Datasets:

sswt

/

arxiv_sample_50K

like 0

Nonconservative Mass Transfer in Massive Binaries and the Formation of WolfRayetO Binaries ; The mass transfer efficiency during the evolution of massive binaries is still uncertain. We model the mass transfer processes in a grid of binaries to investigate the formation of WolfRayetO WRO binaries, taking into account two kinds of nonconservative mass transfer models Model I with rotationdependent mass accretion and Model II of half mass accretion. Generally the mass transfer in Model I is more inefficient, with the average efficiency in a range of sim0.20.7 and lesssim0.2 for Case A and Case B mass transfer, respectively. We present the parameter distributions for the descendant WRO binaries. By comparing the modeled stellar mass distribution with the observed Galactic WRO binaries, we find that highly nonconservative mass transfer is required.

Integrateandfire models with an almost periodic input function ; We investigate leaky integrateandfire models LIF models for short driven by Stepanov and mualmost periodic functions. Special attention is paid to the properties of a firing map and its displacement, which give information about the spiking behaviour of the system under consideration. We provide conditions under which such maps are welldefined for every t in mathbb R and are uniformly continuous. Moreover, we show that the LIF model with a Stepanov almost periodic input has a uniformly almost periodic displacement map. We also show that in the case of a mualmost periodic drive it may happen that the displacement map corresponding to the LIF model is uniformly continuous, but is not mualmost periodic and thus cannot be Stepanov or uniformly almost periodic. By allowing discontinuous inputs, we generalize some results of previous papers, showing, for example, that the firing rate for the LIF model with a Stepanov almost periodic drive exists and is unique. This is a starting point for the investigation of the dynamics of almostperiodically driven integrateandfire systems. The work provides also some contributions to the theory of Stepanov and mualmost periodic functions.

Solar system tests for realistic fT models with nonminimal torsionmatter coupling ; In the previous paper, we have constructed two fT models with nonminimal torsionmatter coupling extension, which are successful in describing the evolution history of the Universe including the radiationdominated era, the matterdominated era, and the present accelerating expansion. Meantime, the significant advantage of these models is that they could avoid the cosmological constant problem of LambdaCDM. However, the nonminimal coupling between matter and torsion will affect the tests of Solar system. In this paper, we study the effects of Solar system in these models, including the gravitation redshift, geodetic effect and perihelion preccesion. We find that Model I can pass all three of the Solar system tests. For Model II, the parameter is constrained by the measure of the perihelion precession of Mercury.

Full Reconstruction of NonStationary StrandSymmetric Models on Rooted Phylogenies ; Understanding the evolutionary relationship among species is of fundamental importance to the biological sciences. The location of the root in any phylogenetic tree is critical as it gives an order to evolutionary events. None of the popular models of nucleotide evolution used in likelihood or Bayesian methods are able to infer the location of the root without exogenous information. It is known that the most general Markov models of nucleotide substitution can also not identify the location of the root or be fitted to multiple sequence alignments with less than three sequences. We prove that the location of the root and the full model can be identified and statistically consistently estimated for a nonstationary, strandsymmetric substitution model given a multiple sequence alignment with two or more sequences. We also generalise earlier work to provide a practical means of overcoming the computationally intractable problem of labelling hidden states in a phylogenetic model.

From EventB to Verified C via HLL ; This work addresses the correct translation of an EventB model to C code via an intermediate formal language, HLL. The proof of correctness follows two main steps. First, the final refinement of the EventB model, including invariants, is translated to HLL. At that point, additional properties e.g., deadlockfreeness, liveness properties, etc. are added to the HLL model. The proof of the invariants and additional properties at the HLL level guarantees the correctness of the translation. Second, the C code is automatically generated from the HLL model for most of the system functions and manually for the remaining ones; in this case, the HLL model provides formal contracts to the software developer. An equivalence proof between the C code and the HLL model guarantees the correctness of the code.

Perfect matchings and Hamiltonian cycles in the preferential attachment model ; In this paper, we study the existence of perfect matchings and Hamiltonian cycles in the preferential attachment model. In this model, vertices are added to the graph one by one, and each time a new vertex is created it establishes a connection with m random vertices selected with probabilities proportional to their current degrees. Constant m is the only parameter of the model. We prove that if m ge 1,260, then asymptotically almost surely there exists a perfect matching. Moreover, we show that there exists a Hamiltonian cycle asymptotically almost surely, provided that m ge 29,500. One difficulty in the analysis comes from the fact that vertices establish connections only with vertices that are older i.e. are created earlier in the process. However, the main obstacle arises from the fact that edges in the preferential attachment model are not generated independently. In view of that, we also consider a simpler settingsometimes called uniform attachmentin which vertices are added one by one and each vertex connects to m older vertices selected uniformly at random and independently of all other choices. We first investigate the existence of perfect matchings and Hamiltonian cycles in the uniform attachment model, and then extend the argument to the preferential attachment version.

Efficient Estimation of COMPoisson Regression and Generalized Additive Model ; The ConwayMaxwellPoisson CMP or COMPoison regression is a popular model for count data due to its ability to capture both under dispersion and over dispersion. However, CMP regression is limited when dealing with complex nonlinear relationships. With today's wide availability of count data, especially due to the growing collection of data on human and social behavior, there is need for count data models that can capture complex nonlinear relationships. One useful approach is additive models; but, there has been no additive model implementation for the CMP distribution. To fill this void, we first propose a flexible estimation framework for CMP regression based on iterative reweighed least squares IRLS and then extend this model to allow for additive components using a penalized splines approach. Because the CMP distribution belongs to the exponential family, convergence of IRLS is guaranteed under some regularity conditions. Further, it is also known that IRLS provides smaller standard errors compared to gradientbased methods. We illustrate the usefulness of this approach through extensive simulation studies and using real data from a bike sharing system in Washington, DC.

Critical properties of the eightvertex model in a field ; The general eightvertex model on a square lattice is studied numerically by using the Corner Transfer Matrix Renormalization Group method. The method is tested on the symmetric zerofield version of the model, the obtained dependence of critical exponents on model's parameters is in agreement with Baxter's exact solution and weak universality is verified with a high accuracy. It was suggested longtime ago that the symmetric eightvertex model is a special exceptional case and in the presence of external fields the eightvertex model falls into the Ising universality class. We confirm numerically this conjecture in a subspace of vertex weights, except for two specific combinations of vertical and horizontal fields for which the system still exhibits weak universality.

Ergodicity breaking and Localization of the Nicolai supersymmetric fermion lattice model ; We investigate dynamics of the supersymmetric fermion lattice model defined by Hermann Nicolai. We provide its local fermionic constants of motion that exist infinitely many. These generate hidden local supersymmetries that the Nicolai model possesses in addition to its defining dynamical supersymmetry. The existence of such local constants directly implies the breaking ergodicity of the model in the sense of Mazur. At zero temperature, there are infinitely many degenerated classical ground states. We discuss these MBLlike properties. First, we show the delocalization scenario proposed by De RoeckHuveneers can not naively apply to the Nicolai model at zero temperature despite its disorderfree translationinvariant quantum interaction. Second, we discuss the quantum integrability of the Nicolai model based on the proposal by CauxMossel.

Adjoint SU2 with Four Fermion Interactions ; Four fermion interactions appear in many models of Beyond Standard Model physics. In Technicolour and composite Higgs models Standard Model fermion masses can be generated by four fermion terms. They are also expected to modify the dynamics of the new strongly interacting sector. In particular in technicolour models it has been suggested that they can be used to break infrared conformality and produce a walking theory with a large mass anomalous dimension. We study the SU2 gauge theory with 2 adjoint fermions and a chirally symmetric four fermion term. We demonstrate chiral symmetry breaking at large four fermion coupling and study the phase diagram of the model.

Validity Examination of the Dissipative Quantum Model of Olfaction ; The validity of the dissipative quantum model of olfaction has not been examined yet and therefore the model suffers from the lack of experimental support. Here, we generalize the model and propose a numerical analysis of the dissipative odorantmediated inelastic electron tunneling mechanism of olfaction, to be used as a potential examination in experiments. Our analysis gives several predictions on the model such as efficiency of elastic and inelastic tunneling of electrons through odorants, sensitivity thresholds in terms of temperature and pressure, isotopic effect on sensitivity, and the chiral recognition for discrimination between the similar and different scents. Our predictions should yield new knowledge to design new experimental protocols for testing the validity of the model.

Inverse problem for multispecies mean field models in the low temperature phase ; In this paper we solve the inverse problem for a class of mean field models CurieWeiss model and its multispecies version when multiple thermodynamic states are present, as in the low temperature phase where the phase space is clustered. The inverse problem consists in reconstructing the model parameters starting from configuration data generated according to the distribution of the model. We show that the application of the inversion procedure without taking into account the presence of many states produces very poor inference results. This problem is overcomed using the clustering algorithm. When the system has two symmetric states of positive and negative magnetization, the parameter reconstruction can be also obtained with smaller computational effort simply by flipping the sign of the magnetizations from positive to negative or viceversa. The parameter reconstruction fails when the system is critical in this case we give the correct inversion formulas for the CurieWeiss model and we show that they can be used to measuring how much the system is close to criticality.

