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Magister Dixit

“Data is new eyes.” Jake Porway

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Book Memo: “Environmental Data Analysis”

Methods and Applications
The book introduces frequently-used mathematical methods, such as time series analysis, statistical methods, approximations, and optimization in analyzing environmental data and demonstrates their application in various case studies. Designed as a practical guide, it suits mathematicians who try to find the way into environmental science, and environmental scientists who struggle to conduct data analysis.

R Packages worth a look

Custom Inputs Widgets for Shiny (shinyWidgets)
Custom inputs widgets to use in Shiny applications.

R Interface for Apache Impala (implyr)
SQL’ back-end to ‘dplyr’ for Apache Impala (incubating), the massively parallel processing query engine for Apache ‘Hadoop’. Impala enables low-latency ‘SQL’ queries on data stored in the ‘Hadoop’ Distributed File System ‘(HDFS)’, Apache ‘HBase’, Apache ‘Kudu’, and Amazon Simple Storage Service ‘(S3)’. See <https://impala.apache.org> for more information about Impala.

Variance Estimation using Difference-Based Methods (VarED)
Generating functions for both optimal and ordinary difference sequences, and the difference-based estimation functions.

Make it Easier to Enter Questionnaire Data (DataEntry)
This is a GUI application for defining attributes and setting valid values of variables, and then, entering questionnaire data in a data.frame.

Document worth reading: “A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing”

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements. A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

Whats new on arXiv

Learning to Predict: A Fast Re-constructive Method to Generate Multimodal Embeddings

Integrating visual and linguistic information into a single multimodal representation is an unsolved problem with wide-reaching applications to both natural language processing and computer vision. In this paper, we present a simple method to build multimodal representations by learning a language-to-vision mapping and using its output to build multimodal embeddings. In this sense, our method provides a cognitively plausible way of building representations, consistent with the inherently re-constructive and associative nature of human memory. Using seven benchmark concept similarity tests we show that the mapped vectors not only implicitly encode multimodal information, but also outperform strong unimodal baselines and state-of-the-art multimodal methods, thus exhibiting more ‘human-like’ judgments—particularly in zero-shot settings.


Who Said What: Modeling Individual Labelers Improves Classification

Data are often labeled by many different experts with each expert only labeling a small fraction of the data and each data point being labeled by several experts. This reduces the workload on individual experts and also gives a better estimate of the unobserved ground truth. When experts disagree, the standard approaches are to treat the majority opinion as the correct label or to model the correct label as a distribution. These approaches, however, do not make any use of potentially valuable information about which expert produced which label. To make use of this extra information, we propose modeling the experts individually and then learning averaging weights for combining them, possibly in sample-specific ways. This allows us to give more weight to more reliable experts and take advantage of the unique strengths of individual experts at classifying certain types of data. Here we show that our approach leads to improvements in computer-aided diagnosis of diabetic retinopathy. We also show that our method performs better than competing algorithms by Welinder and Perona, and by Mnih and Hinton. Our work offers an innovative approach for dealing with the myriad real-world settings that use expert opinions to define labels for training.


Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned

For the past few years, we used Apache Lucene as recommendation frame-work in our scholarly-literature recommender system of the reference-management software Docear. In this paper, we share three lessons learned from our work with Lucene. First, recommendations with relevance scores below 0.025 tend to have significantly lower click-through rates than recommendations with relevance scores above 0.025. Second, by picking ten recommendations randomly from Lucene’s top50 search results, click-through rate decreased by 15%, compared to recommending the top10 results. Third, the number of returned search results tend to predict how high click-through rates will be: when Lucene returns less than 1,000 search results, click-through rates tend to be around half as high as if 1,000+ results are returned.


