Bandit Quickest Changepoint Detection

Detecting abrupt changes in temporal behavior patterns is of interest in many industrial and security applications . Abrupt changes are often local andobservable primarily through a well-aligned sensing action (e.g., a camera with a narrow field-of-view) Due to resource constraints, continuous monitoring of all of the sensors is impractical .…

A Proactive Management Scheme for Data Synopses at the Edge

The combination of the infrastructure provided by the Internet of Things(IoT) with numerous processing nodes present at the Edge Computing (EC)ecosystem opens up new pathways to support intelligent applications . We describe an continuous reasoning model that builds a temporal similarity map of the available datasets to get nodesunderstanding the evolution of data in their peers .…

Distributed Saddle Point Problems Under Similarity

We study solution methods for (strongly-)convex-(strongly)-concaveSaddle-Point Problems (SPPs) over networks of two type – master/workers (thuscentralized) and meshed (thus decentralized) networks . We establish lower complexity bounds for a fairlygeneral class of algorithms solving the SPP . We then propose algorithms matching the lower bounds over either types of networks (up tolog-factors) We assess the effectiveness of the proposed algorithms on arobust logistic regression problem .…

Randomized Online Algorithms for Adwords

The general adwords problem has remained largely unresolved . We define asubcase called $k-TYPICAL, $k \in \Zplus$ as follows: the total budgetof all the bidders is sufficient to buy $k$ bids for each bidder . We also giverandomized online algorithms for other special cases of adwords .…

Impacts Towards a comprehensive assessment of the book impact by integrating multiple evaluation sources

The surge in the number of books published makes the manual evaluation methods difficult to efficiently evaluate books . The use of books’ citationsand alternative evaluation metrics can assist manual evaluation and reduce the cost of evaluation . However, relying on a single resource for book assessment may lead to the risk that theevaluation results cannot be obtained due to the lack of the evaluation data, especially for newly published books .…

FNetAR Mixing Tokens with Autoregressive Fourier Transforms

In this note we examine the autoregressive generalization of the FNetalgorithm . Self-attention layers from the standard Transformerarchitecture are substituted with a trivial sparse-uniformsampling procedure based on Fourier transforms . Using the Wikitext-103 benchmark, FNetAR retains state-of-the-art performance (25.8 ppl) on thetask of causal language modeling .…

Flexible Distributed Matrix Multiplication

The distributed matrix multiplication problem with an unknown number ofstragglers is considered . The goal is to efficiently and flexibly obtain the product of two massive matrices by distributing the computation across Nservers . There are up to N – R stragglers but the exact number is not known apriori.…

Fourier growth of structured mathbb F _2 polynomials and applications

We analyze the Fourier growth of various well-studied classes of “structured”$\mathbb{F}_2$-polynomials . This study is motivated by applications inpseudorandomness, in particular recent results and conjectures due to[CHHL19,CHLT19,CGLSS20] We show that any symmetric degree-$d$ $p$ has $L_1$ Fourier weight at level $k$ and this is tight for any constant $k$.…

Distributed Asynchronous Policy Iteration for Sequential Zero Sum Games and Minimax Control

We introduce a contractive abstract dynamic programming framework and relatedpolicy iteration algorithms . These algorithms are specifically designed for sequential zero-sumgames and minimax problems with a general structure . The advantage of our algorithms over alternatives is that they resolve some long-standing convergence difficulties of the “natural” policyiteration algorithm, which have been known since the Pollatschek and Avi-Itzhakmethod [PoA69] for finite-state Markov games .…

Super Resolution on the Two Dimensional Unit Sphere

We study the problem of recovering an atomic measure on the unit 2-sphere $\mathbb{S}^2$ given finitely many moments with respect to spherical harmonics . We construct a dual certificate using a kernel given in an explicit form and make a concrete analysis of the interpolation problem .…

Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence AI Guided Microassembly