Compression of Deep Neural Networks for Image Instance Retrieval ; Image instance retrieval is the problem of retrieving images from a database which contain the same object. Convolutional Neural Network CNN based descriptors are becoming the dominant approach for generating it global image descriptors for the instance retrieval problem. One major drawback of CNNbased it global descriptors is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware. In this work, we study the problem of neural network model compression focusing on the image instance retrieval task. We study quantization, coding, pruning and weight sharing techniques for reducing model size for the instance retrieval problem. We provide extensive experimental results on the tradeoff between retrieval performance and model size for different types of networks on several data sets providing the most comprehensive study on this topic. We compress models to the order of a few MBs two orders of magnitude smaller than the uncompressed models while achieving negligible loss in retrieval performance.

A Joint Framework for Argumentative Text Analysis Incorporating Domain Knowledge ; For argumentation mining, there are several subtasks such as argumentation component type classification, relation classification. Existing research tends to solve such subtasks separately, but ignore the close relation between them. In this paper, we present a joint framework incorporating logical relation between subtasks to improve the performance of argumentation structure generation. We design an objective function to combine the predictions from individual models for each subtask and solve the problem with some constraints constructed from background knowledge. We evaluate our proposed model on two public corpora and the experiment results show that our model can outperform the baseline that uses a separate model significantly for each subtask. Our model also shows advantages on componentrelated subtasks compared to a stateoftheart joint model based on the evidence graph.

Calibration of a Hybrid LocalStochastic Volatility Stochastic Rates Model with a Control Variate Particle Method ; We propose a novel and generic calibration technique for fourfactor foreignexchange hybrid localstochastic volatility models with stochastic short rates. We build upon the particle method introduced by Guyon and Labordere Nonlinear Option Pricing, Chapter 11, Chapman and Hall, 2013 and combine it with new variance reduction techniques in order to accelerate convergence. We use control variates derived from a calibrated pure local volatility model, a twofactor Hestontype LSV model both with deterministic rates, and the stochastic CIR short rates. The method can be applied to a large class of hybrid LSV models and is not restricted to our particular choice of the diffusion. The calibration procedure is performed on realworld market data for the EURUSD currency pair and has a comparable runtime to the PDE calibration of a twofactor LSV model alone.

Adaptive posterior convergence rates in nonlinear latent variable models ; Nonlinear latent variable models have become increasingly popular in a variety of applications. However, there has been little study on theoretical properties of these models. In this article, we study rates of posterior contraction in univariate density estimation for a class of nonlinear latent variable models where unobserved U0,1 latent variables are related to the response variables via a random nonlinear regression with an additive error. Our approach relies on characterizing the space of densities induced by the above model as kernel convolutions with a general class of continuous mixing measures. The literature on posterior rates of contraction in density estimation almost entirely focuses on finite or countably infinite mixture models. We develop approximation results for our class of continuous mixing measures. Using an appropriate Gaussian process prior on the unknown regression function, we obtain the optimal frequentist rate up to a logarithmic factor under standard regularity conditions on the true density.

Grouped Heterogeneous Mixture Modeling for Clustered Data ; Clustered data is ubiquitous in a variety of scientific fields. In this paper, we propose a flexible and interpretable modeling approach, called grouped heterogenous mixture modeling, for clustered data, which models clusterwise conditional distributions by mixtures of latent conditional distributions common to all the clusters. In the model, we assume that clusters are divided into a finite number of groups and mixing proportions are the same within the same group. We provide a simple generalized EM algorithm for computing the maximum likelihood estimator, and an information criterion to select the numbers of groups and latent distributions. We also propose structured grouping strategies by introducing penalties on grouping parameters in the likelihood function. Under the settings where both the number of clusters and cluster sizes tend to infinity, we present asymptotic properties of the maximum likelihood estimator and the information criterion. We demonstrate the proposed method through simulation studies and an application to crime risk modeling in Tokyo.

Countable models of the theories of BaldwinShi hypergraphs and their regular types ; We continue the study of the theories of BaldwinShi hypergraphs from 5. Restricting our attention to when the rank delta is rational valued, we show that each countable model of the theory of a given BaldwinShi hypergraph is isomorphic to a generic structure built from some suitable subclass of the original class of finite structures with the inherited notion of strong substructure. We introduce a notion of dimension for a model and show that there is a an elementary chain mathfrakMbetabetaomega1 of countable models of the theory of a fixed BaldwinShi hypergraph with mathfrakMbetapreccurlyeqmathfrakMgamma if and only if the dimension of mathfrakMbeta is at most the dimension of mathfrakMgamma and that each countable model is isomorphic to some mathfrakMbeta. We also study the regular types that appear in these theories and show that the dimension of a model is determined by a particular regular type. Further, drawing on the work of Brody and Laskowski, we use these structures to give an example of a pseudofinite, omegastable theory with a nonlocally modular regular type, answering a question of Pillay in 9.

A model of the late universe with viscous Zel'ldovich fluid and decaying vacuum ; Many have speculated about the presence of a stiff fluid in very early stage of the universe. Such a stiff fluid was first introduced by Zel'dovich. Recently the late acceleration of the universe was studied by taking bulk viscous stiff fluid as the dominant cosmic component, but the age predicted by such a model is less than the observed value. We consider a flat universe with viscous stiff fluid and decaying vacuum energy as the cosmic components and found that the model predicts a reasonable background evolution of the universe with de Sitter epoch as end phase of expansion. More over the model also predicts a reasonable value for the age of the present universe. We also perform a dynamical system analysis of the model and found that the end de Sitter phase predicted by the model is stable.

EMBER An Open Dataset for Training Static PE Malware Machine Learning Models ; This paper describes EMBER a labeled benchmark dataset for training machine learning models to statically detect malicious Windows portable executable files. The dataset includes features extracted from 1.1M binary files 900K training samples 300K malicious, 300K benign, 300K unlabeled and 200K test samples 100K malicious, 100K benign. To accompany the dataset, we also release open source code for extracting features from additional binaries so that additional sample features can be appended to the dataset. This dataset fills a void in the information security machine learning community a benignmalicious dataset that is large, open and general enough to cover several interesting use cases. We enumerate several use cases that we considered when structuring the dataset. Additionally, we demonstrate one use case wherein we compare a baseline gradient boosted decision tree model trained using LightGBM with default settings to MalConv, a recently published endtoend featureless deep learning model for malware detection. Results show that even without hyperparameter optimization, the baseline EMBER model outperforms MalConv. The authors hope that the dataset, code and baseline model provided by EMBER will help invigorate machine learning research for malware detection, in much the same way that benchmark datasets have advanced computer vision research.

Learning Deep Sketch Abstraction ; Human freehand sketches have been studied in various contexts including sketch recognition, synthesis and finegrained sketchbased image retrieval FGSBIR. A fundamental challenge for sketch analysis is to deal with drastically different human drawing styles, particularly in terms of abstraction level. In this work, we propose the first strokelevel sketch abstraction model based on the insight of sketch abstraction as a process of trading off between the recognizability of a sketch and the number of strokes used to draw it. Concretely, we train a model for abstract sketch generation through reinforcement learning of a stroke removal policy that learns to predict which strokes can be safely removed without affecting recognizability. We show that our abstraction model can be used for various sketch analysis tasks including 1 modeling stroke saliency and understanding the decision of sketch recognition models, 2 synthesizing sketches of variable abstraction for a given category, or reference object instance in a photo, and 3 training a FGSBIR model with photos only, bypassing the expensive photosketch pair collection step.

Evaluation of the Gradient Boosting of Regression Trees Method on Estimating the Car Following Behavior ; Carfollowing models, as the essential part of traffic microscopic simulations, have been utilized to analyze and estimate longitudinal drivers' behavior since sixty years ago. The conventional car following models use mathematical formulas to replicate human behavior in the carfollowing phenomenon. Incapability of these approaches to capturing the complex interactions between vehicles calls for deploying advanced learning frameworks to consider the more detailed behavior of drivers. In this study, we apply the Gradient Boosting of Regression Tree GBRT algorithm to the vehicle trajectory data sets, which have been collected through the Next Generation Simulation program, so as to develop a new carfollowing model. First, the regularization parameters of the proposed method are tuned using the crossvalidation technique and the sensitivity analysis. Afterward, the prediction performance of the GBRT is compared to the worldfamous GHR model, when both models have been trained on the same data sets. The estimation results of the models on the unseen records indicate the superiority of the GBRT algorithm in capturing the motion characteristics of two successive vehicles.

Physicsdriven Fire Modeling from Multiview Images ; Fire effects are widely used in various computer graphics applications such as visual effects and video games. Modeling the shape and appearance of fire phenomenon is challenging as the underlying effects are driven by complex laws of physics. Stateoftheart fire modeling techniques rely on sophisticated physical simulations which require intensive parameter tuning, or use simplifications which produce physically invalid results. In this paper, we present a novel method of reconstructing physically valid fire models from multiview stereo images. Our method, for the first time, provides plausible estimation of physical properties e.g., temperature, density of a fire volume using RGB cameras. This allows for a number of novel phenomena such as global fire illumination effects. The effectiveness and usefulness of our method are tested by generating fire models from a variety of input data, and applying the reconstructed fire models for realistic illumination of virtual scenes.

MPACT An Open Source Platform for Repeatable Activity Classification Research ; There are many hurdles that prevent the replication of existing work which hinders the development of new activity classification models. These hurdles include switching between multiple deep learning libraries and the development of boilerplate experimental pipelines. We present MPACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing stateoftheart SOTA models available in the platform. MPACT is the first to offer four SOTA activity classification models, I3D, C3D, ResNet50LSTM, and TSN, under a single platform with reproducible competitive results. This platform allows for the generation of models and results over activity recognition datasets through the use of modular code, various preprocessing and neural network layers, and seamless data flow. In this paper, we present the system architecture, detail the functions of various modules, and describe the basic tools to develop a new model in MPACT.