Analysis of Different Approaches of Parallel Block Processing for K-Means Clustering Algorithm

Distributed Computation has been a recent trend in engineering research. Parallel Computation is widely used in different areas of Data Mining, Image Processing, Simulating Models, Aerodynamics and so forth. One of the major usage of Parallel Processing is widely implemented for clustering the satellite images of size more than dimension of 1000×1000 in a legacy system. This paper mainly focuses on the different approaches of parallel block processing such as row-shaped, column-shaped and square-shaped. These approaches are applied for classification problem. These approaches is applied to the K-Means clustering algorithm as this is widely used for the detection of features for high resolution orthoimagery satellite images. The different approaches are analyzed, which lead to reduction in execution time and resulted the influence of improvement in performance measurement compared to sequential K-Means Clustering algorithm.


Multilabel Classification with R Package mlr

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of randomForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.


Efficient Processing of Deep Neural Networks: A Tutorial and Survey

Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Accordingly, techniques that enable efficient processing of deep neural network to improve energy-efficiency and throughput without sacrificing performance accuracy or increasing hardware cost are critical to enabling the wide deployment of DNNs in AI systems. This article aims to provide a comprehensive tutorial and survey about the recent advances towards the goal of enabling efficient processing of DNNs. Specifically, it will provide an overview of DNNs, discuss various platforms and architectures that support DNNs, and highlight key trends in recent efficient processing techniques that reduce the computation cost of DNNs either solely via hardware design changes or via joint hardware design and network algorithm changes. It will also summarize various development resources that can enable researchers and practitioners to quickly get started on DNN design, and highlight important benchmarking metrics and design considerations that should be used for evaluating the rapidly growing number of DNN hardware designs, optionally including algorithmic co-design, being proposed in academia and industry. The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand trade-offs between various architectures and platforms; be able to evaluate the utility of various DNN design techniques for efficient processing; and understand of recent implementation trends and opportunities.


Deep Architectures for Modulation Recognition

We survey the latest advances in machine learning with deep neural networks by applying them to the task of radio modulation recognition. Results show that radio modulation recognition is not limited by network depth and further work should focus on improving learned synchronization and equalization. Advances in these areas will likely come from novel architectures designed for these tasks or through novel training methods.


Channel Impulse Response-based Distributed Physical Layer Authentication

AutonoVi: Autonomous Vehicle Planning with Dynamic Maneuvers and Traffic Constraints

Stochastic Methods for Composite Optimization Problems

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Deep Residual Learning for Instrument Segmentation in Robotic Surgery

Sequence-to-Sequence Models Can Directly Transcribe Foreign Speech

Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees

Analyzing Evolving Stories in News Articles

Low Precision Neural Networks using Subband Decomposition

The Inner Structure of Time-Dependent Signals

Adversarial Examples for Semantic Segmentation and Object Detection

Aversion to Uncertainty and Its Implications for Revenue Maximization

Jointly Optimizing Placement and Inference for Beacon-based Localization

Temporal Non-Volume Preserving Approach to Facial Age-Progression and Age-Invariant Face Recognition

Binarsity: a penalization for one-hot encoded features

LANOVA Penalization for Unreplicated Data

Digraphs with at most one trivial critical ideal

Computing the capacity of a Markoff channel with perfect feedback is PSPACE-hard

Random sampling of Latin squares via binary contingency tables and probabilistic divide-and-conquer

AMAT: Medial Axis Transform for Natural Images

An explicit determination of the $K$-theoretic structure constants of the affine Grassmanian associated to $SL_2$

Informational Substitutes

Regularized Gradient Descent: A Nonconvex Recipe for Fast Joint Blind Deconvolution and Demixing

Real-space analysis of scanning tunneling microscopy topography datasets using sparse modeling approach

Exact Spike Train Inference Via $\ell_0$ Optimization

Simplifying the Bible and Wikipedia Using Statistical Machine Translation

The new concepts of measurement error’s regularities and effect characteristics

Second-Order Necessary Conditions for Optimal Control of Semilinear Elliptic Equations with Leading Term Containing Controls

