**A causal inference framework for cancer cluster investigations using publicly available data**

Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the CDC recommends that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the literature. In this paper we propose a novel causal inference approach to cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we introduce a new estimand called the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation under the counterfactual scenario of no exposure, and 2) make inference on cancer incidence in these unexposed populations using publicly available data that are often available at a much higher level of spatial aggregation than what is desired. We overcome the first challenge by relying on matching. We overcome the second challenge by developing a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired finer level of spatial aggregation. We apply our proposed approach to determine whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, NY.

**Human-like machine learning: limitations and suggestions**

This paper attempts to address the issues of machine learning in its current implementation. It is known that machine learning algorithms require a significant amount of data for training purposes, whereas recent developments in deep learning have increased this requirement dramatically. The performance of an algorithm depends on the quality of data and hence, algorithms are as good as the data they are trained on. Supervised learning is developed based on human learning processes by analysing named (i.e. annotated) objects, scenes and actions. Whether training on large quantities of data (i.e. big data) is the right or the wrong approach, is debatable. The fact is, that training algorithms the same way we learn ourselves, comes with limitations. This paper discusses the issues around applying a human-like approach to train algorithms and the implications of this approach when using limited data. Several current studies involving non-data-driven algorithms and natural examples are also discussed and certain alternative approaches are suggested.

**On the Robustness of Information-Theoretic Privacy Measures and Mechanisms**

Consider a data publishing setting for a dataset composed of non-private features correlated with a set of private features not necessarily present in the dataset. The objective of the publisher is to maximize the amount of information about the non-private features in a revealed dataset (utility), while keeping the information leaked about the private attributes bounded (privacy). Here, both privacy and utility are measured using information leakage measures that arise in adversarial settings. We consider a practical setting wherein the publisher uses an estimate of the joint distribution of the features to design the privacy mechanism. For any privacy mechanism, we provide probabilistic upper bounds for the discrepancy between the privacy guarantees for the empirical and true distributions, and similarly for utility. These bounds follow from our main technical results regarding the Lipschitz continuity of the considered information leakage measures. We also establish the statistical consistency of the notion of optimal privacy mechanism. Finally, we introduce and study the family of uniform privacy mechanisms, mechanisms designed upon an estimate of the joint distribution which are capable of providing privacy to a whole neighborhood of the estimated distribution, and thereby, guaranteeing privacy for the true distribution with high probability.

**Constraint-based Sequential Pattern Mining with Decision Diagrams**

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.

**Unsupervised learning with contrastive latent variable models**

In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings.However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.

**Newton Methods for Convolutional Neural Networks**

Deep learning involves a difficult non-convex optimization problem, which is often solved by stochastic gradient (SG) methods. While SG is usually effective, it may not be robust in some situations. Recently, Newton methods have been investigated as an alternative optimization technique, but nearly all existing studies consider only fully-connected feedforward neural networks. They do not investigate other types of networks such as Convolutional Neural Networks (CNN), which are more commonly used in deep-learning applications. One reason is that Newton methods for CNN involve complicated operations, and so far no works have conducted a thorough investigation. In this work, we give details of all building blocks including function, gradient, and Jacobian evaluation, and Gauss-Newton matrix-vector products. These basic components are very important because with them further developments of Newton methods for CNN become possible. We show that an efficient MATLAB implementation can be done in just several hundred lines of code and demonstrate that the Newton method gives competitive test accuracy.

**Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection**

Timely prediction of clinically critical events in Intensive Care Unit (ICU) is important for improving care and survival rate. Most of the existing approaches are based on the application of various classification methods on explicitly extracted statistical features from vital signals. In this work, we propose to eliminate the high cost of engineering hand-crafted features from multivariate time-series of physiologic signals by learning their representation with a sequence-to-sequence auto-encoder. We then propose to hash the learned representations to enable signal similarity assessment for the prediction of critical events. We apply this methodological framework to predict Acute Hypotensive Episodes (AHE) on a large and diverse dataset of vital signal recordings. Experiments demonstrate the ability of the presented framework in accurately predicting an upcoming AHE.

