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 .…

## 3D Shape Generation with Grid based Implicit Functions

Previous approaches to generate shapes in a 3D setting train a GAN on thelatent space of an autoencoder (AE) This produces convincing results, but it has two major shortcomings . To remedy these issues, we propose to train the GAN .…

## Towards an Understanding of the Role Operator Limb Dynamics Plays in Haptic Perception of Stiffness

Creating haptic interfaces capable of rendering the rich sensation needed fordexterous manipulation is crucial for the advancement of human-in-the-looptelerobotic systems (HiLTS) One limiting factor has been the absence of detailed knowledge of the effect of operator limb dynamics and hapticexploration dynamics on haptic perception .…

## 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 .…

## The Public Good index for games with several levels of approval in the input and output

The Public Good index is a power index for simple games introduced by Holler and Packel . Some authors also speak of the Holler–Packel index . Here we generalize the ideas to games with several levels of approval in the input and output .…

## Lower Bounds for Maximally Recoverable Tensor Code and Higher Order MDS Codes

Maximally Recoverable (MR) TensorCodes, introduced by Gopalan et al., are tensor codes which can correct everyerasure pattern that is theoretically possible to correct . Tensor codes are useful in distributed storage because a single erasure can be correctedquickly either by reading its row or column .…

## A geometric multigrid method for space time finite element discretizations of the Navier Stokes equations and its application to 3d flow simulation

We present a parallelized geometric multigrid (GMG) method, based on thecell-based Vanka smoother, for higher order space-time finite element methods . The GMG solver is applied as apreconditioner for GMRES iterations . Its performance properties aredemonstrated for 2d and 3d benchmarks of flow around a cylinder .…

## 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 .…

## High performance low complexity error pattern generation for ORBGRAND decoding

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed method searching for the error pattern applied to the transmittedcodeword . We propose an improved error pattern schedule that can improve the performance ofORBGRAND of 0.5dB at a block error rate (BLER) of $10^{-5}$, with increasinggains as the BLER decreases .…

## 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 .…

## Target Oriented Fine tuning for Zero Resource Named Entity Recognition

Zero-resource named entity recognition (NER) severely suffers from datascarcity in a specific domain or language . Most studies on zero-resource NERtransfer knowledge from various data by fine-tuning on different auxiliary tasks . In this paper, we tackle the problem by transferring knowledge from three aspects, i.e.,…

## 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 .…

## Evaluation of In Person Counseling Strategies To Develop Physical Activity Chatbot for Women

Artificial intelligence chatbots are the vanguard in technology-based intervention to change people’s behavior . To develop intervention chatbots, the first step is to understand natural language conversation strategies in humanconversation . This work lays the foundation for developing a personalized physical activity intervention bot .…

## 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 .…

## A Distributed Sparse Channel Estimation Technique for mmWave Massive MIMO Systems

In this paper, we study the problem of sparse channel estimation via acollaborative and fully distributed approach . The estimation problem isformulated in the angular domain by exploiting the spatially common sparsitystructure of the involved channels in a multi-user scenario .…

## 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 .…

## Characterizing Social Imaginaries and Self Disclosures of Dissonance in Online Conspiracy Discussion Communities

This paper characterizes self-disclosures ofdissonance about QAnon, a conspiracy theory initiated by a mysterious leader Qand popularized by their followers, anons in conspiracy theory subreddits . We use these social imaginaries to create acomputational framework for distinguishing belief and dissonance from generaldiscussion .…

## 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 .…

## Accuracy analysis of Educational Data Mining using Feature Selection Algorithm

EducationalData Mining (EDM) helps to measure the accuracy of data using relevant attributes and machine learning algorithms performed . EDM removes irrelevant features without changing the original data . The data set used in this study was taken from Kaggle.com. The results were compared on the basis ofrecall, precision and f-measure to check accuracy of the student data.…

## Optimal Rates for Nonparametric Density Estimation under Communication Constraints

We consider density estimation for Besov spaces when each sample is quantized to only a limited number of bits . We provide a noninteractive adaptiveestimator that exploits the sparsity of wavelet bases . We show that our estimator is nearly rate-optimal by derivingminimax lower bounds that hold even when interactive protocols are allowed .…

## 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 .…

## Audit Don t Explain Recommendations Based on a Socio Technical Understanding of ML Based Systems

In this position paper, I provide a socio-technical perspective on machinelearning-based systems . I also explain why systematic audits may be preferable . I make concrete recommendations for how institutionsgoverned by public law can ensure that ML systems operate in the interest of the public .…

## 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…

## Auditing the Biases Enacted by YouTube for Political Topics in Germany

With YouTube’s growing importance as a news platform, its recommendationsystem came under increased scrutiny . We explore the applicability of laws that require broadcasters to give important political, ideological, and social groups adequate opportunity to express themselves in the broadcasted program of the service .…

## 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 .…

## Provenance Anonymisation and Data Environments a Unifying Construction

The Anonymisation Decision-making Framework (ADF) operationalizes the riskmanagement of data exchange between organizations . The second edition of ADF has increased its emphasis on modeling data flows . We show how data environments can be implemented within the W3CPROV in four different ways .…

## 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 .…

## A Deep Reinforcement Learning Approach for Fair Traffic Signal Control

Traffic signal control is one of the most effective methods of traffic management in urban areas . In recent years, traffic control methods based on deep reinforcement learning (DRL) have gained attention due to their ability to exploit real-time traffic data .…

## 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 .…

## Extension of additive valuations to general valuations on the existence of EFX

Envy-freeness is one of the most widely studied notions in fair division . Since envy-free allocations do not always exist when items are indivisible . We show that an EFXallocation always exists (i) when all agents have one of two general valuationsor (ii) when the number of items is at most $n+3$ .…

## 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 .…

## A combined volume penalization selective frequency damping approach for immersed boundary methods applied to high order schemes

There has been an increasing interest in developing efficient immersedboundary method (IBM) based on Cartesian grids . IBM based on volume penalization is a robust and easy to implement method to avoid body-fitted meshes . This work proposes animprovement over the classic penalty formulation for flux reconstruction highorder solvers .…

## 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 .…

## Eigensolution analysis of immersed boundary method based on volume penalization applications to high order schemes

This paper presents eigensolution and non-modal analyses for immersedboundary methods (IBMs) based on volume penalization for the linear advectionequation . This approach is used to analyze the behavior of flux reconstruction(FR) discretization, including the influence of polynomial order andpenalization parameter on numerical errors and stability .…

## 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 .…