
$$
z_{\ell}^{k}:=z_{\ell-1}^k-2\frac{\langle a_{j_{k_{\ell}}},z_{\ell-1}^k\rangle-b_{j_{k_{\ell}}}}{\|a_{j_{k_\ell}}\|^2_2}a_{j_{k_{\ell}}}.
$$
\item[5:] {\bf end for}
\item[6:] Update
$$
x^{k+1}:=(1-\alpha) x^k+\alpha z_{r}^k.
$$
\item[7:] If the stopping rule is satisfied, stop and go to output. Otherwise, set $k=k+1$ and return to Step $1$.
\end{enumerate}
\Ensure
The approximate solution $ x^k $.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2207.04291" | "2207.04291.tar.gz" | "2024-01-09" | {
"title": "randomized douglas-rachford methods for linear systems: improved accuracy and efficiency",
"id": "2207.04291",
"abstract": "the douglas-rachford (dr) method is a widely used method for finding a point in the intersection of two closed convex sets (feasibility problem). however, the method converges weakly and the associated rate of convergence is hard to analyze in general. in addition, the direct extension of the dr method for solving more-than-two-sets feasibility problems, called the $r$-sets-dr method, is not necessarily convergent. to improve the efficiency of the optimization algorithms, the introduction of randomization and the momentum technique has attracted increasing attention. in this paper, we propose the randomized $r$-sets-dr (rrdr) method for solving the feasibility problem derived from linear systems, showing the benefit of the randomization as it brings linear convergence in expectation to the otherwise divergent $r$-sets-dr method. furthermore, the convergence rate does not depend on the dimension of the coefficient matrix. we also study rrdr with heavy ball momentum and establish its accelerated rate. numerical experiments are provided to confirm our results and demonstrate the notable improvements in accuracy and efficiency of the dr method, brought by the randomization and the momentum technique.",
"categories": "math.oc",
"doi": "",
"created": "2022-07-09",
"updated": "2024-01-09",
"authors": [
"deren han",
"yansheng su",
"jiaxin xie"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.04291"
} | "2024-03-15T06:41:30.719861" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"figure/alphabeta8000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_beta.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta1000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/MATLAB-com-5.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_100001.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/MATLAB-com-20.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_10000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/MATLAB-com-10.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta4000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_model1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabetacycle.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cycliccrew1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta500.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_beta100.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicRGG.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/THM2-2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_100cycle.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/THM3-2.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta500-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicaloi.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabetaline.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"references.bib": {
"toxicity_score": 0.005529067,
"severe_toxicity_score": 0.0005340576,
"identity_attack_score": 0.00075845607,
"insult_score": 0.0070378734,
"profanity_score": 0.010638315,
"threat_score": 0.0055859215,
"sexually_explicit_score": 0.0050720368,
"flirtation_score": 0.20497748
},
"figure/compare_r_beta200.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_cycle.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicLinegraph.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"mRDR-revised-09.tex": {
"toxicity_score": 0.014011159,
"severe_toxicity_score": 0.0014305115,
"identity_attack_score": 0.004236255,
"insult_score": 0.008044879,
"profanity_score": 0.017094411,
"threat_score": 0.0078449575,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.20871921
},
"figure/alphabeta2000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_115.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicFranz1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_ijcnn1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabetaFranz1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_125.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_line.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_Franz1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclic10000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_50001.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclic5000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclic1000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_RGG.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_ch8-8-b1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicch8-8-b1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_100line.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_515.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_5000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_10000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"mRDR-revised-09.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"figure/alphabetaRGG.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/THM2-1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta2000-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_crew1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta1000-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabetaWC.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/THM3-4.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta16000-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta4000-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_200001.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_20000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta16000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_1000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/cyclicCycle.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabeta8000-3.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_ash958.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/compare_r_franz1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_100rgg.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/Comp_all_30000.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figure/alphabetaNem.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.36771980565574,
"hash": "003fa0b3f73f800f",
"most_frequent_color_ratio": 94.36771980565574
}
} | [] | "algorithm" | "f374ee2b-64e3-4e75-a4c7-4b2629af9fb0" | 994 | medium |
|
\begin{algorithmic}[1]
\State $\mathcal{D}^{(DD)} \gets \left\{ \right\}$
\For{$i$ s.t. $\mathbf{Y}_i=1$}
\For{$j \in \mathcal{P}^{(DND)}$}
\If{$\mathbf{X}_{i,j}=1$ and $\mathbf{X}_{i,j'}=0,$ $\forall j' \neq j\in \mathcal{P}^{(DND)}$}
\State $\mathcal{D}^{(DD)} \gets \mathcal{D}^{(DD)} \cup \left\{j\right\}$
\EndIf
\EndFor
\EndFor
\State \Return $\mathcal{D}^{(DD)}$
\end{algorithmic}
| \begin{algorithmic}
[1]
\State $\mathcal{D}^{(DD)} \gets \left\{ \right\}$
\For{$i$ s.t. $\mathbf{Y}_i=1$}
\For{$j \in \mathcal{P}^{(DND)}$}
\If{$\mathbf{X}_{i,j}=1$ and $\mathbf{X}_{i,j'}=0,$ $\forall j' \neq j\in \mathcal{P}^{(DND)}$}
\State $\mathcal{D}^{(DD)} \gets \mathcal{D}^{(DD)} \cup \left\{j\right\}$
\EndIf
\EndFor
\EndFor
\State \Return $\mathcal{D}^{(DD)}$
\end{algorithmic} | "https://arxiv.org/src/2402.10018" | "2402.10018.tar.gz" | "2024-02-15" | {
"title": "multi-stage algorithm for group testing with prior statistics",
"id": "2402.10018",
"abstract": "in this paper, we propose an efficient multi-stage algorithm for non-adaptive group testing (gt) with general correlated prior statistics. the proposed solution can be applied to any correlated statistical prior represented in trellis, e.g., finite state machines and markov processes. we introduce a variation of list viterbi algorithm (lva) to enable accurate recovery using much fewer tests than objectives, which efficiently gains from the correlated prior statistics structure. our numerical results demonstrate that the proposed multi-stage gt (msgt) algorithm can obtain the optimal maximum a posteriori (map) performance with feasible complexity in practical regimes, such as with covid-19 and sparse signal recovery applications, and reduce in the scenarios tested the number of pooled tests by at least $25\\%$ compared to existing classical low complexity gt algorithms. moreover, we analytically characterize the complexity of the proposed msgt algorithm that guarantees its efficiency.",
"categories": "cs.it math.it q-bio.qm stat.ap",
"doi": "",
"created": "2024-02-15",
"updated": "",
"authors": [
"ayelet c. portnoy",
"alejandro cohen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.10018"
} | "2024-03-15T04:20:09.862331" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"GT_with_prior_statistics_for_ISIT.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"GT_with_prior_statistics_for_ISIT.tex": {
"toxicity_score": 0.0074139764,
"severe_toxicity_score": 0.0010490417,
"identity_attack_score": 0.002192123,
"insult_score": 0.00654387,
"profanity_score": 0.013644327,
"threat_score": 0.0063238298,
"sexually_explicit_score": 0.01209032,
"flirtation_score": 0.31215188
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.05211947265502,
"hash": "0f3c3f17c73f1f1f",
"most_frequent_color_ratio": 95.05211947265502
}
} | [] | "algorithm" | "be1745d6-9739-47fa-aceb-73f91edde679" | 388 | easy |
|
\begin{algorithm}
\caption{MARK and REFINE for the time-stepping approach}
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^m}
(\eta_\bullet^{i,m} + \eta_\bullet^{i,m,*})$
\If{$|\eta_k|*c \geq |\eta_h|$}
\State mark $I_m$ for temporal refinement based on chosen strategy
\EndIf
\If{$|\eta_h|*c \geq |\eta_k|$}
\For{$m=1,\dots,M$}
\State mark and refine elements in $\mathcal{T}_h^m$ based on chosen strategy
\EndFor
\EndIf
\For{$m=1,\dots,M$}
\If{$I_m$ is marked}
\State Split/Refine $I_m$ into two intervals with (possibly new) mesh $\mathcal{T}_h^m$
\EndIf
\EndFor
\end{algorithmic}
\label{algo_mark_refine_timestep}
\end{algorithm}
| \begin{algorithm}
\caption{MARK and REFINE for the time-stepping approach}
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^m}
(\eta_\bullet^{i,m} + \eta_\bullet^{i,m,*})$
\If{$|\eta_k|*c \geq |\eta_h|$}
\State mark $I_m$ for temporal refinement based on chosen strategy
\EndIf
\If{$|\eta_h|*c \geq |\eta_k|$}
\For{$m=1,\dots,M$}
\State mark and refine elements in $\mathcal{T}_h^m$ based on chosen strategy
\EndFor
\EndIf
\For{$m=1,\dots,M$}
\If{$I_m$ is marked}
\State Split/Refine $I_m$ into two intervals with (possibly new) mesh $\mathcal{T}_h^m$
\EndIf
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2207.04764" | "2207.04764.tar.gz" | "2024-02-04" | {
"title": "numerical modeling and open-source implementation of variational partition-of-unity localizations of space-time dual-weighted residual estimators for parabolic problems",
"id": "2207.04764",
"abstract": "in this work, we consider space-time goal-oriented a posteriori error estimation for parabolic problems. temporal and spatial discretizations are based on galerkin finite elements of continuous and discontinuous type. the main objectives are the development and analysis of space-time estimators, in which the localization is based on a weak form employing a partition-of-unity. the resulting error indicators are used for temporal and spatial adaptivity. our developments are substantiated with several numerical examples.",
"categories": "math.na cs.na math.oc",
"doi": "",
"created": "2022-07-11",
"updated": "2024-02-04",
"authors": [
"jan philipp thiele",
"thomas wick"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.04764"
} | "2024-03-15T07:54:29.015400" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
},
"main.fls": {
"toxicity_score": 0.0035184973,
"severe_toxicity_score": 0.00045776367,
"identity_attack_score": 0.0011515582,
"insult_score": 0.0056556147,
"profanity_score": 0.009903891,
"threat_score": 0.0058059995,
"sexually_explicit_score": 0.005248968,
"flirtation_score": 0.37866667
},
"main.fdb_latexmk": {
"toxicity_score": 0.009173225,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0026453468,
"insult_score": 0.007474876,
"profanity_score": 0.012722028,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.17961895
},
"Intro_nonstat_PU.bib": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0018024445,
"identity_attack_score": 0.0042732526,
"insult_score": 0.0096028885,
"profanity_score": 0.018392462,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.3638164
},
"main.tex": {
"toxicity_score": 0.018095128,
"severe_toxicity_score": 0.0018024445,
"identity_attack_score": 0.0064006294,
"insult_score": 0.008823884,
"profanity_score": 0.017914234,
"threat_score": 0.009074804,
"sexually_explicit_score": 0.01769315,
"flirtation_score": 0.2485177
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 85.58447442609747,
"hash": "00092f142707ff7f",
"most_frequent_color_ratio": 85.58447442609747
}
} | [] | "algorithm" | "2c4998fb-e078-4069-b9db-64c9c4e0143f" | 869 | medium |
|
\begin{algorithm}
\caption{Construction of a random network from a degree sequence with configuration model}
\label{alg: Config}
Suppose we want to generate a realization of a finite graph with $N$ vertices and a given degree sequence $\textbf{d}=(d_1,d_2,...,d_N)$.
\begin{enumerate}
\item Following the algorithm described by \cite{Newman:2010}, we first create $N$ vertices labeled from $i=1$ to $N$, and attach $d_i$ \textbf{stubs} (half edges) to the $i$-th vertex.
\item Then we randomly pair any of the two distinct stubs to form edges by the configuration model:
\begin{enumerate}
\item Vertices are treated as a partition of all stubs, which each vertex labeled by $i$ is isomorphic to a set $w_i$ of the partition contains all the stubs attach to it, so the set $w_i$ has $d_i$ element.
\item Randomly picking any two of the distinct stubs among all stubs and form a pair, even they come from the same partition set or there are already other pairs between the same two sets.
Once two stubs are paired, they will be recorded and removed from later paring candidates.
\item Repeat Step (b) until no unpaired stubs are left.
\item Map the paired stubs and partition sets back to graph, as edges and vertices, accordingly.
\item If there are multi-edges and loops in the result graph reject the result and restart from (a), until a simple graph is attained.
\end{enumerate}
\item Output the simple graph as final result.
\end{enumerate}
\end{algorithm}
| \begin{algorithm}
\caption{Construction of a random network from a degree sequence with configuration model}
Suppose we want to generate a realization of a finite graph with $N$ vertices and a given degree sequence $\textbf{d}=(d_1,d_2,...,d_N)$.
\begin{enumerate}
\item Following the algorithm described by \cite{Newman:2010}, we first create $N$ vertices labeled from $i=1$ to $N$, and attach $d_i$ \textbf{stubs} (half edges) to the $i$-th vertex.
\item Then we randomly pair any of the two distinct stubs to form edges by the configuration model:
\begin{enumerate}
\item Vertices are treated as a partition of all stubs, which each vertex labeled by $i$ is isomorphic to a set $w_i$ of the partition contains all the stubs attach to it, so the set $w_i$ has $d_i$ element.
\item Randomly picking any two of the distinct stubs among all stubs and form a pair, even they come from the same partition set or there are already other pairs between the same two sets.
Once two stubs are paired, they will be recorded and removed from later paring candidates.
\item Repeat Step (b) until no unpaired stubs are left.
\item Map the paired stubs and partition sets back to graph, as edges and vertices, accordingly.
\item If there are multi-edges and loops in the result graph reject the result and restart from (a), until a simple graph is attained.
\end{enumerate}
\item Output the simple graph as final result.
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/2401.06872" | "2401.06872.tar.gz" | "2024-01-12" | {
"title": "disease transmission on random graphs using edge-based percolation",
"id": "2401.06872",
"abstract": "edge-based percolation methods can be used to analyze disease transmission on complex social networks. this allows us to include complex social heterogeneity in our models while maintaining tractability. here we review the seminal works on this field by newman et al (2001); newman (2002, 2003), and miller et al (2012). we present a systematic discussion of the theoretical background behind these models, including an extensive derivation of the major results. we also connect these results relate back to the classical literature in random graph theory molloy and reed (1995, 1998). finally, we also present an accompanying r package that takes epidemic and network parameters as input and generates estimates of the epidemic trajectory and final size. this manuscript and the r package was developed to help researchers easily understand and use network models to investigate the interaction between different community structures and disease transmission.",
"categories": "cs.si math.ds q-bio.pe",
"doi": "",
"created": "2024-01-12",
"updated": "",
"authors": [
"s. zhao",
"f. m. g. magpantay"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.06872"
} | "2024-03-15T07:30:11.562367" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"figure1.tex": {
"toxicity_score": 0.011497946,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0035887922,
"insult_score": 0.007493876,
"profanity_score": 0.015215653,
"threat_score": 0.0074565844,
"sexually_explicit_score": 0.012149297,
"flirtation_score": 0.21014765
},
"main.tex": {
"toxicity_score": 0.009047564,
"severe_toxicity_score": 0.0009059906,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0069238725,
"profanity_score": 0.013780965,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.2085626
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"Bibliography.bib": {
"toxicity_score": 0.007759543,
"severe_toxicity_score": 0.0013637543,
"identity_attack_score": 0.0012024303,
"insult_score": 0.0070853736,
"profanity_score": 0.014874061,
"threat_score": 0.0059160385,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.3658314
},
"images/epidemic_curves.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"main.bbl": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.0039587705,
"insult_score": 0.007531876,
"profanity_score": 0.0147374235,
"threat_score": 0.007573096,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.22497389
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.69585849870577,
"hash": "d39f7f13d0fffede",
"most_frequent_color_ratio": 94.69585849870577
}
} | [] | "algorithm" | "78832468-e3fe-48f5-83ac-0682df6e2705" | 1439 | hard |
|
\begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-occurrence frequency}
\State $r \gets \frac{e_{i,j}}{(\Vert e_{i,j} - e^{\prime}_{i,j} \Vert_2)}$ \Comment{Update co-occurrence}
\State $e_{i,j} = 2 \sqrt{max(r - 1, 1)} + (min(r-2,2) + 2)$
\State \textbf{return} $e_{i,j}$\Comment{Relative co-occurrence frequency}
\EndProcedure
\Procedure{NormalizeImage}{$I_r, \lambda_m$}
\State $N\gets\sum_{i=0}^{4^k}{\sum_{j=0}^{4^k}{I[i,j]}}$
\For{$i \in 4^k$}
\For{$j \in 4^k$}
\If{$I_r[i,j] > \lambda_m$}
\State$I_r[i,j]\gets 255*I[i,j]/N$
\Else
\State$I_r[i,j]\gets 0$
\EndIf
\EndFor
\EndFor
\State \textbf{return} $I_r$\Comment{Normalized Pseudo-Image}
\EndProcedure
\State $I_r \gets ones(4^k,4^k)$ \Comment{Initial pseudo-image}
\For{$ X_i \in \mathcal{X}_i $} \Comment{Iterate through each sequence}
\For{$ x_i, x_j \in X_i $} \Comment{Iterate through successive k-mers}
\State $I_r[i,j]\gets$\Call{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\EndFor
\EndFor
\State $I_r\gets$ \Call{NormalizeImage}{$I_r, \lambda_m$}
\State \textbf{return} $I_r$\Comment{Final pseudo-image}
\end{algorithmic}
| \begin{algorithmic}
\Require Sequence reads $X_i = \{X_0, X_1, \ldots X_N\}, \forall X_i \in \mathcal{X}_i$
\Ensure Pseudo-image $I_r$
\Procedure{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\State $e_{i,j} \gets I_r[i,j]$ \Comment{Current co-occurrence frequency}
\State $e^\prime_{i,j} \gets e_{i,j} + 1$ \Comment{New co-occurrence frequency}
\State $r \gets \frac{e_{i,j}}{(\Vert e_{i,j} - e^{\prime}_{i,j} \Vert_2)}$ \Comment{Update co-occurrence}
\State $e_{i,j} = 2 \sqrt{max(r - 1, 1)} + (min(r-2,2) + 2)$
\State \textbf{return} $e_{i,j}$\Comment{Relative co-occurrence frequency}
\EndProcedure
\Procedure{NormalizeImage}{$I_r, \lambda_m$}
\State $N\gets\sum_{i=0}^{4^k}{\sum_{j=0}^{4^k}{I[i,j]}}$
\For{$i \in 4^k$}
\For{$j \in 4^k$}
\If{$I_r[i,j] > \lambda_m$}
\State$I_r[i,j]\gets 255*I[i,j]/N$
\Else
\State$I_r[i,j]\gets 0$
\EndIf
\EndFor
\EndFor
\State \textbf{return} $I_r$\Comment{Normalized Pseudo-Image}
\EndProcedure
\State $I_r \gets ones(4^k,4^k)$ \Comment{Initial pseudo-image}
\For{$ X_i \in \mathcal{X}_i $} \Comment{Iterate through each sequence}
\For{$ x_i, x_j \in X_i $} \Comment{Iterate through successive k-mers}
\State $I_r[i,j]\gets$\Call{relativeCoOccurrence}{$x_i, x_j$, $I_r$}
\EndFor
\EndFor
\State $I_r\gets$ \Call{NormalizeImage}{$I_r, \lambda_m$}
\State \textbf{return} $I_r$\Comment{Final pseudo-image}