**Predictive Modeling with Delayed Information: a Case Study in E-commerce Transaction Fraud Control**

In Business Intelligence, accurate predictive modeling is the key for providing adaptive decisions. We studied predictive modeling problems in this research which was motivated by real-world cases that Microsoft data scientists encountered while dealing with e-commerce transaction fraud control decisions using transaction streaming data in an uncertain probabilistic decision environment. The values of most online transactions related features can return instantly, while the true fraud labels only return after a stochastic delay. Using partially mature data directly for predictive modeling in an uncertain probabilistic decision environment would lead to significant inaccuracy on risk decision-making. To improve accurate estimation of the probabilistic prediction environment, which leads to more accurate predictive modeling, two frameworks, Current Environment Inference (CEI) and Future Environment Inference (FEI), are proposed. These frameworks generated decision environment related features using long-term fully mature and short-term partially mature data, and the values of those features were estimated using varies of learning methods, including linear regression, random forest, gradient boosted tree, artificial neural network, and recurrent neural network. Performance tests were conducted using some e-commerce transaction data from Microsoft. Testing results suggested that proposed frameworks significantly improved the accuracy of decision environment estimation.

**Deep Learning in the Wavelet Domain**

This paper examines the possibility of, and the possible advantages to learning the filters of convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are stimulated by both Mallat’s scattering transform and the idea of filtering in the Fourier domain. It is important to explore new spaces in which to learn, as these may provide inherent advantages that are not available in the pixel space. However, the scattering transform is limited by its inability to learn in between scattering orders, and any Fourier domain filtering is limited by the large number of filter parameters needed to get localized filters. Instead we consider filtering in the wavelet domain with learnable filters. The wavelet space allows us to have local, smooth filters with far fewer parameters, and learnability can give us flexibility. We present a novel layer which takes CNN activations into the wavelet space, learns parameters and returns to the pixel space. This allows it to be easily dropped in to any neural network without affecting the structure. As part of this work, we show how to pass gradients through a multirate system and give preliminary results.

**Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks**

Deep neural networks have shown superior performance in many regimes to remember familiar patterns with large amounts of data. However, the standard supervised deep learning paradigm is still limited when facing the need to learn new concepts efficiently from scarce data. In this paper, we present a memory-augmented neural network which is motivated by the process of human concept learning. The training procedure, imitating the concept formation course of human, learns how to distinguish samples from different classes and aggregate samples of the same kind. In order to better utilize the advantages originated from the human behavior, we propose a sequential process, during which the network should decide how to remember each sample at every step. In this sequential process, a stable and interactive memory serves as an important module. We validate our model in some typical one-shot learning tasks and also an exploratory outlier detection problem. In all the experiments, our model gets highly competitive to reach or outperform those strong baselines.

**Theoretical Perspective of Deep Domain Adaptation**

Deep domain adaptation has recently undergone a big success. Compared with shallow domain adaptation, deep domain adaptation has shown higher predictive performance and stronger capacity to tackle structural data (e.g., image and sequential data). The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint feature space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain. This idea is certainly appealing and motivating, but under the theoretical perspective, none of the theory has been developed to support this. In this paper, we have developed a rigorous theory to explain why we can bridge the relevant gap in an intermediate joint space. Under the light of our proposed theory, it turns out that there is a strong connection between deep domain adaptation and Wasserstein (WS) distance. More specifically, our theory revolves the following points: i) first, we propose a context wherein we can perfectly perform a transfer learning and ii) second, we further prove that by means of bridging the relevant gap and minimizing some reconstruction errors we are minimizing a WS distance between the push forward source distribution and the target distribution via a transport that maps from the source to target domains.

**Short-Term Wind-Speed Forecasting Using Kernel Spectral Hidden Markov Models**

In machine learning, a nonparametric forecasting algorithm for time series data has been proposed, called the kernel spectral hidden Markov model (KSHMM). In this paper, we propose a technique for short-term wind-speed prediction based on KSHMM. We numerically compared the performance of our KSHMM-based forecasting technique to other techniques with machine learning, using wind-speed data offered by the National Renewable Energy Laboratory. Our results demonstrate that, compared to these methods, the proposed technique offers comparable or better performance.

