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Inflationary predictions of Geometric Inflation ; In the framework of gravitational models obtained from the Geometric Inflation's proposal, where an infinite tower of curvature scalars are included into the action, we compute the slowroll parameters by the Hubble slowroll approach. We test the viability of such models as inflationary scenarios, focusing on the tensortoscalar ratio, r, and spectral scalar index, ns, relation. We find that all models considered here produce inflation and, most of them coincide, some better than others, with the marginalized 95 CL region given by Planck's data collaboration.

Gaussian Random Embeddings of Multigraphs ; This paper generalizes the Gaussian random walk and Gaussian random polygon models for linear and ring polymers to polymer topologies specified by an arbitrary multigraph G. Probability distributions of monomer positions and edge displacements are given explicitly and the spectrum of the graph Laplacian of G is shown to predict the geometry of the configurations. This provides a new perspective on the JamesGuthFlory theory of phantom elastic networks. The model is based on linear algebra motivated by ideas from homology and cohomology theory. It provides a robust theoretical foundation for more detailed models of topological polymers.

Scale symmetry, the Higgs and the Cosmos ; I review the HiggsDilaton model a scaleinvariant extension of the Standard Model and gravity able to support inflation and dark energy with just an additional degree of freedom on top of the Standard Model content. Potential extensions of the simplest realization on the basis of transverse diffeomorphisms are also discussed.

Origin of reversible and irreversible atomicscale rearrangements in a model twodimensional network glass ; In this contribution, we investigate the fundamental mechanism of plasticity in a model twodimensional network glass. The glass is generated by using a Monte Carlo bondswitching algorithm and subjected to athermal simple shear deformation, followed by subsequent unloading at selected deformation states. This enables us to investigate the topological origin of reversible and irreversible atomicscale rearrangements. It is shown that some events that are triggered during loading recover during unloading, while some do not. Thus, two kinds of elementary plastic events are observed, which can be linked to the network topology of the model glass.

Modelbased occlusion disentanglement for imagetoimage translation ; Imagetoimage translation is affected by entanglement phenomena, which may occur in case of target data encompassing occlusions such as raindrops, dirt, etc. Our unsupervised modelbased learning disentangles scene and occlusions, while benefiting from an adversarial pipeline to regress physical parameters of the occlusion model. The experiments demonstrate our method is able to handle varying types of occlusions and generate highly realistic translations, qualitatively and quantitatively outperforming the stateoftheart on multiple datasets.

On the homotopy theory of equivariant colored operads ; We build model structures on the category of equivariant simplicial operads with weak equivalences determined by families of subgroups, in the context of operads with a varying set of colors and building on the fixed color model structures in the prequel. In particular, by specifying to the family of graph subgroups or, more generally, one of the indexing systems of BlumbergHill, we obtain model structures on the category of equivariant simplicial operads whose weak equivalences are determined by norm map data.

Model Theory for Cptheorists ; We survey discrete and continuous modeltheoretic notions which have important connections to general topology. We present a selfcontained exposition of several interactions between continuous logic and Cptheory which have applications to a classification problem involving Banach spaces not including c0 or lp, following recent results obtained by P. Casazza and J. Iovino for compact continuous logics. Using Cptheoretic results involving Grothendieck spaces and double limit conditions, we extend their results to a broader family of logics, namely those with a first countable weakly Grothendieck space of types. We pose Cptheoretic problems which have modeltheoretic implications.

A Class of Models with the Potential to Represent Fundamental Physics ; A class of models intended to be as minimal and structureless as possible is introduced. Even in cases with simple rules, rich and complex behavior is found to emerge, and striking correspondences to some important core known features of fundamental physics are seen, suggesting the possibility that the models may provide a new approach to finding a fundamental theory of physics.

A System Dynamics Model of Bitcoin Mining as an Efficient Market and the Possibility of Peak Hash ; The mining of bitcoin is modeled using system dynamics, showing that the past evolution of the network hash rate can be explained to a large extent by an efficient market hypothesis applied to the mining of blocks. The possibility of a decrease in the network hash rate from the next halving event May 2020 is exposed, implying that the network may be close to 'peak hash', if the price of bitcoin and the revenues from transaction fees will remain at approximately the present level.

Team Performance Evaluation Model based on Network Feature Extraction ; Teamwork is increasingly important in today's society. This paper aims at the problem of team performance evaluation. Through complex network feature extraction, we establishes the passing network and team performance evaluation model. Finally, this paper proposes strategy for Huskies team and extend the model to the general team.

Improving Factual Consistency Between a Response and Persona Facts ; Neural models for response generation produce responses that are semantically plausible but not necessarily factually consistent with facts describing the speaker's persona. These models are trained with fully supervised learning where the objective function barely captures factual consistency. We propose to finetune these models by reinforcement learning and an efficient reward function that explicitly captures the consistency between a response and persona facts as well as semantic plausibility. Our automatic and human evaluations on the PersonaChat corpus confirm that our approach increases the rate of responses that are factually consistent with persona facts over its supervised counterpart while retaining the language quality of responses.

Learning to Segment Actions from Observation and Narration ; We apply a generative segmental model of task structure, guided by narration, to action segmentation in video. We focus on unsupervised and weaklysupervised settings where no action labels are known during training. Despite its simplicity, our model performs competitively with previous work on a dataset of naturalistic instructional videos. Our model allows us to vary the sources of supervision used in training, and we find that both task structure and narrative language provide large benefits in segmentation quality.

Compositional Scientific Computing with Catlab and SemanticModels ; Scientific computing is currently performed by writing domain specific modeling frameworks for solving special classes of mathematical problems. Since applied category theory provides abstract reasoning machinery for describing and analyzing diverse areas of math, it is a natural platform for building generic and reusable software components for scientific computing. We present Catlab.jl, which provides the categorytheoretic infrastructure for this project, together with SemanticModels.jl, which leverages this infrastructure for particular modeling tasks. This approach enhances and automates scientific computing workflows by applying recent advances in mathematical modeling of interconnected systems as cospan algebras.

Discreteevent simulation of an extended EinsteinPodolskyRosenBohm experiment ; We use discreteevent simulation to construct a subquantum model that can reproduce the quantumtheoretical prediction for the statistics of data produced by the EinsteinPodolskyRosenBohm experiment and an extension thereof. This model satisfies Einstein's criterion of locality and generates data in an eventbyevent and causeandeffect manner. We show that quantum theory can describe the statistics of the simulation data for a certain range of model parameters only.