\end{algorithmic} | "https://arxiv.org/src/2401.13219" | "2401.13219.tar.gz" | "2024-01-23" | {
"title": "tepi: taxonomy-aware embedding and pseudo-imaging for scarcely-labeled zero-shot genome classification",
"id": "2401.13219",
"abstract": "a species' genetic code or genome encodes valuable evolutionary, biological, and phylogenetic information that aids in species recognition, taxonomic classification, and understanding genetic predispositions like drug resistance and virulence. however, the vast number of potential species poses significant challenges in developing a general-purpose whole genome classification tool. traditional bioinformatics tools have made notable progress but lack scalability and are computationally expensive. machine learning-based frameworks show promise but must address the issue of large classification vocabularies with long-tail distributions. in this study, we propose addressing this problem through zero-shot learning using tepi, taxonomy-aware embedding and pseudo-imaging. we represent each genome as pseudo-images and map them to a taxonomy-aware embedding space for reasoning and classification. this embedding space captures compositional and phylogenetic relationships of species, enabling predictions in extensive search spaces. we evaluate tepi using two rigorous zero-shot settings and demonstrate its generalization capabilities qualitatively on curated, large-scale, publicly sourced data.",
"categories": "q-bio.gn cs.ai cs.lg",
"doi": "",
"created": "2024-01-23",
"updated": "",
"authors": [
"sathyanarayanan aakur",
"vishalini r. laguduva",
"priyadharsini ramamurthy",
"akhilesh ramachandran"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.13219"
} | "2024-03-15T06:48:41.356948" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"arxiv.tex": {
"toxicity_score": 0.008293601,
"severe_toxicity_score": 0.0011968613,
"identity_attack_score": 0.0026083488,
"insult_score": 0.0068003717,
"profanity_score": 0.014566627,
"threat_score": 0.0065050707,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.2487834
},
"arxiv.bbl": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0008869171,
"identity_attack_score": 0.0030338243,
"insult_score": 0.006999873,
"profanity_score": 0.013336895,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.5114233
},
"generic.sty": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.0010633469,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0074178753,
"profanity_score": 0.014669105,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013033954,
"flirtation_score": 0.32449564
},
"IEEEtran.cls": {
"toxicity_score": 0.0049635945,
"severe_toxicity_score": 0.0007343292,
"identity_attack_score": 0.0012024303,
"insult_score": 0.006011867,
"profanity_score": 0.012312118,
"threat_score": 0.005861019,
"sexually_explicit_score": 0.008374758,
"flirtation_score": 0.4981806
},
"egbib.bib": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.003311308,
"insult_score": 0.0075508766,
"profanity_score": 0.015932998,
"threat_score": 0.007094103,
"sexually_explicit_score": 0.014862247,
"flirtation_score": 0.30444452
},
"logo.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"ieeecolor.cls": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0013828278,
"identity_attack_score": 0.004162259,
"insult_score": 0.009241886,
"profanity_score": 0.017094411,
"threat_score": 0.008220384,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.29178214
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.8674953616059,
"hash": "03383f1f9f3f381f",
"most_frequent_color_ratio": 92.8674953616059
}
} | [] | "algorithm" | "89d120bd-adad-4b31-9ab5-92cc3252c676" | 1353 | hard |
|
\begin{algorithm}
Smoothed Probabilities
\end{algorithm}
| \begin{algorithm}
Smoothed Probabilities
\end{algorithm} | "https://arxiv.org/src/2402.08051" | "2402.08051.tar.gz" | "2024-02-12" | {
"title": "on bayesian filtering for markov regime switching models",
"id": "2402.08051",
"abstract": "this paper presents a framework for empirical analysis of dynamic macroeconomic models using bayesian filtering, with a specific focus on the state-space formulation of dynamic stochastic general equilibrium (dsge) models with multiple regimes. we outline the theoretical foundations of model estimation, provide the details of two families of powerful multiple-regime filters, imm and gpb, and construct corresponding multiple-regime smoothers. a simulation exercise, based on a prototypical new keynesian dsge model, is used to demonstrate the computational robustness of the proposed filters and smoothers and evaluate their accuracy and speed for a selection of filters from each family. we show that the canonical imm filter is faster and is no less, and often more, accurate than its competitors within imm and gpb families, the latter including the commonly used kim and nelson (1999) filter. using it with the matching smoother improves the precision in recovering unobserved variables by about 25 percent. furthermore, applying it to the u.s. 1947-2023 macroeconomic time series, we successfully identify significant past policy shifts including those related to the post-covid-19 period. our results demonstrate the practical applicability and potential of the proposed routines in macroeconomic analysis.",
"categories": "econ.em",
"doi": "",
"created": "2024-02-12",
"updated": "",
"authors": [
"nigar hashimzade",
"oleg kirsanov",
"tatiana kirsanova",
"junior maih"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.08051"
} | "2024-03-15T04:21:14.605813" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"paper_draft_8_arxiv.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"empirics.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"demo.bib": {
"toxicity_score": 0.0068799187,
"severe_toxicity_score": 0.0009441376,
"identity_attack_score": 0.001655654,
"insult_score": 0.0068668723,
"profanity_score": 0.013029462,
"threat_score": 0.0062332097,
"sexually_explicit_score": 0.008669644,
"flirtation_score": 0.25294632
},
"Figure1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"paper_draft_8_arxiv.tex": {
"toxicity_score": 0.011686437,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0032928092,
"insult_score": 0.007360875,
"profanity_score": 0.015181494,
"threat_score": 0.007974415,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.6102261
},
"dataupdate.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"Missp1.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.52194925493355,
"hash": "00ff7f3f3f3fff00",
"most_frequent_color_ratio": 88.52194925493355
}
} | [] | "algorithm" | "d7f2dceb-4b3b-4c01-9743-7cc67bc8d389" | 56 | easy |
|
\begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
\State $PA\_block$
\For {$B_i \in$ OC\_block}
\State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
\EndFor
\State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\State $PA\_set$
\For { OC\_block in OC\_blocks}
\State $PA\_block$
\For {$B_i \in$ OC\_block}
\State $PA\_block \gets B_i$ if $B_i \in \mathcal{F}_{a}^{PA}$
\EndFor
\State $PA\_set \gets PA\_block$
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2208.01756" | "2208.01756.tar.gz" | "2024-02-26" | {
"title": "permutation-adapted complete and independent basis for atomic cluster expansion descriptors",
"id": "2208.01756",
"abstract": "atomic cluster expansion (ace) methods provide a systematic way to describe particle local environments of arbitrary body order. for practical applications it is often required that the basis of cluster functions be symmetrized with respect to rotations and permutations. existing methodologies yield sets of symmetrized functions that are over-complete. these methodologies thus require an additional numerical procedure, such as singular value decomposition (svd), to eliminate redundant functions. in this work, it is shown that analytical linear relationships for subsets of cluster functions may be derived using recursion and permutation properties of generalized wigner symbols. from these relationships, subsets (blocks) of cluster functions can be selected such that, within each block, functions are guaranteed to be linearly independent. it is conjectured that this block-wise independent set of permutation-adapted rotation and permutation invariant (pa-rpi) functions forms a complete, independent basis for ace. along with the first analytical proofs of block-wise linear dependence of ace cluster functions and other theoretical arguments, numerical results are offered to demonstrate this. the utility of the method is demonstrated in the development of an ace interatomic potential for tantalum. using the new basis functions in combination with bayesian compressive sensing sparse regression, some high degree descriptors are observed to persist and help achieve high-accuracy models.",
"categories": "cond-mat.mtrl-sci",
"doi": "",
"created": "2022-08-02",
"updated": "2024-02-26",
"authors": [
"james m. goff",
"charles sievers",
"mitchell a. wood",
"aidan p. thompson"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.01756"
} | "2024-03-15T02:45:54.507286" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bib": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0041007996,
"identity_attack_score": 0.0052166977,
"insult_score": 0.010590895,
"profanity_score": 0.023652986,
"threat_score": 0.009877442,
"sexually_explicit_score": 0.023001095,
"flirtation_score": 0.266283
},
"output.bbl": {
"toxicity_score": 0.06025757,
"severe_toxicity_score": 0.004005432,
"identity_attack_score": 0.0076955543,
"insult_score": 0.017163089,
"profanity_score": 0.02973333,
"threat_score": 0.014566151,
"sexually_explicit_score": 0.023119051,
"flirtation_score": 0.48334706
},
"main.tex": {
"toxicity_score": 0.0143253105,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0053276913,
"insult_score": 0.008025879,
"profanity_score": 0.01627459,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014095544,
"flirtation_score": 0.34552434
},
"appendices.tex": {
"toxicity_score": 0.01859777,
"severe_toxicity_score": 0.001821518,
"identity_attack_score": 0.0047727237,
"insult_score": 0.0096028885,
"profanity_score": 0.017914234,
"threat_score": 0.008738215,
"sexually_explicit_score": 0.019462466,
"flirtation_score": 0.19818714
}
},
"reason": null
}
} | {
"num_done": {
"equation": 3,
"table": 3,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.59773286598553,
"hash": "3f019f83c09f873f",
"most_frequent_color_ratio": 90.59773286598553
}
} | [] | "algorithm" | "002fbb31-bf14-4293-b495-21532297dfc8" | 244 | easy |
|
\begin{algorithm}[t]
\caption{\underline{NC-1-WL} \emph{vs.} 1-WL for graph isomorphism test}
\label{alg:NC-1-WL}
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\ell=1,2,\cdots$)
\If{$\{\!\{c_v^{(\ell-1)}|v \in V\}\!\} \neq \{\!\{d_p^{(\ell-1)}|p \in P\}\!\}$}
\State \Return $\mathcal{G} \not\simeq \mathcal{H}$
\EndIf
\For{$v\in V$}
\State $c_v^{(\ell)} \leftarrow \textsc{Hash}\Bigl(c_v^{(\ell-1)}, \{\!\{c_u^{(\ell-1)}|u\in\mathcal{N}_v\}\!\},
\underline{\{\!\{\{\!\{c_{u_1}^{(\ell-1)}, c_{u_2}^{(\ell-1)}\}\!\}|u_1,u_2\in\mathcal{N}_v, (u_1,u_2)\in E\}\!\}} \Bigr)$
\EndFor
\For{$p\in P$}
\State $d_p^{(\ell)} \leftarrow \textsc{Hash}\Bigl(d_p^{(\ell-1)}, \{\!\{d_q^{(\ell-1)}|q\in\mathcal{N}_p\}\!\}, \underline{\{\!\{\{\!\{d_{q_1}^{(\ell-1)}, d_{q_2}^{(\ell-1)}\}\!\}|q_1,q_2\in\mathcal{N}_p, (q_1,q_2)\in F\}\!\}}\Bigr)$
\EndFor
\Until convergence
\State \Return $\mathcal{G} \simeq \mathcal{H}$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[t]
\caption{\underline{NC-1-WL} \emph{vs.} 1-WL for graph isomorphism test}
\begin{algorithmic}
\State \textbf{Input:} Two graphs $\mathcal{G}=(V, E, \boldsymbol{X})$ and $\mathcal{H}=(P, F, \boldsymbol{Y})$
\State $c_v^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{x}_v), \forall v \in V$
\State $d_p^{(0)} \leftarrow \textsc{Hash}(\boldsymbol{y}_p), \forall p \in P$
\Repeat \ ($\ell=1,2,\cdots$)
\If{$\{\!\{c_v^{(\ell-1)}|v \in V\}\!\} \neq \{\!\{d_p^{(\ell-1)}|p \in P\}\!\}$}
\State \Return $\mathcal{G} \not\simeq \mathcal{H}$
\EndIf
\For{$v\in V$}
\State $c_v^{(\ell)} \leftarrow \textsc{Hash}\Bigl(c_v^{(\ell-1)}, \{\!\{c_u^{(\ell-1)}|u\in\mathcal{N}_v\}\!\},
\underline{\{\!\{\{\!\{c_{u_1}^{(\ell-1)}, c_{u_2}^{(\ell-1)}\}\!\}|u_1,u_2\in\mathcal{N}_v, (u_1,u_2)\in E\}\!\}} \Bigr)$
\EndFor
\For{$p\in P$}
\State $d_p^{(\ell)} \leftarrow \textsc{Hash}\Bigl(d_p^{(\ell-1)}, \{\!\{d_q^{(\ell-1)}|q\in\mathcal{N}_p\}\!\}, \underline{\{\!\{\{\!\{d_{q_1}^{(\ell-1)}, d_{q_2}^{(\ell-1)}\}\!\}|q_1,q_2\in\mathcal{N}_p, (q_1,q_2)\in F\}\!\}}\Bigr)$
\EndFor
\Until convergence
\State \Return $\mathcal{G} \simeq \mathcal{H}$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2206.02059" | "2206.02059.tar.gz" | "2024-01-23" | {
"title": "empowering gnns via edge-aware weisfeiler-leman algorithm",
"id": "2206.02059",
"abstract": "message passing graph neural networks (gnns) are known to have their expressiveness upper-bounded by 1-dimensional weisfeiler-leman (1-wl) algorithm. to achieve more powerful gnns, existing attempts either require ad hoc features, or involve operations that incur high time and space complexities. in this work, we propose a general and provably powerful gnn framework that preserves the scalability of the message passing scheme. in particular, we first propose to empower 1-wl for graph isomorphism test by considering edges among neighbors, giving rise to nc-1-wl. the expressiveness of nc-1-wl is shown to be strictly above 1-wl and below 3-wl theoretically. further, we propose the nc-gnn framework as a differentiable neural version of nc-1-wl. our simple implementation of nc-gnn is provably as powerful as nc-1-wl. experiments demonstrate that our nc-gnn performs effectively and efficiently on various benchmarks.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2022-06-04",
"updated": "2024-01-23",
"authors": [
"meng liu",
"haiyang yu",
"shuiwang ji"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2206.02059"
} | "2024-03-15T09:04:06.314342" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"math_commands.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0074368757,
"profanity_score": 0.011526455,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.0059272056,
"flirtation_score": 0.37736848
},
"tmlr.bst": {
"toxicity_score": 0.02048268,
"severe_toxicity_score": 0.0017642975,
"identity_attack_score": 0.003921773,
"insult_score": 0.00983089,
"profanity_score": 0.023379711,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.021585643,
"flirtation_score": 0.53546125
},
"fancyhdr.sty": {
"toxicity_score": 0.06065326,
"severe_toxicity_score": 0.0034332275,
"identity_attack_score": 0.0056236745,
"insult_score": 0.022255141,
"profanity_score": 0.05268429,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.036801755,
"flirtation_score": 0.3852209
},
"main.bib": {
"toxicity_score": 0.0131943645,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.0032373124,
"insult_score": 0.007797878,
"profanity_score": 0.016752819,
"threat_score": 0.008039144,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.3190224
},
"NCGNN.bbl": {
"toxicity_score": 0.013320025,
"severe_toxicity_score": 0.0012159348,
"identity_attack_score": 0.00414376,
"insult_score": 0.007797878,
"profanity_score": 0.01535229,
"threat_score": 0.0076507707,
"sexually_explicit_score": 0.012739069,
"flirtation_score": 0.195667
},
"tmlr.sty": {
"toxicity_score": 0.006754258,
"severe_toxicity_score": 0.00047922134,
"identity_attack_score": 0.0013781702,
"insult_score": 0.006999873,
"profanity_score": 0.011458136,
"threat_score": 0.0057606893,
"sexually_explicit_score": 0.005543854,
"flirtation_score": 0.48204565
},
"LICENSE": {
"toxicity_score": 0.00823077,
"severe_toxicity_score": 0.0007581711,
"identity_attack_score": 0.001951637,
"insult_score": 0.0069713728,
"profanity_score": 0.012175481,
"threat_score": 0.0068028234,
"sexually_explicit_score": 0.0065759546,
"flirtation_score": 0.17881343
},
"NCGNN.tex": {
"toxicity_score": 0.017969469,
"severe_toxicity_score": 0.0016021729,
"identity_attack_score": 0.0061416444,
"insult_score": 0.009127886,
"profanity_score": 0.018392462,
"threat_score": 0.008764107,
"sexually_explicit_score": 0.01875474,
"flirtation_score": 0.36146027
},
"README.md": {
"toxicity_score": 0.013822667,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0036997858,
"insult_score": 0.00810188,
"profanity_score": 0.016342908,
"threat_score": 0.00780612,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.31661955
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.82406955040274,
"hash": "0f0f1f1f871f853f",
"most_frequent_color_ratio": 91.82406955040274
}
} | [] | "algorithm" | "b8b7f04e-bbb0-49dd-a061-ec109b7a2c6b" | 1173 | hard |
|
\begin{algorithm}
\caption{Optimal algorithm for the approximate solution $u^{(L)}$ in $Q_L$ \label{alg:truealg}}
\begin{algorithmic}[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation}\label{eqn:phiTL}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L)} =\nabla\cdot ae_i \, \mbox{ in }Q_{2L}, \hspace{0.3in} \phi_{i,T}^{(L)}=0 \, \mbox{ on }\partial Q_{2L}.
\end{equation}
\State Calculate the approximate homogenized coefficients via \begin{equation}\label{eqn:algahL}
a_h^{(L)}e_i=\int \omega q_{i,T}^{(L)},
\end{equation} where \begin{equation}\label{eqn:defqiTL} q_{i,T}^{(L)}:=a(e_i+\nabla\phi_{i,T}^{(L)})\end{equation} and $\omega(x)=\frac{1}{L^d}\hat{\omega}(\frac{x}{L})$ with $\hat{\omega}$ as in Theorem \ref{thm:luottooptimal}.
\State Find $\tilde{u}_h^{(L)}$ on $\partial Q_L$:
\begin{equation}\label{eqn:alguhtildeL}
\tilde{u}_h^{(L)} =\int G_h^{(L)}* (\nabla\cdot g),
\end{equation}where $G_h^{(L)}(x) := \frac{1}{4\pi\left|(a_h^{(L)})^{-1/2}x\right|}$ is the Green function for the constant-coefficient operator $-\nabla\cdot a_h^{(L)} \nabla$.
\State Solve for approximate first-order flux correctors $\sigma_{i,T}^{(L)}=\{\sigma_{ijk,T}^{(L)}\}_{j,k}$: \begin{equation}\label{eqn:algsigma}
\dfrac{1}{T}\sigma_{ijk,T}^{(L)}-\Delta \sigma_{ijk,T}^{(L)} =\partial_j q_{ik,T}^{(L)}-\partial_k q_{ij,T}^{(L)} \, \mbox{ in }Q_{\frac{7}{4}L}, \hspace{0.3in} \sigma_{ijk,T}^{(L)}=0 \, \mbox{ on }\partial Q_{\frac{7}{4}L}.
\end{equation}
\State Solve for approximate second-order correctors $\psi_{ij,T}^{(L)}$: \begin{equation}\label{eqn:2ndcorapprox}
\dfrac{1}{T}\psi_{ij,T}^{(L)} - \nabla\cdot a \nabla \psi_{ij,T}^{(L)} = \nabla\cdot (\phi_{i,T}^{(L)}a-\sigma_{i,T}^{(L)})e_j \, \mbox{ in }Q_{\frac{3}{2}L}, \hspace{0.3in} \psi_{ij,T}^{(L)}=0 \,\mbox{ on }\partial Q_{\frac{3}{2}L}.
\end{equation}
\State For the indices \begin{equation}\label{eqn:calJ}(i,j)\in \mathcal{J}=\{(1,2),(1,3),(2,3),(2,2),(3,3)\},\end{equation} calculate \begin{equation}\label{eqn:cijlt}
c_{ij,T}^{(L)}=-\int g\cdot \nabla \Bigl(\sum_{k=1}^3\phi_{k,T}^{(L)}\partial_k v_{h,ij}^{(L)}+(2-\delta_{ij})(\psi_{ij,T}^{(L)}-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}\psi_{11,T}^{(L)})\Bigr) ,
\end{equation} where $v_{h,ij}^{(L)}$ denote the $a_h^{(L)}$-harmonic polynomials \begin{equation}\label{eqn:harmpolL}
v_{h,ij}^{(L)}=(1-\dfrac{1}{2}\delta_{ij})(x_ix_j-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}x_1^2).
\end{equation}
\State Obtain $u_h^{(L)}$ as \begin{equation}\label{eqn:algapproxbdry}
u_h^{(L)}=\tilde{u}_h^{(L)}+ \sum_{i=1}^3(\int g \cdot\nabla \phi_{i,T}^{(L)})\partial_i G_h^{(L)} +\sum_{(i,j)\in\mathcal{J}}c_{ij,T}^{(L)}\partial_{ij} G_h^{(L)}.
\end{equation}
\State Solve for $u^{(L)}$ (here and for the rest of the paper we adopt Einstein's summation convention for repeated indices): \begin{equation}\label{eqn:finalapprox}
-\nabla \cdot a \nabla u^{(L)}=\nabla \cdot g\text{ in }Q_L,\hspace{0.3in} u^{(L)}=(1+\phi_{i,T}^{(L)}\partial_i+\psi_{ij,T}^{(L)}\partial_{ij}) u_h^{(L)}\text{ on }\partial Q_L.
\end{equation}
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Optimal algorithm for the approximate solution $u^{(L)}$ in $Q_L$ }
\begin{algorithmic}
[1]
\State For $\beta\in (1,\frac{3}{2})$ set $\varepsilon = \frac{3}{2}-\beta$ and $T = L^{2(1-\varepsilon)}$. For $i=1,\cdots,d$, solve for the approximate first-order corrector $\phi_{i,T}^{(L)}$:
\begin{equation*}
\dfrac{1}{T}\phi_{i,T}^{(L)}-\nabla \cdot a \nabla \phi_{i,T}^{(L)} =\nabla\cdot ae_i \, \mbox{ in }Q_{2L}, \hspace{0.3in} \phi_{i,T}^{(L)}=0 \, \mbox{ on }\partial Q_{2L}.
\end{equation*}
\State Calculate the approximate homogenized coefficients via \begin{equation*}
a_h^{(L)}e_i=\int \omega q_{i,T}^{(L)},
\end{equation*} where \begin{equation*}
q_{i,T}^{(L)}:=a(e_i+\nabla\phi_{i,T}^{(L)})\end{equation*} and $\omega(x)=\frac{1}{L^d}\hat{\omega}(\frac{x}{L})$ with $\hat{\omega}$ as in Theorem \ref{thm:luottooptimal}.
\State Find $\tilde{u}_h^{(L)}$ on $\partial Q_L$:
\begin{equation*}
\tilde{u}_h^{(L)} =\int G_h^{(L)}* (\nabla\cdot g),
\end{equation*}where $G_h^{(L)}(x) := \frac{1}{4\pi\left|(a_h^{(L)})^{-1/2}x\right|}$ is the Green function for the constant-coefficient operator $-\nabla\cdot a_h^{(L)} \nabla$.
\State Solve for approximate first-order flux correctors $\sigma_{i,T}^{(L)}=\{\sigma_{ijk,T}^{(L)}\}_{j,k}$: \begin{equation*}
\dfrac{1}{T}\sigma_{ijk,T}^{(L)}-\Delta \sigma_{ijk,T}^{(L)} =\partial_j q_{ik,T}^{(L)}-\partial_k q_{ij,T}^{(L)} \, \mbox{ in }Q_{\frac{7}{4}L}, \hspace{0.3in} \sigma_{ijk,T}^{(L)}=0 \, \mbox{ on }\partial Q_{\frac{7}{4}L}.
\end{equation*}
\State Solve for approximate second-order correctors $\psi_{ij,T}^{(L)}$: \begin{equation*}
\dfrac{1}{T}\psi_{ij,T}^{(L)} - \nabla\cdot a \nabla \psi_{ij,T}^{(L)} = \nabla\cdot (\phi_{i,T}^{(L)}a-\sigma_{i,T}^{(L)})e_j \, \mbox{ in }Q_{\frac{3}{2}L}, \hspace{0.3in} \psi_{ij,T}^{(L)}=0 \,\mbox{ on }\partial Q_{\frac{3}{2}L}.