More is Less: A More Complicated Network with Less Inference Complexity

Perfect codes in circulant graphs

Bayesian Optimization for Refining Object Proposals

Multipair Massive MIMO Relaying Systems with One-Bit ADCs and DACs

Maximizing the area of intersection of rectangles

Peterson Isomorphism in $K$-theory and Relativistic Toda Lattice

Full likelihood inference for max-stable data

Exploration–Exploitation in MDPs with Options

On the $h$-vector of ($S_r$) simplicial complexes

The (theta, wheel)-free graphs Part II: structure theorem

Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation

The Fractal Dimension of Interfaces in Edwards-Anderson and Long-range Ising Spin Glasses: Determining the Applicability of Different Theoretical Descriptions

On Completely Regular Codes

An interesting geometry for positive random variables

Improving the Accuracy of the CogniLearn System for Cognitive Behavior Assessment

Hiring Expert Consultants in E-Healthcare: A Two Sided Matching Approach

Morphological Analysis for the Maltese Language: The Challenges of a Hybrid System

Randomized Load Balancing on Networks with Stochastic Inputs

Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

Greedy walks on two lines

Count-ception: Counting by Fully Convolutional Redundant Counting

A new upper bound for subspace codes

On well-covered Cartesian products

Extending Growth Mixture Models Using Continuous Non-Elliptical Distributions

Graphs cospectral with multicone graphs Kw+L(P)

Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality

Roller Coaster Permutations and Partition Numbers

Sketch-based Face Editing in Video Using Identity Deformation Transfer

Digraphs with degree two and excess two are diregular

Clustering and Variable Selection in the Presence of Mixed Variable Types and Missing Data

Continued fractions for permutation statistics

Proof Verification Can Be Hard!

LEPOR: An Augmented Machine Translation Evaluation Metric

Game-Theoretic Protection Against Networked SIS Epidemics by Human Decision-Makers

Denoising-based Turbo Compressed Sensing

Team Formation for Scheduling Educational Material in Massive Online Classes

Structured Learning of Tree Potentials in CRF for Image Segmentation

$R(5,5) \le 48$

Open Vocabulary Scene Parsing

SCAN: Structure Correcting Adversarial Network for Chest X-rays Organ Segmentation

Multivariate Regression with Gross Errors on Manifold-valued Data

Assortative Mixing Equilibria in Social Network Games

A Unified Ensemble of Concatenated Convolutional Codes

Steiner Point Removal — Distant Terminals Don’t (Really) Bother

Derivation of mean-field equations for stochastic particle systems

Uncertainty Quantification in the Classification of High Dimensional Data

Stochastic flows of two-dimensional second grade fluids

Localization for $N$-particle continuous models with strongly mixing correlated random potentials

Surrogate Model of Multi-Period Flexibility from a Home Energy Management System

Counting faces of nestohedra

Token-based Function Computation with Memory

On connectedness of power graphs of finite groups

Learned multi-patch similarity

Person Re-Identification by Camera Correlation Aware Feature Augmentation

Distributed Voting/Ranking with Optimal Number of States per Node

Distributions of a particle’s position and their asymptotics in the $q$-deformed totally asymmetric zero range process with site dependent jumping rates

Inferring The Latent Structure of Human Decision-Making from Raw Visual Inputs

Approximate moment dynamics for polynomial and trigonometric stochastic systems

Testing independence with high-dimensional correlated samples

Driving an Ornstein–Uhlenbeck Process to Desired First-Passage Time Statistics

On the number of geodesics of Petersen graph $GP(n,2)$

A categorical characterization of relative entropy on Polish spaces

Socially Aware Motion Planning with Deep Reinforcement Learning

Learning Simpler Language Models with the Delta Recurrent Neural Network Framework

Multi-View Deep Learning for Consistent Semantic Mapping with RGB-D Cameras

Solvability regions of affinely parameterized quadratic equations

A Mixture of Matrix Variate Skew-t Distributions

Question Answering from Unstructured Text by Retrieval and Comprehension

Nonlinear Large Deviations: Beyond the Hypercube

Transductive Zero-Shot Learning with a Self-training dictionary approach

Distributed Adaptive Gradient Optimization Algorithm

Transductive Zero-Shot Learning with Adaptive Structural Embedding

Distributed optimization with nonuniform unbounded convex constraint sets and nonuniform step-sizes