Island in the Presence of Higher Derivative Terms ; Using extended island formula we compute entanglement entropy of Hawking radiation for black hole solutions of certain gravitational models containing higher derivative terms. To be concrete we consider two different four dimensional models to compute entropy for both asymptotically flat and AdS black holes. One observes that the resultant entropy follows the Page curve, thanks to the contribution of the island, despite the fact that the corresponding gravitational models might be nonunitary.

Color Visual Illusions A Statisticsbased Computational Model ; Visual illusions may be explained by the likelihood of patches in realworld images, as argued by inputdriven paradigms in NeuroScience. However, neither the data nor the tools existed in the past to extensively support these explanations. The era of big data opens a new opportunity to study inputdriven approaches. We introduce a tool that computes the likelihood of patches, given a large dataset to learn from. Given this tool, we present a model that supports the approach and explains lightness and color visual illusions in a unified manner. Furthermore, our model generates visual illusions in natural images, by applying the same tool, reversely.

KaLM at SemEval2020 Task 4 Knowledgeaware Language Models for Comprehension And Generation ; This paper presents our strategies in SemEval 2020 Task 4 Commonsense Validation and Explanation. We propose a novel way to search for evidence and choose the different largescale pretrained models as the backbone for three subtasks. The results show that our evidencesearching approach improves model performance on commonsense explanation task. Our team ranks 2nd in subtask C according to human evaluation score.

Secondorder traffic flow models on networks ; This paper deals with the AwRascleZhang model for traffic flow on unidirectional road networks. For the conservation of the mass and the generalized momentum, we construct weak solutions for Riemann problems at the junctions. We particularly focus on a novel approximation to the homogenized pressure by introducing an additional equation for the propagation of a reference pressure. The resulting system of coupled conservation laws is then solved using an appropriate numerical scheme of Godunov type. Numerical simulations show that the proposed approximation is able to approximate the homogenized pressure sufficiently well. The difference of the new approach compared with the LighthillWhithamRichards model is also illustrated.

Realistic GUT Yukawa Couplings from a Random Clockwork Model ; We present realistic models of flavor in SU5 and SO10 grand unified theories GUTs. The models are renormalizable and do not require any exotic representations in order to accommodate the necessary GUT breaking effects in the Yukawa couplings. They are based on a simple clockwork Lagrangian whose structure is enforced with just two one vectorlike U1 symmetries in the case of SU5 and SO10 respectively. The intergenerational hierarchies arise spontaneously from products of matrices with order one random entries.

GoodPoint unsupervised learning of keypoint detection and description ; This paper introduces a new algorithm for unsupervised learning of keypoint detectors and descriptors, which demonstrates fast convergence and good performance across different datasets. The training procedure uses homographic transformation of images. The proposed model learns to detect points and generate descriptors on pairs of transformed images, which are easy for it to distinguish and repeatedly detect. The trained model follows SuperPoint architecture for ease of comparison, and demonstrates similar performance on natural images from HPatches dataset, and better performance on retina images from Fundus Image Registration Dataset, which contain low number of cornerlike features. For HPatches and other datasets, coverage was also computed to provide better estimation of model quality.

Lambda1405 as a Kbar N Feshbach resonance in the Skyrme model ; We describe the Lambda1405 hyperon as a Feshbach resonance of a barKN quasibound state coupled by a decaying channel of pi Sigma in the Skyrme model. A weakly bound barKN state is generated in the laboratory frame, while the Sigma hyperon as a strongly bound state of barKN in the intrinsic frame. We obtain a coupling of barKN and pi Sigma channels by computing a baryon matrix element of the axial current. This coupling enables the decay of the barKN bound state to piSigma . It is shown that the Skyrme model supports the Lambda1405 as a narrow Feshbach resonance.

A GraphBased Modeling Abstraction for Optimization Concepts and Implementation in Plasmo.jl ; We present a general graphbased modeling abstraction for optimization that we call an OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical hypergraph in which nodes represent optimization subproblems and edges represent connectivity between such subproblems. The abstraction enables the modular construction of highly complex models in an intuitive manner, facilitates the use of graph analysis tools to perform partitioning, aggregation, and visualization tasks, and facilitates communication of structures to decomposition algorithms. We provide an opensource implementation of the abstraction in the Juliabased package Plasmo.jl. We provide tutorial examples and large application case studies to illustrate the capabilities.

An analytic treatment of Quartic Hilltop Inflation ; Quartic hilltop inflation remains one of the most successful inflationary models. Yet, the expectations of early treatments of hilltop inflation would contradict the observations and render the model excluded. However, recent numerical treatment has demonstrated that quartic hilltop inflation actually fares well with observations. In this work, a fully analytic treatment of the model aims to dispel the mystery surrounding the behaviour of quartic hilltop inflation. The results obtained are in excellent agreement with numerical works on the subject, yet offer simple analytic formulas to calculate observables and easily test thereby quartic hilltop inflation, hopefully revealing information on the theoretical background.

New models with independent dynamical torsion and nonmetricity fields ; We propose a gravitational model which allows the independent dynamical behaviour of the torsion and nonmetricity fields to be displayed in the framework of MetricAffine gauge theory of gravity. For this task, we derive a new exact black hole solution referred to this model which extends the role of torsion of the main wellknown exact solutions based on WeylCartan geometry and constitutes the first known isolated gravitational system characterized by a metric tensor with independent spin and dilation charges.

Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations ; Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the mixture components to be nonparametric and have substantial or even total overlap. This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations. Our analysis leverages an oracle inequality for weighted kernel density estimators of the distribution on groups, together with a general result showing that consistent estimation of the distribution on groups implies consistent estimation of mixture components. A practical implementation is provided for paired observations, and the approach is shown to outperform existing methods, especially when mixture components overlap significantly.

Fractal FRW Model within Domain Wall ; In the present paper an attempt has been made to study the flat fractal Friedmann Robertson Walker model filled with domain walls. We have obtained the fractal equation of deceleration parameter and tension of the domain wall. It is observed that, while domain walls exist at early times, they disappear at late time. Finally, some physical parameters of the model are discussed using graphs.