\end{equation*}
\State For the indices \begin{equation*}
(i,j)\in \mathcal{J}=\{(1,2),(1,3),(2,3),(2,2),(3,3)\},\end{equation*} calculate \begin{equation*}
c_{ij,T}^{(L)}=-\int g\cdot \nabla \Bigl(\sum_{k=1}^3\phi_{k,T}^{(L)}\partial_k v_{h,ij}^{(L)}+(2-\delta_{ij})(\psi_{ij,T}^{(L)}-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}\psi_{11,T}^{(L)})\Bigr) ,
\end{equation*} where $v_{h,ij}^{(L)}$ denote the $a_h^{(L)}$-harmonic polynomials \begin{equation*}
v_{h,ij}^{(L)}=(1-\dfrac{1}{2}\delta_{ij})(x_ix_j-\dfrac{a_{hij}^{(L)}}{a_{h11}^{(L)}}x_1^2).
\end{equation*}
\State Obtain $u_h^{(L)}$ as \begin{equation*}
u_h^{(L)}=\tilde{u}_h^{(L)}+ \sum_{i=1}^3(\int g \cdot\nabla \phi_{i,T}^{(L)})\partial_i G_h^{(L)} +\sum_{(i,j)\in\mathcal{J}}c_{ij,T}^{(L)}\partial_{ij} G_h^{(L)}.
\end{equation*}
\State Solve for $u^{(L)}$ (here and for the rest of the paper we adopt Einstein's summation convention for repeated indices): \begin{equation*}
-\nabla \cdot a \nabla u^{(L)}=\nabla \cdot g\text{ in }Q_L,\hspace{0.3in} u^{(L)}=(1+\phi_{i,T}^{(L)}\partial_i+\psi_{ij,T}^{(L)}\partial_{ij}) u_h^{(L)}\text{ on }\partial Q_L.
\end{equation*}
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2109.01616" | "2109.01616.tar.gz" | "2024-01-11" | {
"title": "optimal artificial boundary conditions based on second-order correctors for three dimensional random elliptic media",
"id": "2109.01616",
"abstract": "we are interested in numerical algorithms for computing the electrical field generated by a charge distribution localized on scale $\\ell$ in an infinite heterogeneous medium, in a situation where the medium is only known in a box of diameter $l\\gg\\ell$ around the support of the charge. we propose a boundary condition that with overwhelming probability is (near) optimal with respect to scaling in terms of $\\ell$ and $l$, in the setting where the medium is a sample from a stationary ensemble with a finite range of dependence (set to be unity and with the assumption that $\\ell \\gg 1$). the boundary condition is motivated by quantitative stochastic homogenization that allows for a multipole expansion [bgo20]. this work extends [lo21], the algorithm in which is optimal in two dimension, and thus we need to take quadrupoles, next to dipoles, into account. this in turn relies on stochastic estimates of second-order, next to first-order, correctors. these estimates are provided for finite range ensembles under consideration, based on an extension of the semi-group approach of [go15].",
"categories": "math.ap cs.na math.na math.pr",
"doi": "",
"created": "2021-09-03",
"updated": "2024-01-11",
"authors": [
"jianfeng lu",
"felix otto",
"lihan wang"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2109.01616"
} | "2024-03-15T06:31:22.744786" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"Revised_Manuscript.tex": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010824203,
"identity_attack_score": 0.0035517942,
"insult_score": 0.007322875,
"profanity_score": 0.015147334,
"threat_score": 0.007417747,
"sexually_explicit_score": 0.012208274,
"flirtation_score": 0.21162485
},
"Revised_Manuscript.bbl": {
"toxicity_score": 0.01646154,
"severe_toxicity_score": 0.0016784668,
"identity_attack_score": 0.0030523231,
"insult_score": 0.00808288,
"profanity_score": 0.01941724,
"threat_score": 0.008129764,
"sexually_explicit_score": 0.01710338,
"flirtation_score": 0.283342
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.38415906151924,
"hash": "00f7f7070537ffcf",
"most_frequent_color_ratio": 94.38415906151924
}
} | [] | "algorithm" | "b92119b2-9612-4cce-88b5-02a8c89b3abb" | 3011 | hard |
|
\begin{algorithm}[!htbp]
\caption{Algorithm for CPI Prediction (Multimodal)}
\label{algo:1}
\begin{algorithmic}[1]
\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
\Ensure Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\State \# \textit{Pre-training}
\While{\textit{not convergence}}
\State Generate a set of subsequences and subgraphs by ${\color[rgb]{1.0, 1.0, 1.0}AA}$ length-variable augmentation;
\State Pass the augmented subsequences and subgraphs ${\color[rgb]{1.0, 1.0, 1.0}AA}$ through the sequence and structure encoders;
\State Pre-train the two encoders by intra-modality and ${\color[rgb]{1.0, 1.0, 1.0}AA}$ cross-modality contrastive losses in Eq.~(\ref{eq:7})(\ref{eq:8})(\ref{eq:9}).
\EndWhile
\State \# \textit{Fine-tuning}
\While{\textit{not convergence}}
\State Jointly train the two pre-trained encoders and com- ${\color[rgb]{1.0, 1.0, 1.0}AA}$ pound encoder by loss in Eq.~(\ref{eq:10}) or (\ref{eq:11}).
\EndWhile
\State \# \textit{Inference}
\State Predict interaction pattern $\mathbf{P}^{\text{cont}}$ and interaction strength $y^{\text{aff}}$ for any given compound-protein pair.
\State \textbf{return} Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[!htbp]
\caption{Algorithm for CPI Prediction (Multimodal)}
\begin{algorithmic}
[1]
\Require Compound Graphs $\{\mathcal{G}_C^{(j)}\}_{j=1}^M$, Protein Sequences $\{S^{(i)}\}_{i=1}^N$, and Protein Graphs $\{\mathcal{G}_{\mathcal{P}}^{(i)}\}_{i=1}^N$.
\Ensure Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\State \# \textit{Pre-training}
\While{\textit{not convergence}}
\State Generate a set of subsequences and subgraphs by ${\color[rgb]{1.0, 1.0, 1.0}AA}$ length-variable augmentation;
\State Pass the augmented subsequences and subgraphs ${\color[rgb]{1.0, 1.0, 1.0}AA}$ through the sequence and structure encoders;
\State Pre-train the two encoders by intra-modality and ${\color[rgb]{1.0, 1.0, 1.0}AA}$ cross-modality contrastive losses in Eq.~(\ref{eq:7})(\ref{eq:8})(\ref{eq:9}).
\EndWhile
\State \# \textit{Fine-tuning}
\While{\textit{not convergence}}
\State Jointly train the two pre-trained encoders and com- ${\color[rgb]{1.0, 1.0, 1.0}AA}$ pound encoder by loss in Eq.~(\ref{eq:10}) or (\ref{eq:11}).
\EndWhile
\State \# \textit{Inference}
\State Predict interaction pattern $\mathbf{P}^{\text{cont}}$ and interaction strength $y^{\text{aff}}$ for any given compound-protein pair.
\State \textbf{return} Interaction pattern $\mathbf{P}^{\text{cont}}$ and strength $y^{\text{aff}}$.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2402.08198" | "2402.08198.tar.gz" | "2024-02-12" | {
"title": "psc-cpi: multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction",
"id": "2402.08198",
"abstract": "compound-protein interaction (cpi) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two modalities, which may lead to significant performance drops in complex real-world scenarios due to various factors, e.g., modality missing and domain shifting. more importantly, these methods only model protein sequences and structures at a single fixed scale, neglecting more fine-grained multi-scale information, such as those embedded in key protein fragments. in this paper, we propose a novel multi-scale protein sequence-structure contrasting framework for cpi prediction (psc-cpi), which captures the dependencies between protein sequences and structures through both intra-modality and cross-modality contrasting. we further apply length-variable protein augmentation to allow contrasting to be performed at different scales, from the amino acid level to the sequence level. finally, in order to more fairly evaluate the model generalizability, we split the test data into four settings based on whether compounds and proteins have been observed during the training stage. extensive experiments have shown that psc-cpi generalizes well in all four settings, particularly in the more challenging ``unseen-both\" setting, where neither compounds nor proteins have been observed during training. furthermore, even when encountering a situation of modality missing, i.e., inference with only single-modality protein data, psc-cpi still exhibits comparable or even better performance than previous approaches.",
"categories": "q-bio.bm cs.ai cs.lg",
"doi": "",
"created": "2024-02-12",
"updated": "",
"authors": [
"lirong wu",
"yufei huang",
"cheng tan",
"zhangyang gao",
"bozhen hu",
"haitao lin",
"zicheng liu",
"stan z. li"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.08198"
} | "2024-03-15T04:40:50.031648" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"aaai24.bib": {
"toxicity_score": 0.009990019,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0018036455,
"insult_score": 0.0073988754,
"profanity_score": 0.0157622,
"threat_score": 0.0065568537,
"sexually_explicit_score": 0.012444182,
"flirtation_score": 0.30022034
},
"aaai24.bst": {
"toxicity_score": 0.016712861,
"severe_toxicity_score": 0.0015354156,
"identity_attack_score": 0.0039402717,
"insult_score": 0.009165886,
"profanity_score": 0.018324144,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.40847883
},
"algorithms.tex": {
"toxicity_score": 0.014953613,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.00414376,
"insult_score": 0.00810188,
"profanity_score": 0.01805087,
"threat_score": 0.008116818,
"sexually_explicit_score": 0.014272475,
"flirtation_score": 0.27245644
},
"table_3.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.003921773,
"insult_score": 0.009184887,
"profanity_score": 0.022149978,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.3918558
},
"main.bbl": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0035887922,
"insult_score": 0.0074558756,
"profanity_score": 0.014361672,
"threat_score": 0.0073918556,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.23219578
},
"main.tex": {
"toxicity_score": 0.012063419,
"severe_toxicity_score": 0.0011825562,
"identity_attack_score": 0.00362579,
"insult_score": 0.0075888764,
"profanity_score": 0.015591404,
"threat_score": 0.0076507707,
"sexually_explicit_score": 0.014449406,
"flirtation_score": 0.4688245
},
"table_A1.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.003921773,
"insult_score": 0.009184887,
"profanity_score": 0.022149978,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.3918558
},
"table_1.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.003921773,
"insult_score": 0.009184887,
"profanity_score": 0.022149978,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.3918558
},
"table_A2.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.003921773,
"insult_score": 0.009184887,
"profanity_score": 0.022149978,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.3918558
},
"appendix.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0015735626,
"identity_attack_score": 0.009286462,
"insult_score": 0.008804884,
"profanity_score": 0.016821137,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.012798046,
"flirtation_score": 0.25620487
},
"table_2.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.003921773,
"insult_score": 0.009184887,
"profanity_score": 0.022149978,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.017575197,
"flirtation_score": 0.3918558
},
"aaai24.sty": {
"toxicity_score": 0.004837934,
"severe_toxicity_score": 0.0011110306,
"identity_attack_score": 0.00069833454,
"insult_score": 0.0057743657,
"profanity_score": 0.015079016,
"threat_score": 0.0055859215,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.38160232
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.66391084380813,
"hash": "071f0e063f1f3f07",
"most_frequent_color_ratio": 90.66391084380813
}
} | [] | "algorithm" | "1449d316-9230-46f7-97f3-7e81e66a9874" | 1378 | hard |
|
\begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^m}
(\eta_\bullet^{i,m} + \eta_\bullet^{i,m,*})$
\If{$|\eta_k|*c \geq |\eta_h|$}
\State mark $I_m$ for temporal refinement based on chosen strategy
\EndIf
\If{$|\eta_h|*c \geq |\eta_k|$}
\For{$m=1,\dots,M$}
\State mark and refine elements in $\mathcal{T}_h^m$ based on chosen strategy
\EndFor
\EndIf
\For{$m=1,\dots,M$}
\If{$I_m$ is marked}
\State Split/Refine $I_m$ into two intervals with (possibly new) mesh $\mathcal{T}_h^m$
\EndIf
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\Require indicators on each interval $I_m$ and equilibration factor $c>0$
\State Calculate global temporal estimator
$\eta_k= \frac12 \sum\limits_{m=1}^M (\eta_\star^m+ \eta_\star^{m,*})$
\State Calculate global spatial estimator
$\eta_h= \frac12 \sum\limits_{m=1}^M \sum\limits_{i\in\mathcal{T}_h^m}
(\eta_\bullet^{i,m} + \eta_\bullet^{i,m,*})$
\If{$|\eta_k|*c \geq |\eta_h|$}
\State mark $I_m$ for temporal refinement based on chosen strategy
\EndIf
\If{$|\eta_h|*c \geq |\eta_k|$}
\For{$m=1,\dots,M$}
\State mark and refine elements in $\mathcal{T}_h^m$ based on chosen strategy
\EndFor
\EndIf
\For{$m=1,\dots,M$}
\If{$I_m$ is marked}
\State Split/Refine $I_m$ into two intervals with (possibly new) mesh $\mathcal{T}_h^m$
\EndIf
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2207.04764" | "2207.04764.tar.gz" | "2024-02-04" | {
"title": "numerical modeling and open-source implementation of variational partition-of-unity localizations of space-time dual-weighted residual estimators for parabolic problems",
"id": "2207.04764",
"abstract": "in this work, we consider space-time goal-oriented a posteriori error estimation for parabolic problems. temporal and spatial discretizations are based on galerkin finite elements of continuous and discontinuous type. the main objectives are the development and analysis of space-time estimators, in which the localization is based on a weak form employing a partition-of-unity. the resulting error indicators are used for temporal and spatial adaptivity. our developments are substantiated with several numerical examples.",
"categories": "math.na cs.na math.oc",
"doi": "",
"created": "2022-07-11",
"updated": "2024-02-04",
"authors": [
"jan philipp thiele",
"thomas wick"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.04764"
} | "2024-03-15T07:54:29.015400" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
},
"main.fls": {
"toxicity_score": 0.0035184973,
"severe_toxicity_score": 0.00045776367,
"identity_attack_score": 0.0011515582,
"insult_score": 0.0056556147,
"profanity_score": 0.009903891,
"threat_score": 0.0058059995,
"sexually_explicit_score": 0.005248968,
"flirtation_score": 0.37866667
},
"main.fdb_latexmk": {
"toxicity_score": 0.009173225,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0026453468,
"insult_score": 0.007474876,
"profanity_score": 0.012722028,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.17961895
},
"Intro_nonstat_PU.bib": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0018024445,
"identity_attack_score": 0.0042732526,
"insult_score": 0.0096028885,
"profanity_score": 0.018392462,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.015805881,
"flirtation_score": 0.3638164
},
"main.tex": {
"toxicity_score": 0.018095128,
"severe_toxicity_score": 0.0018024445,
"identity_attack_score": 0.0064006294,
"insult_score": 0.008823884,
"profanity_score": 0.017914234,
"threat_score": 0.009074804,
"sexually_explicit_score": 0.01769315,
"flirtation_score": 0.2485177
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 85.58447442609747,
"hash": "00092f142707ff7f",
"most_frequent_color_ratio": 85.58447442609747
}
} | [] | "algorithm" | "eed5ae79-d4d4-4888-a412-3cc8bfbca634" | 778 | medium |
|
\begin{algorithm}
\caption{generalized\_sparsemax($z$, $r$)}
\begin{algorithmic}[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - r}{\kappa(z)}
\]
\State \textbf{return} \( p \) such that \( p_i = \max(z_i - \rho(z), 0) \)
\end{algorithmic}
\label{algo:sparsemax_generalized}
\end{algorithm}
| \begin{algorithm}
\caption{generalized\_sparsemax($z$, $r$)}
\begin{algorithmic}
[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - r}{\kappa(z)}
\]
\State \textbf{return} \( p \) such that \( p_i = \max(z_i - \rho(z), 0) \)
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2309.16883" | "2309.16883.tar.gz" | "2024-02-06" | {
"title": "the lipschitz-variance-margin tradeoff for enhanced randomized smoothing",
"id": "2309.16883",
"abstract": "real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. the certified radius is in this context a crucial indicator of the robustness of models. however how to design an efficient classifier with an associated certified radius? randomized smoothing provides a promising framework by relying on noise injection into the inputs to obtain a smoothed and robust classifier. in this paper, we first show that the variance introduced by the monte-carlo sampling in the randomized smoothing procedure estimate closely interacts with two other important properties of the classifier, \\textit{i.e.} its lipschitz constant and margin. more precisely, our work emphasizes the dual impact of the lipschitz constant of the base classifier, on both the smoothed classifier and the empirical variance. moreover, to increase the certified robust radius, we introduce a different way to convert logits to probability vectors for the base classifier to leverage the variance-margin trade-off. we leverage the use of bernstein's concentration inequality along with enhanced lipschitz bounds for randomized smoothing. experimental results show a significant improvement in certified accuracy compared to current state-of-the-art methods. our novel certification procedure allows us to use pre-trained models that are used with randomized smoothing, effectively improving the current certification radius in a zero-shot manner.",
"categories": "cs.lg stat.ml",
"doi": "",
"created": "2023-09-28",
"updated": "2024-02-06",
"authors": [
"blaise delattre",
"alexandre araujo",
"quentin barth\u00e9lemy",
"alexandre allauzen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.16883"
} | "2024-03-15T07:05:43.113842" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"math_commands.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0074368757,
"profanity_score": 0.011526455,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.0059272056,
"flirtation_score": 0.37736848
},
"sections/related_works.tex": {
"toxicity_score": 0.0062516155,
"severe_toxicity_score": 0.0006389618,
"identity_attack_score": 0.002173624,
"insult_score": 0.006182868,
"profanity_score": 0.012107162,
"threat_score": 0.006589218,
"sexually_explicit_score": 0.008197826,
"flirtation_score": 0.19155842
},
"sections/theoritical_results.tex": {
"toxicity_score": 0.02048268,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0040142676,
"insult_score": 0.010628895,
"profanity_score": 0.016957775,
"threat_score": 0.0069387536,
"sexually_explicit_score": 0.011913388,
"flirtation_score": 0.23629771
},
"sections/mind_map.tex": {
"toxicity_score": 0.017341165,
"severe_toxicity_score": 0.0020694733,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008576883,
"profanity_score": 0.024882717,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.023590868,
"flirtation_score": 0.32007554
},
"iclr2024_conference.bib": {
"toxicity_score": 0.01017851,
"severe_toxicity_score": 0.0012683868,
"identity_attack_score": 0.0011700572,
"insult_score": 0.008234881,
"profanity_score": 0.017162729,
"threat_score": 0.0057347976,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.62930804
},
"sections/experiments.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.002552852,
"insult_score": 0.0072468747,
"profanity_score": 0.014805742,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.017339287,
"flirtation_score": 0.23475203
},
"references.bib": {
"toxicity_score": 0.013885498,
"severe_toxicity_score": 0.0020503998,
"identity_attack_score": 0.0030153254,
"insult_score": 0.008025879,
"profanity_score": 0.016172111,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.015452018,
"flirtation_score": 0.1781632
},
"figures/figurev3.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"sections/appendix.tex": {
"toxicity_score": 0.007508222,
"severe_toxicity_score": 0.000910759,
"identity_attack_score": 0.0019053896,
"insult_score": 0.00654387,
"profanity_score": 0.014122557,
"threat_score": 0.006576272,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.2905964
},
"iclr2020_conference.sty": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0094128875,
"profanity_score": 0.01220964,
"threat_score": 0.0058739646,
"sexually_explicit_score": 0.0057207854,
"flirtation_score": 0.47478196
},
"sections/conclusion.tex": {
"toxicity_score": 0.007853788,
"severe_toxicity_score": 0.00081539154,
"identity_attack_score": 0.0022846176,
"insult_score": 0.00646787,
"profanity_score": 0.012722028,
"threat_score": 0.006912862,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.18686433
},
"main.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007531876,
"profanity_score": 0.0147374235,
"threat_score": 0.007624879,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.20824872
},
"main.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0038107792,
"insult_score": 0.007835878,
"profanity_score": 0.015386449,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.35838574
},
"iclr2024_conference.sty": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0094128875,
"profanity_score": 0.01220964,
"threat_score": 0.0058739646,
"sexually_explicit_score": 0.0057207854,
"flirtation_score": 0.47478196
},
"figures/figurev2.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"figures/figure.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"refv2.bib": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0016464046,
"insult_score": 0.0066863713,
"profanity_score": 0.012926984,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.1781632
},
"iclr2024_conference.bst": {
"toxicity_score": 0.014765122,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0030153254,
"insult_score": 0.008709883,
"profanity_score": 0.019553876,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.01686747,
"flirtation_score": 0.5479165
},
"natbib.sty": {
"toxicity_score": 0.05750068,
"severe_toxicity_score": 0.0035858154,
"identity_attack_score": 0.008139528,
"insult_score": 0.02173949,
"profanity_score": 0.043415543,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.028662905,
"flirtation_score": 0.41074932
},
"sections/introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.03881567069868,
"hash": "001fffc3ff77e707",
"most_frequent_color_ratio": 95.03881567069868
}
} | [] | "algorithm" | "f23724b9-1ad1-4f63-a8a3-06ed5c6daaad" | 507 | easy |
|
\begin{algorithm}{({\bf DR})}\label{alg:DR}
\begin{description}
\item[Step 1] ({\em Initialization}) Choose a parameter $\lambda\in\left]0,1\right[$ and the initial iterate $u^0$ arbitrarily.
Choose a small parameter $\varepsilon>0$, and set $k=0$.
