Wikistat 2.0: Educational Resources for Artificial Intelligence
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation
Evaluation has always been a key challenge in the development of artificial intelligence (AI) based software, due to the technical complexity of the software artifact and, often, its embedding in complex sociotechnical processes. Recent advances in machine learning (ML) enabled by deep neural networks has exacerbated the challenge of evaluating such software due to the opaque nature of these ML-based artifacts. A key related issue is the (in)ability of such systems to generate useful explanations of their outputs, and we argue that the explanation and evaluation problems are closely linked. The paper models the elements of a ML-based AI system in the context of public sector decision (PSD) applications involving both artificial and human intelligence, and maps these elements against issues in both evaluation and explanation, showing how the two are related. We consider a number of common PSD application patterns in the light of our model, and identify a set of key issues connected to explanation and evaluation in each case. Finally, we propose multiple strategies to promote wider adoption of AI/ML technologies in PSD, where each is distinguished by a focus on different elements of our model, allowing PSD policy makers to adopt an approach that best fits their context and concerns.
Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks
Salient object detection is a problem that has been considered in detail and many solutions proposed. In this paper, we argue that work to date has addressed a problem that is relatively ill-posed. Specifically, there is not universal agreement about what constitutes a salient object when multiple observers are queried. This implies that some objects are more likely to be judged salient than others, and implies a relative rank exists on salient objects. Initially, we present a novel deep learning solution based on a hierarchical representation of relative saliency and stage-wise refinement. Furthermore, we present data, analysis and benchmark baseline results towards addressing the problem of salient object ranking. Methods for deriving suitable ranked salient object instances are presented, along with metrics suitable to measuring algorithm performance. In addition, we show how a derived dataset can be successively refined to provide cleaned results that correlate well with pristine ground truth. Finally, we provide a comparison among prevailing algorithms that address salient object ranking or detection to establish initial baselines.
An Efficient Approach for Removing Look-ahead Bias in the Least Square Monte Carlo Algorithm: Leave-One-Out
The least square Monte Carlo (LSM) algorithm proposed by Longstaff and Schwartz [2001] is the most widely used method for pricing options with early exercise features. The LSM estimator contains look-ahead bias, and the conventional technique of removing it necessitates an independent set of simulations. This study proposes a new approach for efficiently eliminating look-ahead bias by using the leave-one-out method, a well-known cross-validation technique for machine learning applications. The leave-one-out LSM (LOOLSM) method is illustrated with examples, including multi-asset options whose LSM price is biased high. The asymptotic behavior of look-ahead bias is also discussed with the LOOLSM approach.
Convex Clustering: Model, Theoretical Guarantee and Efficient Algorithm
Projective Inference in High-dimensional Problems: Prediction and Feature Selection
This paper discusses predictive inference and feature selection for generalized linear models with scarce but high-dimensional data. We argue that in many cases one can benefit from a decision theoretically justified two-stage approach: first, construct a possibly non-sparse model that predicts well, and then find a minimal subset of features that characterize the predictions. The model built in the first step is referred to as the \emph{reference model} and the operation during the latter step as predictive \emph{projection}. The key characteristic of this approach is that it finds an excellent tradeoff between sparsity and predictive accuracy, and the gain comes from utilizing all available information including prior and that coming from the left out features. We review several methods that follow this principle and provide novel methodological contributions. We present a new projection technique that unifies two existing techniques and is both accurate and fast to compute. We also propose a way of evaluating the feature selection process using fast leave-one-out cross-validation that allows for easy and intuitive model size selection. Furthermore, we prove a theorem that helps to understand the conditions under which the projective approach could be beneficial. The benefits are illustrated via several simulated and real world examples.
Spatial evolution of Hindmarsh-Rose neural network with time delays
Spatial relations between neurons in the network with time delays play a crucial role in determining dynamics of the system. During the development of the nervous system different types of neurons group together to enable specific functions of the network. Right spatial distances, thus right time delays between cells are crucial for an appropriate functioning of the system. To model the process of neural migration we proposed simple but effective model of network spatial evolution based on Hindmarsh-Rose neurons and Metropoli-Hastings Monte Carlo algorithm. Under the specific assumptions and using appropriate parameters of the neural evolution the network can converge to the desirable state giving the opportunity of achieving large variety of spectra. We show that there is a specific range of network size in space which allows it to generate assumed output. A network or generally speaking a system with time delays (corresponding to the arrangement in the physical space) of specific output properties has a specific spatial dimension that allows it to function properly.