Wellposedness of the twodimensional AbelsGarckeGrun model for twophase flows with unmatched densities ; We study the AbelsGarckeGrun AGG model for a mixture of two viscous incompressible fluids with different densities. The AGG model consists of a NavierStokesCahnHilliard system characterized by a nonconstant concentrationdependent density and an additional flux term due to interface diffusion. In this paper we address the wellposedness problem in the twodimensional case. We first prove the existence of local strong solutions in general bounded domains. In the space periodic setting we show that the strong solutions exist globally in time. In both cases we prove the uniqueness and the continuous dependence on the initial data of the strong solutions.

Exchange interactions, YangBaxter relations and transparent particles ; We introduce a class of particle models in one dimension involving exchange interactions that have scattering properties satisfying the YangBaxter consistency condition. A subclass of these models exhibits reflectionless scattering, in which particles are transparent to each other, generalizing a property hitherto only known for the exchange Calogero model. The thermodynamics of these systems can be derived using the asymptotic Betheansatz method.

Simulating human interactions in supermarkets to measure the risk of COVID19 contagion at scale ; Taking the context of simulating a retail environment using agent based modelling, a theoretical model is presented that describes the probability distribution of customer collisions using a novel space transformation to the Torus Tor2. A method for generating the distribution of customer paths based on historical basket data is developed. Finally a calculation of the number of simulations required for statistical significance is developed. An implementation of this modelling approach to run simulations on multiple store geometries at industrial scale is being developed with current progress detailed in the technical appendix.

Social Networks as a Mechanism for Discrimination ; I study labor markets in which firms hire via referrals. I develop an employment model showing thatdespite initial equality in ability, employment, wages, and network structureminorities receive fewer jobs through referral and lower expected wages, simply because their social group is smaller. This disparity, termed social network discrimination, falls outside the dominant economics discrimination modelstastebased and statistical. Social network discrimination can be mitigated by minorities having more social ties or a strongerknit network. I calibrate the model using a nationallyrepresentative U.S. sample and estimate the lowerbound welfare gap caused by social network discrimination at over four percent, disadvantaging black workers.

Binary Random Projections with Controllable Sparsity Patterns ; Random projection is often used to project higherdimensional vectors onto a lowerdimensional space, while approximately preserving their pairwise distances. It has emerged as a powerful tool in various data processing tasks and has attracted considerable research interest. Partly motivated by the recent discoveries in neuroscience, in this paper we study the problem of random projection using binary matrices with controllable sparsity patterns. Specifically, we proposed two sparse binary projection models that work on general data vectors. Compared with the conventional random projection models with dense projection matrices, our proposed models enjoy significant computational advantages due to their sparsity structure, as well as improved accuracies in empirical evaluations.

Semiclassical analysis of the Starobinsky inflationary model ; In this work we study the scalar power spectrum and the spectral index for the Starobinsky inflationary model using the phase integral method upto thirdorder of approximation. We show that the semiclassical methods reproduce the scalar power spectrum for the Starobinsky model with a good accuracy, and the value of the spectral index compares favorably with observations. Also, we compare the results with the uniform approximation method and the secondorder slowroll approximation.

Solvable twoparticle systems with timeasymmetric interactions in de Sitter space ; The twoparticle models in de Sitter spacetime with timeasymmetric retardedadvanced interactions are constructed. Particular cases of the fieldtype electromagnetic and scalar interactions are considered. The manifestly covariant descriptions of the models within the Lagrangian and Hamiltonian formalisms with constraints are proposed. It is shown that the models are de Sitterinvariant and integrable. An explicit solution of equations of motion is derived in quadratures by means of projection operator technique.

The curious case of developmental BERTology On sparsity, transfer learning, generalization and the brain ; In this essay, we explore a point of intersection between deep learning and neuroscience, through the lens of large language models, transfer learning and network compression. Just like perceptual and cognitive neurophysiology has inspired effective deep neural network architectures which in turn make a useful model for understanding the brain, here we explore how biological neural development might inspire efficient and robust optimization procedures which in turn serve as a useful model for the maturation and aging of the brain.

Reconstruction Bottlenecks in ObjectCentric Generative Models ; A range of methods with suitable inductive biases exist to learn interpretable objectcentric representations of images without supervision. However, these are largely restricted to visually simple images; robust object discovery in realworld sensory datasets remains elusive. To increase the understanding of such inductive biases, we empirically investigate the role of reconstruction bottlenecks for scene decomposition in GENESIS, a recent VAEbased model. We show such bottlenecks determine reconstruction and segmentation quality and critically influence model behaviour.

A Spectral Condition for Spectral Gap Fast Mixing in HighTemperature Ising Models ; We prove that Ising models on the hypercube with general quadratic interactions satisfy a Poincar'e inequality with respect to the natural Dirichlet form corresponding to Glauber dynamics, as soon as the operator norm of the interaction matrix is smaller than 1. The inequality implies a control on the mixing time of the Glauber dynamics. Our techniques rely on a localization procedure which establishes a structural result, stating that Ising measures may be decomposed into a mixture of measures with quadratic potentials of rank one, and provides a framework for proving concentration bounds for high temperature Ising models.

A Technical Report for VIPriors Image Classification Challenge ; Image classification has always been a hot and challenging task. This paper is a brief report to our submission to the VIPriors Image Classification Challenge. In this challenge, the difficulty is how to train the model from scratch without any pretrained weight. In our method, several strong backbones and multiple loss functions are used to learn more representative features. To improve the models' generalization and robustness, efficient image augmentation strategies are utilized, like autoaugment and cutmix. Finally, ensemble learning is used to increase the performance of the models. The final Top1 accuracy of our team DeepBlueAI is 0.7015, ranking second in the leaderboard.

Random perturbations of an ecoepidemiological model ; We consider random perturbations of a general ecoepidemiological model. We prove the existence of a global random attractor, the persistence of susceptibles preys and provide conditions for the simultaneous extinction of infectives and predators. We also discuss the dynamics of the corresponding random epidemiological SI and predatorprey models. We obtain for this cases a global random attractor, prove the prevalence of susceptiblespreys and provide conditions for the extinctions of infectivespredators.

Image reconstruction in dynamic inverse problems with temporal models ; The paper surveys variational approaches for image reconstruction in dynamic inverse problems. Emphasis is on methods that rely on parametrised temporal models. These are here encoded as diffeomorphic deformations with time dependent parameters, or as motion constrained reconstruction where the motion model is given by a partial differential equation. The survey also includes recent development in integrating deep learning for solving these computationally demanding variational methods. Examples are given for 2D dynamic tomography, but methods apply to general inverse problems.