Universal statistics of incubation periods and other detection times via diffusion models ; We suggest an explanation of typical incubation times statistical features based on the universal behavior of exit times for diffusion models. We give a mathematically rigorous proof of the characteristic right skewness of the incubation time distribution for very general onedimensional diffusion models. Imposing natural simple conditions on the drift coefficient, we also study these diffusion models under the assumption of noise smallness and show that the limiting exit time distributions in the limit of vanishing noise are Gaussian and Gumbel. Thus they match the existing data as well as the other existing models do. The character of our models, however, allows us to argue that the features of the exit time distributions that we describe are universal and manifest themselves in various other situations where the times involved can be described as detection or halting times, for example, response times studied in psychology.

Stationarily ordered types and the number of countable models ; We introduce notions of stationarily ordered types and theories; the latter generalizes weak ominimality and the first is a relaxed version of weak ominimality localized at the locus of a single type. We show that forking, as a binary relation on elements realizing stationarily ordered types, is an equivalence relation and that each stationarily ordered type in a model determines some ordertype as an invariant of the model. We study weak and forking nonorthogonality of stationarily ordered types, show that they are equivalence relations and prove that invariants of nonorthogonal types are closely related. The developed techniques are applied to prove that in the case of a binary, stationarily ordered theory with fewer than 2aleph0 countable models, the isomorphism type of a countable model is determined by a certain sequence of invariants of the model. In particular, we confirm Vaught's conjecture for binary, stationarily ordered theories.

Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers ; Call centers' managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars i flexibility of the loss function; ii statistical evaluation of forecast accuracy; iii economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second moments modeling is important both for point and density forecasting and that the simple Seasonal Random Walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.

Structure Discrimination in BlockOriented Models Using Linear Approximations A Theoretic Framework ; In this paper we show that it is possible to retrieve structural information about complex blockoriented nonlinear systems, starting from linear approximations of the nonlinear system around different setpoints.The key idea is to monitor the movements of the poles and zeros of the linearized models and to reduce the number of candidate models on the basis of these observations. Besides the well known open loop single branch Wiener, Hammerstein, and WienerHammerstein systems, we also cover a number of more general structures like parallel multi branch WienerHammerstein models, and closed loop block oriented models, including linear fractional representation LFR models.

Entity Set Search of Scientific Literature An Unsupervised Ranking Approach ; Literature search is critical for any scientific research. Different from Web or general domain search, a large portion of queries in scientific literature search are entityset queries, that is, multiple entities of possibly different types. Entityset queries reflect user's need for finding documents that contain multiple entities and reveal interentity relationships and thus pose nontrivial challenges to existing search algorithms that model each entity separately. However, entityset queries are usually sparse i.e., not so repetitive, which makes ineffective many supervised ranking models that rely heavily on associated click history. To address these challenges, we introduce SetRank, an unsupervised ranking framework that models interentity relationships and captures entity type information. Furthermore, we develop a novel unsupervised model selection algorithm, based on the technique of weighted rank aggregation, to automatically choose the parameter settings in SetRank without resorting to a labeled validation set. We evaluate our proposed unsupervised approach using datasets from TREC Genomics Tracks and Semantic Scholar's query log. The experiments demonstrate that SetRank significantly outperforms the baseline unsupervised models, especially on entityset queries, and our model selection algorithm effectively chooses suitable parameter settings.

Hybrid Forests for Left Ventricle Segmentation using only the first slice label ; Machine learning models produce stateoftheart results in many MRI images segmentation. However, most of these models are trained on very large datasets which come from experts manual labeling. This labeling process is very time consuming and costs experts work. Therefore finding a way to reduce this cost is on high demand. In this paper, we propose a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task. Only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation. Inference result is another datum used to train the model again. The updated model then infers the third slice and the same process is carried out until the last slice. The proposed model is an combination of two Random Forest algorithms the classical one and a recent one namely Mondrian Forests. We applied our method on human left ventricle segmentation and results are very promising. This method can also be used to generate labels.

Towards Empathetic Opendomain Conversation Models a New Benchmark and Dataset ; One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publiclyavailable datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on largescale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy retraining of the full model.

Deep Learning for Tube Amplifier Emulation ; Analog audio effects and synthesizers often owe their distinct sound to circuit nonlinearities. Faithfully modeling such significant aspect of the original sound in virtual analog software can prove challenging. The current work proposes a generic datadriven approach to virtual analog modeling and applies it to the Fender Bassman 56FA vacuumtube amplifier. Specifically, a feedforward variant of the WaveNet deep neural network is trained to carry out a regression on audio waveform samples from input to output of a SPICE model of the tube amplifier. The output signals are preemphasized to assist the model at learning the highfrequency content. The results of a listening test suggest that the proposed model accurately emulates the reference device. In particular, the model responds to user control changes, and faithfully restitutes the range of sonic characteristics found across the configurations of the original device.

Neural Task Representations as Weak Supervision for Model Agnostic CrossLingual Transfer ; Natural language processing is heavily Anglocentric, while the demand for models that work in languages other than English is greater than ever. Yet, the task of transferring a model from one language to another can be expensive in terms of annotation costs, engineering time and effort. In this paper, we present a general framework for easily and effectively transferring neural models from English to other languages. The framework, which relies on task representations as a form of weak supervision, is model and task agnostic, meaning that many existing neural architectures can be ported to other languages with minimal effort. The only requirement is unlabeled parallel data, and a loss defined over task representations. We evaluate our framework by transferring an English sentiment classifier to three different languages. On a battery of tests, we show that our models outperform a number of strong baselines and rival stateoftheart results, which rely on more complex approaches and significantly more resources and data. Additionally, we find that the framework proposed in this paper is able to capture semantically rich and meaningful representations across languages, despite the lack of direct supervision.

Detecting Structural Changes in Longitudinal Network Data ; Dynamic modeling of longitudinal networks has been an increasingly important topic in applied research. While longitudinal network data commonly exhibit dramatic changes in its structures, existing methods have largely focused on modeling smooth topological changes over time. In this paper, we develop a hidden Markov multilinear tensor model HMTM that combines the multilinear tensor regression model Hoff 2011 with a hidden Markov model using Bayesian inference. We model changes in network structure as shifts in discrete states yielding particular sets of network generating parameters. Our simulation results demonstrate that the proposed method correctly detects the number, locations, and types of changes in latent node characteristics. We apply the proposed method to international military alliance networks to find structural changes in the coalition structure among nations.

Learning Shared Dynamics with MetaWorld Models ; Humans have consciousness as the ability to perceive events and objects a mental model of the world developed from the most impoverished of visual stimuli, enabling humans to make rapid decisions and take actions. Although spatial and temporal aspects of different scenes are generally diverse, the underlying physics among environments still work the same way, thus learning an abstract description of shared physical dynamics helps human to understand the world. In this paper, we explore building this mental world with neural network models through multitask learning, namely the metaworld model. We show through extensive experiments that our proposed metaworld models successfully capture the common dynamics over the compact representations of visually different environments from Atari Games. We also demonstrate that agents equipped with our metaworld model possess the ability of visual selfrecognition, i.e., recognize themselves from the reflected mirrored environment derived from the classic mirror selfrecognition test MSR.

Improving MultiPerson Pose Estimation using Label Correction ; Significant attention is being paid to multiperson pose estimation methods recently, as there has been rapid progress in the field owing to convolutional neural networks. Especially, recent method which exploits part confidence maps and Part Affinity Fields PAFs has achieved accurate realtime prediction of multiperson keypoints. However, human annotated labels are sometimes inappropriate for learning models. For example, if there is a limb that extends outside an image, a keypoint for the limb may not have annotations because it exists outside of the image, and thus the labels for the limb can not be generated. If a model is trained with data including such missing labels, the output of the model for the location, even though it is correct, is penalized as a false positive, which is likely to cause negative effects on the performance of the model. In this paper, we point out the existence of some patterns of inappropriate labels, and propose a novel method for correcting such labels with a teacher model trained on such incomplete data. Experiments on the COCO dataset show that training with the corrected labels improves the performance of the model and also speeds up training.

Effect of data reduction on sequencetosequence neural TTS ; Recent speech synthesis systems based on sampling from autoregressive neural networks models can generate speech almost undistinguishable from human recordings. However, these models require large amounts of data. This paper shows that the lack of data from one speaker can be compensated with data from other speakers. The naturalness of Tacotron2like models trained on a blend of 5k utterances from 7 speakers is better than that of speaker dependent models trained on 15k utterances, but in terms of stability multispeaker models are always more stable. We also demonstrate that models mixing only 1250 utterances from a target speaker with 5k utterances from another 6 speakers can produce significantly better quality than stateoftheart DNNguided unit selection systems trained on more than 10 times the data from the target speaker.

Multifidelity Approximate Bayesian Computation ; A vital stage in the mathematical modelling of realworld systems is to calibrate a model's parameters to observed data. Likelihoodfree parameter inference methods, such as Approximate Bayesian Computation, build Monte Carlo samples of the uncertain parameter distribution by comparing the data with large numbers of model simulations. However, the computational expense of generating these simulations forms a significant bottleneck in the practical application of such methods. We identify how simulations of cheap, lowfidelity models have been used separately in two complementary ways to reduce the computational expense of building these samples, at the cost of introducing additional variance to the resulting parameter estimates. We explore how these approaches can be unified so that cost and benefit are optimally balanced, and we characterise the optimal choice of how often to simulate from cheap, lowfidelity models in place of expensive, highfidelity models in Monte Carlo ABC algorithms. The resulting early acceptreject multifidelity ABC algorithm that we propose is shown to give improved performance over existing multifidelity and highfidelity approaches.