Restarting Frank-Wolfe
Abstracting Probabilistic Relational Models
Abstraction is a powerful idea widely used in science, to model, reason and explain the behavior of systems in a more tractable search space, by omitting irrelevant details. While notions of abstraction have matured for deterministic systems, the case for abstracting probabilistic models is not yet fully understood. In this paper, we develop a foundational framework for abstraction in probabilistic relational models from first principles. These models borrow syntactic devices from first-order logic and are very expressive, thus naturally allowing for relational and hierarchical constructs with stochastic primitives. We motivate a definition of consistency between a high-level model and its low-level counterpart, but also treat the case when the high-level model is missing critical information present in the low-level model. We prove properties of abstractions, both at the level of the parameter as well as the structure of the models.
The Dynamics of Differential Learning I: Information-Dynamics and Task Reachability
We study the topology of the space of learning tasks, which is critical to understanding transfer learning whereby a model such as a deep neural network is pre-trained on a task, and then used on a different one after some fine-tuning. First we show that using the Kolmogorov structure function we can define a distance between tasks, which is independent on any particular model used and, empirically, correlates with the semantic similarity between tasks. Then, using a path integral approximation, we show that this plays a central role in the learning dynamics of Deep Networks, and in particular in the reachability of one task from another. We show that the probability of paths connecting two tasks, is asymmetric and has a static component that depends on the geometry of the loss function, in particular on the curvature, and a dynamic component that is model dependent and relates to the ease of traversing such paths. Surprisingly, the static component corresponds to the distance derived from the Kolmogorov Structure Function. With the dynamic component, this gives strict lower bounds on the complexity necessary to learn a task starting from the solution to another. Our analysis also explains more complex phenomena where semantically similar tasks may be unreachable from one another, a phenomenon called Information Plasticity and observed in diverse learning systems such as animals and deep neural networks.
Comparing Averaged Relaxed Cutters and Projection Methods: Theory and Examples
We focus on the convergence analysis of averaged relaxations of cutters, specifically for variants that—depending upon how parameters are chosen—resemble \emph{alternating projections}, the \emph{Douglas–Rachford method}, \emph{relaxed reflect-reflect}, or the \emph{Peaceman–Rachford} method. Such methods are frequently used to solve convex feasibility problems. The standard convergence analyses of projection algorithms are based on the \emph{firm nonexpansivity} property of the relevant operators. However if the projections onto the constraint sets are replaced by cutters (projections onto separating hyperplanes), the firm nonexpansivity is lost. We provide a proof of convergence for a family of related averaged relaxed cutter methods under reasonable assumptions, relying on a simple geometric argument. This allows us to clarify fine details related to the allowable choice of the relaxation parameters, highlighting the distinction between the exact (firmly nonexpansive) and approximate (strongly quasi-nonexpansive) settings. We provide illustrative examples and discuss practical implementations of the method.
Compositional planning in Markov decision processes: Temporal abstraction meets generalized logic composition
In hierarchical planning for Markov decision processes (MDPs), temporal abstraction allows planning with macro-actions that take place at different time scale in form of sequential composition. In this paper, we propose a novel approach to compositional reasoning and hierarchical planning for MDPs under temporal logic constraints. In addition to sequential composition, we introduce a composition of policies based on generalized logic composition: Given sub-policies for sub-tasks and a new task expressed as logic compositions of subtasks, a semi-optimal policy, which is optimal in planning with only sub-policies, can be obtained by simply composing sub-polices. Thus, a synthesis algorithm is developed to compute optimal policies efficiently by planning with primitive actions, policies for sub-tasks, and the compositions of sub-policies, for maximizing the probability of satisfying temporal logic specifications. We demonstrate the correctness and efficiency of the proposed method in stochastic planning examples with a single agent and multiple task specifications.