Efficient GraphBased Active Learning with Probit Likelihood via Gaussian Approximations ; We present a novel adaptation of active learning to graphbased semisupervised learning SSL under nonGaussian Bayesian models. We present an approximation of nonGaussian distributions to adapt previously Gaussianbased acquisition functions to these more general cases. We develop an efficient rankone update for applying lookahead based methods as well as model retraining. We also introduce a novel model change acquisition function based on these approximations that further expands the available collection of active learning acquisition functions for such methods.

StructurePreserving Interpolation for Model Reduction of Parametric Bilinear Systems ; In this paper, we present an interpolation framework for structurepreserving model order reduction of parametric bilinear dynamical systems. We introduce a general setting, covering a broad variety of different structures for parametric bilinear systems, and then provide conditions on projection spaces for the interpolation of structured subsystem transfer functions such that the system structure and parameter dependencies are preserved in the reducedorder model. Two benchmark examples with different parameter dependencies are used to demonstrate the theoretical analysis.

Stability of Hyperbolic and MatterDominated Bounce Cosmologies From FR,G Modified Gravity at Late Evolution Stages ; The stability of two different bounce scenarios from FR,G modified gravity at later times is studied, namely a hyperbolic cosine bounce model and a matterdominated one. After describing the main characteristics of FR,G modified gravity, the two different bounce scenarios stemming from this theory are reconstructed and their stability at late stages is discussed. The stability of the hyperbolic cosine model is proven, while the concrete matterbounce model here chosen does not seem to accomplish the necessary conditions to be stable at later times.

Nonlinear diffusion in the KellerSegel model of parabolicparabolic type ; In this paper we study the initial boundary value problem for the system utDelta ummboxdivuqnabla v, vtDelta vvu. This problem is the socalled KellerSegel model with nonlinear diffusion. Our investigation reveals that nonlinear diffusion can prevent overcrowding. To be precise, we show that solutions are bounded as long as mq0, thereby substantially generalizing the known results in this area. Furthermore, our result seems to imply that the KellerSegel model can have bounded solutions and blowup ones simultaneously.

Asymptotic behavior of Nfields Chiral Cosmology ; We perform a detailed analysis for the asymptotic behaviour for the multiscalar field Chiral cosmological scenario. We present the asymptotic behaviour for the onefield, twofields and threefields Chiral models. From these results, and deriving conserved quantities, we present a Theorem for the Nfields model for the Chiral model with Nfields. We find that the maximum number of scalar fields which provide interesting physical results is twofields, while for N2 the new stationary points are only of mathematical interest since they do not describe new exact solutions different from those recovered for N2.

The Winfree model with noninfinitesimal phaseresponse curve OttAntonsen theory ; A novel generalization of the Winfree model of globally coupled phase oscillators, representing phase reduction under finite coupling, is studied analytically. We consider interactions through a noninfinitesimal or finite phaseresponse curve PRC, in contrast to the infinitesimal PRC of the original model. For a family of noninfinitesimal PRCs, the global dynamics is captured by one complexvalued ordinary differential equation resorting to the OttAntonsen ansatz. The phase diagrams are thereupon obtained for four illustrative cases of noninfinitesimal PRC. Bistability between collective synchronization and full desynchronization is observed in all cases.

Optical clocks based on molecular vibrations as probes of variation of the protontoelectron mass ratio ; Some new physics models of quantum gravity or dark matter predict drifts or oscillations of the fundamental constants. A relatively simple model relates molecular vibrations to the protontoelectron mass ratio mu. Many vibrational transitions are at optical frequencies with prospects for use as highly accurate optical clocks. We give a brief summary of new physics models that lead to changes in mu and the current limits on drifts and oscillation amplitudes. After an overview of laboratory procedures, we give examples of molecules with experiments currently in development or underway. These experiments' projected systematic and statistical uncertainties make them leading candidates in nextgeneration searches for timevariation of mu.

PMSD DataDriven Simulation Using System Dynamics and Process Mining ; Process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck analysis and organizational mining. However, these techniques are mostly backwardlooking. PMSD is a web application tool that supports forwardlooking simulation techniques. It transforms the event data and process mining results into a simulation model which can be executed and validated. PMSD includes log transformation, time window selection, relation detection, interactive model generation, simulating and validating the models in the form of system dynamics, i.e., a technique for an aggregated simulation. The results of the modules are visualized in the tool for a better interpretation

Barrow holographic dark energy with Hubble horizon as IR cutoff ; In this work, we propose a noninteracting model of Barrow holographic dark energy BHDE using Barrow entropy in a spatially flat FLRW Universe considering the IR cutoff as the Hubble horizon. We study the evolutionary history of important cosmological parameters, in particular, EoS omegaB, deceleration parameter and, the BHDE and matter density parameter and also observe satisfactory behaviours in the BHDE the model. In addition, to describe the accelerated expansion of the Universe the correspondence of the BHDE model with the quintessence scalar field has been reconstructed.

New Properties of the Data Distillation Method When Working With Tabular Data ; Data distillation is the problem of reducing the volume oftraining data while keeping only the necessary information. With thispaper, we deeper explore the new data distillation algorithm, previouslydesigned for image data. Our experiments with tabular data show thatthe model trained on distilled samples can outperform the model trainedon the original dataset. One of the problems of the considered algorithmis that produced data has poor generalization on models with differenthyperparameters. We show that using multiple architectures during distillation can help overcome this problem.

Dynamics of a homogeneous and isotropic space in pure cubic fR gravity ; We consider a possible ways of the dynamics of a homogeneous and isotropic space described by the FLRW metric in the framework of cubic fR gravity in the absence of matter. This article points an method for limiting the parameters of extended gravity models. We propose and develop a method for fR gravity models based on the dynamics of metrics for various model parameters in the simplest example. The influence of parameters and initial conditions on further dynamics are discussed. The parameters can be limited by 1 slow growth of space, 2 instability, 3 divergence with the inflationary scenario.

Neural Approximate Sufficient Statistics for Implicit Models ; We consider the fundamental problem of how to automatically construct summary statistics for implicit generative models where the evaluation of the likelihood function is intractable, but sampling data from the model is possible. The idea is to frame the task of constructing sufficient statistics as learning mutual information maximizing representations of the data with the help of deep neural networks. The infomax learning procedure does not need to estimate any density or density ratio. We apply our approach to both traditional approximate Bayesian computation and recent neural likelihood methods, boosting their performance on a range of tasks.