\item[Step 2] ({\em Projection onto ${\cal B}$}) Set $u^- = \lambda u^{k}$.
Compute $\widetilde{u} = P_{{\cal B}}(u^-)$ by using \eqref{eqn:projB}.
\item[Step 3] ({\em Projection onto ${\cal A}$}) Set $u^- := 2\widetilde{u}-u^k$.
Compute $\widehat{u} = P_{{\cal A}}(u^-)$ by using \eqref{eqn:projA} or Algorithm~\ref{alg:projA}.
\item[Step 4] ({\em Update}) Set $u^{k+1} := u^k + \widehat{u} - \widetilde{u}$.
\item[Step 5] ({\em Stopping criterion}) If $\|u^{k+1} - u^k\|_{L^\infty} \le \varepsilon$, then return $\widetilde{u}$ and stop.
Otherwise, set $k := k+1$ and go to Step 2.
\end{description}
\end{algorithm}
| \begin{algorithm}
{({\bf DR})}\begin{description}
\item[Step 1] ({\em Initialization}) Choose a parameter $\lambda\in\left]0,1\right[$ and the initial iterate $u^0$ arbitrarily.
Choose a small parameter $\varepsilon>0$, and set $k=0$.
\item[Step 2] ({\em Projection onto ${\cal B}$}) Set $u^- = \lambda u^{k}$.
Compute $\widetilde{u} = P_{{\cal B}}(u^-)$ by using \eqref{eqn:projB}.
\item[Step 3] ({\em Projection onto ${\cal A}$}) Set $u^- := 2\widetilde{u}-u^k$.
Compute $\widehat{u} = P_{{\cal A}}(u^-)$ by using \eqref{eqn:projA} or Algorithm~\ref{alg:projA}.
\item[Step 4] ({\em Update}) Set $u^{k+1} := u^k + \widehat{u} - \widetilde{u}$.
\item[Step 5] ({\em Stopping criterion}) If $\|u^{k+1} - u^k\|_{L^\infty} \le \varepsilon$, then return $\widetilde{u}$ and stop.
Otherwise, set $k := k+1$ and go to Step 2.
\end{description}
\end{algorithm} | "https://arxiv.org/src/2210.17279" | "2210.17279.tar.gz" | "2024-01-11" | {
"title": "douglas--rachford algorithm for control-constrained minimum-energy control problems",
"id": "2210.17279",
"abstract": "splitting and projection-type algorithms have been applied to many optimization problems due to their simplicity and efficiency, but the application of these algorithms to optimal control is less common. in this paper we utilize the douglas--rachford (dr) algorithm to solve control-constrained minimum-energy optimal control problems. instead of the traditional approach where one discretizes the problem and solves it using large-scale finite-dimensional numerical optimization techniques we split the problem in two subproblems and use the dr algorithm to find an optimal point in the intersection of the solution sets of these two subproblems hence giving a solution to the original problem. we derive general expressions for the projections and propose a numerical approach. we obtain analytic closed-form expressions for the projectors of pure, under-, critically- and over-damped harmonic oscillators. we illustrate the working of our approach to solving not only these example problems but also a challenging machine tool manipulator problem. through numerical case studies, we explore and propose desirable ranges of values of an algorithmic parameter which yield smaller number of iterations.",
"categories": "math.oc",
"doi": "",
"created": "2022-10-31",
"updated": "2024-01-11",
"authors": [
"regina s. burachik",
"bethany i. caldwell",
"c. yal\u00e7\u0131n kaya"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2210.17279"
} | "2024-03-15T06:24:12.690702" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"DR_min_en-revised_11Jan2024.tex": {
"toxicity_score": 0.012691722,
"severe_toxicity_score": 0.0012779236,
"identity_attack_score": 0.00414376,
"insult_score": 0.0077028773,
"profanity_score": 0.016342908,
"threat_score": 0.0077802283,
"sexually_explicit_score": 0.014154521,
"flirtation_score": 0.204693
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.73551335522228,
"hash": "7f010b7f1b3f0f07",
"most_frequent_color_ratio": 91.73551335522228
}
} | [] | "algorithm" | "bdf7c5dd-81f6-42a9-ab9e-33997175cc18" | 852 | medium |
|
\begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
\begin{algorithmic}[1]
\State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
\State $loss^{*}\gets \infty$ \Comment{Initialize best model}
\For {$fold \in D$}
\State $reset(w_{BidNet})$ \Comment{Reset parameters before entering each new fold}
\State $D_{val}\gets D(fold)$, $D_{train}\gets D(-fold)$
\While {has not converged}
\For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
\State $d \sim D_{train}$ \Comment{Sample batch of real examples}
\State $\hat{\theta}\gets BidNet(d)$
\State $L^{train}\gets m^{-1}\sum_i NLL(\hat{\theta})_i$ \Comment{compute NLL on training batch}
\State $w \gets w + Adam(\nabla L^{train})$ \Comment{Update BidNet}
\EndFor
\State $converged \gets ES(L^{val})$ \Comment{Early stopping}
\State $L^{val}\gets n^{-1} \sum_j NLL(BidNet(D_{val}))_j$ \Comment{compute NLL on validation fold}
\If {$L^{val}<loss^{*}$}
\State $loss^{*} \gets L^{val}$
\State save model
\EndIf
\EndWhile
\EndFor
\end{algorithmic}
\label{alg:bidnet}
\end{algorithm}
| \begin{algorithm}
\caption{K-folds cross-validation BidNet training procedure}
\begin{algorithmic}
[1]
\State $D \gets \{D_1,\dots,D_K\}$ \Comment{Initialize K-folds}
\State $loss^{*}\gets \infty$ \Comment{Initialize best model}
\For {$fold \in D$}
\State $reset(w_{BidNet})$ \Comment{Reset parameters before entering each new fold}
\State $D_{val}\gets D(fold)$, $D_{train}\gets D(-fold)$
\While {has not converged}
\For {$batch \in \{1,\dots,N_{batches}\}$} \Comment{Gradient descent with mini-batch}
\State $d \sim D_{train}$ \Comment{Sample batch of real examples}
\State $\hat{\theta}\gets BidNet(d)$
\State $L^{train}\gets m^{-1}\sum_i NLL(\hat{\theta})_i$ \Comment{compute NLL on training batch}
\State $w \gets w + Adam(\nabla L^{train})$ \Comment{Update BidNet}
\EndFor
\State $converged \gets ES(L^{val})$ \Comment{Early stopping}
\State $L^{val}\gets n^{-1} \sum_j NLL(BidNet(D_{val}))_j$ \Comment{compute NLL on validation fold}
\If {$L^{val}<loss^{*}$}
\State $loss^{*} \gets L^{val}$
\State save model
\EndIf
\EndWhile
\EndFor
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2207.12255" | "2207.12255.tar.gz" | "2024-02-15" | {
"title": "implementing a hierarchical deep learning approach for simulating multi-level auction data",
"id": "2207.12255",
"abstract": "we present a deep learning solution to address the challenges of simulating realistic synthetic first-price sealed-bid auction data. the complexities encountered in this type of auction data include high-cardinality discrete feature spaces and a multilevel structure arising from multiple bids associated with a single auction instance. our methodology combines deep generative modeling (dgm) with an artificial learner that predicts the conditional bid distribution based on auction characteristics, contributing to advancements in simulation-based research. this approach lays the groundwork for creating realistic auction environments suitable for agent-based learning and modeling applications. our contribution is twofold: we introduce a comprehensive methodology for simulating multilevel discrete auction data, and we underscore the potential of dgm as a powerful instrument for refining simulation techniques and fostering the development of economic models grounded in generative ai.",
"categories": "econ.gn q-fin.ec",
"doi": "",
"created": "2022-07-25",
"updated": "2024-02-15",
"authors": [
"igor sadoune",
"andrea lodi",
"marcelin joanis"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.12255"
} | "2024-03-15T04:09:20.272119" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"manuscript.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0010728836,
"identity_attack_score": 0.003311308,
"insult_score": 0.0073798755,
"profanity_score": 0.014395831,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.011736456,
"flirtation_score": 0.27859175
},
"manuscript.tex": {
"toxicity_score": 0.01218908,
"severe_toxicity_score": 0.0011634827,
"identity_attack_score": 0.0037367835,
"insult_score": 0.007664877,
"profanity_score": 0.015454767,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21971871
},
"utphys.bst": {
"toxicity_score": 0.0154562555,
"severe_toxicity_score": 0.0013160706,
"identity_attack_score": 0.004384246,
"insult_score": 0.008310881,
"profanity_score": 0.017436003,
"threat_score": 0.007624879,
"sexually_explicit_score": 0.015452018,
"flirtation_score": 0.34099135
}
},
"reason": null
}
} | {
"num_done": {
"table": 0,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 89.76724660105671,
"hash": "0983819c9cc4fe3f",
"most_frequent_color_ratio": 89.76724660105671
}
} | [] | "algorithm" | "17d4903b-04cf-4df4-82c3-3b34ea656b4d" | 1074 | medium |
|
\begin{algorithmic}[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - r}{\kappa(z)}
\]
\State \textbf{return} \( p \) such that \( p_i = \max(z_i - \rho(z), 0) \)
\end{algorithmic}
| \begin{algorithmic}
[1]
\State Sort \( z \) in decreasing order \( z_{1} \geq \dots \geq z_{c} \)
\State Find \( \kappa(z) \) such that
\[
\kappa(z) = \max_{k = 1 \ldots c} \left\{ k \, \bigg| \, r + k z_{k} > \sum_{j \leq k} z_{j} \right\}
\]
\State Define
\[
\rho(z) = \frac{\left(\sum_{j \leq \kappa(z)} z_j\right) - r}{\kappa(z)}
\]
\State \textbf{return} \( p \) such that \( p_i = \max(z_i - \rho(z), 0) \)
\end{algorithmic} | "https://arxiv.org/src/2309.16883" | "2309.16883.tar.gz" | "2024-02-06" | {
"title": "the lipschitz-variance-margin tradeoff for enhanced randomized smoothing",
"id": "2309.16883",
"abstract": "real-life applications of deep neural networks are hindered by their unsteady predictions when faced with noisy inputs and adversarial attacks. the certified radius is in this context a crucial indicator of the robustness of models. however how to design an efficient classifier with an associated certified radius? randomized smoothing provides a promising framework by relying on noise injection into the inputs to obtain a smoothed and robust classifier. in this paper, we first show that the variance introduced by the monte-carlo sampling in the randomized smoothing procedure estimate closely interacts with two other important properties of the classifier, \\textit{i.e.} its lipschitz constant and margin. more precisely, our work emphasizes the dual impact of the lipschitz constant of the base classifier, on both the smoothed classifier and the empirical variance. moreover, to increase the certified robust radius, we introduce a different way to convert logits to probability vectors for the base classifier to leverage the variance-margin trade-off. we leverage the use of bernstein's concentration inequality along with enhanced lipschitz bounds for randomized smoothing. experimental results show a significant improvement in certified accuracy compared to current state-of-the-art methods. our novel certification procedure allows us to use pre-trained models that are used with randomized smoothing, effectively improving the current certification radius in a zero-shot manner.",
"categories": "cs.lg stat.ml",
"doi": "",
"created": "2023-09-28",
"updated": "2024-02-06",
"authors": [
"blaise delattre",
"alexandre araujo",
"quentin barth\u00e9lemy",
"alexandre allauzen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2309.16883"
} | "2024-03-15T07:05:43.113842" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"math_commands.tex": {
"toxicity_score": 0.01005285,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0023678627,
"insult_score": 0.0074368757,
"profanity_score": 0.011526455,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.0059272056,
"flirtation_score": 0.37736848
},
"sections/related_works.tex": {
"toxicity_score": 0.0062516155,
"severe_toxicity_score": 0.0006389618,
"identity_attack_score": 0.002173624,
"insult_score": 0.006182868,
"profanity_score": 0.012107162,
"threat_score": 0.006589218,
"sexually_explicit_score": 0.008197826,
"flirtation_score": 0.19155842
},
"sections/theoritical_results.tex": {
"toxicity_score": 0.02048268,
"severe_toxicity_score": 0.0011062622,
"identity_attack_score": 0.0040142676,
"insult_score": 0.010628895,
"profanity_score": 0.016957775,
"threat_score": 0.0069387536,
"sexually_explicit_score": 0.011913388,
"flirtation_score": 0.23629771
},
"sections/mind_map.tex": {
"toxicity_score": 0.017341165,
"severe_toxicity_score": 0.0020694733,
"identity_attack_score": 0.0032188136,
"insult_score": 0.008576883,
"profanity_score": 0.024882717,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.023590868,
"flirtation_score": 0.32007554
},
"iclr2024_conference.bib": {
"toxicity_score": 0.01017851,
"severe_toxicity_score": 0.0012683868,
"identity_attack_score": 0.0011700572,
"insult_score": 0.008234881,
"profanity_score": 0.017162729,
"threat_score": 0.0057347976,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.62930804
},
"sections/experiments.tex": {
"toxicity_score": 0.013634177,
"severe_toxicity_score": 0.0012588501,
"identity_attack_score": 0.002552852,
"insult_score": 0.0072468747,
"profanity_score": 0.014805742,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.017339287,
"flirtation_score": 0.23475203
},
"references.bib": {
"toxicity_score": 0.013885498,
"severe_toxicity_score": 0.0020503998,
"identity_attack_score": 0.0030153254,
"insult_score": 0.008025879,
"profanity_score": 0.016172111,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.015452018,
"flirtation_score": 0.1781632
},
"figures/figurev3.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"sections/appendix.tex": {
"toxicity_score": 0.007508222,
"severe_toxicity_score": 0.000910759,
"identity_attack_score": 0.0019053896,
"insult_score": 0.00654387,
"profanity_score": 0.014122557,
"threat_score": 0.006576272,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.2905964
},
"iclr2020_conference.sty": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0094128875,
"profanity_score": 0.01220964,
"threat_score": 0.0058739646,
"sexually_explicit_score": 0.0057207854,
"flirtation_score": 0.47478196
},
"sections/conclusion.tex": {
"toxicity_score": 0.007853788,
"severe_toxicity_score": 0.00081539154,
"identity_attack_score": 0.0022846176,
"insult_score": 0.00646787,
"profanity_score": 0.012722028,
"threat_score": 0.006912862,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.18686433
},
"main.bbl": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.0039957687,
"insult_score": 0.007531876,
"profanity_score": 0.0147374235,
"threat_score": 0.007624879,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.20824872
},
"main.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0038107792,
"insult_score": 0.007835878,
"profanity_score": 0.015386449,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.0129749775,
"flirtation_score": 0.35838574
},
"iclr2024_conference.sty": {
"toxicity_score": 0.011937759,
"severe_toxicity_score": 0.00068187714,
"identity_attack_score": 0.0021643746,
"insult_score": 0.0094128875,
"profanity_score": 0.01220964,
"threat_score": 0.0058739646,
"sexually_explicit_score": 0.0057207854,
"flirtation_score": 0.47478196
},
"figures/figurev2.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"figures/figure.tex": {
"toxicity_score": 0.05378982,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.006511623,
"insult_score": 0.01838776,
"profanity_score": 0.06596114,
"threat_score": 0.010265815,
"sexually_explicit_score": 0.04009293,
"flirtation_score": 0.29042256
},
"refv2.bib": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0016464046,
"insult_score": 0.0066863713,
"profanity_score": 0.012926984,
"threat_score": 0.0060908063,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.1781632
},
"iclr2024_conference.bst": {
"toxicity_score": 0.014765122,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0030153254,
"insult_score": 0.008709883,
"profanity_score": 0.019553876,
"threat_score": 0.00746953,
"sexually_explicit_score": 0.01686747,
"flirtation_score": 0.5479165
},
"natbib.sty": {
"toxicity_score": 0.05750068,
"severe_toxicity_score": 0.0035858154,
"identity_attack_score": 0.008139528,
"insult_score": 0.02173949,
"profanity_score": 0.043415543,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.028662905,
"flirtation_score": 0.41074932
},
"sections/introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 95.03881567069868,
"hash": "001fffc3ff77e707",
"most_frequent_color_ratio": 95.03881567069868
}
} | [] | "algorithm" | "4785c6d2-3bf1-4533-93f1-cb845da8cc8a" | 430 | easy |
|
\begin{algorithmic}[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${\bf M}_\ell$ on domain [a,b]}
\State {Zero out ${\bf M}_\ell$ outside domain $D_\ell$}
\EndFor
\State \Return{ ${\bf M}_T \leftarrow \sum_{\ell=0}^{2^k} {\bf M}_\ell$}
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require{ A degree-$p$ piece-wise function $f_\ell(x) = \sum_{j=0}^p a_{j}^{(\ell)} x^j $. System size ${N}$. Domain [a,b]. Support bit $k$.}
\Ensure{A $\chi \le 2^k(p+1)$ MPS, $\bf{M}_T$ which encodes $f_\ell(x)$}
\Statex
\For{$\ell \gets 1$ to $2^k$}
\State {Encode $f_\ell(x)$ into ${\bf M}_\ell$ on domain [a,b]}
\State {Zero out ${\bf M}_\ell$ outside domain $D_\ell$}
\EndFor
\State \Return{ ${\bf M}_T \leftarrow \sum_{\ell=0}^{2^k} {\bf M}_\ell$}
\end{algorithmic} | "https://arxiv.org/src/2303.01562" | "2303.01562.tar.gz" | "2024-02-16" | {
"title": "quantum state preparation of normal distributions using matrix product states",
"id": "2303.01562",
"abstract": "state preparation is a necessary component of many quantum algorithms. in this work, we combine a method for efficiently representing smooth differentiable probability distributions using matrix product states with recently discovered techniques for initializing quantum states to approximate matrix product states. using this, we generate quantum states encoding a class of normal probability distributions in a trapped ion quantum computer for up to 20 qubits. we provide an in depth analysis of the different sources of error which contribute to the overall fidelity of this state preparation procedure. our work provides a study in quantum hardware for scalable distribution loading, which is the basis of a wide range of algorithms that provide quantum advantage.",
"categories": "quant-ph",
"doi": "10.1038/s41534-024-00805-0",
"created": "2023-03-02",
"updated": "2024-02-16",
"authors": [
"jason iaconis",
"sonika johri",
"elton yechao zhu"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.01562"
} | "2024-03-15T04:16:05.461100" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.0137598375,
"severe_toxicity_score": 0.0016403198,
"identity_attack_score": 0.0041992567,
"insult_score": 0.007949878,
"profanity_score": 0.01846078,
"threat_score": 0.007909686,
"sexually_explicit_score": 0.018164968,
"flirtation_score": 0.42432743
},
"naturemag-doi.bst": {
"toxicity_score": 0.013571346,
"severe_toxicity_score": 0.0011110306,
"identity_attack_score": 0.003921773,
"insult_score": 0.00810188,
"profanity_score": 0.01402008,
"threat_score": 0.0073789097,
"sexually_explicit_score": 0.008905552,
"flirtation_score": 0.43227512
},
"jabbrv.sty": {
"toxicity_score": 0.0059688794,
"severe_toxicity_score": 0.00064849854,
"identity_attack_score": 0.0018498929,
"insult_score": 0.0066008708,
"profanity_score": 0.011167783,
"threat_score": 0.006375613,
"sexually_explicit_score": 0.0069298176,
"flirtation_score": 0.4899393
},
"wlscirep.cls": {
"toxicity_score": 0.01608456,
"severe_toxicity_score": 0.0009727478,
"identity_attack_score": 0.0045322375,
"insult_score": 0.00971689,
"profanity_score": 0.014566627,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.33412078
},
"jabbrv-ltwa-en.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
},
"jabbrv-ltwa-all.ldf": {
"toxicity_score": 0.009675868,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.0018406434,
"insult_score": 0.007303875,
"profanity_score": 0.013336895,
"threat_score": 0.0066927844,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.2559753
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.64233317725848,
"hash": "070c0f7fbf8f3f1f",
"most_frequent_color_ratio": 91.64233317725848
}
} | [] | "algorithm" | "a558a599-57f0-48b5-a1a5-916d7f9e4be3" | 496 | easy |
|
\begin{algorithm}
\caption{Node subsampling/resampling bootstrap for two-sample inference}
\label{algorithm::node-resample}
{\bf Input:} Networks $A,B$; bootstrap repetition $N_{\rm boot}$; if subsampling: subsample sizes $m_{\rm sub},n_{\rm sub}$\\
{\bf Output:} Bootstrapped studentized empirical moment discrepancies $\{\hat T_{m,n}^{(b)}\}_{b=1,\ldots,N_{\rm boot}}$\\
{\bf Steps:} For $b=1,\ldots,N_{\rm boot}$, do
\begin{enumerate}
\item Node subsample/resample $A,B$, obtain $A^{(b)}, B^{(b)}$. If resampling, randomly sample $m$ nodes ${\cal J}_A$ from $[1:m]$ with replacement; if subsampling, randomly sample $m_{\rm sub}$ nodes ${\cal J}_A$ from $[1:m]$ without replacement; In either case, set $A^{(b)}\leftarrow A_{{\cal J}_A}$; do the same for $B$
\item Compute $\hat T_{m,n}^{(b)}$ using \eqref{def::S_m,n} and \eqref{def::T_m,n}, with $A^{(b)}, B^{(b)}$ as the input
\end{enumerate}
\end{algorithm}
| \begin{algorithm}
\caption{Node subsampling/resampling bootstrap for two-sample inference}
{\bf Input:} Networks $A,B$; bootstrap repetition $N_{\rm boot}$; if subsampling: subsample sizes $m_{\rm sub},n_{\rm sub}$\\
{\bf Output:} Bootstrapped studentized empirical moment discrepancies $\{\hat T_{m,n}^{(b)}\}_{b=1,\ldots,N_{\rm boot}}$\\
{\bf Steps:} For $b=1,\ldots,N_{\rm boot}$, do
\begin{enumerate}
\item Node subsample/resample $A,B$, obtain $A^{(b)}, B^{(b)}$. If resampling, randomly sample $m$ nodes ${\cal J}_A$ from $[1:m]$ with replacement; if subsampling, randomly sample $m_{\rm sub}$ nodes ${\cal J}_A$ from $[1:m]$ without replacement; In either case, set $A^{(b)}\leftarrow A_{{\cal J}_A}$; do the same for $B$
\item Compute $\hat T_{m,n}^{(b)}$ using \eqref{def::S_m,n} and \eqref{def::T_m,n}, with $A^{(b)}, B^{(b)}$ as the input