Some new bounds of placement delivery arrays

Multileader WAN Paxos: Ruling the Archipelago with Fast Consensus

Exploiting Color Name Space for Salient Object Detection

A Visual Measure of Changes to Weighted Self-Organizing Map Patterns

MIHash: Online Hashing with Mutual Information

Modeling high dimensional multichannel brain signals

On Automating the Doctrine of Double Effect

Heat Kernels for Non-symmetric Non-local Operators

Resource-monotonicity and Population-monotonicity in Connected Cake-cutting

Palindromic Decompositions with Gaps and Errors

Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric

A circuit-preserving mapping from multilevel to Boolean dynamics

Security Constrained Multi-Stage Transmission Expansion Planning Considering a Continuously Variable Series Reactor

A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis

Tree Edit Distance Cannot be Computed in Strongly Subcubic Time (unless APSP can)

Physical Layer Security in Wireless Ad Hoc Networks Under A Hybrid Full-/Half-Duplex Receiver Deployment Strategy

Equivalence of recurrence and Liouville property for symmetric Dirichlet forms

Intelligent bidirectional rapidly-exploring random trees for optimal motion planning in complex cluttered environments

Uniform description of the rigged configuration bijection

Gene tree species tree reconciliation with gene conversion

Exact and approximate limit behaviour of the Yule tree’s cophenetic index

Optimal insider control of stochastic Volterra equations

Scaling the Scattering Transform: Deep Hybrid Networks

Mastering Sketching: Adversarial Augmentation for Structured Prediction

Multimodal deep learning approach for joint EEG-EMG data compression and classification

Thompson Sampling for Linear-Quadratic Control Problems

Equivalence of Palm measures for determinantal point processes governed by Bergman kernels

On the minimum output entropy of random orthogonal quantum channels

Freeness of multi-reflection arrangements via primitive vector fields

MURS: Mitigating Memory Pressure in Data Processing Systems for Service

TCP in 5G mmWave Networks: Link Level Retransmissions and MP-TCP

Simultaneous Perception and Path Generation Using Fully Convolutional Neural Networks

Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis

On the Limit Imbalanced Logistic Regression by Binary Predictors

Rational ergodicity of Step function Skew Products

On Infinite Linear Programming and the Moment Approach to Deterministic Infinite Horizon Discounted Optimal Control Problems

Detection of Spatiotemporally Coherent Rainfall Anomalies Using Markov Random Fields

Group Cooperation with Optimal Resource Allocation in Wireless Powered Communication Networks

Percolation on an infinitely generated group

Factorization of Saddle-point Matrices in Dynamical Systems Optimization—Updating Bunch-Parlett

A Sentence Simplification System for Improving Relation Extraction

Gamma relaxation in bulk metallic glasses

Trespassing the Boundaries: Labeling Temporal Bounds for Object Interactions in Egocentric Video

One- and Two-Way Relay Optimization for MIMO Interference Networks

Shift-Symmetric Configurations in Two-Dimensional Cellular Automata: Irreversibility, Insolvability, and Enumeration

Deep Deterministic Policy Gradient for Urban Traffic Light Control

Accessibility and Delay in Random Temporal Networks

Density classification performance of the Gacs-Kurdyumov-Levin four-state cellular automaton model IV

Maximum matchings in scale-free networks with identical degree distribution

Multiple Access for 5G New Radio: Categorization, Evaluation, and Challenges

Bootstrapping a Lexicon for Emotional Arousal in Software Engineering

Bayesian Repulsive Gaussian Mixture Model

A numerical method for the estimation of time-varying parameter models in large dimensions

Randomized CP Tensor Decomposition

Active Convolution: Learning the Shape of Convolution for Image Classification

The weighted stable matching problem

Automating decision making to help establish norm-based regulations

Fractional Herglotz variational problems of variable order

Reflected solutions of Anticipated Backward Doubly SDEs driven by Teugels Martingales

Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia

Towards Effective Research-Paper Recommender Systems and User Modeling based on Mind Maps