LeeCarter method for forecasting mortality for Peruvian Population ; In this article, we have modeled mortality rates of Peruvian female and male populations during the period of 19502017 using the LeeCarter LC model. The stochastic mortality model was introduced by Lee and Carter 1992 and has been used by many authors for fitting and forecasting the human mortality rates. The Singular Value Decomposition SVD approach is used for estimation of the parameters of the LC model. Utilizing the best fitted auto regressive integrated moving average ARIMA model we forecast the values of the time dependent parameter of the LC model for the next thirty years. The forecasted values of life expectancy at different age group with 95 confidence intervals are also reported for the next thirty years. In this research we use the data, obtained from the Peruvian National Institute of Statistics INEI.

Ontology Matching Techniques A Gold Standard Model ; Typically an ontology matching technique is a combination of much different type of matchers operating at various abstraction levels such as structure, semantic, syntax, instance etc. An ontology matching technique which employs matchers at all possible abstraction levels is expected to give, in general, best results in terms of precision, recall and Fmeasure due to improvement in matching opportunities and if we discount efficiency issues which may improve with better computing resources such as parallel processing. A gold standard ontology matching model is derived from a model classification of ontology matching techniques. A suitable metric is also defined based on gold standard ontology matching model. A review of various ontology matching techniques specified in recent research papers in the area was undertaken to categorize an ontology matching technique as per newly proposed gold standard model and a metric value for the whole group was computed. The results of the above study support proposed gold standard ontology matching model.

Arena Model Inference About Competitions ; The authors propose a parametric model called the arena model for prediction in paired competitions, i.e. paired comparisons with eliminations and bifurcations. The arena model has a number of appealing advantages. First, it predicts the results of competitions without rating many individuals. Second, it takes full advantage of the structure of competitions. Third, the model provides an easy method to quantify the uncertainty in competitions. Fourth, some of our methods can be directly generalized for comparisons among three or more individuals. Furthermore, the authors identify an invariant Bayes estimator with regard to the prior distribution and prove the consistency of the estimations of uncertainty. Currently, the arena model is not effective in tracking the change of strengths of individuals, but its basic framework provides a solid foundation for future study of such cases.

LHC Constraints on a BL Gauge Model using Contur ; The large and growing library of measurements from the Large Hadron Collider has significant power to constrain extensions of the Standard Model. We consider such constraints on a wellmotivated model involving a gauged and spontaneouslybroken BL symmetry, within the Contur framework. The model contains an extra Higgs boson, a gauge boson, and righthanded neutrinos with Majorana masses. This new particle content implies a varied phenomenology highly dependent on the parameters of the model, very wellsuited to a general study of this kind. We find that existing LHC measurements significantly constrain the model in interesting regions of parameter space. Other regions remain open, some of which are within reach of future LHC data.

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints ; The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residueresidue contact prediction even for shallow sequence alignments. Here we introduce DMPfold, which uses deep learning to predict interatomic distance bounds, the main chain hydrogen bond network, and torsion angles, which it uses to build models in an iterative fashion. DMPfold produces more accurate models than two popular methods for a test set of CASP12 domains, and works just as well for transmembrane proteins. Applied to all Pfam domains without known structures, confident models for 25 of these socalled dark families were produced in under a week on a small 200 core cluster. DMPfold provides models for 16 of human proteome UniProt entries without structures, generates accurate models with fewer than 100 sequences in some cases, and is freely available.

Status of backgroundindependent coarsegraining in tensor models for quantum gravity ; A backgroundindependent route towards a universal continuum limit in discrete models of quantum gravity proceeds through a backgroundindependent form of coarse graining. This review provides a pedagogical introduction to the conceptual ideas underlying the use of the number of degrees of freedom as a scale for a Renormalization Group flow. We focus on tensor models, for which we explain how the tensor size serves as the scale for a backgroundindependent coarsegraining flow. This flow provides a new probe of a universal continuum limit in tensor models. We review the development and setup of this tool and summarize results in the 2 and 3dimensional case. Moreover, we provide a stepbystep guide to the practical implementation of these ideas and tools by deriving the flow of couplings in a rank4tensor model. We discuss the phenomenon of dimensional reduction in these models and find tentative first hints for an interacting fixed point with potential relevance for the continuum limit in fourdimensional quantum gravity.

Training with the Invisibles Obfuscating Images to Share Safely for Learning Visual Recognition Models ; Highperformance visual recognition systems generally require a large collection of labeled images to train. The expensive data curation can be an obstacle for improving recognition performance. Sharing more data allows training for better models. But personal and private information in the data prevent such sharing. To promote sharing visual data for learning a recognition model, we propose to obfuscate the images so that humans are not able to recognize their detailed contents, while machines can still utilize them to train new models. We validate our approach by comprehensive experiments on three challenging visual recognition tasks; image classification, attribute classification, and facial landmark detection on several datasets including SVHN, CIFAR10, Pascal VOC 2012, CelebA, and MTFL. Our method successfully obfuscates the images from humans recognition, but a machine model trained with them performs within about 1 margin up to 0.48 of the performance of a model trained with the original, nonobfuscated data.

Personalised network modelling in epilepsy ; Epilepsy is a disorder characterised by spontaneous, recurrent seizures. Both local and network abnormalities have been associated with epilepsy, and the exact processes generating seizures are thought to be heterogeneous and patientspecific. Due to the heterogeneity, treatments such as surgery and medication are not always effective in achieving full seizure control and choosing the best treatment for the individual patient can be challenging. Predictive models constrained by the patient's own data therefore offer the potential to assist in clinical decision making. In this chapter, we describe how personalised patientderived networks from structural or functional connectivity can be incorporated into predictive models. We focus specifically on dynamical systems models which are composed of differential equations capable of simulating brain activity over time. Here we review recent studies which have used these models, constrained by patient data, to make personalised patientspecific predictions about seizure features such as propagation patterns or treatment outcomes such as the success of surgical resection. Finally, we suggest future research directions for patientspecific network models in epilepsy, including their application to integrate information from multiple modalities, to predict longterm disease evolution, and to account for withinsubject variability for treatment.

Smoothing Spline Semiparametric Density Models ; Density estimation plays a fundamental role in many areas of statistics and machine learning. Parametric, nonparametric and semiparametric density estimation methods have been proposed in the literature. Semiparametric density models are flexible in incorporating domain knowledge and uncertainty regarding the shape of the density function. Existing literature on semiparametric density models is scattered and lacks a systematic framework. In this paper, we consider a unified framework based on the reproducing kernel Hilbert space for modeling, estimation, computation and theory. We propose general semiparametric density models for both a single sample and multiple samples which include many existing semiparametric density models as special cases. We develop penalized likelihood based estimation methods and computational methods under different situations. We establish joint consistency and derive convergence rates of the proposed estimators for both the finite dimensional Euclidean parameters and an infinitedimensional functional parameter. We validate our estimation methods empirically through simulations and an application.

Exponential random graph models for the Japanese bipartite network of banks and firms ; We use the exponential random graph models to understand the network structure and its generative process for the Japanese bipartite network of banks and firms. One of the well known and simple model of exponential random graph is the Bernoulli model which shows the links in the bankfirm network are not independent from each other. Another popular exponential random graph model, the two star model, indicates that the bankfirms are in a state where macroscopic variables of the system can show large fluctuations. Moreover, the presence of high fluctuations reflect a fragile nature of the bankfirm network.

Renormalization of crossing probabilities in the planar randomcluster model ; The study of crossing probabilities i.e. probabilities of existence of paths crossing rectangles has been at the heart of the theory of twodimensional percolation since its beginning. They may be used to prove a number of results on the model, including speed of mixing, tails of decay of the connectivity probabilities, scaling relations, etc. In this article, we develop a renormalization scheme for crossing probabilities in the twodimensional randomcluster model. The outcome of the process is a precise description of an alternative between four behaviors Subcritical Crossing probabilities, even with favorable boundary conditions, converge exponentially fast to 0. Supercritical Crossing probabilities, even with unfavorable boundary conditions, converge exponentially fast to 1. Critical discontinuous Crossing probabilities converge to 0 exponentially fast with unfavorable boundary conditions and to 1 with favorable boundary conditions. Critical continuous Crossing probabilities remain bounded away from 0 and 1 uniformly in the boundary conditions. The approach does not rely on selfduality, enabling it to apply in a much larger generality, including the randomcluster model on arbitrary graphs with sufficient symmetry, but also other models like certain random height models.

Social power evolution in influence networks with stubborn individuals ; This paper studies the evolution of social power in influence networks with stubborn individuals. Based on the FriedkinJohnsen opinion dynamics and the reflected appraisal mechanism, two models are proposed over issue sequences and over a single issue, respectively. These models generalize the original DeGrootFriedkin DF model by including stubbornness. To the best of our knowledge, this paper is the first attempt to investigate the social power evolution of stubborn individuals basing on the reflected appraisal mechanism. Properties of equilibria and convergence are provided. We show that the models have same equilibrium social power and convergence property, where the equilibrium social power depends only upon interpersonal influence and individuals' stubbornness. Roughly speaking, more stubborn individual has more equilibrium social power. Moreover, unlike the DF model without stubbornness, we prove that for the models with stubbornness, autocracy can never be achieved, while democracy can be achieved under any network topology.