Many current micro-assembly methods are serial in nature, resulting in unfeasibly low throughput . Alternatively, parallel self-assembly ordirected-assembly techniques can be employed by utilizing forces dominant atthe micro and nano scales such as electro-kinetic, thermal, and capillaryforces . However, these forces are governed by complex equations and often act on microparts simultaneously and competitively, making modeling and simulation difficult .…

Sampling from Potts on random graphs of unbounded degree via random cluster dynamics

The random-cluster model is parametrized by an edge probability $p \in (0,1) and a cluster weight $q 0$ We establish that for every $q\ge 1$ the random-Cluster Glauber dynamics mixes in optimal$\Theta(n\log n)$ steps on $n$-vertex random graphs having a prescribed degreesequence with bounded average branching $pp_u(q,\gamma)$ We provide the first polynomial-timesampling algorithm for the ferromagnetic Potts model on the Erd\H{o}s--R\'enyirandom graphs that works for all $q$ in the full uniqueness regime .…

Reinforcement Learning Agent Training with Goals for Real World Tasks

Reinforcement Learning (RL) is a promising approach for solving various control, optimization, and sequential decision making tasks . But designing reward functions for complex tasks (e.g., with multiple objectives and safetyconstraints) can be challenging for most users . In this paper we propose aspecification language (Inkling Goal Specification) for complex control andoptimization tasks, which is very close to natural language and allows apractitioner to focus on problem specification instead of reward function hacking .…

Peer Selection with Noisy Assessments

In this paper we extend PeerNomination, the most accurate peer reviewing algorithm to date, into WeightedPeerNomination . We show analytically that a weighting scheme can improve the overall accuracy of the selection significantly . We explicitly formulate assessors’ reliability weights in a way that doesn’t violate strategyproofness, and use this information to reweight their scores .…

Decidability of Liveness on the TSO Memory Model

An important property of concurrent objects is whether they support progress-a special case of liveness-guarantees, which ensure the termination of method calls under system fairness assumptions . Typical liveness propertiesincludelock-freedom,wait-freedom,.deadlock-freedom and starvation-freedom are undecidable on TSO for a bounded number of processes, while obstruction-freedom is decidable .…

Convergence of the implicit MAC discretized Navier Stokes equations with variable density and viscosity on non uniform grids

A priori-estimates on the unknownsare obtained, and along with a topological degree argument they lead to theexistence of a solution of the discrete scheme at each time step . We conclude the proof of the convergence of the scheme toward the continuous problem as mesh size and time step tend toward zero with the limit of the sequence ofdiscrete solutions being a solution to the weak formulation of the problem .…

Training Electric Vehicle Charging Controllers with Imitation Learning

The problem of coordinating the charging of electric vehicles gains more importance as the number of such vehicles grows . In order to train the controllers, we use the idea of imitation learning . The method is evaluated on realistic data and shows improved performance and training speed compared to similar controllers trained using evolutionary algorithms .…

Towards Plug and Play Visual Graph Query Interfaces Data driven Canned Pattern Selection for Large Networks

Canned patterns (i.e. small subgraph patterns) in visual graph queryinterfaces (a.k.a GUI) facilitate efficient query formulation by enablingpattern-at-a-time construction mode . TATTOO takes a data-driven approach to automaticallyselecting canned patterns for a . GUI from large networks . It first decomposes the underlying network into truss-infested and truss .oblivious…

Towards Plug and Play Visual Graph Query Interfaces Data driven Canned Pattern Selection for Large Networks

Canned patterns (i.e. small subgraph patterns) in visual graph queryinterfaces (a.k.a GUI) facilitate efficient query formulation by enablingpattern-at-a-time construction mode . TATTOO takes a data-driven approach to automaticallyselecting canned patterns for a . GUI from large networks . It first decomposes the underlying network into truss-infested and truss .oblivious…

Machine Learning Characterization of Cancer Patients Derived Extracellular Vesicles using Vibrational Spectroscopies