Learning To Simulate
Simulation is a useful tool in situations where training data for machine learning models is costly to annotate or even hard to acquire. In this work, we propose a reinforcement learning-based method for automatically adjusting the parameters of any (non-differentiable) simulator, thereby controlling the distribution of synthesized data in order to maximize the accuracy of a model trained on that data. In contrast to prior art that hand-crafts these simulation parameters or adjusts only parts of the available parameters, our approach fully controls the simulator with the actual underlying goal of maximizing accuracy, rather than mimicking the real data distribution or randomly generating a large volume of data. We find that our approach (i) quickly converges to the optimal simulation parameters in controlled experiments and (ii) can indeed discover good sets of parameters for an image rendering simulator in actual computer vision applications.
Where Did My Optimum Go?: An Empirical Analysis of Gradient Descent Optimization in Policy Gradient Methods
Recent analyses of certain gradient descent optimization methods have shown that performance can degrade in some settings – such as with stochasticity or implicit momentum. In deep reinforcement learning (Deep RL), such optimization methods are often used for training neural networks via the temporal difference error or policy gradient. As an agent improves over time, the optimization target changes and thus the loss landscape (and local optima) change. Due to the failure modes of those methods, the ideal choice of optimizer for Deep RL remains unclear. As such, we provide an empirical analysis of the effects that a wide range of gradient descent optimizers and their hyperparameters have on policy gradient methods, a subset of Deep RL algorithms, for benchmark continuous control tasks. We find that adaptive optimizers have a narrow window of effective learning rates, diverging in other cases, and that the effectiveness of momentum varies depending on the properties of the environment. Our analysis suggests that there is significant interplay between the dynamics of the environment and Deep RL algorithm properties which aren’t necessarily accounted for by traditional adaptive gradient methods. We provide suggestions for optimal settings of current methods and further lines of research based on our findings.
An Introduction to fast-Super Paramagnetic Clustering
We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly compared to a modified implementation of a maximum likelihood approach to fast-Super-Paramagnetic Clustering (f-SPC). The methods are first applied standard toy test-case problems, and then to a dataset of 447 stocks traded on the New York Stock Exchange (NYSE) over 1249 days. The signal to noise ratio of stock market correlation matrices is briefly considered. Our result recover approximately clusters representative of standard economic sectors and mixed clusters whose dynamics shine light on the adaptive nature of financial markets and raise concerns relating to the effectiveness of industry based static financial market classification in the world of real-time data-analytics. A key result is that we show that the standard maximum likelihood methods are confirmed to converge to solutions within a Super-Paramagnetic (SP) phase. We use insights arising from this to discuss the implications of using a Maximum Entropy Principle (MEP) as opposed to the Maximum Likelihood Principle (MLP) as an optimization device for this class of problems.
Randomized Consensus based Distributed Kalman Filtering over Wireless Sensor Networks
This paper is concerned with developing a novel distributed Kalman filtering algorithm over wireless sensor networks based on randomized consensus strategy. Compared with the centralized algorithm, distributed filtering techniques require less computation per sensor and lead to more robust estimation since they simply use the information from the neighboring nodes in the network. However, poor local sensor estimation caused by limited observability and network topology changes which interfere the global consensus are challenging issues. Motivated by this observation, we propose a novel randomized gossip-based distributed Kalman filtering algorithm. Information exchange and computation in the proposed algorithm can be carried out in an arbitrarily connected network of nodes. In addition, the computational burden can be distributed for a sensor which communicates with a stochastically selected neighbor at each clock step under schemes of gossip algorithm. In this case, the error covariance matrix changes stochastically at every clock step, thus the convergence is considered in a probabilistic sense. We provide the mean square convergence analysis of the proposed algorithm. Under a sufficient condition, we show that the proposed algorithm is quite appealing as it achieves better mean square error performance theoretically than the noncooperative decentralized Kalman filtering algorithm. Besides, considering the limited computation, communication, and energy resources in the wireless sensor networks, we propose an optimization problem which minimizes the average expected state estimation error based on the proposed algorithm. To solve the proposed problem efficiently, we transform it into a convex optimization problem. And a sub-optimal solution is attained. Examples and simulations are provided to illustrate the theoretical results.
Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
Stochastic optimization techniques are standard in variational inference algorithms. These methods estimate gradients by approximating expectations with independent Monte Carlo samples. In this paper, we explore a technique that uses correlated, but more representative , samples to reduce estimator variance. Specifically, we show how to generate antithetic samples that match sample moments with the true moments of an underlying importance distribution. Combining a differentiable antithetic sampler with modern stochastic variational inference, we showcase the effectiveness of this approach for learning a deep generative model.
GPdoemd: a python package for design of experiments for model discrimination
GPdoemd is an open-source python package for design of experiments for model discrimination that uses Gaussian process surrogate models to approximate and maximise the divergence between marginal predictive distributions of rival mechanistic models. GPdoemd uses the divergence prediction to suggest a maximally informative next experiment.
Continuous-time Models for Stochastic Optimization Algorithms
We propose a new continuous-time formulation for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance reduced techniques. We exploit this continuous-time model, together with a simple Lyapunov analysis as well as tools from stochastic calculus, in order to derive convergence bounds for various types of non-convex functions. We contrast these bounds to their known equivalent in discrete-time as well as derive new bounds. Our model also includes SVRG, for which we derive a linear convergence rate for the class of weakly quasi-convex and quadratically growing functions.
Online Learning to Rank with Features
We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.
Spatially-weighted Anomaly Detection
Many types of anomaly detection methods have been proposed recently, and applied to a wide variety of fields including medical screening and production quality checking. Some methods have utilized images, and, in some cases, a part of the anomaly images is known beforehand. However, this kind of information is dismissed by previous methods, because the methods can only utilize a normal pattern. Moreover, the previous methods suffer a decrease in accuracy due to negative effects from surrounding noises. In this study, we propose a spatially-weighted anomaly detection method (SPADE) that utilizes all of the known patterns and lessens the vulnerability to ambient noises by applying Grad-CAM, which is the visualization method of a CNN. We evaluated our method quantitatively using two datasets, the MNIST dataset with noise and a dataset based on a brief screening test for dementia.
Replica analysis of Bayesian data clustering
We use statistical mechanics to study model-based Bayesian data clustering. In this approach, each partitioning of the data into clusters is regarded as a microscopic system state, the negative data log-likelihood gives the energy of each state, and the data set realisation acts as disorder. Optimal clustering corresponds to the ground state of the system, and is hence obtained from the free energy via a low `temperature’ limit. We assume that for large sample sizes the free energy density is self-averaging, and we use the replica method to compute the asymptotic free energy density. The main order parameter in the resulting (replica symmetric) theory, the distribution of the data over the clusters, satisfies a self-consistent equation which can be solved by a population dynamics algorithm. From this order parameter one computes the average free energy, and all relevant macroscopic characteristics of the problem. The theory describes numerical experiments perfectly, and gives a significant improvement over the mean-field theory that was used to study this model in past.
Local Interpretable Model-agnostic Explanations of Bayesian Predictive Models via Kullback-Leibler Projections
We introduce a method, KL-LIME, for explaining predictions of Bayesian predictive models by projecting the information in the predictive distribution locally to a simpler, interpretable explanation model. The proposed approach combines the recent Local Interpretable Model-agnostic Explanations (LIME) method with ideas from Bayesian projection predictive variable selection methods. The information theoretic basis helps in navigating the trade-off between explanation fidelity and complexity. We demonstrate the method in explaining MNIST digit classifications made by a Bayesian deep convolutional neural network.
Clust-LDA: Joint Model for Text Mining and Author Group Inference
Social media corpora pose unique challenges and opportunities, including typically short document lengths and rich meta-data such as author characteristics and relationships. This creates great potential for systematic analysis of the enormous body of the users and thus provides implications for industrial strategies such as targeted marketing. Here we propose a novel and statistically principled method, clust-LDA, which incorporates authorship structure into the topical modeling, thus accomplishing the task of the topical inferences across documents on the basis of authorship and, simultaneously, the identification of groupings between authors. We develop an inference procedure for clust-LDA and demonstrate its performance on simulated data, showing that clust-LDA out-performs the ‘vanilla’ LDA on the topic identification task where authors exhibit distinctive topical preference. We also showcase the empirical performance of clust-LDA based on a real-world social media dataset from Reddit.