Deep Learning Volatility ; We present a neural network based calibration method that performs the calibration task within a few milliseconds for the full implied volatility surface. The framework is consistently applicable throughout a range of volatility models including the rough volatility family and a range of derivative contracts. The aim of neural networks in this work is an offline approximation of complex pricing functions, which are difficult to represent or timeconsuming to evaluate by other means. We highlight how this perspective opens new horizons for quantitative modelling The calibration bottleneck posed by a slow pricing of derivative contracts is lifted. This brings several numerical pricers and model families such as rough volatility models within the scope of applicability in industry practice. The form in which information from available data is extracted and stored influences network performance This approach is inspired by representing the implied volatility and option prices as a collection of pixels. In a number of applications we demonstrate the prowess of this modelling approach regarding accuracy, speed, robustness and generality and also its potentials towards model recognition.

MultiAgent Reinforcement Learning with MultiStep Generative Models ; We consider modelbased reinforcement learning MBRL in 2agent, highfidelity continuous control problems an important domain for robots interacting with other agents in the same workspace. For nontrivial dynamical systems, MBRL typically suffers from accumulating errors. Several recent studies have addressed this problem by learning latent variable models for trajectory segments and optimizing over behavior in the latent space. In this work, we investigate whether this approach can be extended to 2agent competitive and cooperative settings. The fundamental challenge is how to learn models that capture interactions between agents, yet are disentangled to allow for optimization of each agent behavior separately. We propose such models based on a disentangled variational autoencoder, and demonstrate our approach on a simulated 2robot manipulation task, where one robot can either help or distract the other. We show that our approach has better sample efficiency than a strong modelfree RL baseline, and can learn both cooperative and adversarial behavior from the same data.

Orthogonalized smoothing for rescaled spike and slab models ; Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model selection properties. We review background theory and discuss applications of rescaled spike and slab models to prediction problems involving orthogonal polynomials. We first consider global smoothing and discuss potential weaknesses. Some of these deficiencies are remedied by using local regression. The local regression approach relies on an intimate connection between local weighted regression and weighted generalized ridge regression. An important implication is that one can trace the effective degrees of freedom of a curve as a way to visualize and classify curvature. Several motivating examples are presented.

On adaptive Bayesian inference ; We study the rate of Bayesian consistency for hierarchical priors consisting of prior weights on a model index set and a prior on a density model for each choice of model index. Ghosal, Lember and Van der Vaart 2 have obtained general inprobability theorems on the rate of convergence of the resulting posterior distributions. We extend their results to almost sure assertions. As an application we study log spline densities with a finite number of models and obtain that the Bayes procedure achieves the optimal minimax rate ngamma2gamma1 of convergence if the true density of the observations belongs to the Holder space Cgamma0,1. This strengthens a result in 1; 2. We also study consistency of posterior distributions of the model index and give conditions ensuring that the posterior distributions concentrate their masses near the index of the best model.

Poisson Cloning Model for Random Graphs ; In the random graph Gn,p with pn bounded, the degrees of the vertices are almost i.i.d Poisson random variables with mean gl pn1. Motivated by this fact, we introduce the Poisson cloning model GPC n,p for random graphs in which the degrees are i.i.d Poisson random variables with mean gl. Then, we first establish a theorem that shows the new model is equivalent to the classical model Gn,p in an asymptotic sense. Next, we introduce a useful algorithm, called the cutoff line algorithm, to generate the random graph GPC n,p. The Poisson cloning model GPCn,p equipped with the cutoff line algorithm enables us to very precisely analyze the sizes of the largest component and the tcore of Gn,p. This new approach to the problems yields not only elegant proofs but also improved bounds that are essentially best possible. We also consider the Poisson cloning models for random hypergraphs and random kSAT problems. Then, the tcore problem for random hypergraphs and the pure literal algorithm for random kSAT problems are analyzed.

The torsion cosmology in KaluzaKlein theory ; We have studied the torsion cosmology model in KaluzaKlein theory. We considered two simple models in which the torsion vectors are Amualpha,0,0,0 and Amuat20,beta,beta,beta, respectively. For the first model, the accelerating expansion of the Universe can be not explained without dark energy which is similar to that in the standard cosmology. But for the second model, we find that without dark energy the effect of torsion can give rise to the accelerating expansion of the universe and the alleviation of the wellknown age problem of the three old objects for appropriated value of the model parameter beta. These outstanding features of the second torsion cosmology model have been supported by the Type Ia supernovae SNIa data.

Warping the Universal Extra Dimensions ; We develop the necessary ingredients for the construction of realistic models with warped universal extra dimensions. Our investigations are based on the seven dimensional 7D spacetime AdS5 x T2 and we derive the KaluzaKlein KK spectra for gravitons, bulk vectors and the TeV brane localized Higgs boson. We show that, starting with a massive 7D fermion, one may obtain a single chiral massless mode whose profile is readily localized towards the Planck or TeV brane. This allows one to place the standard model fermions in the bulk and construct models of flavor as in RandallSundrum models. Our solution also admits the familiar KK parity of UED models so that the lightest odd KK state is stable and may be a dark matter DM candidate. As an additional feature the AdS5 warping ensures that the excited modes on the torus, including the DM candidate, appear at TeV energies as is usually assumed in UED models even though the Planck scale sets the dimensions for the torus.

Bayesian model comparison and model averaging for smallarea estimation ; This paper considers smallarea estimation with lung cancer mortality data, and discusses the choice of upperlevel model for the variation over areas. Inference about the random effects for the areas may depend strongly on the choice of this model, but this choice is not a straightforward matter. We give a general methodology for both evaluating the data evidence for different models and averaging over plausible models to give robust area effect distributions. We reanalyze the data of Tsutakawa Biometrics 41 1985 6979 on lung cancer mortality rates in Missouri cities, and show the differences in conclusions about the city rates from this methodology.

Flexible objective Bayesian linear regression with applications in survival analysis ; We study objective Bayesian inference for linear regression models with residual errors distributed according to the class of twopiece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general noninformative, scaleinvariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in Rubio and Hong 2015. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.

Analytical Model for Outdoor Millimeter Wave Channels using GeometryBased Stochastic Approach ; The severe bandwidth shortage in conventional microwave bands has spurred the exploration of the millimeter wave MMW spectrum for the next revolution in wireless communications. However, there is still lack of proper channel modeling for the MMW wireless propagation, especially in the case of outdoor environments. In this paper, we develop a geometrybased stochastic channel model to statistically characterize the effect of all the firstorder reflection paths between the transmitter and receiver. These firstorder reflections are generated by the singlebounce of signals reflected from the walls of randomly distributed buildings. Based on this geometric model, a closedform expression for the power delay profile PDP contributed by all the firstorder reflection paths is obtained and then used to evaluate their impact on the MMW outdoor propagation characteristics. Numerical results are provided to validate the accuracy of the proposed model under various channel parameter settings. The findings in this paper provide a promising step towards more complex and practical MMW propagation channel modeling.

Polyglot Neural Language Models A Case Study in CrossLingual Phonetic Representation Learning ; We introduce polyglot language models, recurrent neural network models trained to predict symbol sequences in many different languages using shared representations of symbols and conditioning on typological information about the language to be predicted. We apply these to the problem of modeling phone sequencesa domain in which universal symbol inventories and crosslinguistically shared feature representations are a natural fit. Intrinsic evaluation on heldout perplexity, qualitative analysis of the learned representations, and extrinsic evaluation in two downstream applications that make use of phonetic features show i that polyglot models better generalize to heldout data than comparable monolingual models and ii that polyglot phonetic feature representations are of higher quality than those learned monolingually.

Fredkin Spin Chain ; We introduce a new model of interacting spin 12. It describes interaction of three nearest neighbors. The Hamiltonian can be expressed in terms of Fredkin gates. The Fredkin gate also known as the CSWAP gate is a computational circuit suitable for reversible computing. Our construction generalizes the work of Ramis Movassagh and Peter Shor. Our model can be solved by means of Catalan combinatorics in the form of random walks on the upper half of a square lattice Dyck walks. Each Dyck path can be mapped to a wave function of the spins. The ground state is an equally weighted superposition of Dyck walks instead of Motzkin walks. We can also express it as a matrix product state. We further construct the model of interacting spins 32 and greater halfinteger spins. The models with higher spins require coloring of Dyck walks. We construct SUk symmetric model here k is the number of colors. The leading term of the entanglement entropy is then proportional to the square root of the length of the lattice like in ShorMovassagh model. The gap closes as a high power of the length of the lattice.

Phase transitions for Quantum Markov Chains associated with Ising type models on a Cayley tree ; The main aim of the present paper is to prove the existence of a phase transition in quantum Markov chain QMC scheme for the Ising type models on a Cayley tree. Note that this kind of models do not have onedimensional analogous, i.e. the considered model persists only on trees. In this paper, we provide a more general construction of forward QMC. In that construction, a QMC is defined as a weak limit of finite volume states with boundary conditions, i.e. QMC depends on the boundary conditions. Our main result states the existence of a phase transition for the Ising model with competing interactions on a Cayley tree of order two. By the phase transition we mean the existence of two distinct QMC which are not quasiequivalent and their supports do not overlap. We also study some algebraic property of the disordered phase of the model, which is a new phenomena even in a classical setting.