Datadriven RANS closures for threedimensional flows around bluff bodies ; In this short note we apply the recently proposed datadriven RANS closure modelling framework of Schmelzer et al. 2020 to fully threedimensional, high Reynolds number flows namely wallmounted cubes and cuboids at Re40,000, and a cylinder at Re140,000. For each flow, a new RANS closure is generated using sparse symbolic regression based on LES or DES reference data. This new model is implemented in a CFD solver, and subsequently applied to prediction of the other flows. We see consistent improvements compared to the baseline komega SST model in predictions of meanvelocity in the complete flow domain.

Random hyperbolic graphs in d1 dimensions ; We consider random hyperbolic graphs in hyperbolic spaces of any dimension d1 geq 2. We present a rescaling of model parameters that casts the random hyperbolic graph model of any dimension to a unified mathematical framework, leaving the degree distribution invariant with respect to d. We analyze the limiting regimes of the model and release a software package that generates random hyperbolic graphs and their limits in hyperbolic spaces of any dimension.

ClosedForm Solutions for a LowOrder System Fast Frequency Response Model ; This paper presents a novel closedform solution for a loworder system frequency response SFR model that is accurate for all time periods and an accompanying approximation for representing primary frequency responses at two different speeds while still maintaining mathematical tractability. This allows for the inclusion of both fast frequency responses e.g. from battery energy storage systems and more conventional responses e.g. from thermal generation in a single SFR formulation. The closedform expressions can be efficiently used in applications that use the SFR model such as frequency stability studies and securityconstrained unit commitment SCUC studies.

Potential linear and angular momentum in the scalar diquark model ; We present an analytic twoloop calculation within the scalar diquark model of the potential linear and angular momenta, defined as the difference between the JaffeManohar and Ji notions of linear and angular momenta. As expected by parity and timereversal symmetries, a direct calculation confirms that the potential transverse momentum coincides with the JaffeManohar or canonical definition of average quark transverse momentum, also known as the quark Sivers shift. We examine whether initialfinalstate interactions at the origin of the Sivers asymmetry can also generate a potential angular momentum in the scalar diquark model.

Liputan6 A Largescale Indonesian Dataset for Text Summarization ; In this paper, we introduce a largescale Indonesian summarization dataset. We harvest articles from Liputan6.com, an online news portal, and obtain 215,827 documentsummary pairs. We leverage pretrained language models to develop benchmark extractive and abstractive summarization methods over the dataset with multilingual and monolingual BERTbased models. We include a thorough error analysis by examining machinegenerated summaries that have low ROUGE scores, and expose both issues with ROUGE itself, as well as with extractive and abstractive summarization models.

Matrix model for the total descendant potential of a simple singularity of type D ; We construct a Hermitian matrix model for the total descendant potential of a simple singularity of type D similar to the Kontsevich matrix model for the generating function of intersection numbers on the DeligneMumford moduli spaces overlinemathcalMg,n.

MADVAE Manifold Awareness Defense Variational Autoencoder ; Although deep generative models such as DefenseGAN and DefenseVAE have made significant progress in terms of adversarial defenses of image classification neural networks, several methods have been found to circumvent these defenses. Based on DefenseVAE, in our research we introduce several methods to improve the robustness of defense models. The methods introduced in this paper are straight forward yet show promise over the vanilla DefenseVAE. With extensive experiments on MNIST data set, we have demonstrated the effectiveness of our algorithms against different attacks. Our experiments also include attacks on the latent space of the defensive model. We also discuss the applicability of existing adversarial latent space attacks as they may have a significant flaw.

Modeling Event Salience in Narratives via Barthes' Cardinal Functions ; Events in a narrative differ in salience some are more important to the story than others. Estimating event salience is useful for tasks such as story generation, and as a tool for text analysis in narratology and folkloristics. To compute event salience without any annotations, we adopt Barthes' definition of event salience and propose several unsupervised methods that require only a pretrained language model. Evaluating the proposed methods on folktales with event salience annotation, we show that the proposed methods outperform baseline methods and find finetuning a language model on narrative texts is a key factor in improving the proposed methods.

Monetary Policy and Firm Dynamics ; Do firm dynamics matter for the transmission of monetary policy Empirically, the startup rate declines following a monetary contraction, while the exit rate increases, both of which reduce aggregate employment. I present a model that combines firm dynamics in the spirit of Hopenhayn 1992 with NewKeynesian frictions and calibrate it to match crosssectional evidence. The model can qualitatively account for the responses of entry and exit rates to a monetary policy shock. However, the responses of macroeconomic variables closely resemble those in a representativefirm model. I discuss the equilibrium forces underlying this approximate equivalence, and what may overturn this result.

Twodimensional model of a highTc superconducting dynamo ; High temperature superconducting dynamos are capable of low loss contactless pumping large dc currents into superconducting magnet coils. We present a model of such devices and use efficient numerical methods for computing the twodimensional loop currents and electric fields induced in the thin superconducting stator strip by a dynamo rotor mounted permanent magnet. We find also the voltages generated in the stator under the open circuit conditions. It is shown that if the length of a permanent magnet is comparable to or smaller than the strip width, the onedimensional model employed in the previous works can be inaccurate.

Bohr compactifications of groups and rings ; We introduce and study modeltheoretic connected components of rings as an analogue of modeltheoretic connected components of definable groups. We develop their basic theory and use them to describe both the definable and classical Bohr compactifications of rings. We then use modeltheoretic connected components to explicitly calculate Bohr compactifications of some classical matrix groups, such as the discrete Heisenberg group UT3Z, the continuous Heisenberg group UT3R, and, more generally, groups of upper unitriangular and invertible upper triangular matrices over unital rings.

Efficiency Models for GaNbased LightEmitting Diodes Status and Challenges ; Light emitting diodes LEDs based on Gallium Nitride GaN have been revolutionizing various applications in lighting, displays, medical equipment, and other fields. However, their energy efficiency is still below expectations in many cases. An unprecedented diversity of theoretical models has been developed for efficiency analysis and GaNLED design optimization. This review paper provides an overview of the modeling landscape and pays special attention to the influence of IIInitride material properties. It thereby identifies some key challenges and directions for future improvements.