**Model-based Approximate Query Processing**

Interactive visualizations are arguably the most important tool to explore, understand and convey facts about data. In the past years, the database community has been working on different techniques for Approximate Query Processing (AQP) that aim to deliver an approximate query result given a fixed time bound to support interactive visualizations better. However, classical AQP approaches suffer from various problems that limit the applicability to support the ad-hoc exploration of a new data set: (1) Classical AQP approaches that perform online sampling can support ad-hoc exploration queries but yield low quality if executed over rare subpopulations. (2) Classical AQP approaches that rely on offline sampling can use some form of biased sampling to mitigate these problems but require a priori knowledge of the workload, which is often not realistic if users want to explore a new database. In this paper, we present a new approach to AQP called Model-based Approximate Query Processing that leverages generative models learned over the complete database to answer SQL queries at interactive speeds. Different from classical AQP approaches, generative models allow us to compute responses to ad-hoc queries and deliver high-quality estimates also over rare subpopulations at the same time. In our experiments with real and synthetic data sets, we show that Model-based AQP can in many scenarios return more accurate results in a shorter runtime. Furthermore, we think that our techniques of using generative models presented in this paper can not only be used for AQP in databases but also has applications for other database problems including Query Optimization as well as Data Cleaning.

**Selective Feature Connection Mechanism: Concatenating Multi-layer CNN Features with a Feature Selector**

Different layers of deep convolutional neural networks(CNN) can encode different-level information. High-layer features always contain more semantic information, and low-layer features contain more detail information. However, low-layer features suffer from the background clutter and semantic ambiguity. During visual recognition, the feature combination of the low-layer and high-level features plays an important role in context modulation. Directly combining the high-layer and low-layer features, the background clutter and semantic ambiguity may be caused due to the introduction of detailed information.In this paper, we propose a general network architecture to concatenate CNN features of different layers in a simple and effective way, called Selective Feature Connection Mechanism (SFCM). Low level features are selectively linked to high-level features with an feature selector which is generated by high-level features. The proposed connection mechanism can effectively overcome the above-mentioned drawbacks. We demonstrate the effectiveness, superiority, and universal applicability of this method on many challenging computer vision tasks, such as image classification, scene text detection, and image-to-image translation.

**Contextual Care Protocol using Neural Networks and Decision Trees**

A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model.

**Minimizing the Age of Information from Sensors with Correlated Observations**

The Age of Information (AoI) metric has recently received much attention as a measure of freshness of information in a network. In this paper, we study the average AoI in a generic setting with correlated sensor information, which can be related to multiple Internet of Things (IoT) scenarios. The system consists of physical sources with discrete-time updates that are observed by a set of sensors. Each source update may be observed by multiple sensors, and hence the sensor observations are correlated. We devise a model that is simple, but still capable to capture the main tradeoffs. We propose two sensor scheduling policies that minimize the AoI of the sources; one that requires the system parameters to be known a priori, and one based on contextual bandits in which the parameters are unknown and need to be learned. We show that both policies are able to exploit the sensor correlation to reduce the AoI, which result in a large reduction in AoI compared to the use of schedules that are random or based on round-robin.

**Towards Explainable Deep Learning for Credit Lending: A Case Study**

Deep learning adoption in the financial services industry has been limited due to a lack of model interpretability. However, several techniques have been proposed to explain predictions made by a neural network. We provide an initial investigation into these techniques for the assessment of credit risk with neural networks.

**Multi-cell LSTM Based Neural Language Model**

Language models, being at the heart of many NLP problems, are always of great interest to researchers. Neural language models come with the advantage of distributed representations and long range contexts. With its particular dynamics that allow the cycling of information within the network, `Recurrent neural network’ (RNN) becomes an ideal paradigm for neural language modeling. Long Short-Term Memory (LSTM) architecture solves the inadequacies of the standard RNN in modeling long-range contexts. In spite of a plethora of RNN variants, possibility to add multiple memory cells in LSTM nodes was seldom explored. Here we propose a multi-cell node architecture for LSTMs and study its applicability for neural language modeling. The proposed multi-cell LSTM language models outperform the state-of-the-art results on well-known Penn Treebank (PTB) setup.