Estimation of a Multiplicative Correlation Structure in the Large Dimensional Case ; We propose a Kronecker product model for correlation or covariance matrices in the large dimensional case. The number of parameters of the model increases logarithmically with the dimension of the matrix. We propose a minimum distance MD estimator based on a loglinear property of the model, as well as a onestep estimator, which is a onestep approximation to the quasimaximum likelihood estimator QMLE. We establish rates of convergence and central limit theorems CLT for our estimators in the large dimensional case. A specification test and tools for Kronecker product model selection and inference are provided. In a Monte Carlo study where a Kronecker product model is correctly specified, our estimators exhibit superior performance. In an empirical application to portfolio choice for SP500 daily returns, we demonstrate that certain Kronecker product models are good approximations to the general covariance matrix.

Constraining minimal anomaly free mathrmU1 extensions of the Standard Model ; We consider a class of minimal anomaly free mathrmU1 extensions of the Standard Model with three generations of righthanded neutrinos and a complex scalar. Using electroweak precision constraints, new 13 TeV LHC data, and considering theoretical limitations such as perturbativity, we show that it is possible to constrain a wide class of models. By classifying these models with a single parameter, kappa, we can put a model independent upper bound on the new mathrmU1 gauge coupling gz. We find that the new dilepton data puts strong bounds on the parameters, especially in the mass region MZ'lesssim 3 mathrmTeV.

Light scattering from dense cold atomic media ; We theoretically study the propagation of light through a cold atomic medium, where the effects of motion, laser intensity, atomic density, and polarization can all modify the properties of the scattered light. We present two different microscopic models the coherent dipole model and the random walk model, both suitable for modeling recent experimental work done in large atomic arrays in the low light intensity regime. We use them to compute relevant observables such as the linewidth, peak intensity and line center of the emitted light. We further develop generalized models that explicitly take into account atomic motion. Those are relevant for hotter atoms and beyond the low intensity regime. We show that atomic motion can lead to drastic dephasing and to a reduction of collective effects, together with a distortion of the lineshape. Our results are applicable to model a full gamut of quantum systems that rely on atomlight interactions including atomic clocks, quantum simulators and nanophotonic systems.

A Bivariate Copula Additive Model for Location, Scale and Shape ; Rigby Stasinopoulos 2005 introduced generalized additive models for location, scale and shape GAMLSS where the response distribution is not restricted to belong to the exponential family and its parameters can be specified as functions of additive predictors that allows for several types of covariate effects e.g., linear, nonlinear, random and spatial effects. In many empirical situations, however, modeling simultaneously two or more responses conditional on some covariates can be of considerable relevance. In this article, we extend the scope of GAMLSS by introducing a bivariate copula additive model with continuous margins for location, scale and shape. The framework permits the copula dependence and marginal distribution parameters to be estimated simultaneously and, like in GAMLSS, each parameter to be modeled using an additive predictor. Parameter estimation is achieved within a penalized likelihood framework using a trust region algorithm with integrated automatic multiple smoothing parameter selection. The proposed approach allows for straightforward inclusion of potentially any parametric continuous marginal distribution and copula function. The models can be easily used via the copulaReg function in the R package SemiParBIVProbit. The usefulness of the proposal is illustrated on two case studies which use electricity price and demand data, and birth records and on simulated data.

Models and Algorithms for Graph Watermarking ; We introduce models and algorithmic foundations for graph watermarking. Our frameworks include security definitions and proofs, as well as characterizations when graph watermarking is algorithmically feasible, in spite of the fact that the general problem is NPcomplete by simple reductions from the subgraph isomorphism or graph edit distance problems. In the digital watermarking of many types of files, an implicit step in the recovery of a watermark is the mapping of individual pieces of data, such as image pixels or movie frames, from one object to another. In graphs, this step corresponds to approximately matching vertices of one graph to another based on graph invariants such as vertex degree. Our approach is based on characterizing the feasibility of graph watermarking in terms of keygen, marking, and identification functions defined over graph families with known distributions. We demonstrate the strength of this approach with exemplary watermarking schemes for two random graph models, the classic ErdHosR'enyi model and a random powerlaw graph model, both of which are used to model realworld networks.

Quantum interference in a Cooper pair splitter The three sites model ; New generation of Cooper pair splitters defined on hybrid nanostructures are devices with high tunable coupling parameters. Transport measurements through these devices revealed clear signatures of interference effects and motivated us to introduce a new model, called the 3sites model. These devices provide an ideal playground to tune the Cooper pair splitting CPS efficency on demand, and displays a rich variety of physical phenomena. In the present work we analyze theoretically the conductance of the 3sites model in the linear and nonlinear regimes and characterize the most representative features that arise by the interplay of the different model parameters. In the linear regime we find that the local processes typically exhibit Fanoshape resonances, while the CPS contribution exhibits Lorentzianshapes. Remarkably, we find that under certain conditions, the transport is blocked by the presence of a dark state. In the nonlinear regime we established a hierarchy of the model parameters to obtain the conditions for optimal efficency.

Quark Orbital Angular Momentum in the MIT Bag Model ; We present the results for the Generalized Transverse Momentum Distribution related to quark Orbital Angular Momentum, it i.e. F14, in the MIT bag model. This model has been modified to include the PeierlsYoccoz projection to restore translational invariance. Such a modification allows to fulfill more satisfactorily basic sum rules, that would otherwise be less elegantly carried out with the original version. Using the same model, we have calculated the twist3 GPD that corresponds to Orbital Angular Momentum a la Ji, through the PenttinenPolyakovShuvaevStrikman sum rule. Recently, a new relation between the two definitions of the quark Orbital Angular Momentum at the density level has been proposed, which we illustrate here within the model. The sum rule is fulfilled. Still within the framework of the MIT bag model, we analyze the WandzuraWilczek expression for the GPD of interest. The genuine quarkgluon contribution is evaluated directly thanks to the equation of motion of the bag, which allows for a direct control of the kinematical contributions to the twist3 GPD.

Linguistically Regularized LSTMs for Sentiment Classification ; Sentiment understanding has been a longterm goal of AI in the past decades. This paper deals with sentencelevel sentiment classification. Though a variety of neural network models have been proposed very recently, however, previous models either depend on expensive phraselevel annotation, whose performance drops substantially when trained with only sentencelevel annotation; or do not fully employ linguistic resources e.g., sentiment lexicons, negation words, intensity words, thus not being able to produce linguistically coherent representations. In this paper, we propose simple models trained with sentencelevel annotation, but also attempt to generating linguistically coherent representations by employing regularizers that model the linguistic role of sentiment lexicons, negation words, and intensity words. Results show that our models are effective to capture the sentiment shifting effect of sentiment, negation, and intensity words, while still obtain competitive results without sacrificing the models' simplicity.

Discontinuous polaron transition in a twoband model ; We present exact diagonalization and momentum average approximation MA results for the single polaron properties of a onedimensional twoband model with phononmodulated hopping. At strong electronphonon coupling, we find a novel type of sharp transition, where the polaron ground state momentum jumps discontinuously from kpi to k0. The nature and origin of this transition is investigated and compared to that of the SuSchriefferHeeger SSH model, where a sharp but smooth transition was previously reported. We argue that such discontinuous transitions are a consequence of the multiband nature of the model, and are unlikely to be observed in oneband models. We also show that MA describes qualitatively and even quantitatively accurately this polaron and its transition. Given its computationally efficient generalization to higher dimensions, MA thus promises to allow for accurate studies of electronphonon coupling in multiband models in higher dimensions.

Z boson mediated dark matter beyond the effective theory ; Direct detection bounds are beginning to constrain a very simple model of weakly interacting dark mattera Majorana fermion with a coupling to the Z boson. In a particularly straightforward gaugeinvariant realization, this coupling is introduced via a higherdimensional operator. While attractive in its simplicity, this model generically induces a large rho parameter. An ultraviolet completion that avoids an overly large contribution to rho is the singletdoublet model. We revisit this model, focusing on the Higgs blind spot region of parameter space where spinindependent interactions are absent. This model successfully reproduces dark matter with direct detection mediated by the Z boson, but whose cosmology may depend on additional couplings and states. Future direct detection experiments should effectively probe a significant portion of this parameter space, aside from a small coannihilating region. As such, Zmediated thermal dark matter as realized in the singletdoublet model represents an interesting target for future searches.

What Do Recurrent Neural Network Grammars Learn About Syntax ; Recurrent neural network grammars RNNG are a recently proposed probabilistic generative modeling family for natural language. They show stateoftheart language modeling and parsing performance. We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism GARNNG to enable closer inspection. We find that explicit modeling of composition is crucial for achieving the best performance. Through the attention mechanism, we find that headedness plays a central role in phrasal representation with the model's latent attention largely agreeing with predictions made by handcrafted head rules, albeit with some important differences. By training grammars without nonterminal labels, we find that phrasal representations depend minimally on nonterminals, providing support for the endocentricity hypothesis.

Constraining Logotropic Unified Dark Energy Models ; A unification of dark matter and dark energy in terms of a logotropic perfect dark fluid has recently been proposed, where deviations with respect to the standard Lambda rm CDM model are dependent on a single parameter B. In this paper we show that the requirement that the linear growth of cosmic structures on comoving scales larger than 8 h1 , rm Mpc is not significantly affected with respect to the standard Lambda rm CDM result provides the strongest constraint to date on the model B 6 times 107, an improvement of more than three orders of magnitude over previous constraints on the value of B. We further show that this constraint rules out the logotropic Unified Dark Energy model as a possible solution to the small scale problems of the LambdaCDM model, including the cusp problem of Dark Matter halos or the missing satellite problem, as well as the original version of the model where the Planck energy density was taken as one of the two parameters characterizing the logotropic dark fluid.