Hierarchical Recurrent Filtering for Fully Convolutional DenseNets
Generating a robust representation of the environment is a crucial ability of learning agents. Deep learning based methods have greatly improved perception systems but still fail in challenging situations. These failures are often not solvable on the basis of a single image. In this work, we present a parameter-efficient temporal filtering concept which extends an existing single-frame segmentation model to work with multiple frames. The resulting recurrent architecture temporally filters representations on all abstraction levels in a hierarchical manner, while decoupling temporal dependencies from scene representation. Using a synthetic dataset, we show the ability of our model to cope with data perturbations and highlight the importance of recurrent and hierarchical filtering.
GraphBolt: Streaming Graph Approximations on Big Data
Graphs are found in a plethora of domains, including online social networks, the World Wide Web and the study of epidemics, to name a few. With the advent of greater volumes of information and the need for continuously updated results under temporal constraints, it is necessary to explore novel approaches that further enable performance improvements. In the scope of stream processing over graphs, we research the trade-offs between result accuracy and the speedup of approximate computation techniques. We believe this to be a natural path towards these performance improvements. Herein we present GraphBolt, through which we conducted our research. It is an innovative model for approximate graph processing, implemented in Apache Flink. We analyze our model and evaluate it with the case study of the PageRank algorithm, perhaps the most famous measure of vertex centrality used to rank websites in search engine results. In light of our model, we discuss the challenges driven by relations between result accuracy and potential performance gains. Our experiments show that GraphBolt can reduce computational time by over 50% while achieving result quality above 95% when compared to results of the traditional version of PageRank without any summarization or approximation techniques.
Interpretable Convolutional Neural Networks via Feedforward Design
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any BP as a reference. The FF design adopts a data-centric approach. It derives network parameters of the current layer based on data statistics from the output of the previous layer in a one-pass manner. To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform. It is a variant of the principal component analysis (PCA) with an added bias vector to annihilate activation’s nonlinearity. Multiple Saab transforms in cascade yield multiple convolutional layers. As to fully-connected (FC) layers, we construct them using a cascade of multi-stage linear least squared regressors (LSRs). The classification and robustness (against adversarial attacks) performances of BP- and FF-designed CNNs applied to the MNIST and the CIFAR-10 datasets are compared. Finally, we comment on the relationship between BP and FF designs.
Doubly Semi-Implicit Variational Inference
We extend the existing framework of semi-implicit variational inference (SIVI) and introduce doubly semi-implicit variational inference (DSIVI), a way to perform variational inference and learning when both the approximate posterior and the prior distribution are semi-implicit. In other words, DSIVI performs inference in models where the prior and the posterior can be expressed as an intractable infinite mixture of some analytic density with a highly flexible implicit mixing distribution. We provide a sandwich bound on the evidence lower bound (ELBO) objective that can be made arbitrarily tight. Unlike discriminator-based and kernel-based approaches to implicit variational inference, DSIVI optimizes a proper lower bound on ELBO that is asymptotically exact. We evaluate DSIVI on a set of problems that benefit from implicit priors. In particular, we show that DSIVI gives rise to a simple modification of VampPrior, the current state-of-the-art prior for variational autoencoders, which improves its performance.
POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset
Many services that perform information retrieval for Points of Interest (POI) utilize a Lucene-based setup with spatial filtering. While this type of system is easy to implement it does not make use of semantics but relies on direct word matches between a query and reviews leading to a loss in both precision and recall. To study the challenging task of semantically enriching POIs from unstructured data in order to support open-domain search and question answering (QA), we introduce a new dataset POIReviewQA. It consists of 20k questions (e.g.’is this restaurant dog friendly?’) for 1022 Yelp business types. For each question we sampled 10 reviews, and annotated each sentence in the reviews whether it answers the question and what the corresponding answer is. To test a system’s ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question. We build a Lucene-based baseline model, which achieves 77.0% AUC and 48.8% MAP. A sentence embedding-based model achieves 79.2% AUC and 41.8% MAP, indicating that the dataset presents a challenging problem for future research by the GIR community. The result technology can help exploit the thematic content of web documents and social media for characterisation of locations.
• RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification• Human Indignity: From Legal AI Personhood to Selfish Memes• Transfer Learning via Unsupervised Task Discovery for Visual Question Answering• Computing the Nucleolus of Weighted Voting Games in Pseudo-polynomial Time• On involutions in the Weyl group and $B$-orbit closures in the orthogonal case• Perturbation-based FEC-assisted Iterative Nonlinearity Compensation for WDM Systems• Learning Depth with Convolutional Spatial Propagation Network• Recurrent Transition Networks for Character Locomotion• Approximate Leave-One-Out for High-Dimensional Non-Differentiable Learning Problems• A Generic Complementary Sequence Encoder• First-order interpretations of bounded expansion classes• On the First Hitting Time Density of an Ornstein-Uhlenbeck Process• On Block Sensitivity and Fractional Block Sensitivity• On the Inner Product Predicate and a Generalization of Matching Vector Families• Bayesian Model Selection for a Class of Spatially-Explicit Capture Recapture Models• A method to Suppress Facial Expression in Posed and Spontaneous Videos• Optimal Transport Based Distributionally Robust Optimization: Structural Properties and Iterative Schemes• Sensitivity analysis for the active manipulation of Helmholtz fields in 3D• Prefix-Free Code Distribution Matching for Probabilistic Constellation Shaping• Langevin equation and fractional dynamics• Adaptive finite element methods for the pointwise tracking optimal control problem of the Stokes equations• Estimation of Parameters in Avian Movement Models• Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs• Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations• Feature prioritization and regularization improve standard accuracy and adversarial robustness• Quantitative and Local Limit Theorems• Permutation graphs and the Abelian sandpile model, tiered trees and non-ambiguous binary trees• Sticky-disk limit of planar $N$-bubbles• FashionNet: Personalized Outfit Recommendation with Deep Neural Network• Comment on `Detecting Topology Variations in Networks of Linear Dynamical Systems’• Tight Information Theoretic Converse Results for some Pliable Index Coding Problems• Parameterized Leaf Power Recognition via Embedding into Graph Products• Correcting the bias in least squares regression with volume-rescaled sampling• Image-based Guidance of Autonomous Aircraft for Wildfire Surveillance and Prediction• Graph-Theoretic Analysis of Belief System Dynamics under Logic Constraints• Synthetic likelihood method for reaction network inference• Applications of robust estimators of covariance in examination of inter-laboratory study data• Prototyping Formal System Models with Active Objects• Meeting Real-Time Constraint of Spectrum Management in TV Black-Space Access• Co-Learning Feature Fusion Maps from PET-CT Images of Lung Cancer• A note on spanoid rank• Medical Images Analysis in Cancer Diagnostic• High-Dimensional Poisson DAG Model Learning Using $\ell_1$-Regularized Regression• Uplink Time Scheduling with Power Level Modulation in Wireless Powered Communication Networks• MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations• An Edge-Computing Based Architecture for Mobile Augmented Reality• Performance Analysis of Multi-Cell Millimeter Wave Massive MIMO Networks with Low-Precision ADCs• Multi-Vehicle Trajectory Optimisation On Road Networks• Social Choice Random Utility Models of Intransitive Pairwise Comparisons• Local Stability and Performance of Simple Gradient Penalty mu-Wasserstein GAN• Advanced Traffic Management Systems: An Overview and A Development Strategy• PAPR Reduction in OFDM-IM Using Multilevel Dither Signals• Corrections to ‘Wyner’s Common Information under Rényi Divergence Measures’• Performance of Two-Hop Cooperative Cognitive Networks with an Energy Harvesting Relay• The role of memory in transition from direct to indirect reciprocity• Large deviations for the largest eigenvalue of the sum of two random matrices• PPO-CMA: Proximal Policy Optimization with Covariance Matrix Adaptation• Optimal Denial-of-Service Attack Energy Management over an SINR-Based Network• On the