**Exploiting Class Learnability in Noisy Data**

In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged Web data. Noisy labels occur frequently in data sets harvested via these means, sometimes resulting in entire classes of data on which learned classifiers generalize poorly. For real world applications, we argue that it can be beneficial to avoid training on such classes entirely. In this work, we aim to explore the classes in a given data set, and guide supervised training to spend time on a class proportional to its learnability. By focusing the training process, we aim to improve model generalization on classes with a strong signal. To that end, we develop an online algorithm that works in conjunction with classifier and training algorithm, iteratively selecting training data for the classifier based on how well it appears to generalize on each class. Testing our approach on a variety of data sets, we show our algorithm learns to focus on classes for which the model has low generalization error relative to strong baselines, yielding a classifier with good performance on learnable classes.

• DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning

• Enhancing Operation of a Sewage Pumping Station for Inter Catchment Wastewater Transfer by Using Deep Learning and Hydraulic Model

• Modelling student online behaviour in a virtual learning environment

• Cluster analysis of homicide rates in the Brazilian state of Goias from 2002 to 2014

• Baire categorical aspects of first passage percolation II

• Modeling car-following behavior on urban expressways in Shanghai: A naturalistic driving study

• Prediction of Preliminary Maximum Wing Bending Moments under Discrete Gust

• Transportation inequalities for stochastic wave equation

• The Amortized Analysis of a Non-blocking Chromatic Tree

• On the Further Structure of the Finite Free Convolutions

• Fault-Tolerant Metric Dimension of $P(n,2)$ with Prism Graph

• Randomisation Algorithms for Large Sparse Matrices

• How to get meaningful and correct results from your finite element model

• Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions

• On the use of FHT, its modification for practical applications and the structure of Hough image

• Describing many-body localized systems in thermal environments

• Catch and Prolong: recurrent neural network for seeking track-candidates

• Estimation of Multivariate Wrapped Models for Data in Torus

• Faster manipulation of large quantum circuits using wire label reference diagrams

• Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning

• Distinguishing number of Urysohn metric spaces

• Incentivizing Exploration with Unbiased Histories

• Verification of Recurrent Neural Networks Through Rule Extraction

• A Fast Method for Array Response Adjustment with Phase-Only Constraint

• A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks

• Natural Environment Benchmarks for Reinforcement Learning

• Limit distributions of the upper order statistics for the Lévy-frailty Marshall-Olkin distribution

• Dynamic Behavior Control of Induction Motor with STATCOM

• Focus Quality Assessment of High-Throughput Whole Slide Imaging in Digital Pathology

• Arterial Blood Pressure Feature Estimation Using Photoplethysmography

• Unsupervised domain adaptation for medical imaging segmentation with self-ensembling

• A Performance Vocabulary for Affine Loop Transformations

• Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

• A multi-level convolutional LSTM model for the segmentation of left ventricle myocardium in infarcted porcine cine MR images

• Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing

• Lattice bijections for string modules, snake graphs and the weak Bruhat order

• Spatial Logics and Model Checking for Medical Imaging (Extended Version)

• Interpretable deep learning for guided structure-property explorations in photovoltaics

• Derivation of an aggregated band pseudo phasor for single phase pulse width modulation voltage waveforms

• Communication-Optimal Distributed Dynamic Graph Clustering

• Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN

• ReSIFT: Reliability-Weighted SIFT-based Image Quality Assessment

• Automatic Grammar Augmentation for Robust Voice Command Recognition

• On the specification and verification of atomic swap smart contracts

• Deep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples

• Operator-Theoretical Treatment of Ergodic Theorem and Its Application to Dynamic Models in Economics

• Layered Belief Propagation for Low-complexity Large MIMO Detection Based on Statistical Beams

• Learning from Past Bids to Participate Strategically in Day-Ahead Electricity Markets

• Prophet Inequalities for Independent Random Variables from an Unknown Distribution

• The case for shifting the Renyi Entropy

• Some Moderate Deviations for Ewens-Pitman Sampling Model

• Cooperation Enforcement and Collusion Resistance in Repeated Public Goods Games