Thermal conductivity for stochastic energy exchange models ; We consider a class of stochastic models for energy transport and study relations between the thermal conductivity and some static observables, such as the static conductivity, which is defined as the contribution of static correlations in GreenKubo formula. The class of models is a generalization of two specific models derived by Gaspard and Gilbert as mesoscopic dynamics of energies for twodimensional and threedimensional locally confined harddiscs. They claim some equalities hold between the thermal conductivity and several static observables and also conjecture that these equations are universal in the sense that they hold for mesoscopic dynamics of energies for confined particles interacting through hardcore collisions. In this paper, we give sufficient and necessary conditions for these equalities to hold in the class we introduce. In particular, we prove that the equality between the thermal conductivity and other static observables holds if and only if the model obeys the gradient condition. Since the gradient condition does not hold for models derived by Gaspard and Gilbert, our result implies a part of their claim is incorrect.

There is Something Beyond the Twitter Network ; How information spreads through a social network Can we assume, that the information is spread only through a given social network graph What is the correct way to compare the models of information flow These are the basic questions we address in this work. We focus on meticulous comparison of various, wellknown models of rumor propagation in the social network. We introduce the model incorporating mass media and effects of absent nodes. In this model the information appears spontaneously in the graph. Using the most conservative metric, we showed that the distribution of cascades sizes generated by this model fits the real data much better than the previously considered models.

CompactlySupported Smooth Interpolators for Shape Modeling with Varying Resolution ; In applications that involve interactive curve and surface modeling, the intuitive manipulation of shapes is crucial. For instance, user interaction is facilitated if a geometrical object can be manipulated through control points that interpolate the shape itself. Additionally, models for shape representation often need to provide local shape control and they need to be able to reproduce common shape primitives such as ellipsoids, spheres, cylinders, or tori. We present a general framework to construct families of compactlysupported interpolators that are piecewiseexponential polynomial. They can be designed to satisfy regularity constraints of any order and they enable one to build parametric deformable shape models by suitable linear combinations of interpolators. They allow to change the resolution of shapes based on the refinability of Bsplines. We illustrate their use on examples to construct shape models that involve curves and surfaces with applications to interactive modeling and character design.

MultiValue Rule Sets ; We present the MultivAlue Rule Set MARS model for interpretable classification with feature efficient presentations. MARS introduces a more generalized form of association rules that allows multiple values in a condition. Rules of this form are more concise than traditional singlevalued rules in capturing and describing patterns in data. MARS mitigates the problem of dealing with continuous features and highcardinality categorical features faced by rulebased models. Our formulation also pursues a higher efficiency of feature utilization, which reduces the cognitive load to understand the decision process. We propose an efficient inference method for learning a maximum a posteriori model, incorporating theoretically grounded bounds to iteratively reduce the search space to improve search efficiency. Experiments with synthetic and realworld data demonstrate that MARS models have significantly smaller complexity and fewer features, providing better interpretability while being competitive in predictive accuracy. We conducted a usability study with human subjects and results show that MARS is the easiest to use compared with other competing rulebased models, in terms of the correct rate and response time. Overall, MARS introduces a new approach to rulebased models that balance accuracy and interpretability with featureefficient representations.

Performance Guaranteed Inertia Emulation for DieselWind System Feed Microgrid via Model Reference Control ; In this paper, a model reference control based inertia emulation strategy is proposed. Desired inertia can be precisely emulated through this control strategy so that guaranteed performance is ensured. A typical frequency response model with parametrical inertia is set to be the reference model. A measurement at a specific location delivers the information of disturbance acting on the dieselwind system to the reference model. The objective is for the speed of the dieselwind system to track the reference model. Since active power variation is dominantly governed by mechanical dynamics and modes, only mechanical dynamics and states, i.e., a swingenginegovernor system plus a reducedorder wind turbine generator, are involved in the feedback control design. The controller is implemented in a threephase dieselwind system feed microgrid. The results show exact synthetic inertia is emulated, leading to guaranteed performance and safety bounds.

Time Series Prediction Predicting Stock Price ; Time series forecasting is widely used in a multitude of domains. In this paper, we present four models to predict the stock price using the SPX index as input time series data. The martingale and ordinary linear models require the strongest assumption in stationarity which we use as baseline models. The generalized linear model requires lesser assumptions but is unable to outperform the martingale. In empirical testing, the RNN model performs the best comparing to other two models, because it will update the input through LSTM instantaneously, but also does not beat the martingale. In addition, we introduce an online to batch algorithm and discrepancy measure to inform readers the newest research in time series predicting method, which doesn't require any stationarity or non mixing assumptions in time series data. Finally, to apply these forecasting to practice, we introduce basic trading strategies that can create Win win and Zero sum situations.

Algorithmic detectability threshold of the stochastic block model ; The assumption that the values of model parameters are known or correctly learned, i.e., the Nishimori condition, is one of the requirements for the detectability analysis of the stochastic block model in statistical inference. In practice, however, there is no example demonstrating that we can know the model parameters beforehand, and there is no guarantee that the model parameters can be learned accurately. In this study, we consider the expectationmaximization EM algorithm with belief propagation BP and derive its algorithmic detectability threshold. Our analysis is not restricted to the community structure, but includes general modular structures. Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.

Learning Hidden Quantum Markov Models ; Hidden Quantum Markov Models HQMMs can be thought of as quantum probabilistic graphical models that can model sequential data. We extend previous work on HQMMs with three contributions 1 we show how classical hidden Markov models HMMs can be simulated on a quantum circuit, 2 we reformulate HQMMs by relaxing the constraints for modeling HMMs on quantum circuits, and 3 we present a learning algorithm to estimate the parameters of an HQMM from data. While our algorithm requires further optimization to handle larger datasets, we are able to evaluate our algorithm using several synthetic datasets. We show that on HQMM generated data, our algorithm learns HQMMs with the same number of hidden states and predictive accuracy as the true HQMMs, while HMMs learned with the BaumWelch algorithm require more states to match the predictive accuracy.

Merging the BernoulliGaussian and Symmetric AlphaStable Models for Impulsive Noises in Narrowband Power Line Channels ; To model impulsive noise in power line channels, both the BernoulliGaussian model and the symmetric alphastable model are usually applied. Towards a merge of existing noise measurement databases and a simplification of communication system design, the compatibility between the two models is of interest. In this paper, we show that they can be approximately converted to each other under certain constrains, although never generally unified. Based on this, we propose a fast model conversion.

Neural Variational Inference and Learning in Undirected Graphical Models ; Many problems in machine learning are naturally expressed in the language of undirected graphical models. Here, we propose blackbox learning and inference algorithms for undirected models that optimize a variational approximation to the loglikelihood of the model. Central to our approach is an upper bound on the logpartition function parametrized by a function q that we express as a flexible neural network. Our bound makes it possible to track the partition function during learning, to speedup sampling, and to train a broad class of hybrid directedundirected models via a unified variational inference framework. We empirically demonstrate the effectiveness of our method on several popular generative modeling datasets.

Fine Grained Knowledge Transfer for Personalized Taskoriented Dialogue Systems ; Training a personalized dialogue system requires a lot of data, and the data collected for a single user is usually insufficient. One common practice for this problem is to share training dialogues between different users and train multiple sequencetosequence dialogue models together with transfer learning. However, current sequencetosequence transfer learning models operate on the entire sentence, which might cause negative transfer if different personal information from different users is mixed up. We propose a personalized decoder model to transfer finer granularity phraselevel knowledge between different users while keeping personal preferences of each user intact. A novel personal control gate is introduced, enabling the personalized decoder to switch between generating personalized phrases and shared phrases. The proposed personalized decoder model can be easily combined with various deep models and can be trained with reinforcement learning. Realworld experimental results demonstrate that the phraselevel personalized decoder improves the BLEU over multiple sentencelevel transfer baseline models by as much as 7.5.

An Unstructured Mesh Convergent ReactionDiffusion Master Equation for Reversible Reactions ; The convergent reactiondiffusion master equation CRDME was recently developed to provide a lattice particlebased stochastic reactiondiffusion model that is a convergent approximation in the lattice spacing to an underlying spatiallycontinuous particle dynamics model. The CRDME was designed to be identical to the popular lattice reactiondiffusion master equation RDME model for systems with only linear reactions, while overcoming the RDME's loss of bimolecular reaction effects as the lattice spacing is taken to zero. In our original work we developed the CRDME to handle bimolecular association reactions on Cartesian grids. In this work we develop several extensions to the CRDME to facilitate the modeling of cellular processes within realistic biological domains. Foremost, we extend the CRDME to handle reversible bimolecular reactions on unstructured grids. Here we develop a generalized CRDME through discretization of the spatially continuous volume reactivity model, extending the CRDME to encompass a larger variety of particleparticle interactions. Finally, we conclude by examining several numerical examples to demonstrate the convergence and accuracy of the CRDME in approximating the volume reactivity model.