Learning Models for Actionable Recourse ; As machine learning models are increasingly deployed in highstakes domains such as legal and financial decisionmaking, there has been growing interest in posthoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes e.g., an applicant denied a loan with recourse i.e., a description of how they can change their features to obtain a positive outcome. We propose a novel algorithm that leverages adversarial training and PAC confidence sets to learn models that theoretically guarantee recourse to affected individuals with high probability without sacrificing accuracy. We demonstrate the efficacy of our approach via extensive experiments on real data.

Dynamical large deviations of twodimensional kinetically constrained models using a neuralnetwork state ansatz ; We use a neural network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We obtain the scaled cumulantgenerating function for the dynamical activity of the FredricksonAndersen model, a prototypical kinetically constrained model, in one and two dimensions, and present the first sizescaling analysis of the dynamical activity in two dimensions. These results provide a new route to the study of dynamical largedeviation functions, and highlight the broad applicability of the neuralnetwork state ansatz across domains in physics.

Baryonic and Leptonic GeV Dark Matter ; We perform a systematic analysis of models with GeVscale dark matter coupled to baryons and leptons. Such theories provide a natural framework to explain the matterantimatter asymmetry of the universe. We find that only a few baryonic dark matter models are free from treelevel proton decay without explicitly imposing baryon number conservation. We enumerate those cases and provide a brief overview of their phenomenology. We then focus on a leptonic dark matter model for a more detailed discussion of the baryon asymmetry generation via leptogenesis, the symmetry restoration in the dark sector and the expected dark matter annihilation signals in indirect detection experiments.

Assembling a Pipeline for 3D Face Interpolation ; This paper describes a pipeline built with open source tools for interpolating 3D facial expressions taken from images. The presented approach allows anyone to create 3D face animations from 2 input photos one from the start face expression, and the other from the final face expression. Given the input photos, corresponding 3D face models are constructed and texturemapped using the photos as textures aligned with facial features. Animations are then generated by morphing the models by interpolation of the geometries and textures of the models. This work was performed as a MS project at the University of California, Merced.

Large time behavior to the FENE dumbbell model of polymeric flows near equilibrium ; In this paper we mainly study large time behavior for the strong solutions of the finite extensible nonlinear elastic FENE dumbbell model. There is a lot results about the L2 decay rate of the corotation model. In this paper, we consider the general case. We prove that the optimal L2 decay rate of the velocity is 1tfracd4 with dgeq 2. This result improves the previous result in citeLuoYin.

Lethean Attack An Online Data Poisoning Technique ; Data poisoning is an adversarial scenario where an attacker feeds a specially crafted sequence of samples to an online model in order to subvert learning. We introduce Lethean Attack, a novel data poisoning technique that induces catastrophic forgetting on an online model. We apply the attack in the context of TestTime Training, a modern online learning framework aimed for generalization under distribution shifts. We present the theoretical rationale and empirically compare it against other sample sequences that naturally induce forgetting. Our results demonstrate that using lethean attacks, an adversary could revert a testtime training model back to coinflip accuracy performance using a short sample sequence.

Reciprocal maximum likelihood degrees of diagonal linear concentration models ; We show that the reciprocal maximal likelihood degree rmld of a diagonal linear concentration model mathcal L subseteq mathbbCn of dimension r is equal to 2rchiM textstylefrac12, where chiM is the characteristic polynomial of the matroid M associated to mathcal L. In particular, this establishes the polynomiality of the rmld for general diagonal linear concentration models, positively answering a question of Sturmfels, Timme, and Zwiernik.

Electroweak Form Factor in Sudakov and Threshold Regimes with Effective Field Theories ; We compute the massive gauge and scalar corrections to form factors in both the Sudakov and threshold regimes up to and including twoloop orders. The corrections are calculated for processes involving two external fermions and scalars in the spontaneously broken SUNHiggs model, examining a range of composite operators. Our results are general, so we discuss how our form factors are mappable from our model to the Standard Model and beyond. The effective theory formalism deployed in our work extends previous studies based on infrared evolution equations, which either neglect scalar contributions or are restricted to the Sudakov regime.

Enhanced Sufficient Battery Model for Aggregate Flexibility of Thermostatically Controlled Loads Considering Coupling Constraints ; This letter proposes an enhanced sufficient battery model ESBM as well as a binary search algorithm for a sharp innerapproximation of the aggregate flexibility of thermostatically controlled load TCL arrays. Compared with the previous work on generalized battery models, this ESBM preserves the merits of being sufficient and mitigates the conservativity. Moreover, unlike the work ignoring the coupling constraints that may also restrict TCLs' aggregate flexibility, our ESBM can readily handle these constraints. Numerical tests validate the merits of using the ESBM and its significance for power system operations.

Population genetic models of dormancy ; In the present article, we investigate the effects of dormancy on an abstract population genetic level. We first provide a short review of seed bank models in population genetics, and the role of dormancy for the interplay of evolutionary forces in general, before we discuss two recent paradigmatic models, referring to spontaneous resp. simultaneous switching of individuals between the active and the dormant state. We show that both mechanisms give rise to nontrivial mathematical objects, namely the continuous seed bank diffusion and the seed bank diffusion with jumps, as well as their dual processes, the seed bank coalescent and the seed bank coalescent with simultaneous switching.

SkeleonBased Typing Style Learning For Person Identification ; We present a novel architecture for person identification based on typingstyle, constructed of adaptive nonlocal spatiotemporal graph convolutional network. Since type style dynamics convey meaningful information that can be useful for person identification, we extract the joints positions and then learn their movements' dynamics. Our nonlocal approach increases our model's robustness to noisy input data while analyzing joints locations instead of RGB data provides remarkable robustness to alternating environmental conditions, e.g., lighting, noise, etc. We further present two new datasets for typing style based person identification task and extensive evaluation that displays our model's superior discriminative and generalization abilities, when compared with stateoftheart skeletonbased models.

Likelihood Geometry of Correlation Models ; Correlation matrices are standardized covariance matrices. They form an affine space of symmetric matrices defined by setting the diagonal entries to one. We study the geometry of maximum likelihood estimation for this model and linear submodels that encode additional symmetries. We also consider the problem of minimizing two closely related functions of the covariance matrix the Stein's loss and the symmetrized Stein's loss. Unlike the Gaussian loglikelihood these two functions are convex and hence admit a unique positive definite optimum. Some of our results hold for general affine covariance models.