• Vectorized Character Counting for Faster Pattern Matching

• Machine Learning for Combinatorial Optimization: a Methodological Tour d’Horizon

• Gallai-Ramsey numbers for a class of graphs with five vertices

• Forbidden rainbow subgraphs that force large monochromatic or multicolored connected subgraphs

• It Does Not Follow. Response to ‘Yes They Can! …’

• Empirical Effects of Dynamic Human-Body Blockage in 60 GHz Communications

• Adaptive Full-Duplex Jamming Receiver for Secure D2D Links in Random Networks

• Real-time Power System State Estimation and Forecasting via Deep Neural Networks

• Boosting Search Performance Using Query Variations

• Pure-Exploration for Infinite-Armed Bandits with General Arm Reservoirs

• Effect Handling for Composable Program Transformations in Edward2

• Orthogonal Policy Gradient and Autonomous Driving Application

• Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos

• Cops and robbers on oriented graphs

• Exploiting Sentence Embedding for Medical Question Answering

• Intersecting Families of Perfect Matchings

• Recurrent approach to effective material properties with application to anisotropic binarized random fields

• Improving Skin Condition Classification with a Question Answering Model

• The autoregression bootstrap for kernel estimates of smooth nonlinear functional time series

• Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network

• Monochromatic Schur triples in randomly perturbed dense sets of integers

• Implementing a Portable Clinical NLP System with a Common Data Model – a Lisp Perspective

• A q-analogue and a symmetric function analogue of a result by Carlitz, Scoville and Vaughan

• Phase retrieval for Bragg coherent diffraction imaging at high X-ray energies

• Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms

• Electric Vehicle Valet

• GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition

• Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation

• Equivariant Perturbation in Gomory and Johnson’s Infinite Group Problem. VII. Inverse semigroup theory, closures, decomposition of perturbations

• From Videos to URLs: A Multi-Browser Guide To Extract User’s Behavior with Optical Character Recognition

• Face Verification and Forgery Detection for Ophthalmic Surgery Images

• Distributed Obstacle and Multi-Robot Collision Avoidance in Uncertain Environments

• Minimax Posterior Convergence Rates and Model Selection Consistency in High-dimensional DAG Models based on Sparse Cholesky Factors

• Exotic Steiner Chains in Miquelian Möbius Planes

• Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference

• Time-Varying Formation Control of a Collaborative Multi-Agent System Using Negative-Imaginary Systems Theory

• Fano generalized Bott manifolds

• Quantile Regression Modeling of Recurrent Event Risk

• Predicting thermoelectric properties from crystal graphs and material descriptors – first application for functional materials

• A Schur transform for spatial stochastic processes

• Reward-estimation variance elimination in sequential decision processes

• Graph Convolutional Neural Networks for Polymers Property Prediction

• On Training Targets and Objective Functions for Deep-Learning-Based Audio-Visual Speech Enhancement

• SGR: Self-Supervised Spectral Graph Representation Learning

• Computing Quartet Distance is Equivalent to Counting 4-Cycles

• Fundamental Limits of Caching in Heterogeneous Networks with Uncoded Prefetching

• Effects of Lombard Reflex on the Performance of Deep-Learning-Based Audio-Visual Speech Enhancement Systems

• Network capacity enhancement with various link rewiring strategies

• In-silico Feedback Control of a MIMO Synthetic Toggle Switch via Pulse-Width Modulation

• On Graphs with equal Dominating and C-dominating energy

• Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search

• On Infinite Prefix Normal Words

• Effect of correlations on routing and modeling of Time Varying Communication Networks

• Image declipping with deep networks

• Survey of Computational Approaches to Diachronic Conceptual Change

• Decentralized Data Storages: Technologies of Construction

• Guiding the One-to-one Mapping in CycleGAN via Optimal Transport

• Sketch based Reduced Memory Hough Transform

• On a new class of score functions to estimate tail probabilities of some stochastic processes with Adaptive Multilevel Splitting

• Unique ergodicity for stochastic hyperbolic equations with additive space-time white noise