Vibrationalspectroscopies provide non-invasive approaches for assessment of structural and biophysical properties in complex biological samples . The AdaBoost Random Forest Classifier, Decision Trees, and Support VectorMachines (SVM) distinguished the baseline corrected Raman spectra of cancer EVs from those of healthy controls (18 spectra) with a classification accuracy of greater than 90% when reduced to a spectral frequency range of 1800 to 1940inverse cm .…

Neural Fixed Point Acceleration for Convex Optimization

Fixed-point iterations are at the heart of numerical computing and are often a computational bottleneck in real-time applications that typically need a fast solution of moderate accuracy . We present neural fixed-point acceleration which combines ideas from meta-learning and classical acceleration methods .…

Fairness aware Maximal Clique Enumeration

Cohesive subgraph mining on attributed graphs is a fundamental problem ingraph data analysis . Existing mining algorithms on attributedgraphs do not consider the fairness of attributes in the subgraph . In this paper, we for the first time introduce fairness into the widely-used cliquemodel to mine fairness-aware cohesive subgraphs .…

Efficient Top k Ego Betweenness Search

Betweenness centrality has been recognized as a key indicator for the importance of a vertice in a network . The betweenness of a vertex is hard to compute because it needs to explore all the shortest paths between the other vertices .…

How to Tell Deep Neural Networks What We Know

We present a short survey of ways in which existing scientific knowledge are included when constructing models with neural networks . The inclusion of domain-knowledge is of special interest not just to constructing scientificassistants, but also, many other areas that involve understanding data using human-machine collaboration .…

Answer Set Programs for Reasoning about Counterfactual Interventions and Responsibility Scores for Classification

We describe how answer-set programs can be used to declaratively specifycounterfactual interventions on entities under classification, and reason about them . In particular, they can define and compute responsibilityscores as attribution-based explanations for outcomes from classificationmodels . The approach allows for the inclusion of domain knowledge and supports query answering .…

Demonstration Guided Reinforcement Learning with Learned Skills

Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations . We propose Skill-based Learning with Demonstrations(SkiLD), an algorithm for demonstration-guided RL that efficiently leveragesthe provided demonstrations by following the demonstrated skills instead of theprimitive actions .…

Bridging the Gap between Spatial and Spectral Domains A Theoretical Framework for Graph Neural Networks

Graphneural networks (GNN) are a type of deep learning that is designed to handlenon-Euclidean issues using graph-structured data that are difficult to solve with traditional deep learning techniques . The majority of GNNs were createdusing a variety of processes, including random walk, PageRank, graphconvolution, and heat diffusion, making direct comparisons impossible .…

Uncertainty Aware Task Allocation for Distributed Autonomous Robots

This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs) It has great potential to be employed . The proposed framework was tested in a simulated environment where the decision-maker needsto determine an optimal allocation of multiple locations assigned to multiple mobile flying robots .…

Investigating External Interaction Modality and Design Between Automated Vehicles and Pedestrians at Crossings

In this study, we investigated the effectiveness and user acceptance of threeexternal interaction modalities (i.e., visual, auditory, and visual+auditory) in promoting communications between automated vehicle systems (AVS) and pedestrians at a crosswalk . We alsotested different visual and auditory interaction methods, and found that”Pedestrian silhouette on the front of the vehicle” was the best preferredoption .…

SkyCell A Space Pruning Based Parallel Skyline Algorithm

Skyline computation is an essential database operation that has many applications in multi-criteria decision making scenarios such as recommendersystems . Existing algorithms have focused on checking point domination, which lack efficiency over large datasets . We propose a grid-based structure that enables grid cell domination checks .…

Distribution of Classification Margins Are All Data Equal

Recent theoretical results show that gradient descent on deep neural networks locally maximizes classification margin . This property of the solution however does not fully characterizethe generalization performance . We motivate theoretically and show empiricallythat the area under the curve of the margin distribution on the training set is a good measure of generalization .…