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/2208.07573" | "2208.07573.tar.gz" | "2024-02-02" | {
"title": "higher-order accurate two-sample network inference and network hashing",
"id": "2208.07573",
"abstract": "two-sample hypothesis testing for network comparison presents many significant challenges, including: leveraging repeated network observations and known node registration, but without requiring them to operate; relaxing strong structural assumptions; achieving finite-sample higher-order accuracy; handling different network sizes and sparsity levels; fast computation and memory parsimony; controlling false discovery rate (fdr) in multiple testing; and theoretical understandings, particularly regarding finite-sample accuracy and minimax optimality. in this paper, we develop a comprehensive toolbox, featuring a novel main method and its variants, all accompanied by strong theoretical guarantees, to address these challenges. our method outperforms existing tools in speed and accuracy, and it is proved power-optimal. our algorithms are user-friendly and versatile in handling various data structures (single or repeated network observations; known or unknown node registration). we also develop an innovative framework for offline hashing and fast querying as a very useful tool for large network databases. we showcase the effectiveness of our method through comprehensive simulations and applications to two real-world datasets, which revealed intriguing new structures.",
"categories": "stat.me math.st stat.ml stat.th",
"doi": "",
"created": "2022-08-16",
"updated": "2024-02-02",
"authors": [
"meijia shao",
"dong xia",
"yuan zhang",
"qiong wu",
"shuo chen"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2208.07573"
} | "2024-03-15T04:50:33.625258" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"section-model.tex": {
"toxicity_score": 0.008482091,
"severe_toxicity_score": 0.00076293945,
"identity_attack_score": 0.003089321,
"insult_score": 0.0067433715,
"profanity_score": 0.012961143,
"threat_score": 0.0070552654,
"sexually_explicit_score": 0.009495324,
"flirtation_score": 0.24089618
},
"section-FDR.tex": {
"toxicity_score": 0.017089844,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0047357255,
"insult_score": 0.009336887,
"profanity_score": 0.017094411,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.010792822,
"flirtation_score": 0.21245912
},
"agsm.bst": {
"toxicity_score": 0.02308189,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0073625734,
"insult_score": 0.011160898,
"profanity_score": 0.016889455,
"threat_score": 0.007754337,
"sexually_explicit_score": 0.014154521,
"flirtation_score": 0.35954246
},
"section-future-work.tex": {
"toxicity_score": 0.012000589,
"severe_toxicity_score": 0.0013923645,
"identity_attack_score": 0.0041067624,
"insult_score": 0.0075508766,
"profanity_score": 0.015454767,
"threat_score": 0.0079873605,
"sexually_explicit_score": 0.012444182,
"flirtation_score": 0.22646497
},
"Yuan-symbol_macro.tex": {
"toxicity_score": 0.015519086,
"severe_toxicity_score": 0.0013065338,
"identity_attack_score": 0.0041067624,
"insult_score": 0.008709883,
"profanity_score": 0.016547862,
"threat_score": 0.0074048014,
"sexually_explicit_score": 0.017457243,
"flirtation_score": 0.39367503
},
"section-numerical-studies.tex": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0008583069,
"identity_attack_score": 0.0021551251,
"insult_score": 0.0069333725,
"profanity_score": 0.013780965,
"threat_score": 0.006757513,
"sexually_explicit_score": 0.013269863,
"flirtation_score": 0.26155546
},
"section-introduction.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007390976,
"identity_attack_score": 0.0022753682,
"insult_score": 0.0064488696,
"profanity_score": 0.012687869,
"threat_score": 0.0066798385,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.2300378
},
"section-pooling.tex": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.005253696,
"insult_score": 0.00971689,
"profanity_score": 0.017436003,
"threat_score": 0.008414571,
"sexually_explicit_score": 0.0122672515,
"flirtation_score": 0.21245912
},
"supp-section-numerical.tex": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0042177555,
"insult_score": 0.008500882,
"profanity_score": 0.013712646,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.0080798725,
"flirtation_score": 0.19446386
},
"main-JASA-revision-1.bbl": {
"toxicity_score": 0.012000589,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.003903274,
"insult_score": 0.007531876,
"profanity_score": 0.014532468,
"threat_score": 0.0074565844,
"sexually_explicit_score": 0.011382594,
"flirtation_score": 0.20557849
},
"section-method2.tex": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0008678436,
"identity_attack_score": 0.0032558115,
"insult_score": 0.0072658747,
"profanity_score": 0.013507691,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.00979021,
"flirtation_score": 0.22210616
},
"section-method.tex": {
"toxicity_score": 0.010429831,
"severe_toxicity_score": 0.0008773804,
"identity_attack_score": 0.0036997858,
"insult_score": 0.0073988754,
"profanity_score": 0.0138151245,
"threat_score": 0.0072365063,
"sexually_explicit_score": 0.010910776,
"flirtation_score": 0.27015415
},
"latexmkrc": {
"toxicity_score": 0.009424546,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0021273769,
"insult_score": 0.007360875,
"profanity_score": 0.0128245065,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.00937737,
"flirtation_score": 0.24744678
},
"supp-section-definitions.tex": {
"toxicity_score": 0.0126288915,
"severe_toxicity_score": 0.0009584427,
"identity_attack_score": 0.004162259,
"insult_score": 0.007607877,
"profanity_score": 0.014634945,
"threat_score": 0.007327127,
"sexually_explicit_score": 0.011972365,
"flirtation_score": 0.33827403
},
"section-degeneracy.tex": {
"toxicity_score": 0.017089844,
"severe_toxicity_score": 0.0012207031,
"identity_attack_score": 0.0047357255,
"insult_score": 0.009336887,
"profanity_score": 0.017094411,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.010792822,
"flirtation_score": 0.21245912
},
"references.bib": {
"toxicity_score": 0.014073989,
"severe_toxicity_score": 0.0010538101,
"identity_attack_score": 0.0025343532,
"insult_score": 0.008956885,
"profanity_score": 0.01309778,
"threat_score": 0.0066345283,
"sexually_explicit_score": 0.0077849864,
"flirtation_score": 0.32811013
},
"main-JASA-revision-1.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 88.45244807548472,
"hash": "007f7f7f8080ffff",
"most_frequent_color_ratio": 88.45244807548472
}
} | [] | "algorithm" | "9e0f944a-a23c-4821-a3a5-2176db9c30a5" | 881 | medium |
|
\begin{algorithmic}[1]
\Procedure{$\mathrm{CS}_t$}{$R, S$}
\Comment{$t$: log number of parallel control-swaps, $R$: control bit data register with at least $2^t$ qubits, $S$: target bit angle register with at least $2^{t+1}$ qubits (note that the subscript here labels the qubit indices of every \textit{single} register)}
\For{$i$ in range($2^t$)} \Comment{All values of $i$ performed in parallel}
\State CSWAP$(R_{i}, S_{i}, S_{i + 2^t})$
\EndFor
\EndProcedure
\end{algorithmic}
| \begin{algorithmic}
[1]
\Procedure{$\mathrm{CS}_t$}{$R, S$}
\Comment{$t$: log number of parallel control-swaps, $R$: control bit data register with at least $2^t$ qubits, $S$: target bit angle register with at least $2^{t+1}$ qubits (note that the subscript here labels the qubit indices of every \textit{single} register)}
\For{$i$ in range($2^t$)} \Comment{All values of $i$ performed in parallel}
\State CSWAP$(R_{i}, S_{i}, S_{i + 2^t})$
\EndFor
\EndProcedure
\end{algorithmic} | "https://arxiv.org/src/2303.02131" | "2303.02131.tar.gz" | "2024-02-09" | {
"title": "spacetime-efficient low-depth quantum state preparation with applications",
"id": "2303.02131",
"abstract": "we propose a novel deterministic method for preparing arbitrary quantum states. when our protocol is compiled into cnot and arbitrary single-qubit gates, it prepares an $n$-dimensional state in depth $o(\\log(n))$ and spacetime allocation (a metric that accounts for the fact that oftentimes some ancilla qubits need not be active for the entire circuit) $o(n)$, which are both optimal. when compiled into the $\\{\\mathrm{h,s,t,cnot}\\}$ gate set, we show that it requires asymptotically fewer quantum resources than previous methods. specifically, it prepares an arbitrary state up to error $\\epsilon$ with optimal depth of $o(\\log(n) + \\log (1/\\epsilon))$ and spacetime allocation $o(n\\log(\\log(n)/\\epsilon))$, improving over $o(\\log(n)\\log(\\log (n)/\\epsilon))$ and $o(n\\log(n/\\epsilon))$, respectively. we illustrate how the reduced spacetime allocation of our protocol enables rapid preparation of many disjoint states with only constant-factor ancilla overhead -- $o(n)$ ancilla qubits are reused efficiently to prepare a product state of $w$ $n$-dimensional states in depth $o(w + \\log(n))$ rather than $o(w\\log(n))$, achieving effectively constant depth per state. we highlight several applications where this ability would be useful, including quantum machine learning, hamiltonian simulation, and solving linear systems of equations. we provide quantum circuit descriptions of our protocol, detailed pseudocode, and gate-level implementation examples using braket.",
"categories": "quant-ph cs.cc cs.lg",
"doi": "10.22331/q-2024-02-15-1257",
"created": "2023-03-03",
"updated": "2024-02-09",
"authors": [
"kaiwen gui",
"alexander m. dalzell",
"alessandro achille",
"martin suchara",
"frederic t. chong"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2303.02131"
} | "2024-03-15T03:55:47.804290" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"quantum_template.tex": {
"toxicity_score": 0.0071626552,
"severe_toxicity_score": 0.0007247925,
"identity_attack_score": 0.0022753682,
"insult_score": 0.00649637,
"profanity_score": 0.012277958,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.009023507,
"flirtation_score": 0.22418799
},
"sections/03sp+csp.tex": {
"toxicity_score": 0.0056547276,
"severe_toxicity_score": 0.00056505203,
"identity_attack_score": 0.0017296498,
"insult_score": 0.006173368,
"profanity_score": 0.010672474,
"threat_score": 0.0062332097,
"sexually_explicit_score": 0.0065759546,
"flirtation_score": 0.26993847
},
"sections/07acknowledgment.tex": {
"toxicity_score": 0.009990019,
"severe_toxicity_score": 0.00093460083,
"identity_attack_score": 0.0029783275,
"insult_score": 0.006885872,
"profanity_score": 0.01402008,
"threat_score": 0.007301235,
"sexually_explicit_score": 0.011146685,
"flirtation_score": 0.32888338
},
"sections/_legacy_04_circuit_interleaving_technique.tex": {
"toxicity_score": 0.007790958,
"severe_toxicity_score": 0.0006866455,
"identity_attack_score": 0.002330865,
"insult_score": 0.0066198707,
"profanity_score": 0.011902207,
"threat_score": 0.0067057298,
"sexually_explicit_score": 0.00855169,
"flirtation_score": 0.2309284
},
"sections/05applications.tex": {
"toxicity_score": 0.006942749,
"severe_toxicity_score": 0.0007200241,
"identity_attack_score": 0.0021273769,
"insult_score": 0.0064298697,
"profanity_score": 0.012312118,
"threat_score": 0.0066086366,
"sexually_explicit_score": 0.009495324,
"flirtation_score": 0.19534646
},
"sections/02quantum_computing_background.tex": {
"toxicity_score": 0.0072569004,
"severe_toxicity_score": 0.0007867813,
"identity_attack_score": 0.0021828737,
"insult_score": 0.0064108693,
"profanity_score": 0.013166099,
"threat_score": 0.006763986,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.18547978
},
"sections/08potential_improvement.tex": {
"toxicity_score": 0.007696713,
"severe_toxicity_score": 0.0007867813,
"identity_attack_score": 0.0024418586,
"insult_score": 0.0065533705,
"profanity_score": 0.012243799,
"threat_score": 0.0068999166,
"sexually_explicit_score": 0.008610667,
"flirtation_score": 0.2122394
},
"quantumarticle.cls": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.001001358,
"identity_attack_score": 0.0029783275,
"insult_score": 0.007607877,
"profanity_score": 0.014225035,
"threat_score": 0.0069387536,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.42424405
},
"sections/11blog-proposal.tex": {
"toxicity_score": 0.006722843,
"severe_toxicity_score": 0.000705719,
"identity_attack_score": 0.0021273769,
"insult_score": 0.0067908717,
"profanity_score": 0.012517073,
"threat_score": 0.0064532873,
"sexually_explicit_score": 0.008846575,
"flirtation_score": 0.2300407
},
"sections/06code.tex": {
"toxicity_score": 0.0043038763,
"severe_toxicity_score": 0.0004911423,
"identity_attack_score": 0.0010451894,
"insult_score": 0.0057648653,
"profanity_score": 0.0108774295,
"threat_score": 0.0059840037,
"sexually_explicit_score": 0.0071362373,
"flirtation_score": 0.2409465
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"sections/01intro.tex": {
"toxicity_score": 0.006754258,
"severe_toxicity_score": 0.00067710876,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0064393696,
"profanity_score": 0.012243799,
"threat_score": 0.0065180166,
"sexually_explicit_score": 0.009318393,
"flirtation_score": 0.27036667
},
"main.tex": {
"toxicity_score": 0.014639461,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0047357255,
"insult_score": 0.008766884,
"profanity_score": 0.015864678,
"threat_score": 0.007741391,
"sexually_explicit_score": 0.0136827035,
"flirtation_score": 0.4269956
},
"sections/04multiple_copy.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0060676485,
"insult_score": 0.0088618845,
"profanity_score": 0.015181494,
"threat_score": 0.008168601,
"sexually_explicit_score": 0.011795434,
"flirtation_score": 0.21719036
},
"tikzlibraryquantikz.code.tex": {
"toxicity_score": 0.011435116,
"severe_toxicity_score": 0.00091552734,
"identity_attack_score": 0.003884775,
"insult_score": 0.0077028773,
"profanity_score": 0.0138151245,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.010262027,
"flirtation_score": 0.46759063
},
"reference.bib": {
"toxicity_score": 0.006345861,
"severe_toxicity_score": 0.0010156631,
"identity_attack_score": 0.0013874196,
"insult_score": 0.0068003717,
"profanity_score": 0.013063621,
"threat_score": 0.0059386934,
"sexually_explicit_score": 0.009908165,
"flirtation_score": 0.2611724
},
"dirty_qubit_circuit.tex": {
"toxicity_score": 0.011497946,
"severe_toxicity_score": 0.0011205673,
"identity_attack_score": 0.0035887922,
"insult_score": 0.007493876,
"profanity_score": 0.015215653,
"threat_score": 0.0074565844,
"sexually_explicit_score": 0.012149297,
"flirtation_score": 0.21014765
},
"sections/APPappendix.tex": {
"toxicity_score": 0.015079274,
"severe_toxicity_score": 0.0015735626,
"identity_attack_score": 0.004439743,
"insult_score": 0.008690883,
"profanity_score": 0.019007329,
"threat_score": 0.008336896,
"sexually_explicit_score": 0.01769315,
"flirtation_score": 0.2779886
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.09071459135643,
"hash": "f899870f033f073f",
"most_frequent_color_ratio": 92.09071459135643
}
} | [] | "algorithm" | "a857f638-8b82-4927-9547-f7a55ec58dab" | 481 | easy |
|
\begin{algorithm}
\caption{Calculate $y = x^n$}\label{algo1}
\begin{algorithmic}[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$}\label{algln2}
\State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
\State $X \Leftarrow X \times X$
\State $N \Leftarrow N / 2$
\Else[$N$ is odd]
\State $y \Leftarrow y \times X$
\State $N \Leftarrow N - 1$
\EndIf
\EndWhile
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Calculate $y = x^n$}\begin{algorithmic}
[1]
\Require $n \geq 0 \vee x \neq 0$
\Ensure $y = x^n$
\State $y \Leftarrow 1$
\If{$n < 0$} \State $X \Leftarrow 1 / x$
\State $N \Leftarrow -n$
\Else
\State $X \Leftarrow x$
\State $N \Leftarrow n$
\EndIf
\While{$N \neq 0$}
\If{$N$ is even}
\State $X \Leftarrow X \times X$
\State $N \Leftarrow N / 2$
\Else[$N$ is odd]
\State $y \Leftarrow y \times X$
\State $N \Leftarrow N - 1$
\EndIf
\EndWhile
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2202.05650" | "2202.05650.tar.gz" | "2024-02-23" | {
"title": "bernstein flows for flexible posteriors in variational bayes",
"id": "2202.05650",
"abstract": "variational inference (vi) is a technique to approximate difficult to compute posteriors by optimization. in contrast to mcmc, vi scales to many observations. in the case of complex posteriors, however, state-of-the-art vi approaches often yield unsatisfactory posterior approximations. this paper presents bernstein flow variational inference (bf-vi), a robust and easy-to-use method, flexible enough to approximate complex multivariate posteriors. bf-vi combines ideas from normalizing flows and bernstein polynomial-based transformation models. in benchmark experiments, we compare bf-vi solutions with exact posteriors, mcmc solutions, and state-of-the-art vi methods including normalizing flow based vi. we show for low-dimensional models that bf-vi accurately approximates the true posterior; in higher-dimensional models, bf-vi outperforms other vi methods. further, we develop with bf-vi a bayesian model for the semi-structured melanoma challenge data, combining a cnn model part for image data with an interpretable model part for tabular data, and demonstrate for the first time how the use of vi in semi-structured models.",
"categories": "stat.ml cs.lg",
"doi": "",
"created": "2022-02-11",
"updated": "2024-02-23",
"authors": [
"oliver d\u00fcrr",
"stephan h\u00f6rling",
"daniel dold",
"ivonne kovylov",
"beate sick"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2202.05650"
} | "2024-03-15T03:55:04.902331" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"aistats2022.sty": {
"toxicity_score": 0.025792383,
"severe_toxicity_score": 0.0025939941,
"identity_attack_score": 0.0065486208,
"insult_score": 0.011084898,
"profanity_score": 0.026180768,
"threat_score": 0.009722093,
"sexually_explicit_score": 0.030432219,
"flirtation_score": 0.49416974
},
"fancyhdr.sty": {
"toxicity_score": 0.031449065,
"severe_toxicity_score": 0.0022506714,
"identity_attack_score": 0.005956655,
"insult_score": 0.013440913,
"profanity_score": 0.02795705,
"threat_score": 0.009023022,
"sexually_explicit_score": 0.022883141,
"flirtation_score": 0.34986767
},
"IEEEtran.cls": {
"toxicity_score": 0.0049635945,
"severe_toxicity_score": 0.0007343292,
"identity_attack_score": 0.0012024303,
"insult_score": 0.006011867,
"profanity_score": 0.012312118,
"threat_score": 0.005861019,
"sexually_explicit_score": 0.008374758,
"flirtation_score": 0.4981806
},
"sn-sample-bib.tex": {
"toxicity_score": 0.01237757,
"severe_toxicity_score": 0.0011920929,
"identity_attack_score": 0.0040327664,
"insult_score": 0.007664877,
"profanity_score": 0.015283971,
"threat_score": 0.007754337,
"sexually_explicit_score": 0.011677479,
"flirtation_score": 0.20696951
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"main.bbl": {
"toxicity_score": 0.008293601,
"severe_toxicity_score": 0.0008773804,
"identity_attack_score": 0.003144818,
"insult_score": 0.0070758737,
"profanity_score": 0.013336895,
"threat_score": 0.0069905366,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.5140007
},
"IEEEtran.bst": {
"toxicity_score": 0.11088664,
"severe_toxicity_score": 0.009460449,
"identity_attack_score": 0.03919895,
"insult_score": 0.047935467,
"profanity_score": 0.09775851,
"threat_score": 0.007922632,
"sexually_explicit_score": 0.052283376,
"flirtation_score": 0.47095555
},
"main.out": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.001964569,
"identity_attack_score": 0.0014891637,
"insult_score": 0.007028373,
"profanity_score": 0.02030538,
"threat_score": 0.0061296434,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.40309274
},
"sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"main.tex": {
"toxicity_score": 0.009801528,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.0030153254,
"insult_score": 0.007132874,
"profanity_score": 0.015967157,
"threat_score": 0.006874025,
"sexually_explicit_score": 0.015687928,
"flirtation_score": 0.25975358
},
"main.fdb_latexmk": {
"toxicity_score": 0.009173225,
"severe_toxicity_score": 0.0010585785,
"identity_attack_score": 0.0026453468,
"insult_score": 0.007474876,
"profanity_score": 0.012722028,
"threat_score": 0.0069516995,
"sexually_explicit_score": 0.009731233,
"flirtation_score": 0.17961895
},
"sn-article.tex": {
"toxicity_score": 0.019226074,
"severe_toxicity_score": 0.0013637543,
"identity_attack_score": 0.0025343532,
"insult_score": 0.0096028885,
"profanity_score": 0.01442999,
"threat_score": 0.006686311,
"sexually_explicit_score": 0.008138849,
"flirtation_score": 0.30276403
},
"main.fls": {
"toxicity_score": 0.005529067,
"severe_toxicity_score": 0.00079631805,
"identity_attack_score": 0.0013041744,
"insult_score": 0.0062208683,
"profanity_score": 0.01309778,
"threat_score": 0.0062008454,
"sexually_explicit_score": 0.011441571,
"flirtation_score": 0.30375808
},
"main.bib": {
"toxicity_score": 0.008010864,
"severe_toxicity_score": 0.001206398,
"identity_attack_score": 0.00155391,
"insult_score": 0.006999873,
"profanity_score": 0.014669105,
"threat_score": 0.006149062,
"sexually_explicit_score": 0.013092931,
"flirtation_score": 0.4058236
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
}
},
"reason": null
}
} | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 96.32356487712283,
"hash": "003f3f7f1f1f3f3f",
"most_frequent_color_ratio": 96.32356487712283
}
} | [] | "algorithm" | "2df04537-61d7-47f1-a575-e25388c6720e" | 500 | easy |
|
\begin{algorithmic}[1]
\State The agents gather information about the prices of the products of their providers.
