SiReN Sign Aware Recommendation Using Graph Neural Networks

In recent years, many recommender systems using network embedding (NE) suchas graph neural networks (GNNs) have been extensively studied in the sense ofimproving recommendation accuracy . SiReN has three key components: constructing a signed bipartite graph for moreprecisely representing users’ preferences, splitting into twoedge-disjoint graphs with positive and negative edges each, generating twoembeddings for the partitioned graphs .…

PowerLinear Activation Functions with application to the first layer of CNNs

Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing . PowerLinear activation functions are based on the polynomial kernel generalization of the convolution operator . They are used in CNN models to classify the inversion of grayscale images as accurately as the original graycale images, which is significantly better than commonly used activation functions .…

Does Preprocessing help in Fast Sequence Comparisons

We study edit distance computation with preprocessing: the preprocessingalgorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings . This model is inspired by scenarios wherewe would like to compute edit distance between many pairs in the same pool ofstrings .…

The n ary Initial Literal and Literal Shuffle

The literal and initial literal shuffle have been introduced to model the behavior of two synchronized processes . However, it is not possible to describe the synchronization of multiple processes . Here, we extend the literal shuffle and theinitial literal shuffle to multiple arguments .…

On the Parallel I O Optimality of Linear Algebra Kernels Near Optimal Matrix Factorizations

Matrix factorizations are among the most important building blocks of computing . State-of-the-art libraries, however, are notcommunication-optimal, underutilizing current parallel architectures . Wepresent novel algorithms for Cholesky and LU factorizations that utilize anasymptotically communication-optimistic 2.5D decomposition . Our code is ScaLAPACK-compatible and available as an open-source library.…

Does Preprocessing help in Fast Sequence Comparisons

We study edit distance computation with preprocessing: the preprocessingalgorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings . This model is inspired by scenarios wherewe would like to compute edit distance between many pairs in the same pool ofstrings .…

Does Preprocessing help in Fast Sequence Comparisons

We study edit distance computation with preprocessing: the preprocessingalgorithm acts on each string separately, and then the query algorithm takes as input the two preprocessed strings . This model is inspired by scenarios wherewe would like to compute edit distance between many pairs in the same pool ofstrings .…

Privacy Preserving Batch based Task Assignment in Spatial Crowdsourcing with Untrusted Server

In this paper, we study the privacy-preserving task assignment in spatialcrowdsourcing, where the locations of both workers and tasks, prior to their release to the server, are perturbed with Geo-Indistinguishability . We propose the k-Switch solution, which first divides the workers into small groups based on the perturbed distance between workers/tasks, and then utilizes Homomorphic Encryption (HE) based secure computation to enhancethe task assignment .…

On the Parallel I O Optimality of Linear Algebra Kernels Near Optimal Matrix Factorizations

Matrix factorizations are among the most important building blocks of computing . State-of-the-art libraries, however, are notcommunication-optimal, underutilizing current parallel architectures . Wepresent novel algorithms for Cholesky and LU factorizations that utilize anasymptotically communication-optimistic 2.5D decomposition . Our code is ScaLAPACK-compatible and available as an open-source library.…

Temporal Graph Functional Dependencies Technical Report

We propose a class of functional dependencies for temporal graphs calledTGFDs . TGFDs capture both attribute-value and topologicalstructures of entities over a valid period of time in a temporal graph . We show that thesatisfiability and validation problems are coNP-complete and the implicationproblem is NP-complete .…

Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience driven Procedural Content Generation

Virtual Reality (VR) games that feature physical activities have been shownto increase players’ motivation to do physical exercise . For suchexercises to have a positive healthcare effect, they have to be repeated several times a week . We propose to use experience-driven Procedural ContentGeneration for DDA in VR exercise games by procedurally generating levels thatmatch the player’s current capabilities .…

Selectively Amortized Resource Bounding

We consider the problem of automatically proving resource bounds . We show that proving bounds in any such decomposition yields asound resource bound in the original program . We present a framework for selectively-amortized analysis that mixes worst-caseand amortized reasoning via a property decomposition and a programtransformation .…