Radial solutions for a dynamic debonding model in dimension two ; In this paper we deal with a debonding model for a thin film in dimension two, where the wave equation on a timedependent domain is coupled with a flow rule Griffith's principle for the evolution of the domain. We propose a general definition of energy release rate, which is central in the formulation of Griffith's criterion. Next, by means of an existence result, we show that such definition is well posed in the special case of radial solutions, which allows us to employ representation formulas typical of onedimensional models.

LowOrder Model of Biological Neural Networks ; A biologically plausible loworder model LOM of biological neural networks is a recurrent hierarchical network of dendritic nodestrees, spikingnonspiking neurons, unsupervised supervised covarianceaccumulative learning mechanisms, feedback connections, and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect and recognize multiplehierarchical corrupted, distorted, and occluded temporal and spatial patterns.

nvicinity method for Ising Model with longrange interaction ; The previously developed nvicinity method allows us to calculate accurately critical values of inverse temperatures for Ising models with shortrange interaction. We generalize the method to the case of longrange interactions in spin systems and obtain theoretical formulas for the inverse temperatures in terms of the spin interaction constants. The comparison of our theoretical estimates with computer simulations for the two and threedimensional Ising models shows that the larger the dimension of the problem the better their agreement.

Lagrangian formulation for an extended cosmological equationofstate ; We show that the extended cosmological equationofstate developed starting from a Chaplygin equationofstate, recently applied to stellar modeling, is a viable dark energy model consistent with standard scalar potentials. Moreover we find a Lagrangian formulation based on a canonical scalar field with the appropriate selfinteraction potential. Finally, we fit the scalar potential obtained numerically with concrete functions well studied in the literature. Our results may be of interest to model builders and particle physicists.

The FaddeevReshetikhin model from a 4D ChernSimons theory ; We derive the FaddeevReshetikhin FR model from a fourdimensional Chern Simons theory with two order surface defects by following the work by Costello and Yamazaki arXiv1908.02289. Then we present a trigonometric deformation of the FR model by employing a boundary condition with an Roperator of DrinfeldJimbo type. This is a generalization of the work by Delduc, Lacroix, Magro and Vicedo arXiv1909.13824 from the disorder surface defect case to the order one.

Optimal transport for vector Gaussian mixture models ; Vectorvalued Gaussian mixtures form an important special subset of vectorvalued distributions. In general, vectorvalued distributions constitute natural representations for physical entities, which can mutate or transit among alternative manifestations distributed in a given space. A key example is color imagery. In this note, we vectorize the Gaussian mixture model and study several different optimal mass transport related problems associated to such models. The benefits of using vector Gaussian mixture for optimal mass transport include computational efficiency and the ability to preserve structure.

A deep network approach to multitemporal cloud detection ; We present a deep learning model with temporal memory to detect clouds in image time series acquired by the Seviri imager mounted on the Meteosat Second Generation MSG satellite. The model provides pixellevel cloud maps with related confidence and propagates information in time via a recurrent neural network structure. With a single model, we are able to outline clouds along all year and during day and night with high accuracy.

RealFormer Transformer Likes Residual Attention ; Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants BERT, ETC, etc. on a wide spectrum of tasks including Masked Language Modeling, GLUE, SQuAD, Neural Machine Translation, WikiHop, HotpotQA, Natural Questions, and OpenKP. We also observe empirically that RealFormer stabilizes training and leads to models with sparser attention. Source code and pretrained checkpoints for RealFormer can be found at httpsgithub.comgoogleresearchgoogleresearchtreemasterrealformer.

Global martingale solutions for a stochastic ShigesadaKawasakiTeramoto population model ; The existence of global nonnegative martingale solutions to a crossdiffusion system of ShigesadaKawasakiTeramoto type with multiplicative noise is proven. The model describes the segregation dynamics of populations with an arbitrary number of species. The diffusion matrix is generally neither symmetric nor positive semidefinite, which excludes standard methods. Instead, the existence proof is based on the entropy structure of the model, approximated by a WongZakai argument, and on suitable higher moment estimates and fractional time regularity. In the case without selfdiffusion, the lack of regularity is overcome by carefully exploiting the entropy production terms.

Testing the effectiveness of unconventional monetary policy in Japan and the United States ; Unconventional monetary policy UMP may make the effective lower bound ELB on the shortterm interest rate irrelevant. We develop a theoretical model that underpins our empirical test of this irrelevance hypothesis' based on the simple idea that under the hypothesis, the short rate can be excluded in any empirical model that accounts for alternative measures of monetary policy. We test the hypothesis for Japan and the United States using a structural vector autoregressive model with the ELB. We firmly reject the hypothesis but find that UMP has had strong delayed effects.

On the existence of solutions for FrenkelKontorova models on quasicrystals ; We focus on the recent development of the investigation on the equilibria of the FrenkelKontorova models subjected to potentials generated by quasicrystals. We present a specific onedimensional model with an explicit equivariant potential driven by the Fibonacci quasicrystal. For a given positive number theta , we show that there are multiple equilibria with rotation number theta , e.g., a minimal configuration and a nonminimal equilibrium configuration. Some numerical explorations of of finding these equilibira are provided.

A Model of Market Making and Price Impact ; Traders constantly consider the price impact associated with changing their positions. This paper seeks to understand how price impact emerges from the quoting strategies of market makers. To this end, market making is modeled as a dynamic auction using the mathematical framework of Stochastic Differential Games. In Nash Equilibrium, the market makers' quoting strategies generate a price impact function that is of the same form as the celebrated AlmgrenChriss model. The key insight is that price impact is the mechanism through which market makers earn profits while matching their books. As such, price impact is an essential feature of markets where flow is intermediated by market makers.

Instanced model simplification using combined geometric and appearancerelated metric ; Evolution of 3D graphics and graphical worlds has brought issues like content optimization, realtime processing, rendering, and shared storage limitation under consideration. Generally, different simplification approaches are used to make 3D meshes viable for rendering. However, many of these approaches ignore vertex attributes for instanced 3D meshes. In this paper, we implement and evaluate a simple and improved version to simplify instanced 3D textured models. The approach uses different vertex attributes in addition to geometry to simplify mesh instances. The resulting simplified models demonstrate efficient timespace requirements and better visual quality.