\State They calculate their demand for goods(either as input for production or for consumption) and labour(applicable only for producers), leisure and income for the next period. .
\State All the agents send their demands to their respective providers.
\State The producers sell their demanders goods from their inventory.
\State Labour supply and labour demand of the whole demand is calculated and is bought and sold at an aggregate level, with a wage rate being the same across the economy.
\State After step 5, all trade for that period has been completed. The agents calculate their costs and the producers produce the goods for the next periods and augment that to the inventory. If the producer's inventory falls to zero, then that producer is marked for removal.
\State Each individual producer calculates the excess demand and changes their commodity price in the direction of the excess demand.
\State The aggregate excess labour demand is calculated and the wage rate is changed in the direction of the excess demand.
\State Each individual firm, if it has made profit in that period, redistributes $(1-PRR)$ amount of it among its shareholders. If the firm has been marked for removal, its PRR is set to 0.
\State Each consumer calculates their own utility and income earned in this period and adds it to their stock of wealth.
\State All demanders of a firm marked for removal have a choice to either remove the shut-down firm from their provider set or replace it with one of the available producers not already in their provider set. This decision is assumed to be stochastic and each agent has a probability of 0.5 of choosing either of the two outcomes. This choice is however only there when there exists possible candidates for the outgoing firm to be replaced by. In either case, the output elasticities of the producers are renormalized and multiplied with the degree of homogeneity, which remains unchanged throughout. If there is no producer left in the economy, the model program halts.
\State If any consumer is left with no provider, their utility function parameters and provider set is regenerated.
\State The shut-down firms are removed from the economy and start the next period.
\end{algorithmic}
| \begin{algorithmic}
[1]
\State The agents gather information about the prices of the products of their providers.
\State They calculate their demand for goods(either as input for production or for consumption) and labour(applicable only for producers), leisure and income for the next period. .
\State All the agents send their demands to their respective providers.
\State The producers sell their demanders goods from their inventory.
\State Labour supply and labour demand of the whole demand is calculated and is bought and sold at an aggregate level, with a wage rate being the same across the economy.
\State After step 5, all trade for that period has been completed. The agents calculate their costs and the producers produce the goods for the next periods and augment that to the inventory. If the producer's inventory falls to zero, then that producer is marked for removal.
\State Each individual producer calculates the excess demand and changes their commodity price in the direction of the excess demand.
\State The aggregate excess labour demand is calculated and the wage rate is changed in the direction of the excess demand.
\State Each individual firm, if it has made profit in that period, redistributes $(1-PRR)$ amount of it among its shareholders. If the firm has been marked for removal, its PRR is set to 0.
\State Each consumer calculates their own utility and income earned in this period and adds it to their stock of wealth.
\State All demanders of a firm marked for removal have a choice to either remove the shut-down firm from their provider set or replace it with one of the available producers not already in their provider set. This decision is assumed to be stochastic and each agent has a probability of 0.5 of choosing either of the two outcomes. This choice is however only there when there exists possible candidates for the outgoing firm to be replaced by. In either case, the output elasticities of the producers are renormalized and multiplied with the degree of homogeneity, which remains unchanged throughout. If there is no producer left in the economy, the model program halts.
\State If any consumer is left with no provider, their utility function parameters and provider set is regenerated.
\State The shut-down firms are removed from the economy and start the next period.
\end{algorithmic} | "https://arxiv.org/src/2401.07070" | "2401.07070.tar.gz" | "2024-01-13" | {
"title": "a dynamic agent based model of the real economy with monopolistic competition, perfect product differentiation, heterogeneous agents, increasing returns to scale and trade in disequilibrium",
"id": "2401.07070",
"abstract": "we have used agent-based modeling as our numerical method to artificially simulate a dynamic real economy where agents are rational maximizers of an objective function of cobb-douglas type. the economy is characterised by heterogeneous agents, acting out of local or imperfect information, monopolistic competition, perfect product differentiation, allowance for increasing returns to scale technology and trade in disequilibrium. an algorithm for economic activity in each period is devised and a general purpose open source agent-based model is developed which allows for counterfactual inquiries, testing out treatments, analysing causality of various economic processes, outcomes and studying emergent properties. 10,000 simulations, with 10 firms and 80 consumers are run with varying parameters and the results show that from only a few initial conditions the economy reaches equilibrium while in most of the other cases it remains in perpetual disequilibrium. it also shows that from a few initial conditions the economy reaches a disaster where all the consumer wealth falls to zero or only a single producer remains. furthermore, from some initial conditions, an ideal economy with high wage rate, high consumer utility and no unemployment is also reached. it was also observed that starting from an equal endowment of wealth in consumers and in producers, inequality emerged in the economy. in majority of the cases most of the firms(6-7) shut down because they were not profitable enough and only a few firms remained. our results highlight that all these varying outcomes are possible for a decentralized market economy with rational optimizing agents.",
"categories": "econ.th cs.ma",
"doi": "",
"created": "2024-01-13",
"updated": "",
"authors": [
"subhamon supantha",
"naresh kumar sharma"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.07070"
} | "2024-03-15T06:13:08.276479" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"sn-article.tex": {
"toxicity_score": 0.0046494426,
"severe_toxicity_score": 0.00053167343,
"identity_attack_score": 0.001415168,
"insult_score": 0.006011867,
"profanity_score": 0.010569996,
"threat_score": 0.0062396824,
"sexually_explicit_score": 0.006605443,
"flirtation_score": 0.2874154
},
"empty.eps": {
"toxicity_score": 0.007885204,
"severe_toxicity_score": 0.0008249283,
"identity_attack_score": 0.0019146391,
"insult_score": 0.0067908717,
"profanity_score": 0.013268576,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.008492713,
"flirtation_score": 0.45258012
},
"sn-vancouver.bst": {
"toxicity_score": 0.010995304,
"severe_toxicity_score": 0.0008535385,
"identity_attack_score": 0.0033298072,
"insult_score": 0.007797878,
"profanity_score": 0.013268576,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.008256803,
"flirtation_score": 0.4476301
},
"sn-jnl.cls": {
"toxicity_score": 0.011812098,
"severe_toxicity_score": 0.0011396408,
"identity_attack_score": 0.003366805,
"insult_score": 0.007721877,
"profanity_score": 0.01402008,
"threat_score": 0.007145886,
"sexually_explicit_score": 0.010379982,
"flirtation_score": 0.512107
},
"fig.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"sn-basic.bst": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.0014209747,
"identity_attack_score": 0.0037367835,
"insult_score": 0.010248893,
"profanity_score": 0.018597418,
"threat_score": 0.0071717775,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.29158676
},
"sn-bibliography.bib": {
"toxicity_score": 0.009424546,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0017758971,
"insult_score": 0.0074368757,
"profanity_score": 0.013336895,
"threat_score": 0.006440342,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.20111376
},
"sn-article.bbl": {
"toxicity_score": 0.007351146,
"severe_toxicity_score": 0.000834465,
"identity_attack_score": 0.0024603575,
"insult_score": 0.00654387,
"profanity_score": 0.012585391,
"threat_score": 0.006647474,
"sexually_explicit_score": 0.009200439,
"flirtation_score": 0.5493274
},
"sn-mathphys.bst": {
"toxicity_score": 0.015142105,
"severe_toxicity_score": 0.0013542175,
"identity_attack_score": 0.004236255,
"insult_score": 0.008690883,
"profanity_score": 0.017572641,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.013977589,
"flirtation_score": 0.32881752
},
"sn-chicago.bst": {
"toxicity_score": 0.05205809,
"severe_toxicity_score": 0.0031089783,
"identity_attack_score": 0.00449524,
"insult_score": 0.017872108,
"profanity_score": 0.029050145,
"threat_score": 0.007197669,
"sexually_explicit_score": 0.015334064,
"flirtation_score": 0.22683682
},
"sn-nature.bst": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.0034223017,
"insult_score": 0.008880884,
"profanity_score": 0.015932998,
"threat_score": 0.006861079,
"sexually_explicit_score": 0.010438959,
"flirtation_score": 0.39879754
},
"sn-apacite.bst": {
"toxicity_score": 0.020842785,
"severe_toxicity_score": 0.0014591217,
"identity_attack_score": 0.0035332954,
"insult_score": 0.0105148945,
"profanity_score": 0.019143965,
"threat_score": 0.0075083673,
"sexually_explicit_score": 0.015216109,
"flirtation_score": 0.39166427
},
"sn-aps.bst": {
"toxicity_score": 0.025556687,
"severe_toxicity_score": 0.0018978119,
"identity_attack_score": 0.004994711,
"insult_score": 0.0114649,
"profanity_score": 0.021945024,
"threat_score": 0.00789674,
"sexually_explicit_score": 0.020052237,
"flirtation_score": 0.32421535
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.80746395250212,
"hash": "078ecf9f9f0f0f2f",
"most_frequent_color_ratio": 92.80746395250212
}
} | [] | "algorithm" | "a1612f64-29ac-47cb-81e9-e8e0362829e4" | 2341 | hard |
|
\begin{algorithm}
\caption{Anytime Valid Linear Model Summary in R}
\begin{verbatim}
mod <- summary(lm(outcome ~ . + trt*., data=df))
stderrs <- mod$coefficients[, 'Std. Error']
t2 <- mod$coefficients[, 't value']^2
ols <- mod$coefficients[, 'Estimate']
nu <- nrow(df) - length(ols)
z2 <- (mod$sigma / stderrs)^2
r <- phi / (phi + z2)
spvals <- min(1, sqrt(r) *
((1 + r * (t2 / nu))^(-(nu + 1)/2)) /
((1 + (t2 / nu))^(-(nu + 1)/2)))
alpha <- 0.05
radii <- stderrs * sqrt(nu * ((1 - (r * alpha^2)^(1 / (nu + 1))) /
max(0, ((r * alpha^2)^(1 / (nu + 1))) - r)))
lower_cis <- ols - radii
upper_cis <- ols + radii
\end{verbatim}
\end{algorithm}
| \begin{algorithm}
\caption{Anytime Valid Linear Model Summary in R}
\begin{verbatim}
mod <- summary(lm(outcome ~ . + trt*., data=df))
stderrs <- mod$coefficients[, 'Std. Error']
t2 <- mod$coefficients[, 't value']^2
ols <- mod$coefficients[, 'Estimate']
nu <- nrow(df) - length(ols)
z2 <- (mod$sigma / stderrs)^2
r <- phi / (phi + z2)
spvals <- min(1, sqrt(r) *
((1 + r * (t2 / nu))^(-(nu + 1)/2)) /
((1 + (t2 / nu))^(-(nu + 1)/2)))
alpha <- 0.05
radii <- stderrs * sqrt(nu * ((1 - (r * alpha^2)^(1 / (nu + 1))) /
max(0, ((r * alpha^2)^(1 / (nu + 1))) - r)))
lower_cis <- ols - radii
upper_cis <- ols + radii
\end{verbatim}
\end{algorithm} | "https://arxiv.org/src/2210.08589" | "2210.08589.tar.gz" | "2024-02-07" | {
"title": "anytime-valid linear models and regression adjusted causal inference in randomized experiments",
"id": "2210.08589",
"abstract": "linear regression adjustment is commonly used to analyse randomised controlled experiments due to its efficiency and robustness against model misspecification. current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to provide type-i error and coverage guarantees that hold only at a single sample size. here, we develop the theory for the anytime-valid analogues of such procedures, enabling linear regression adjustment in the sequential analysis of randomised experiments. we first provide sequential $f$-tests and confidence sequences for the parametric linear model, which provide time-uniform type-i error and coverage guarantees that hold for all sample sizes. we then relax all linear model parametric assumptions in randomised designs and provide nonparametric model-free sequential tests and confidence sequences for treatment effects. this formally allows experiments to be continuously monitored for significance, stopped early, and safeguards against statistical malpractices in data collection. a particular feature of our results is their simplicity. our test statistics and confidence sequences all emit closed-form expressions, which are functions of statistics directly available from a standard linear regression table. we illustrate our methodology with the sequential analysis of software a/b experiments at netflix, performing regression adjustment with pre-treatment outcomes.",
"categories": "stat.me math.st stat.th",
"doi": "",
"created": "2022-10-16",
"updated": "2024-02-07",
"authors": [
"michael lindon",
"dae woong ham",
"martin tingley",
"iavor bojinov"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2210.08589"
} | "2024-03-15T04:40:18.918382" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"thebibliography.bib": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003348306,
"insult_score": 0.007664877,
"profanity_score": 0.015932998,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.014685315,
"flirtation_score": 0.41820723
},
"main.bbl": {
"toxicity_score": 0.014827953,
"severe_toxicity_score": 0.0015258789,
"identity_attack_score": 0.0039402717,
"insult_score": 0.008348881,
"profanity_score": 0.018255826,
"threat_score": 0.007637825,
"sexually_explicit_score": 0.0150981555,
"flirtation_score": 0.2647355
},
"main.bib": {
"toxicity_score": 0.011120965,
"severe_toxicity_score": 0.0012302399,
"identity_attack_score": 0.003348306,
"insult_score": 0.007664877,
"profanity_score": 0.015932998,
"threat_score": 0.007365964,
"sexually_explicit_score": 0.014685315,
"flirtation_score": 0.41820723
},
"main.tex": {
"toxicity_score": 0.012126249,
"severe_toxicity_score": 0.0011730194,
"identity_attack_score": 0.0038292783,
"insult_score": 0.0076838774,
"profanity_score": 0.015488927,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.01250316,
"flirtation_score": 0.21894184
},
"agsm.bst": {
"toxicity_score": 0.02308189,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.0073625734,
"insult_score": 0.011160898,
"profanity_score": 0.016889455,
"threat_score": 0.007754337,
"sexually_explicit_score": 0.014154521,
"flirtation_score": 0.35954246
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.58542804832857,
"hash": "03070f1f0f0f1f5f",
"most_frequent_color_ratio": 91.58542804832857
}
} | [] | "algorithm" | "803cc3fc-4c90-425c-9d0a-1bd111e305b3" | 639 | easy |
|
\begin{algorithmic}[1]
\State preds = $f$.predict(\textit{samples})
\Comment{Applying the test data on f to get the prediction results}
\State targets= the opposite label of the preds
\Comment{Get the target labels based on the prediction results}
\For{$\textit{sample}$ $\leftarrow$ $\textit{samples}$}
\If{target(sample) == 0}
\State i, start\_idx, end\_idx = [i\_0, start\_idx\_0, end\_idx\_0]
\Comment{extracted index information of the motif from class 0 }
\Else
\State i, start\_idx, end\_idx = [i\_1, start\_idx\_1, end\_idx\_1]
\Comment{extracted index information of the motif from class 1 }
\EndIf
\State sample[start\_idx, end\_idx] = T[i][start\_idx, end\_idx]
\State cf\_sample = sample
\State CF.append(cf\_sample)
\EndFor
\State \Return $\textbf{CF}$
\end{algorithmic}
| \begin{algorithmic}
[1]
\State preds = $f$.predict(\textit{samples})
\Comment{Applying the test data on f to get the prediction results}
\State targets= the opposite label of the preds
\Comment{Get the target labels based on the prediction results}
\For{$\textit{sample}$ $\leftarrow$ $\textit{samples}$}
\If{target(sample) == 0}
\State i, start\_idx, end\_idx = [i\_0, start\_idx\_0, end\_idx\_0]
\Comment{extracted index information of the motif from class 0 }
\Else
\State i, start\_idx, end\_idx = [i\_1, start\_idx\_1, end\_idx\_1]
\Comment{extracted index information of the motif from class 1 }
\EndIf
\State sample[start\_idx, end\_idx] = T[i][start\_idx, end\_idx]
\State cf\_sample = sample
\State CF.append(cf\_sample)
\EndFor
\State \Return $\textbf{CF}$
\end{algorithmic} | "https://arxiv.org/src/2211.04411" | "2211.04411.tar.gz" | "2024-02-01" | {
"title": "motif-guided time series counterfactual explanations",
"id": "2211.04411",
"abstract": "with the rising need of interpretable machine learning methods, there is a necessity for a rise in human effort to provide diverse explanations of the influencing factors of the model decisions. to improve the trust and transparency of ai-based systems, the explainable artificial intelligence (xai) field has emerged. the xai paradigm is bifurcated into two main categories: feature attribution and counterfactual explanation methods. while feature attribution methods are based on explaining the reason behind a model decision, counterfactual explanation methods discover the smallest input changes that will result in a different decision. in this paper, we aim at building trust and transparency in time series models by using motifs to generate counterfactual explanations. we propose motif-guided counterfactual explanation (mg-cf), a novel model that generates intuitive post-hoc counterfactual explanations that make full use of important motifs to provide interpretive information in decision-making processes. to the best of our knowledge, this is the first effort that leverages motifs to guide the counterfactual explanation generation. we validated our model using five real-world time-series datasets from the ucr repository. our experimental results show the superiority of mg-cf in balancing all the desirable counterfactual explanations properties in comparison with other competing state-of-the-art baselines.",
"categories": "cs.lg",
"doi": "",
"created": "2022-11-08",
"updated": "2024-02-01",
"authors": [
"peiyu li",
"soukaina filali boubrahimi",
"shah muhammad hamdi"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2211.04411"
} | "2024-03-15T08:04:45.521940" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"mybib.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"mybib.bib": {
"toxicity_score": 0.004869349,
"severe_toxicity_score": 0.0008916855,
"identity_attack_score": 0.0009943173,
"insult_score": 0.006173368,
"profanity_score": 0.012585391,
"threat_score": 0.0057347976,
"sexually_explicit_score": 0.00896453,
"flirtation_score": 0.31566563
},
"mybib.tex": {
"toxicity_score": 0.012880214,
"severe_toxicity_score": 0.0010967255,
"identity_attack_score": 0.0040512653,
"insult_score": 0.007892879,
"profanity_score": 0.014976538,
"threat_score": 0.0076766624,
"sexually_explicit_score": 0.012149297,
"flirtation_score": 0.23857248
},
"llncs.cls": {
"toxicity_score": 0.0025289198,
"severe_toxicity_score": 0.00032424927,
"identity_attack_score": 0.00068446033,
"insult_score": 0.0057316152,
"profanity_score": 0.009135309,
"threat_score": 0.005618286,
"sexually_explicit_score": 0.0040399362,
"flirtation_score": 0.61743
},
"readme.txt": {
"toxicity_score": 0.022964042,
"severe_toxicity_score": 0.0014400482,
"identity_attack_score": 0.005512681,
"insult_score": 0.012338906,
"profanity_score": 0.016616182,
"threat_score": 0.007598988,
"sexually_explicit_score": 0.00979021,
"flirtation_score": 0.26585522
},
"splncs04.bst": {
"toxicity_score": 0.0109324735,
"severe_toxicity_score": 0.0010919571,
"identity_attack_score": 0.0029598286,
"insult_score": 0.007797878,
"profanity_score": 0.015454767,
"threat_score": 0.0067445673,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.39611804
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 90.91747693841936,
"hash": "033f3f8f87833f01",
"most_frequent_color_ratio": 90.91747693841936
}
} | [] | "algorithm" | "075e539e-0bde-4c85-a97e-e20874b3c03d" | 784 | medium |
|
\begin{algorithm}[htb]
\caption{Training procedure of the moment-based neural Hawkes}
\label{algo:pinn_training}
\begin{enumerate}
\item
Estimate the first order statistics $\Lambda$ and second order statistics $G$ given by Equation \eqref{eq:def_G_statistics} over the time and mark domains.
\item
For $i=1$ to $D$:
\begin{enumerate}
\item
Represent the solution of the $i$-th characterization equation of Theorem \ref{th:characterization_theorem} by a DGM neural network $u_\theta^i$ and initialize its weights $\theta$ with a Glorot scheme.
\item
Set the number of epochs $E$, the size $N^c$ of the training set $\mathcal{E}$, the batch size $B$ such that $N^c=0\,[B]$, a decreasing sequence of learning rates $(\gamma_e)_{e\geq 1}$ and a random sampling scheme, \textit{e.g.} the procedure described above. We use $\gamma_e=\gamma_0100^{-\frac{e}{E}}$.
\item
For $e=1$ to $E$:
\begin{enumerate}
\item Sample a training set $\mathcal{E}=\{(t_n,x_n), 1\leq n\leq N^c\}\}$ and a validation set $\bar{\mathcal{E}}$ .
\item Apply a standardization and log-scaling to the data points.