Invariants of the Cohomology of Complements of Coxeter Arrangements• Rate Loss Mitigation for 60-GHz mmWave Massive MIMO Lens Antenna Array Systems• End-to-end Networks for Supervised Single-channel Speech Separation• Weakly Supervised Object Detection in Artworks• Underdetermined Blind Source Separation for Sparse Signals based on the Law of Large Numbers and Minimum Intersection Angle Rule• Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime• C-DLSI: An Extended LSI Tailored for Federated Text Retrieval• AIRNet: Self-Supervised Affine Registration for 3D Medical Images using Neural Networks• Deep Learning for micro-Electrocorticographic (μECoG) Data• Graph reconstruction and generation from one card and the degree sequence• A Logic-Based Mixed-Integer Nonlinear Programming Model to Solve Non-Convex and Non-Smooth Economic Dispatch Problems: An Accuracy Analysis• Comment on ‘Analysis of a Charge-Pump PLL: A New Model’ by M. van Paemel• Accelerated Labeling of Discrete Abstractions for Autonomous Driving Subject to LTL Specifications• Integrating Weakly Supervised Word Sense Disambiguation into Neural Machine Translation• Modeling data with zero inflation and overdispersion using GAMLSSs• Metric graphs, cross ratios, and Rayleigh’s laws• Tropical moments of tropical Jacobians• SLIC Based Digital Image Enlargement• Hybrid Active Inference• ReTiCaM: Real-time Human Performance Capture from Monocular Video• Simulating acculturation dynamics between migrants and locals in relation to network formation• FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network• Diffusive Molecular Communication in a Biological Spherical Environment with Partially Absorbing Boundary• IMMIGRATE: A Margin-based Feature Selection Method with Interaction Terms• Accelerated Decentralized Optimization with Local Updates for Smooth and Strongly Convex Objectives• Brownian geometry• Asymptotic Confidence Regions Based on the Adaptive Lasso with Partial Consistent Tuning• Reply to Chen et al.: Parametric methods for cluster inference perform worse for two-sided t-tests• On extreme faces of projective point configurations• Optimal Target Intercept Paths for Vehicles with Turn Radius Constraints• Distributed optimization in wireless sensor networks: an island-model framework• Generating Diffusion MRI scalar maps from T1 weighted images using generative adversarial networks• Microlensing Searches for Exoplanets• Memetic Viability Evolution for Constrained Optimization• Mathematical models for stable matching problems with ties and incomplete lists• Optimizing groups of colluding strong attackers in mobile urban communication networks with evolutionary algorithms• Directed preferential attachment models• Block-Based Spectral Processing of Static and Dynamic 3D Meshes using Orthogonal Iterations• TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation• Balanced Allocation with Random Walk Based Sampling• Automatic Detection of Arousals during Sleep using Multiple Physiological Signals• Intersection sizes of linear subspaces with the hypercube• A Context-free Grammar for the Ramanujan-Shor Polynomials• Sample Complexity of Sinkhorn divergences• Random walks with preferential relocations and fading memory: a study through random recursive trees• Grundy dominating sequences on $X$-join product• Effects of Time Horizons on Influence Maximization in the Voter Dynamics• From direct tagging to Tagging with sentences compression• Improved Weighted Average Consensus in Distributed Cooperative Spectrum Sensing Networks• $\mathbf{M^*}$-Regularized Dictionary Learning• Frozen percolation on inhomogeneous random graphs• Subset selection in sparse matrices• Optimal Mechanism Design with Risk-loving Agents• Local Randomization and Beyond for Regression Discontinuity Designs• Subdeterminants and Concave Integer Quadratic Programming• Efficient Estimation of Smooth Functionals in Gaussian Shift Models• A Relaxation-based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence• Improved Inapproximability of Rainbow Coloring• Linear Queries Estimation with Local Differential Privacy
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