Sparse Multi-Output Gaussian Processes for Medical Time Series Prediction

A conjectural description for real Schur roots of acyclic quivers

Multi-sensor Transmission Management for Remote State Estimation under Coordination

Generalized Gabidulin codes over fields of any characteristic

Where to put the Image in an Image Caption Generator

Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes

Multi-Path Region-Based Convolutional Neural Network for Accurate Detection of Unconstrained ‘Hard Faces’

GPU Activity Prediction using Representation Learning

Two-part models with stochastic processes for modelling longitudinal semicontinuous data: computationally efficient inference and modelling the overall marginal mean

Reweighted Infrared Patch-Tensor Model With Both Non-Local and Local Priors for Single-Frame Small Target Detection

A Dynamic Programming Solution to Bounded Dejittering Problems

Scalable Bayesian shrinkage and uncertainty quantification in high-dimensional regression

PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading

Rates in almost sure invariance principle for Young towers with exponential tails

Nash Equilibrium in Social Media

Transfer learning for music classification and regression tasks

Gradient Method With Inexact Oracle for Composite Non-Convex Optimization

Private Learning on Networks: Part II

Monitoring crystal breakage in wet milling processes using inline imaging and chord length distribution measurements

Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference

Pattern Recognition on Oriented Matroids: Decompositions of Topes, and Orthogonality Relations

Counterexamples to regularities for the derivative processes associated to stochastic evolution equations

Introduction To The Monogenic Signal

Deep Poincare Map For Robust Medical Image Segmentation

Biologically inspired protection of deep networks from adversarial attacks

On period polynomials of degree $2^m$ and weight distributions of certain irreducible cyclic codes

Fairness in Criminal Justice Risk Assessments: The State of the Art

StyleBank: An Explicit Representation for Neural Image Style Transfer

Coherent Online Video Style Transfer

Distilled News

An Introduction to Stock Market Data Analysis with R (Part 1)

This post is the first in a two-part series on stock data analysis using R, based on a lecture I gave on the subject for MATH 3900 (Data Science) at the University of Utah. In these posts, I will discuss basics such as obtaining the data from Yahoo! Finance using pandas, visualizing stock data, moving averages, developing a moving-average crossover strategy, backtesting, and benchmarking. The final post will include practice problems. This first post discusses topics up to introducing moving averages.


Standardization and Specialization in Analytics, Data Science, and BI

We see beginnings of both standardization and specialization, with a baseline graduate analytics curriculum that covers proficiencies in mathematics, statistics, computer science, IT systems, and organizational communications. We also see specializations in data science and BI, and verticals like marketing and healthcare analytics.


From Big Data Platforms to Platform-less Machine Learning

The rise in serverless architectures along with marketplaces from cloud providers creates a significant momentum to democratize big data analytics. Machine learning or AI services are much more valuable, tangible and easier to understand for businesses than clumsy big data platforms.

Whats new on arXiv

LRC: Dependency-Aware Cache Management for Data Analytics Clusters

Memory caches are being aggressively used in today’s data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies–notably the Least Recently Used (LRU) policy–that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed by ‘guessing’ the future data access patterns based on historical information (e.g., the access recency and/or frequency), which frequently results in inefficient, erroneous caching with low hit ratio and a long response time. In this paper, we propose a novel cache replacement policy, Least Reference Count (LRC), which exploits the application-specific DAG information to optimize the cache management. LRC evicts the cached data blocks whose reference count is the smallest. The reference count is defined, for each data block, as the number of dependent child blocks that have not been computed yet. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, compared with LRU, LRC speeds up typical applications by 60%.