Physics informed machine learning improves detection of head impacts

Monitoring player impacts is vitally important tounderstanding and protecting from injuries like concussion . Traditional machine learning approaches fail to detect true positives and prevent falsenegatives . In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard anddetect impacts to the head .…

Second Order Specifications and Quantifier Elimination for Consistent Query Answering in Databases

Repairs are consistent instances that minimally differ from the original inconsistent instance . Consistent answers to a query from a possibly inconsistent database are answers that are simultaneously retrieved from every possible repair of the database . In this paper we show how to use the repair programs to transform the problem ofconsistent query answering into a problem of reasoning w.r.t.…

Separation of P and NP

A new proof method, already used in arXiv:2104.14316, P not equal NP can be proved . A time limit is set for an arbitrary Turing machine and an input word is rejected on a timeout . Due to thehalting problem, whether a word is accepted can only be determined at runtime .…

Parallel time integration using Batched BLAS Basic Linear Algebra Subprograms routines

We present an approach for integrating the time evolution of quantum systems . Our PARAllelized Matrix Exponentiation for Numerical Timeevolution (PARAMENT) implementation runs on CUDA-enabled graphics processing units . The algorithm can largely be implemented using the recently-specified batchedversions of the BLAS routines, and can therefore be easily ported to a variety of platforms .…

Byzantine Cluster Sending in Expected Constant Communication

Traditional resilient systems operate on fully-replicated fault-tolerant clusters, which limits their scalability and performance . One way to make the step towards resilient high-performance systems that can deal with hugeworkloads, is by enabling independent fault tolerantant clusters to efficientlycommunicate and cooperate with each other .…

Separation of P and NP

A new proof method, already used in arXiv:2104.14316, P not equal NP can be proved . A time limit is set for an arbitrary Turing machine and an input word is rejected on a timeout . Due to thehalting problem, whether a word is accepted can only be determined at runtime .…

Physics informed machine learning improves detection of head impacts

Monitoring player impacts is vitally important tounderstanding and protecting from injuries like concussion . Traditional machine learning approaches fail to detect true positives and prevent falsenegatives . In this work we present a new physics-informed machine learning model that can be used to analyze kinematic data from an instrumented mouthguard anddetect impacts to the head .…

On correctness and completeness of an n queens program

Thom Fr\”uhwirth presented a short, elegant and efficient Prolog program for the n queens problem . However the program may be seen as rather tricky and onemay not be convinced about its correctness . This paper explains the program in a declarative way, and provides proofs of its correctness and completeness .…

Convexity via Weak Distributive Laws

We study the canonical weak distributive law of the powerset monadover the semimodule monad for a certain class of semirings containing, inparticular, positive semifields . For this subclass we characterise $delta$ as a convex closure in a set . Using the abstract theory ofweak distributive laws, we compose the monad of convex subsets of the free semimmodule .…

Towards an Automatic Proof of Lamport s Paxos

Lamport’s celebrated Paxos consensus protocol is generally viewed as a hard-to-understand algorithm . We take a step towards automatically proving the safety of Paxos by taking advantage of three structural features in its specification . We note that these structural features are not specific to Paxos and that IC3PO can serve as an automatic general-purpose protocol verification tool .…

Accurate 3D frequency domain seismic wave modeling with the wavelength adaptive 27 point finite difference stencil a tool for full waveform inversion

Efficient frequency-domain Full Waveform Inversion (FWI) of long-offset/wide-azimuth node data can be designed with a few discreterequencies . However, 3D frequency- domain seismic modeling remains challengingsince it requires solving a large and sparse linear indefinite system perfrequency . When such systems are solved with direct methods or hybriddirect/iterative solvers, based upon domain decomposition preconditioner,finite-difference stencils on regular Cartesian grids should be designed to conciliate compactness and accuracy, the former being necessary to mitigate thefill-in induced by the Lower-Upper (LU) factorization .…