• Local theorems for arithmetic compound renewal processes, when Cramer’s condition holds

• End-to-End Learning for Answering Structured Queries Directly over Text

• A Neurodynamical model of Saliency prediction in V1

• Redundancy scheduling with scaled Bernoulli service requirements

• Sum rules and large deviations for spectral matrix measures in the Jacobi ensemble

• Effect of data reduction on sequence-to-sequence neural TTS

• ShuffleDet: Real-Time Vehicle Detection Network in On-board Embedded UAV Imagery

• Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

• Impedance Network of Interconnected Power Electronics Systems: Impedance Operator and Stability Criterion

• State Complexity Characterizations of Parameterized Degree-Bounded Graph Connectivity, Sub-Linear Space Computation, and the Linear Space Hypothesis

• Hörmander’s theorem for semilinear SPDEs

• Sparsely Observed Functional Time Series: Estimation and Prediction

• Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting

• Deep Template Matching for Offline Handwritten Chinese Character Recognition

• Physical Signal Classification Via Deep Neural Networks

• Temporal viability regulation for control affine systems with applications to mobile vehicle coordination under time-varying motion constraints

• State-dependent jump activity estimation for Markovian semimartingales

• Cost of selfishness in the allocation of cities in the Multiple Travelling Salesmen Problem

• On approximations of Value at Risk and Expected Shortfall involving kurtosis

• Semi-perfect 1-Factorizations of the Hypercube

• Leaf-induced subtrees of leaf-Fibonacci trees

• Asynchronous Stochastic Composition Optimization with Variance Reduction

• Offline Biases in Online Platforms: a Study of Diversity and Homophily in Airbnb

• Pairwise Relational Networks using Local Appearance Features for Face Recognition

• Neural Predictive Belief Representations

• Chordal circulant graphs and induced matching number

• LinkNet: Relational Embedding for Scene Graph

• Characterising $k$-connected sets in infinite graphs

• Effects of Beamforming and Antenna Configurations on Mobility in 5G NR

• The Sliding Frank-Wolfe Algorithm and its Application to Super-Resolution Microscopy

• Adversarial Examples from Cryptographic Pseudo-Random Generators

• Learning to Bound the Multi-class Bayes Error

• Latecomers Help to Meet: Deterministic Anonymous Gathering in the Plane

• Introducing Multiobjective Complex Systems

• On a minimum distance procedure for threshold selection in tail analysis

• HCU400: An Annotated Dataset for Exploring Aural Phenomenology Through Causal Uncertainty

• Energy Efficient Precoder in Multi-User MIMO Systems with Imperfect Channel State Information

• Secretary Ranking with Minimal Inversions

• Preliminary Studies on a Large Face Database

• Adversarial Resilience Learning – Towards Systemic Vulnerability Analysis for Large and Complex Systems

• A regularised Dean-Kawasaki model for weakly interacting particles

• MIMO Beampattern and Waveform Design with Low Resolution DACs

• Review of isolation enhancement with the help of Theory of characteristic modes

• Psychophysical evaluation of individual low-level feature influences on visual attention

• Oversampled Adaptive Sensing with Random Projections: Analysis and Algorithmic Approaches

• The q-Hahn PushTASEP

• A Note On Universal Point Sets for Planar Graphs

• Fourier decay, Renewal theorem and Spectral gaps for random walks on split semisimple Lie groups

• Exploring the Deep Feature Space of a Cell Classification Neural Network

• Mathematical Analysis of Adversarial Attacks

• Large-Scale Distributed Algorithms for Facility Location with Outliers

• Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer

• Adjusting for Confounding in Unsupervised Latent Representations of Images

• A Bayesian optimization approach to compute the Nash equilibria of potential games using bandit feedback

• Anti-concentration in most directions

• Tight Bayesian Ambiguity Sets for Robust MDPs

• Kernel Smoothing of the Treatment Effect CDF

• Reward learning from human preferences and demonstrations in Atari

• On transfer learning using a MAC model variant

• Learning to Predict the Cosmological Structure Formation

• Predicting enterprise cyber incidents using social network analysis on the darkweb hacker forums