Vibrational model of thermal conduction for fluids with soft interactions ; A vibrational model of heat transfer in simple liquids with soft pairwise interatomic interactions is discussed. A general expression is derived, which involves an averaging over the liquid collective mode excitation spectrum. The model is applied to quantify heat transfer in a dense LennardJones liquid and a strongly coupled onecomponent plasma. Remarkable agreement with the available numerical results is documented. A similar picture does not apply to the momentum transfer and shear viscosity of liquids.

Sharp detection boundaries on testing dense subhypergraph ; We study the problem of testing the existence of a dense subhypergraph. The null hypothesis is an ErdosRenyi uniform random hypergraph and the alternative hypothesis is a uniform random hypergraph that contains a dense subhypergraph. We establish sharp detection boundaries in both scenarios 1 the edge probabilities are known; 2 the edge probabilities are unknown. In both scenarios, sharp detectable boundaries are characterized by the appropriate model parameters. Asymptotically powerful tests are provided when the model parameters fall in the detectable regions. Our results indicate that the detectable regions for general hypergraph models are dramatically different from their graph counterparts.

On consistency scores in text data with an implementation in R ; In this paper, we introduce a reproducible cleaning process for the text extracted from PDFs using ngram models. Our approach compares the originally extracted text with the text generated from, or expected by, these models using earlier text as stimulus. To guide this process, we introduce the notion of a consistency score, which refers to the proportion of text that is expected by the model. This is used to monitor changes during the cleaning process, and across different corpuses. We illustrate our process on text from the book Jane Eyre and introduce both a Shiny application and an R package to make our process easier for others to adopt.

Selfdriven criticality in a stochastic epidemic model ; We present a generic epidemic model with stochastic parameters, in which the dynamics selforganize to a critical state with suppressed exponential growth. More precisely, the dynamics evolve into a quasisteadystate, where the effective reproduction rate fluctuates close to the critical value one, as observed for different epidemics. The main assumptions underlying the model are that the rate at which each individual becomes infected changes stochastically in time with a heavytailed steady state. The critical regime is characterized by an extremely long duration of the epidemic. Its stability is analyzed both numerically and analytically.

Optimal Reinsurance A RuinRelated Uncertain Programming Approach ; We investigate the role of reinsurance in maximizing the wealth of an insurance company. We use Liu's uncertainty theory B. Liu, 2007 for the problem modeling and followup computations. The uncertainty measure of ruin for the insurance company is considered as the optimization criterion. Since calculating the ruin index is very difficult, we introduce a simple computational method to identify the uncertain measure of ruin for an insurance company. Finally, a generalized model is presented, granting the model be more practical.

An elementary approach to component sizes in critical random graphs ; In this article we introduce a simple tool to derive polynomial upper bounds for the probability of observing unusually large maximal components in some models of random graphs when considered at criticality. Specifically, we apply our method to a model of random intersection graph, a random graph obtained through pbond percolation on a general dregular graph, and a model of inhomogeneous random graph.

Baryonic TullyFisher test of Grumiller's modified gravity model ; We test the Grumiller's quantum motivated modified gravity model, which at large distances modifies the Newtonian potential and describes the galactic rotation curves of disk galaxies in terms of a Rindler acceleration term without the need of any dark matter, against the baryonic TullyFisher feature that relates the total baryonic mass of a galaxy with flat rotation velocity of the galaxy. We estimate the Rindler acceleration parameter from observed baryonic mass versus rotation velocity data of a sample of sixty galaxies. Grumiller's model is found to describe the observed data reasonably well.

Products in a Category with Only One Object ; We consider certain decision problems for the free model of the theory of Cartesian monoids. We introduce a model of computation based on the notion of a single stack oneway PDA due to Ginsburg, Greibach and Harrison. This model allows us to solve problems such as 1 Given a finite set B of elements and an element F, is F a product of members of B 2 Is the submonoid generated by the finite set B infinite for certain fragments of the free Cartesian monoid. These fragments include the submonoid of right invertible elements and so our results apply to the ThompsonHigman groups.

Malware Classification with GMMHMM Models ; Discrete hidden Markov models HMM are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture modelHMMs GMMHMM, are rarely considered in the field of cybersecurity. In this paper, we use GMMHMMs for malware classification and we compare our results to those obtained using discrete HMMs. As features, we consider opcode sequences and entropybased sequences. For our opcode features, GMMHMMs produce results that are comparable to those obtained using discrete HMMs, whereas for our entropybased features, GMMHMMs generally improve significantly on the classification results that we have achieved with discrete HMMs.

Finetuning Pretrained Multilingual BERT Model for Indonesian Aspectbased Sentiment Analysis ; Although previous research on Aspectbased Sentiment Analysis ABSA for Indonesian reviews in hotel domain has been conducted using CNN and XGBoost, its model did not generalize well in test data and high number of OOV words contributed to misclassification cases. Nowadays, most stateoftheart results for wide array of NLP tasks are achieved by utilizing pretrained language representation. In this paper, we intend to incorporate one of the foremost language representation model, BERT, to perform ABSA in Indonesian reviews dataset. By combining multilingual BERT mBERT with task transformation method, we manage to achieve significant improvement by 8 on the F1score compared to the result from our previous study.

Hierarchical Causal Bandit ; Causal bandit is a nascent learning model where an agent sequentially experiments in a causal network of variables, in order to identify the rewardmaximizing intervention. Despite the model's wide applicability, existing analytical results are largely restricted to a parallel bandit version where all variables are mutually independent. We introduce in this work the hierarchical causal bandit model as a viable path towards understanding general causal bandits with dependent variables. The core idea is to incorporate a contextual variable that captures the interaction among all variables with direct effects. Using this hierarchical framework, we derive sharp insights on algorithmic design in causal bandits with dependent arms and obtain nearly matching regret bounds in the case of a binary context.

Presenting the MultiObjective Optimization Model of Search and Rescue Network ; The Search and Rescue Network SAR is a kind of emergency network that pursuit people in need or imminent danger. This paper aims using a priori optimization to demonstrate the optimal assignment of HFDF receivers to the Generalized Search and Rescue GSAR network, which is independent of the weighting of the transmitter areas. The mathematical model seeks two objectives, the first one is maximizing the expected number of LOBs for HFDF receivers. The second is providing a fair share number of HFDF receivers allowed to cover the frequency. The result shown the efficiency of presented model ran by CPLEX toolbox of MATLAB 2020 software.