Improving Classification by Improving Labelling: Introducing Probabilistic Multi-Label Object Interaction Recognition

This work deviates from easy-to-define class boundaries for object interactions. For the task of object interaction recognition, often captured using an egocentric view, we show that semantic ambiguities in verbs and recognising sub-interactions along with concurrent interactions result in legitimate class overlaps (Figure 1). We thus aim to model the mapping between observations and interaction classes, as well as class overlaps, towards a probabilistic multi-label classifier that emulates human annotators. Given a video segment containing an object interaction, we model the probability for a verb, out of a list of possible verbs, to be used to annotate that interaction. The proba- bility is learnt from crowdsourced annotations, and is tested on two public datasets, comprising 1405 video sequences for which we provide annotations on 90 verbs. We outper- form conventional single-label classification by 11% and 6% on the two datasets respectively, and show that learning from annotation probabilities outperforms majority voting and enables discovery of co-occurring labels.


A Hybrid Deep Learning Approach for Texture Analysis

Texture classification is a problem that has various applications such as remote sensing and forest species recognition. Solutions tend to be custom fit to the dataset used but fails to generalize. The Convolutional Neural Network (CNN) in combination with Support Vector Machine (SVM) form a robust selection between powerful invariant feature extractor and accurate classifier. The fusion of experts provides stability in classification rates among different datasets.


K-Means Clustering using Tabu Search with Quantized Means

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering as an alternative to Lloyd’s algorithm, which for all its ease of implementation and fast runtime, has the major drawback of being trapped at local optima. While the TS approach can yield superior performance, it involves a high computational complexity. Moreover, the difficulty in parameter selection in the existing TS approach does not make it any more attractive. This paper presents an alternative, low-complexity formulation of the TS optimization procedure for K-Means clustering. This approach does not require many parameter settings. We initially constrain the centers to points in the dataset. We then aim at evolving these centers using a unique neighborhood structure that makes use of gradient information of the objective function. This results in an efficient exploration of the search space, after which the means are refined. The proposed scheme is implemented in MATLAB and tested on four real-world datasets, and it achieves a significant improvement over the existing TS approach in terms of the intra cluster sum of squares and computational time.


MSE estimates for multitaper spectral estimation and off-grid compressive sensing

Localization of a microtubule organizing center by kinesin motors

A recursive point process model for infectious diseases

Fast and Flexible Successive-Cancellation List Decoders for Polar Codes

Speeding up TestU01 with the use of HTCondor

Flare: Native Compilation for Heterogeneous Workloads in Apache Spark

Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing

3D spatially-resolved optical energy density enhanced by wavefront shaping

A Novel Millimeter-Wave Channel Simulator and Applications for 5G Wireless Communications

Forcing clique immersions through chromatic number

Semi-Automatic Segmentation and Ultrasonic Characterization of Solid Breast Lesions

Millimeter Wave Small-Scale Spatial Statistics in an Urban Microcell Scenario

Efficient regularization with wavelet sparsity constraints in PAT

Mean-Field Controllability and Decentralized Stabilization of Markov Chains, Part I: Global Controllability and Rational Feedbacks

TokTrack: A Complete Token Provenance and Change Tracking Dataset for the English Wikipedia

On the Robustness of Convolutional Neural Networks to Internal Architecture and Weight Perturbations

SINR and Throughput of Dense Cellular Networks with Stretched Exponential Path Loss

The Dependence of Machine Learning on Electronic Medical Record Quality

Improved NN-JPDAF for Joint Multiple Target Tracking and Feature Extraction

Mixing Time of Random Walk on Poisson Geometry Small World

Supervisor Synthesis of POMDP based on Automata Learning

A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality

Combinatorial metrics: MacWilliams-type identities, isometries and extension property

An Asymptotically Tighter Bound on Sampling for Frequent Itemsets Mining

View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data

Experimental Identification of Hard Data Sets for Classification and Feature Selection Methods with Insights on Method Selection

Diffusion L0-norm constraint improved proportionate LMS algorithm for sparse distributed estimation

The Multi-Armed Bandit Problem: An Efficient Non-Parametric Solution

Evolutionary Stability of Reputation Management System in Peer to Peer Networks

Deep Direct Regression for Multi-Oriented Scene Text Detection

Projective divisible binary codes

Multi-Level Discovery of Deep Options

An online slow manifold approach for efficient optimal control of multiple time-scale kinetics

Interacting Conceptual Spaces I : Grammatical Composition of Concepts

Arc-transitive cyclic and dihedral covers of pentavalent symmetric graphs of order twice a prime

Are crossing dependencies really scarce?