Modeling Binary Time Series Using Gaussian Processes with Application to Predicting Sleep States ; Motivated by the problem of predicting sleep states, we develop a mixed effects model for binary time series with a stochastic component represented by a Gaussian process. The fixed component captures the effects of covariates on the binaryvalued response. The Gaussian process captures the residual variations in the binary response that are not explained by covariates and past realizations. We develop a frequentist modeling framework that provides efficient inference and more accurate predictions. Results demonstrate the advantages of improved prediction rates over existing approaches such as logistic regression, generalized additive mixed model, models for ordinal data, gradient boosting, decision tree and random forest. Using our proposed model, we show that previous sleep state and heart rates are significant predictors for future sleep states. Simulation studies also show that our proposed method is promising and robust. To handle computational complexity, we utilize Laplace approximation, golden section search and successive parabolic interpolation. With this paper, we also submit an Rpackage HIBITS that implements the proposed procedure.

Advances in Variational Inference ; Many modern unsupervised or semisupervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference VI lets us approximate a highdimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and largescale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects a scalable VI, which includes stochastic approximations, b generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as nonconjugate models, c accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and d amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

Deep TemporalRecurrentReplicatedSoftmax for Topical Trends over Time ; Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural NetworkReplicated Softmax Model RNNRSM, where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNNRSM to 19 years of articles on NLP research, we demonstrate that compared to stateofthe art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric named as SPAN to quantify the capability of dynamic topic model to capture word evolution in topics over time.

Hierarchical Modeling of Seed Variety Yields and Decision Making for Future Planting Plans ; Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risksensitive decisionmaking framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting crop yield the yield of different seed varieties of the same crop. We integrate this prediction mechanism with a weather forecasting model, and propose three different approaches for decision making under uncertainty to select seed varieties for planting so as to balance yield maximization and risk.We apply our model to the problem of soybean variety selection given in the 2016 Syngenta Crop Challenge. Our prediction model achieves a median absolute error of 3.74 bushels per acre and thus provides good estimates for input into the decision models.Our decision models identify the selection of soybean varieties that appropriately balance yield and risk as a function of the farmer's risk aversion level. More generally, our models support farmers in decision making about which seed varieties to plant.

Speech Dereverberation with Contextaware Recurrent Neural Networks ; In this paper, we propose a model to perform speech dereverberation by estimating its spectral magnitude from the reverberant counterpart. Our models are capable of extracting features that take into account both short and longterm dependencies in the signal through a convolutional encoder which extracts features from a short, bounded context of frames and a recurrent neural network for extracting longterm information. Our model outperforms a recently proposed model that uses different context information depending on the reverberation time, without requiring any sort of additional input, yielding improvements of up to 0.4 on PESQ, 0.3 on STOI, and 1.0 on POLQA relative to reverberant speech. We also show our model is able to generalize to real room impulse responses even when only trained with simulated room impulse responses, different speakers, and high reverberation times. Lastly, listening tests show the proposed method outperforming benchmark models in reduction of perceived reverberation.

Evolution of Social Power for Opinion Dynamics Networks ; This article studies the evolution of opinions and interpersonal influence structures in a group of agents as they discuss a sequence of issues, each of which follows an opinion dynamics model. In this work, we propose a general opinion dynamics model and an evolution of interpersonal influence structures based on the model of reflected appraisals proposed by Friedkin. Our contributions can be summarized as follows i we introduce a model of opinion dynamics and evolution of interpersonal influence structures between issues viewed as a best response cost minimization to the neighbor's actions, ii we show that DeGroot's and FriedkinJohnsen's models of opinion dynamics and their evolution of interpersonal influence structures are particular cases of our proposed model, and iii we prove the existence of an equilibrium. This work is a step towards providing a solid formulation of the evolution of opinions and interpersonal influence structures over a sequence of issues.

Global stability of a piecewise linear macroeconomic model with a continuum of equilibrium states and sticky expectation ; We consider piecewise linear discrete time macroeconomic models, which possess a continuum of equilibrium states. These systems are obtained by replacing rational inflation expectations with a boundedly rational, and genuinely sticky, response of agents to changes in the actual inflation rate in a standard Dynamic Stochastic General Equilibrium model. Both for a lowdimensional variant of the model, with one representative agent, and the multiagent model, we show that, when exogenous noise is absent from the system, the continuum of equilibrium states is the global attractor. Further, when a uniformly bounded noise is present, or the equilibrium states are destabilized by an imperfect Central Bank policy or both, we estimate the size of the domain that attracts all the trajectories. The proofs are based on introducing a family of Lyapunov functions and, for the multiagent model, deriving a formula for the inverse of the PrandtlIshlinskii operator acting in the space of discrete time inputs and outputs.

Continuous Semantic Topic Embedding Model Using Variational Autoencoder ; This paper proposes the continuous semantic topic embedding model CSTEM which finds latent topic variables in documents using continuous semantic distance function between the topics and the words by means of the variational autoencoderVAE. The semantic distance could be represented by any symmetric bellshaped geometric distance function on the Euclidean space, for which the Mahalanobis distance is used in this paper. In order for the semantic distance to perform more properly, we newly introduce an additional model parameter for each word to take out the global factor from this distance indicating how likely it occurs regardless of its topic. It certainly improves the problem that the Gaussian distribution which is used in previous topic model with continuous word embedding could not explain the semantic relation correctly and helps to obtain the higher topic coherence. Through the experiments with the dataset of 20 Newsgroup, NIPS papers and CNNDailymail corpus, the performance of the recent stateoftheart models is accomplished by our model as well as generating topic embedding vectors which makes possible to observe where the topic vectors are embedded with the word vectors in the real Euclidean space and how the topics are related each other semantically.

A Formal Specification Framework for Smart Grid Components ; Smart grid can be considered as the next step in the evolution of power systems. It comprises of different entities and objects ranging from smart appliances, smart meters, generators, smart storages, and more. One key problem in modeling smart grid is that while currently there is a considerable focus on the practical aspects of smart grid, there are very few modeling attempts and even lesser attempts at formalization. To the best of our knowledge, among other formal methods, formal specification has previously not been applied in the domain of smart grid. In this paper, we attempt to bridge this gap by presenting a novel approach to modeling smart grid components using a formal specification approach. We use a statebased formal specification language namely Z pronounced as Zed' since we believe Z is particularly suited for modeling smart grid components.We demonstrate the application of Z on key smart grid components. The presented formal specification can be considered as first steps towards modeling of smart grid using a Software Engineering formalism. It also demonstrates how complex systems, such as the smart grid, can be modeled elegantly using formal specification.

Variational Inference for Gaussian Process Models with Linear Complexity ; Largescale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size. While sparse variational Gaussian process models are capable of learning from largescale data, standard strategies for sparsifying the model can prevent the approximation of complex functions. In this work, we propose a novel variational Gaussian process model that decouples the representation of mean and covariance functions in reproducing kernel Hilbert space. We show that this new parametrization generalizes previous models. Furthermore, it yields a variational inference problem that can be solved by stochastic gradient ascent with time and space complexity that is only linear in the number of mean function parameters, regardless of the choice of kernels, likelihoods, and inducing points. This strategy makes the adoption of largescale expressive Gaussian process models possible. We run several experiments on regression tasks and show that this decoupled approach greatly outperforms previous sparse variational Gaussian process inference procedures.

Partially composite Higgs models Phenomenology and RG analysis ; We study the phenomenology of partially compositeHiggs models where electroweak symmetry breaking is dynamically induced, and the Higgs is a mixture of a composite and an elementary state. The models considered have explicit realizations in terms of gaugeYukawa theories with new strongly interacting fermions coupled to elementary scalars and allow for a very SMlike Higgs state. We study constraints on their parameter spaces from vacuum stability and perturbativity as well as from LHC results and find that requiring vacuum stability up to the compositeness scale already imposes relevant constraints. A small part of parameter space around the classically conformal limit is stable up to the Planck scale. This is however already strongly disfavored by LHC results. In different limits, the models realize both partially compositeHiggs and bosonic technicolor models and a dynamical extension of the fundamental GoldstoneHiggs model. Therefore, they provide a general framework for exploring the phenomenology of composite dynamics.

A shortranged memory model with preferential growth ; In this work we introduce a variant of the YuleSimon model for preferential growth by incorporating a finite kernel to model the effects of bounded memory. We characterize the properties of the model combining analytical arguments with extensive numerical simulations. In particular, we analyze the lifetime and popularity distributions by mapping the model dynamics to corresponding Markov chains and branching processes, respectively. These distributions follow powerlaws with well defined exponents that are within the range of the empirical data reported in ecologies. Interestingly, by varying the innovation rate, this simple outofequilibrium model exhibits many of the characteristics of a continuous phase transition and, around the critical point, it generates time series with powerlaw popularity, lifetime and interevent time distributions, and nontrivial temporal correlations, such as a bursty dynamics in analogy with the activity of solar flares. Our results suggest that an appropriate balance between innovation and oblivion rates could provide an explanatory framework for many of the properties commonly observed in many complex systems.

SelfDestructing Dark Matter ; We present SelfDestructing Dark Matter SDDM, a new class of dark matter models which are detectable in large neutrino detectors. In this class of models, a component of dark matter can transition from a longlived state to a shortlived one by scattering off of a nucleus or an electron in the Earth. The shortlived state then decays to Standard Model particles, generating a dark matter signal with a visible energy of order the dark matter mass rather than just its recoil. This leads to striking signals in large detectors with high energy thresholds. We present a few examples of models which exhibit self destruction, all inspired by bound state dynamics in the Standard Model. The models under consideration exhibit a rich phenomenology, possibly featuring events with one, two, or even three lepton pairs, each with a fixed invariant mass and a fixed energy, as well as nontrivial directional distributions. This motivates dedicated searches for dark matter in large underground detectors such as SuperK, Borexino, SNO, and DUNE.