\item Over the elements of $\mathcal{E}$: compute the equation residuals $(\varepsilon_n^{ij}(\theta))_{1\leq j\leq D}$ and the temporal weights $(\omega_n^{ij}(\theta))_{1\leq j\leq D}$.
\item For $s = 1$ to $\frac{N^c}{B}$:
\begin{itemize}
\item Using Equation \eqref{eq:weighted_loss_function_nn_i}, update the weights via a mini-batch gradient descent
\begin{equation}
\theta \leftarrow \theta - \gamma_e\frac{1}{B}\sum_{n=(s-1)B+1}^{sB}\nabla_\theta\mathcal{L}_{i,n}^{\omega}(\theta).
\end{equation}
\end{itemize}
\item Using Equation \eqref{eq:unweighted_loss_function_nn_i}, compute the unweighted validation loss $\mathcal{L}_i(\theta, \bar{\mathcal{E}})$.
\end{enumerate}
\item \textbf{Outputs:} An estimate $\theta^i$ of the optimal weights for the $i$-th model and a list of validation losses over the epochs.
\end{enumerate}
\item \textbf{Outputs:} $D$ models $(u_{\theta^i}^i)_{1\leq i\leq D}$, the $i$-th model representing the $i$-th row of the kernel matrix.
\end{enumerate}
\end{algorithm}
| \begin{algorithm}
[htb]
\caption{Training procedure of the moment-based neural Hawkes}
\begin{enumerate}
\item
Estimate the first order statistics $\Lambda$ and second order statistics $G$ given by Equation \eqref{eq:def_G_statistics} over the time and mark domains.
\item
For $i=1$ to $D$:
\begin{enumerate}
\item
Represent the solution of the $i$-th characterization equation of Theorem \ref{th:characterization_theorem} by a DGM neural network $u_\theta^i$ and initialize its weights $\theta$ with a Glorot scheme.
\item
Set the number of epochs $E$, the size $N^c$ of the training set $\mathcal{E}$, the batch size $B$ such that $N^c=0\,[B]$, a decreasing sequence of learning rates $(\gamma_e)_{e\geq 1}$ and a random sampling scheme, \textit{e.g.} the procedure described above. We use $\gamma_e=\gamma_0100^{-\frac{e}{E}}$.
\item
For $e=1$ to $E$:
\begin{enumerate}
\item Sample a training set $\mathcal{E}=\{(t_n,x_n), 1\leq n\leq N^c\}\}$ and a validation set $\bar{\mathcal{E}}$ .
\item Apply a standardization and log-scaling to the data points.
\item Over the elements of $\mathcal{E}$: compute the equation residuals $(\varepsilon_n^{ij}(\theta))_{1\leq j\leq D}$ and the temporal weights $(\omega_n^{ij}(\theta))_{1\leq j\leq D}$.
\item For $s = 1$ to $\frac{N^c}{B}$:
\begin{itemize}
\item Using Equation \eqref{eq:weighted_loss_function_nn_i}, update the weights via a mini-batch gradient descent
\begin{equation*}
\theta \leftarrow \theta - \gamma_e\frac{1}{B}\sum_{n=(s-1)B+1}^{sB}\nabla_\theta\mathcal{L}_{i,n}^{\omega}(\theta).
\end{equation*}
\end{itemize}
\item Using Equation \eqref{eq:unweighted_loss_function_nn_i}, compute the unweighted validation loss $\mathcal{L}_i(\theta, \bar{\mathcal{E}})$.
\end{enumerate}
\item \textbf{Outputs:} An estimate $\theta^i$ of the optimal weights for the $i$-th model and a list of validation losses over the epochs.
\end{enumerate}
\item \textbf{Outputs:} $D$ models $(u_{\theta^i}^i)_{1\leq i\leq D}$, the $i$-th model representing the $i$-th row of the kernel matrix.
\end{enumerate}
\end{algorithm} | "https://arxiv.org/src/2401.09361" | "2401.09361.tar.gz" | "2024-01-18" | {
"title": "neural hawkes: non-parametric estimation in high dimension and causality analysis in cryptocurrency markets",
"id": "2401.09361",
"abstract": "we propose a novel approach to marked hawkes kernel inference which we name the moment-based neural hawkes estimation method. hawkes processes are fully characterized by their first and second order statistics through a fredholm integral equation of the second kind. using recent advances in solving partial differential equations with physics-informed neural networks, we provide a numerical procedure to solve this integral equation in high dimension. together with an adapted training pipeline, we give a generic set of hyperparameters that produces robust results across a wide range of kernel shapes. we conduct an extensive numerical validation on simulated data. we finally propose two applications of the method to the analysis of the microstructure of cryptocurrency markets. in a first application we extract the influence of volume on the arrival rate of btc-usd trades and in a second application we analyze the causality relationships and their directions amongst a universe of 15 cryptocurrency pairs in a centralized exchange.",
"categories": "q-fin.tr q-fin.mf",
"doi": "",
"created": "2024-01-17",
"updated": "2024-01-18",
"authors": [
"timoth\u00e9e fabre",
"ioane muni toke"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.09361"
} | "2024-03-15T05:59:43.122977" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"main.bib": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.0011587143,
"identity_attack_score": 0.0022013725,
"insult_score": 0.0072088744,
"profanity_score": 0.013917602,
"threat_score": 0.006912862,
"sexually_explicit_score": 0.010674868,
"flirtation_score": 0.21914493
},
"main.tex": {
"toxicity_score": 0.01225191,
"severe_toxicity_score": 0.0012683868,
"identity_attack_score": 0.003903274,
"insult_score": 0.007721877,
"profanity_score": 0.01627459,
"threat_score": 0.0076637166,
"sexually_explicit_score": 0.013859635,
"flirtation_score": 0.21216936
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 91.12043668667552,
"hash": "019b809f81ff9383",
"most_frequent_color_ratio": 91.12043668667552
}
} | [] | "algorithm" | "3526bd02-0f50-49a9-893e-5a5a4f7c252a" | 2065 | hard |
|
\begin{algorithm}[H]
\caption{D-Mapper}%标题
\label{D-Mapper}%标签
\begin{algorithmic}[1]
\State Choose a proper filter function $f$ to project data on the real line,$f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and quantile $\alpha$.
\State Fit projected data to a mixture model.
\For{$i$th component of the mixture model}
\State Set the $\frac{\alpha}{2}$ quantile $s_i$ as the start point of the interval,
\State Set the $1-\frac{\alpha}{2}$ quantile $e_i$ as the end point of the interval,
\State The interval of $i$th component is $u_i = (s_i,e_i)$.
\EndFor
\State The collection of intervals $\mathcal{U} = (u_i) ,i=1...n,$ is a cover on projected data $f(X)$.
\State Pull back the intervals of projected data, $f^{-1}(\mathcal{U})$.
\State Clustering on the refined cover and build the nerve by clustering result.
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
[H]
\caption{D-Mapper}%标题
%标签
\begin{algorithmic}
[1]
\State Choose a proper filter function $f$ to project data on the real line,$f: X \rightarrow \mathbb{R}$.
\State Choose a component number $n$ and quantile $\alpha$.
\State Fit projected data to a mixture model.
\For{$i$th component of the mixture model}
\State Set the $\frac{\alpha}{2}$ quantile $s_i$ as the start point of the interval,
\State Set the $1-\frac{\alpha}{2}$ quantile $e_i$ as the end point of the interval,
\State The interval of $i$th component is $u_i = (s_i,e_i)$.
\EndFor
\State The collection of intervals $\mathcal{U} = (u_i) ,i=1...n,$ is a cover on projected data $f(X)$.
\State Pull back the intervals of projected data, $f^{-1}(\mathcal{U})$.
\State Clustering on the refined cover and build the nerve by clustering result.
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2401.12237" | "2401.12237.tar.gz" | "2024-01-19" | {
"title": "a distribution-guided mapper algorithm",
"id": "2401.12237",
"abstract": "motivation: the mapper algorithm is an essential tool to explore shape of data in topology data analysis. with a dataset as an input, the mapper algorithm outputs a graph representing the topological features of the whole dataset. this graph is often regarded as an approximation of a reeb graph of data. the classic mapper algorithm uses fixed interval lengths and overlapping ratios, which might fail to reveal subtle features of data, especially when the underlying structure is complex. results: in this work, we introduce a distribution guided mapper algorithm named d-mapper, that utilizes the property of the probability model and data intrinsic characteristics to generate density guided covers and provides enhanced topological features. our proposed algorithm is a probabilistic model-based approach, which could serve as an alternative to non-prababilistic ones. moreover, we introduce a metric accounting for both the quality of overlap clustering and extended persistence homology to measure the performance of mapper type algorithm. our numerical experiments indicate that the d-mapper outperforms the classical mapper algorithm in various scenarios. we also apply the d-mapper to a sars-cov-2 coronavirus rna sequences dataset to explore the topological structure of different virus variants. the results indicate that the d-mapper algorithm can reveal both vertical and horizontal evolution processes of the viruses. availability: our package is available at https://github.com/shufeige/d-mapper.",
"categories": "math.at cs.lg q-bio.qm",
"doi": "",
"created": "2024-01-19",
"updated": "",
"authors": [
"yuyang tao",
"shufei ge"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2401.12237"
} | "2024-03-15T06:54:42.856784" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"main.tex": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0026453468,
"insult_score": 0.0070473733,
"profanity_score": 0.012277958,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.0068413517,
"flirtation_score": 0.3889268
},
"oup-authoring-template.cls": {
"toxicity_score": 0.009236055,
"severe_toxicity_score": 0.00084400177,
"identity_attack_score": 0.0026453468,
"insult_score": 0.0070473733,
"profanity_score": 0.012277958,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.0068413517,
"flirtation_score": 0.3889268
},
"cat.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 85.546875,
"hash": "000311303f3f0700",
"most_frequent_color_ratio": 85.546875
}
} | [] | "algorithm" | "8a95aa39-1412-4043-9baf-8bbc24ce302f" | 858 | medium |
|
\begin{algorithm}
\caption{Sequential coupling for one time-step}\label{alg:stag}
$\mathbf{u}_{mesh}$ = $\left(\mathbf{x}_{n} - \mathbf{x}_{n-1} \right) \frac{1}{\Delta t}$\\
$\mathbf{u}_{n+1}$ = \text{Navier-Stokes} $(\mathbf{u}_n, \phi_n, \mathbf{u}_{mesh})$\\
$\phi_{adv}$ = \text{level set} $(\mathbf{u}_{n+1}, \phi_n)$\\
$\mathbf{x}_{n+1}$, $\text{front}_{n+1}$ = \text{X-Mesh} $(\mathbf{x}_n, \phi_{adv}, \phi_n, \text{front}_n)$\\
$\phi_{n+1}$ = \text{Fastmarching} $(\mathbf{x}_{n+1}, \text{front}_{n+1})$
$t$ = $t+\Delta t$ \\
\end{algorithm}
| \begin{algorithm}
\caption{Sequential coupling for one time-step}
$\mathbf{u}_{mesh}$ = $\left(\mathbf{x}_{n} - \mathbf{x}_{n-1} \right) \frac{1}{\Delta t}$\\
$\mathbf{u}_{n+1}$ = \text{Navier-Stokes} $(\mathbf{u}_n, \phi_n, \mathbf{u}_{mesh})$\\
$\phi_{adv}$ = \text{level set} $(\mathbf{u}_{n+1}, \phi_n)$\\
$\mathbf{x}_{n+1}$, $\text{front}_{n+1}$ = \text{X-Mesh} $(\mathbf{x}_n, \phi_{adv}, \phi_n, \text{front}_n)$\\
$\phi_{n+1}$ = \text{Fastmarching} $(\mathbf{x}_{n+1}, \text{front}_{n+1})$
$t$ = $t+\Delta t$ \\
\end{algorithm} | "https://arxiv.org/src/2302.03983" | "2302.03983.tar.gz" | "2024-02-01" | {
"title": "x-mesh: a new approach for the simulation of two-phase flow with sharp interface",
"id": "2302.03983",
"abstract": "accurate modeling of moving boundaries and interfaces is a difficulty present in many situations of computational mechanics. we use the extreme mesh deformation approach (x-mesh) to simulate the interaction between two immiscible flows using the finite element method, while maintaining an accurate and sharp description of the interface without remeshing. in this new approach, the mesh is locally deformed to conform to the interface at all times, which can result in degenerated elements. the surface tension between the two fluids is added by imposing the pressure jump condition at the interface, which, when combined with the x-mesh framework, allows us to have an exactly sharp interface. if a numerical scheme fails to properly balance surface tension and pressure gradients, it leads to numerical artefacts called spurious or parasitic currents. the method presented here is well balanced and reduces such currents down to the level of machine precision.",
"categories": "cs.ce",
"doi": "10.1016/j.jcp.2024.112775",
"created": "2023-02-08",
"updated": "2024-02-01",
"authors": [
"antoine quiriny",
"jonathan lambrechts",
"nicolas mo\u00ebs",
"jean-fran\u00e7ois remacle"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2302.03983"
} | "2024-03-15T08:12:32.496033" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"refs.bib": {
"toxicity_score": 0.010492661,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0025158543,
"insult_score": 0.0075128763,
"profanity_score": 0.015898837,
"threat_score": 0.007068211,
"sexually_explicit_score": 0.012916001,
"flirtation_score": 0.27045402
},
"main.tex": {
"toxicity_score": 0.011372286,
"severe_toxicity_score": 0.0010347366,
"identity_attack_score": 0.0029968263,
"insult_score": 0.0074558756,
"profanity_score": 0.014839902,
"threat_score": 0.006977591,
"sexually_explicit_score": 0.010556913,
"flirtation_score": 0.35724726
},
"thetafem.tikz": {
"toxicity_score": 0.056016337,
"severe_toxicity_score": 0.008049011,
"identity_attack_score": 0.0058086636,
"insult_score": 0.01819439,
"profanity_score": 0.11354194,
"threat_score": 0.008285114,
"sexually_explicit_score": 0.06306043,
"flirtation_score": 0.21970871
},
"model1-num-names.bst": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.00093460083,
"identity_attack_score": 0.0032743104,
"insult_score": 0.007189874,
"profanity_score": 0.013336895,
"threat_score": 0.0071588317,
"sexually_explicit_score": 0.008787598,
"flirtation_score": 0.4130954
},
"main.bbl": {
"toxicity_score": 0.011497946,
"severe_toxicity_score": 0.0011253357,
"identity_attack_score": 0.0037922803,
"insult_score": 0.0074558756,
"profanity_score": 0.01442999,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.011323617,
"flirtation_score": 0.20881563
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 1
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.37404829027865,
"hash": "003f0f1f070f7fff",
"most_frequent_color_ratio": 92.37404829027865
}
} | [] | "algorithm" | "d34f1e9e-0fc7-4c2b-a79d-dec77dfe6883" | 535 | easy |
|
\begin{algorithm}\label{alg:sbo1}
To place the blocks of a partition in size-biased order, sample uniformly without replacement from the underlying set, then list the blocks in the order they are discovered. In other words, given $A$ and $P$, and $B(u), u\in A$ as above, let $n=|A|$ and $u:\{1,\dots,n\}\to A$ be a uniform random bijection, then let $B_1=B(u(1))$ and for $i>1$, $I_i:=\min\{i\colon u(i) \notin \bigcup_{j<i}B_j\}$ and $B_i = B(u(I_i))$. The property quoted in Definition \ref{def:sbo} follows from the fact that, conditioned on $u(1),\dots,u(I_{i-1})$, $u(I_i)$ is uniform on $A \setminus \bigcup_{j<i}B_j$.
\end{algorithm}
| \begin{algorithm}
To place the blocks of a partition in size-biased order, sample uniformly without replacement from the underlying set, then list the blocks in the order they are discovered. In other words, given $A$ and $P$, and $B(u), u\in A$ as above, let $n=|A|$ and $u:\{1,\dots,n\}\to A$ be a uniform random bijection, then let $B_1=B(u(1))$ and for $i>1$, $I_i:=\min\{i\colon u(i) \notin \bigcup_{j<i}B_j\}$ and $B_i = B(u(I_i))$. The property quoted in Definition \ref{def:sbo} follows from the fact that, conditioned on $u(1),\dots,u(I_{i-1})$, $u(I_i)$ is uniform on $A \setminus \bigcup_{j<i}B_j$.
\end{algorithm} | "https://arxiv.org/src/2104.00193" | "2104.00193.tar.gz" | "2024-01-12" | {
"title": "takeover, fixation and identifiability in finite neutral genealogy models",
"id": "2104.00193",
"abstract": "for neutral genealogy models in a finite, possibly non-constant population, there is a convenient ordered rearrangement of the particles, known as the lookdown representation, that greatly simplifies the analysis of the family trees. by introducing the dual notions of forward and backward neutrality, we give a more intuitive implementation of this rearrangement. we also show that the lookdown arranges subtrees in size-biased order of the number of their descendants, a property that is familiar in other settings but appears not to have been previously established in this context. in addition, we use the lookdown to study three properties of finite neutral models, as a function of the sequence of unlabelled litter sizes of the model: uniqueness of the infinite path (fixation), existence of a single lineage to which almost all individuals can trace their ancestry (takeover) and whether or not we can infer the lookdown rearrangement by examining the unlabelled genealogy model (identifiability). identifiability of the spine path in size-biased galton-watson trees was previously studied, so we also discuss connections to those results, by relating the spinal decomposition to the lookdown.",
"categories": "math.pr",
"doi": "",
"created": "2021-03-31",
"updated": "2024-01-12",
"authors": [
"eric foxall",
"jen labossiere"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2104.00193"
} | "2024-03-15T06:08:13.487657" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"EJP2104-001revised3.tex": {
"toxicity_score": 0.018723432,
"severe_toxicity_score": 0.0012779236,
"identity_attack_score": 0.0026083488,
"insult_score": 0.009393888,
"profanity_score": 0.014156717,
"threat_score": 0.0067316215,
"sexually_explicit_score": 0.007726009,
"flirtation_score": 0.30276403
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 83.32385938668662,
"hash": "005b4ffbfd20dfc7",
"most_frequent_color_ratio": 83.32385938668662
}
} | [] | "algorithm" | "e9cff77a-5306-449c-839f-f220c1fd734a" | 625 | easy |
|
\begin{algorithmic}[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the machine learning model class: $\mathcal{M}=\{M_\theta: \mathcal{X} \rightarrow \mathcal{Y} , \theta\in \Theta\}$; loss functions: $\ell(M(X),Y)$ (could be $m$-dimensional).}
\State Initialize two models, the treatment model ${M}_{\theta_T}$ and the control model ${M}_{\theta_C}$, both of which are set to the current production model.
\For{$t \gets 1$ to the end of the experiment }
\For{$i \gets 1$ to $n_t$ }
\State User $i$ arrives. The platform randomly assigns user $i$ to the treatment group with probability $p$.
\State When a user is assigned to the treatment group, the platform recommends an item based on the treatment algorithm and model, and vice versa.
\State Collect data $(X_{i,t},Y_{i,t},Z_{i,t})$.
\EndFor
\State Compute weights:
$$W_{T,i,t}=\frac{G_{\theta_W}(X_{i,t})}{p} \text{ and } W_{C,i,t}=\frac{1-G_{\theta_W}(X_{i,t})}{1-p} \text{ , for } i=1,2,\ldots,n_t. $$
\State Update the treatment model ${M}_{\theta_T}$ by minimizing the weighted loss
$$\frac{1}{n_t}\sum_{i=1}^{n_t}W_{T,i,t}\ell({M}_{\theta_T}(X_{i,t}),Y_{i,t}).$$
\State Update the control model ${M}_{\theta_C}$ by minimizing the weighted loss
$$\frac{1}{n_t}\sum_{i=1}^{n_t}W_{C,i,t}\ell({M}_{\theta_C}(X_{i,t}),Y_{i,t}).$$
\State Update the model $G_{\theta_W}$ using data $\{(X_{i,t},Z_{i,t}),i=1,\ldots,n_t\}$.
\EndFor
\Return the estimator (\ref{naive_estimator}).
\end{algorithmic}
| \begin{algorithmic}
[1]
\Require{The probability of treatment assignment: $p$; a model class for the weight prediction: $\mathcal{G}=\{G_{\theta_W}: \mathbb{R}^d \rightarrow \{0,1\}, {\theta_W}\in \Theta_W\}$; the machine learning model class: $\mathcal{M}=\{M_\theta: \mathcal{X} \rightarrow \mathcal{Y} , \theta\in \Theta\}$; loss functions: $\ell(M(X),Y)$ (could be $m$-dimensional).}
\State Initialize two models, the treatment model ${M}_{\theta_T}$ and the control model ${M}_{\theta_C}$, both of which are set to the current production model.
\For{$t \gets 1$ to the end of the experiment }
\For{$i \gets 1$ to $n_t$ }
\State User $i$ arrives. The platform randomly assigns user $i$ to the treatment group with probability $p$.
\State When a user is assigned to the treatment group, the platform recommends an item based on the treatment algorithm and model, and vice versa.
\State Collect data $(X_{i,t},Y_{i,t},Z_{i,t})$.
\EndFor
\State Compute weights:
$$W_{T,i,t}=\frac{G_{\theta_W}(X_{i,t})}{p} \text{ and } W_{C,i,t}=\frac{1-G_{\theta_W}(X_{i,t})}{1-p} \text{ , for } i=1,2,\ldots,n_t. $$
\State Update the treatment model ${M}_{\theta_T}$ by minimizing the weighted loss
$$\frac{1}{n_t}\sum_{i=1}^{n_t}W_{T,i,t}\ell({M}_{\theta_T}(X_{i,t}),Y_{i,t}).$$
\State Update the control model ${M}_{\theta_C}$ by minimizing the weighted loss
$$\frac{1}{n_t}\sum_{i=1}^{n_t}W_{C,i,t}\ell({M}_{\theta_C}(X_{i,t}),Y_{i,t}).$$
\State Update the model $G_{\theta_W}$ using data $\{(X_{i,t},Z_{i,t}),i=1,\ldots,n_t\}$.