Hyper Zagreb Index of Bridge and Chain Grpahs

Taming Tail Latency for Erasure-coded, Distributed Storage Systems

Anderson localization of a Rydberg electron along a classical orbit

Event-based State Estimation: An Emulation-based Approach

Nonparametric Bayesian analysis for support boundary recovery

Scalable Person Re-identification on Supervised Smoothed Manifold

A new class of three-weight linear codes from weakly regular plateaued functions

A randomized primal distributed algorithm for partitioned and big-data non-convex optimization

Optimal Service Elasticity in Large-Scale Distributed Systems

A duality-based approach for distributed min-max optimization with application to demand side management

Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot

Smart Augmentation – Learning an Optimal Data Augmentation Strategy

The KMS Condition for the homoclinic equivalence relation and Gibbs probabilities

Self-organized pattern formation of run-and-tumble chemotactic bacteria: Instability analysis of a kinetic chemotaxis model

DeepVisage: Making face recognition simple yet with powerful generalization skills

Smart Meter Privacy with Renewable Energy and a Storage Device

Stochastic Calculus with respect to Gaussian Processes: Part I

Zero controllability in discrete-time structured systems

Volterra differential equations with singular kernels

Reasoning by Cases in Structured Argumentation

Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces

A Bitcoin-inspired infinite-server model with a random fluid limit

A vehicle-to-infrastructure communication based algorithm for urban traffic control

Combinatorial Ricci curvature on cell-complex and Gauss-Bonnnet Theorem

On the compensator in the Doob-Meyer decomposition of the Snell envelope

A bijective proof of the hook-length formula for skew shapes

Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop

Modeling and Estimation for Self-Exciting Spatio-Temporal Models of Terrorist Activity

Metric random matchings with applications

Linear classifier design under heteroscedasticity in Linear Discriminant Analysis

Moments of the Hermitian Matrix Jacobi process

Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach

Constant Threshold Intersection Graphs of Orthodox Paths in Trees

Virtualization technology for distributed time sensitive domains

Batch-normalized joint training for DNN-based distant speech recognition

Medical Image Retrieval using Deep Convolutional Neural Network

Overcoming Catastrophic Forgetting by Incremental Moment Matching

Long-Term Evolution of Genetic Programming Populations

Multiscale Granger causality

regsem: Regularized Structural Equation Modeling

Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors

Multi-stage Multi-recursive-input Fully Convolutional Networks for Neuronal Boundary Detection

Local Deep Neural Networks for Age and Gender Classification

Partitions of multigraphs under degree constraints

Generalized Nash Equilibrium Problem by the Alternating Direction Method of Multipliers

ALLSAT compressed with wildcards. Part 2: All k-models of a BDD

An Extension of Feller’s Strong Law of Large Numbers

Interactive Natural Language Acquisition in a Multi-modal Recurrent Neural Architecture

Mean-Field Controllability and Decentralized Stabilization of Markov Chains, Part II: Asymptotic Controllability and Polynomial Feedbacks

Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer

Rejection-free Ensemble MCMC with applications to Factorial Hidden Markov Models

Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks

An Algorithmic Approach to the Asynchronous Computability Theorem

Turing instability in a model with two interacting Ising lines: hydrodynamic limit

A Dynamic Programming Principle for Distribution-Constrained Optimal Stopping

PonyGE2: Grammatical Evolution in Python

Crowdsourcing Universal Part-Of-Speech Tags for Code-Switching

Document worth reading: “Distributed Constraint Optimization Problems and Applications: A Survey”

The field of Multi-Agent System (MAS) is an active area of research within Artificial Intelligence, with an increasingly important impact in industrial and other real-world applications. Within a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent agent architectures to govern the agents’ autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have enabled them to support MAS in complex, real-time, and uncertain environments. This survey aims at providing an overview of the DCOP model, giving a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas. Distributed Constraint Optimization Problems and Applications: A Survey