\EndFor
\Return the estimator (\ref{naive_estimator}).
\end{algorithmic} | "https://arxiv.org/src/2310.17496" | "2310.17496.tar.gz" | "2024-02-03" | {
"title": "tackling interference induced by data training loops in a/b tests: a weighted training approach",
"id": "2310.17496",
"abstract": "in modern recommendation systems, the standard pipeline involves training machine learning models on historical data to predict user behaviors and improve recommendations continuously. however, these data training loops can introduce interference in a/b tests, where data generated by control and treatment algorithms, potentially with different distributions, are combined. to address these challenges, we introduce a novel approach called weighted training. this approach entails training a model to predict the probability of each data point appearing in either the treatment or control data and subsequently applying weighted losses during model training. we demonstrate that this approach achieves the least variance among all estimators that do not cause shifts in the training distributions. through simulation studies, we demonstrate the lower bias and variance of our approach compared to other methods.",
"categories": "stat.me cs.lg econ.em",
"doi": "",
"created": "2023-10-26",
"updated": "2024-02-03",
"authors": [
"nian si"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2310.17496"
} | "2024-03-15T04:57:23.023908" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.00092983246,
"identity_attack_score": 0.002848835,
"insult_score": 0.0072278744,
"profanity_score": 0.014259194,
"threat_score": 0.0070164283,
"sexually_explicit_score": 0.011205662,
"flirtation_score": 0.25324678
},
"main.bbl": {
"toxicity_score": 0.012566062,
"severe_toxicity_score": 0.0011873245,
"identity_attack_score": 0.0040327664,
"insult_score": 0.0076268767,
"profanity_score": 0.014976538,
"threat_score": 0.007637825,
"sexually_explicit_score": 0.012444182,
"flirtation_score": 0.21330994
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 92.06317286562798,
"hash": "000700bfdfefef0f",
"most_frequent_color_ratio": 92.06317286562798
}
} | [] | "algorithm" | "a782dcb8-0763-4306-9476-e7f1910425a2" | 1590 | hard |
|
\begin{algorithm}
\caption{Training Step for Model $f_k$ (with parameters $\theta_k$) using decorrelation. $\lambda$ and $r$ are hyperparameters.}\label{alg:dec_alg}
\begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $Z_{k,b} \gets f^l_k(X_b)$
\State $N, D \gets shape(Z_{k,b})$
\State $\hat{Y_b} \gets f_k(X_b)$
\State $\mathcal{L} \gets \mathcal{L}_{ce}(\hat{Y_b}, Y_b)$
\State $i \gets 0$
\While{$i \leq k-1$}
\State $Z_1, Z_2 \gets Z_{k,b}, Z_{i,b}$
\If {$t \sim Uniform[0,1] < 0.5$}
$Z_1, Z_2 \gets Z_2, Z_1$
\EndIf
\State $R \sim N(0,1/\sqrt{D}) \in \mathbb{R}^{D+1 \times r}$
\State $Z_1 \gets [Z_1, \mathbf{1}]R$
\State $\mathcal{L} \gets \mathcal{L} + \frac{\lambda}{k} \mathcal{L}_R(Z_1, Z_2)$ \Comment{Apply decorrelation loss from Equation \ref{eqn:LR}}
\State $i \gets i+1$
\EndWhile
\State $\theta_k \gets \theta_k - \eta \nabla_{\mathcal{L}} \theta_k$
\end{algorithmic}
\end{algorithm}
| \begin{algorithm}
\caption{Training Step for Model $f_k$ (with parameters $\theta_k$) using decorrelation. $\lambda$ and $r$ are hyperparameters.}\begin{algorithmic}
\State Draw $X_b, Y_b, [Z_{k-1,b} \cdots Z_{0,b}]$ \Comment{Draw training batch and corresponding features from prior models}
\State $Z_{k,b} \gets f^l_k(X_b)$
\State $N, D \gets shape(Z_{k,b})$
\State $\hat{Y_b} \gets f_k(X_b)$
\State $\mathcal{L} \gets \mathcal{L}_{ce}(\hat{Y_b}, Y_b)$
\State $i \gets 0$
\While{$i \leq k-1$}
\State $Z_1, Z_2 \gets Z_{k,b}, Z_{i,b}$
\If {$t \sim Uniform[0,1] < 0.5$}
$Z_1, Z_2 \gets Z_2, Z_1$
\EndIf
\State $R \sim N(0,1/\sqrt{D}) \in \mathbb{R}^{D+1 \times r}$
\State $Z_1 \gets [Z_1, \mathbf{1}]R$
\State $\mathcal{L} \gets \mathcal{L} + \frac{\lambda}{k} \mathcal{L}_R(Z_1, Z_2)$ \Comment{Apply decorrelation loss from Equation \ref{eqn:LR}}
\State $i \gets i+1$
\EndWhile
\State $\theta_k \gets \theta_k - \eta \nabla_{\mathcal{L}} \theta_k$
\end{algorithmic}
\end{algorithm} | "https://arxiv.org/src/2207.09031" | "2207.09031.tar.gz" | "2024-02-16" | {
"title": "decorrelative network architecture for robust electrocardiogram classification",
"id": "2207.09031",
"abstract": "artificial intelligence has made great progress in medical data analysis, but the lack of robustness and trustworthiness has kept these methods from being widely deployed. as it is not possible to train networks that are accurate in all scenarios, models must recognize situations where they cannot operate confidently. bayesian deep learning methods sample the model parameter space to estimate uncertainty, but these parameters are often subject to the same vulnerabilities, which can be exploited by adversarial attacks. we propose a novel ensemble approach based on feature decorrelation and fourier partitioning for teaching networks diverse complementary features, reducing the chance of perturbation-based fooling. we test our approach on single and multi-channel electrocardiogram classification, and adapt adversarial training and dverge into the bayesian ensemble framework for comparison. our results indicate that the combination of decorrelation and fourier partitioning generally maintains performance on unperturbed data while demonstrating superior robustness and uncertainty estimation on projected gradient descent and smooth adversarial attacks of various magnitudes. furthermore, our approach does not require expensive optimization with adversarial samples, adding much less compute to the training process than adversarial training or dverge. these methods can be applied to other tasks for more robust and trustworthy models.",
"categories": "cs.lg cs.ai",
"doi": "",
"created": "2022-07-18",
"updated": "2024-02-16",
"authors": [
"christopher wiedeman",
"ge wang"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2207.09031"
} | "2024-03-15T04:53:44.109126" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"main.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.66384118977147,
"hash": "103f3f3f071f303f",
"most_frequent_color_ratio": 94.66384118977147
}
} | [] | "algorithm" | "e48ab977-e08a-4350-beaf-9c7f7a5b1929" | 982 | medium |
|
\begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ by \eqref{eq:funnel_update}}
\If{\eqref{eq:stopping_criterion} is True}
\State{break}
\EndIf
\State{update $(\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k)\leftarrow (\bar{x}_k,\bar{u}_k,Q_k,K_k)$}
\EndFor
\Ensure{$(\bar{x}_k,\bar{u}_k,Q_k,K_k)$}
\end{algorithmic}
| \begin{algorithmic}
\Require{($\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k$)}
\For{$i=1\ldots N_{max}$}
\State{optimize $\bar{x}_k,\bar{u}_k$ by \eqref{eq:traj_update}}
\State{estimate $\gamma_k$ via \eqref{eq:gamma_update} or \eqref{eq:approximate_outer_optimization}}
\State{optimize $Q_k,K_k$ by \eqref{eq:funnel_update}}
\If{\eqref{eq:stopping_criterion} is True}
\State{break}
\EndIf
\State{update $(\hat{x}_k,\hat{u}_k\,\hat{Q}_k,\hat{K}_k)\leftarrow (\bar{x}_k,\bar{u}_k,Q_k,K_k)$}
\EndFor
\Ensure{$(\bar{x}_k,\bar{u}_k,Q_k,K_k)$}
\end{algorithmic} | "https://arxiv.org/src/2209.03535" | "2209.03535.tar.gz" | "2024-01-12" | {
"title": "joint synthesis of trajectory and controlled invariant funnel for discrete-time systems with locally lipschitz nonlinearities",
"id": "2209.03535",
"abstract": "this paper presents a joint synthesis algorithm of trajectory and controlled invariant funnel (cif) for locally lipschitz nonlinear systems subject to bounded disturbances. the cif synthesis refers to a procedure of computing controlled invariance sets and corresponding feedback gains. in contrast to existing cif synthesis methods that compute the cif with a pre-defined nominal trajectory, our work aims to optimize the nominal trajectory and the cif jointly to satisfy feasibility conditions without the relaxation of constraints and obtain a more cost-optimal nominal trajectory. the proposed work has a recursive scheme that mainly optimize trajectory update and funnel update. the trajectory update step optimizes the nominal trajectory while ensuring the feasibility of the cif. then, the funnel update step computes the funnel around the nominal trajectory so that the cif guarantees an invariance property. as a result, with the optimized trajectory and cif, any resulting trajectory propagated from an initial set by the control law with the computed feedback gain remains within the feasible region around the nominal trajectory under the presence of bounded disturbances. we validate the proposed method via two applications from robotics.",
"categories": "math.oc",
"doi": "",
"created": "2022-09-07",
"updated": "2024-01-12",
"authors": [
"taewan kim",
"purnanand elango",
"behcet acikmese"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2209.03535"
} | "2024-03-15T06:18:49.073482" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"NJDnatbib.sty": {
"toxicity_score": 0.018974753,
"severe_toxicity_score": 0.002231598,
"identity_attack_score": 0.0035887922,
"insult_score": 0.0107048955,
"profanity_score": 0.02249157,
"threat_score": 0.006874025,
"sexually_explicit_score": 0.019108603,
"flirtation_score": 0.47491857
},
"figures/comparison.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"WileyNJD-AMA.bst": {
"toxicity_score": 0.049584184,
"severe_toxicity_score": 0.0034713745,
"identity_attack_score": 0.008620501,
"insult_score": 0.019032324,
"profanity_score": 0.037904397,
"threat_score": 0.00951496,
"sexually_explicit_score": 0.0353863,
"flirtation_score": 0.33491865
},
"WileyNJD-v2.cls": {
"toxicity_score": 0.020607091,
"severe_toxicity_score": 0.0013256073,
"identity_attack_score": 0.0026453468,
"insult_score": 0.00983089,
"profanity_score": 0.014190876,
"threat_score": 0.006699257,
"sexually_explicit_score": 0.007902941,
"flirtation_score": 0.30276403
},
"wileyNJD-AMA.bbl": {
"toxicity_score": 0.011874928,
"severe_toxicity_score": 0.0011348724,
"identity_attack_score": 0.003866276,
"insult_score": 0.0075508766,
"profanity_score": 0.014532468,
"threat_score": 0.007521313,
"sexually_explicit_score": 0.010969753,
"flirtation_score": 0.27629572
},
"wileyNJD-AMA.pag": {
"toxicity_score": 0.0026231653,
"severe_toxicity_score": 0.0005412102,
"identity_attack_score": 0.00047403504,
"insult_score": 0.005332613,
"profanity_score": 0.010433359,
"threat_score": 0.005547084,
"sexually_explicit_score": 0.0071362373,
"flirtation_score": 0.235439
},
"wileyNJD-AMA.bib": {
"toxicity_score": 0.01024134,
"severe_toxicity_score": 0.0009822845,
"identity_attack_score": 0.0016186562,
"insult_score": 0.0084818825,
"profanity_score": 0.013883443,
"threat_score": 0.0059937127,
"sexually_explicit_score": 0.009849187,
"flirtation_score": 0.34238678
},
"figures/Block_diagram_control.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/problem_decomposition.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"figures/Block_diagram.eps": {
"toxicity_score": 0.0058118035,
"severe_toxicity_score": 0.00071525574,
"identity_attack_score": 0.0011515582,
"insult_score": 0.006344369,
"profanity_score": 0.012175481,
"threat_score": 0.0060196044,
"sexually_explicit_score": 0.0072836806,
"flirtation_score": 0.49011314
},
"wileyNJD-AMA.tex": {
"toxicity_score": 0.010681152,
"severe_toxicity_score": 0.0014019012,
"identity_attack_score": 0.0029043318,
"insult_score": 0.007360875,
"profanity_score": 0.017299367,
"threat_score": 0.0068869707,
"sexually_explicit_score": 0.016159745,
"flirtation_score": 0.3864505
}
},
"reason": null
}
} | {
"num_done": {
"figure": 0,
"algorithm": 2
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 93.85126919927545,
"hash": "001f1f1f1f3f071f",
"most_frequent_color_ratio": 93.85126919927545
}
} | [] | "algorithm" | "0ac2d7f9-a8c4-45cb-b460-cf280df12e6f" | 552 | easy |
|
\begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} > 0}$\\
\State \textbf{\textit{Initialization of the first slice $i = 1$}}
\State $V_{1} \gets \min(V^{s}, \max\limits_{t \in T_{div}} bn_{t})$\\
\State \textbf{\textit{Recursion}}
\For{$i > 1$}
\While{$\mkern9mu \sum\limits_{1 \leq j < i} (V_{j}) < \max\limits_{t \in T_{div}} bn_{t}$}
\State $V_{i} \gets \min(V^{s}, \min\limits_{t \in T_{div}} (bn_{t} - \sum\limits_{1 \leq j < i} (V_{j})))$\Comment{Compute the maximum size of $V_{i}$}\\
\For{$t \in T_{div}$}
\State $q_{t,i} \gets |V_{i}|$\Comment{Extract the order quantity for relevant time steps}
\State $\sigma_{t,i} \gets -1$\Comment{Set the order direction}
\If{$bn_{t} - \sum\limits_{1 \leq j \leq i} (V_{j}) = 0$}\Comment{Remove now "empty" time steps from $T_{div}$}
\State $T_{div} \gets T_{div} - \{t\}$
\EndIf
\EndFor
\State $i \gets i+1$
\EndWhile
\EndFor
\end{algorithmic}
| \begin{algorithmic}
\State \textbf{\textit{Initialization of the set of time steps}}
\State $T_{div} \gets {t \in T_{m} \mkern9mu | \mkern9mu bn_{t} > 0}$\\
\State \textbf{\textit{Initialization of the first slice $i = 1$}}
\State $V_{1} \gets \min(V^{s}, \max\limits_{t \in T_{div}} bn_{t})$\\
\State \textbf{\textit{Recursion}}
\For{$i > 1$}
\While{$\mkern9mu \sum\limits_{1 \leq j < i} (V_{j}) < \max\limits_{t \in T_{div}} bn_{t}$}
\State $V_{i} \gets \min(V^{s}, \min\limits_{t \in T_{div}} (bn_{t} - \sum\limits_{1 \leq j < i} (V_{j})))$\Comment{Compute the maximum size of $V_{i}$}\\
\For{$t \in T_{div}$}
\State $q_{t,i} \gets |V_{i}|$\Comment{Extract the order quantity for relevant time steps}
\State $\sigma_{t,i} \gets -1$\Comment{Set the order direction}
\If{$bn_{t} - \sum\limits_{1 \leq j \leq i} (V_{j}) = 0$}\Comment{Remove now "empty" time steps from $T_{div}$}
\State $T_{div} \gets T_{div} - \{t\}$
\EndIf
\EndFor
\State $i \gets i+1$
\EndWhile
\EndFor
\end{algorithmic} | "https://arxiv.org/src/2402.12859" | "2402.12859.tar.gz" | "2024-02-20" | {
"title": "atlas: a model of short-term european electricity market processes under uncertainty -- balancing modules",
"id": "2402.12859",
"abstract": "the atlas model simulates the various stages of the electricity market chain in europe, including the formulation of offers by different market actors, the coupling of european markets, strategic optimization of production portfolios and, finally, real-time system balancing processes. atlas was designed to simulate the various electricity markets and processes that occur from the day ahead timeframe to real-time with a high level of detail. its main aim is to capture impacts from imperfect actor coordination, evolving forecast errors and a high-level of technical constraints -- both regarding different production units and the different market constraints. this working paper describes the simulated balancing processes in detail and is the second part of the atlas documentation.",
"categories": "econ.gn math.oc q-fin.ec",
"doi": "",
"created": "2024-02-20",
"updated": "",
"authors": [
"florent cogen",
"emily little",
"virginie dussartre",
"quentin bustarret"
],
"affiliation": [],
"url": "https://arxiv.org/abs/2402.12859"
} | "2024-03-15T03:21:13.620616" | {
"ToxicityFilter": {
"text_to_toxicity_attributes": {
"main.bbl": {
"toxicity_score": 0.012314741,
"severe_toxicity_score": 0.0012016296,
"identity_attack_score": 0.0041067624,
"insult_score": 0.007797878,
"profanity_score": 0.015181494,
"threat_score": 0.007586042,
"sexually_explicit_score": 0.011264639,
"flirtation_score": 0.20508216
},
"example_main.tex": {
"toxicity_score": 0.010304171,
"severe_toxicity_score": 0.0010061264,
"identity_attack_score": 0.0032558115,
"insult_score": 0.007189874,
"profanity_score": 0.014259194,
"threat_score": 0.0072882893,
"sexually_explicit_score": 0.0108517995,
"flirtation_score": 0.18085068
},
"Sections/Introduction.tex": {
"toxicity_score": 0.0068799187,
"severe_toxicity_score": 0.00072956085,
"identity_attack_score": 0.0024973555,
"insult_score": 0.006315869,
"profanity_score": 0.012722028,
"threat_score": 0.006602164,
"sexually_explicit_score": 0.009554301,
"flirtation_score": 0.23678802
},
"main.tex": {
"toxicity_score": 0.009047564,
"severe_toxicity_score": 0.0009059906,
"identity_attack_score": 0.0030153254,
"insult_score": 0.0069238725,
"profanity_score": 0.013780965,
"threat_score": 0.0071847234,
"sexually_explicit_score": 0.01020305,
"flirtation_score": 0.2085626
},
"Sections/Modules/Post_Clearing_Aggregation.tex": {
"toxicity_score": 0.09568449,
"severe_toxicity_score": 0.0045776367,
"identity_attack_score": 0.0233943,
"insult_score": 0.02051482,
"profanity_score": 0.054437835,
"threat_score": 0.034422375,
"sexually_explicit_score": 0.05369676,
"flirtation_score": 0.20790628
},
"Notes.tex": {
"toxicity_score": 0.00816794,
"severe_toxicity_score": 0.0011587143,
"identity_attack_score": 0.0021551251,
"insult_score": 0.006667371,
"profanity_score": 0.015523086,
"threat_score": 0.0065568537,
"sexually_explicit_score": 0.01374168,
"flirtation_score": 0.2103531
},
"Sections/Modules/BalancingMarket_BSPOrders.tex": {
"toxicity_score": 0.0043038763,
"severe_toxicity_score": 0.0005364418,
"identity_attack_score": 0.0011654325,
"insult_score": 0.0059833666,
"profanity_score": 0.010740792,
"threat_score": 0.0056506502,
"sexually_explicit_score": 0.0067233974,
"flirtation_score": 0.20176986
},
"header.tex": {
"toxicity_score": 0.019980038,
"severe_toxicity_score": 0.0017261505,
"identity_attack_score": 0.0046987277,
"insult_score": 0.009450888,
"profanity_score": 0.021603432,
"threat_score": 0.008000307,
"sexually_explicit_score": 0.020995872,
"flirtation_score": 0.49595475
},
"Sections/Modules/BalancingMechanism.tex": {
"toxicity_score": 0.09872158,
"severe_toxicity_score": 0.0050354004,
"identity_attack_score": 0.022791367,
"insult_score": 0.019741343,
"profanity_score": 0.058946952,
"threat_score": 0.035458352,
"sexually_explicit_score": 0.06482717,
"flirtation_score": 0.20523615
},
"style.tex": {
"toxicity_score": 0.0038326487,
"severe_toxicity_score": 0.00069618225,
"identity_attack_score": 0.00085557543,
"insult_score": 0.0055986145,
"profanity_score": 0.012346277,
"threat_score": 0.005754216,
"sexually_explicit_score": 0.010262027,
"flirtation_score": 0.20146786
},
"bibliography.bib": {
"toxicity_score": 0.01834645,
"severe_toxicity_score": 0.0023555756,
"identity_attack_score": 0.0029783275,
"insult_score": 0.010438894,
"profanity_score": 0.021808386,
"threat_score": 0.0069387536,
"sexually_explicit_score": 0.023119051,
"flirtation_score": 0.22071634
},
"Sections/Modules/BalancingMarket_TSOOrders.tex": {
"toxicity_score": 0.06817148,
"severe_toxicity_score": 0.004005432,
"identity_attack_score": 0.01656105,
"insult_score": 0.017614283,
"profanity_score": 0.04266402,
"threat_score": 0.021127338,
"sexually_explicit_score": 0.05033997,
"flirtation_score": 0.23178892
}
},
"reason": null
}
} | {
"num_done": {
"table": 1,
"figure": 0,
"algorithm": 3,
"plot": 0
}
} | {
"NonTrivialRenderingFilter": {
"white_pixels_ratio": 94.28531714775518,
"hash": "000f3f0fbf80bf3f",
"most_frequent_color_ratio": 94.28531714775518
}
} | [] | "algorithm" | "9fae7478-ae9e-44ed-bead-b393c41bed37" | 990 | medium |