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 .…

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 .…

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 .…

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 .…

Truthful Information Elicitation from Hybrid Crowds

A decision maker wants to predict weather tomorrow by eliciting andaggregating information from crowd . How can the decision maker incentivize the crowd to report their information truthfully? Ignoring the heterogeneity among the agent may lead to inefficient of biased information, which would lead to suboptimal decisions .…

Error estimates for fully discrete generalized FEMs with locally optimal spectral approximations

This paper is concerned with the error estimates of the fully discrete generalized finite element method (GFEM) with optimal local approximationspaces for solving elliptic problems with heterogeneous coefficients . The error bound of the discrete GFEM approximation isproved to converge as $h\rightarrow 0$ towards that of the continuous GFEMapproximation, which was shown to decay nearly exponentially in previous works .…

On Fair and Efficient Allocations of Indivisible Public Goods

We study fair allocation of indivisible public goods subject to cardinality(budget) constraints . In this model, we have n agents and m available publicgoods, and we want to select $k \leq m$ goods in a fair and efficient manner . We prove that MNW allocations provide fairness guarantees of Proportionality upto one good (Prop1), $1/n$ approximation to Round Robin Share (RRS) and theefficiency guarantee of Pareto Optimality (PO) Further, we show that theproblems of finding MNW or leximin-optimal allocations are NP-hard, even in thecase of constantly many agents, or binary valuations .…

ECG Heartbeat Classification Using Multimodal Fusion

Electrocardiogram (ECG) is an authoritative source to diagnose and countercritical cardiovascular syndromes such as arrhythmia and myocardial infarction(MI) Current machine learning techniques either depend on manually extractedfeatures or large and complex deep learning networks which merely utilize the1D ECG signal directly .…

THz Transmission meets Untrusted UAV Relaying Trajectory and Communication Co design for Secrecy Energy Efficiency Maximization

Unmanned aerial vehicles (UAVs) and Terahertz (THz) technology are envisioned to play paramount roles in next-generation wireless communications . We present a secure two-phase transmission strategy with cooperative jamming . We assume that the UAV-mounted relay may act, besides providing services, as a potential adversary called the untrusted UAV relay.…

Multi Agent Belief Sharing through Autonomous Hierarchical Multi Level Clustering

Coordination in multi-agent systems is challenging for agile robots such as UAVs, where relative agent positions frequentlychange due to unconstrained movement . This work proposes autonomous hierarchical multi-level clustering (MLC), which forms aclustering hierarchy utilizing decentralized methods . Using observation aggregation, compression, and dissemination, agentsshare local observations throughout the hierarchy, giving every agent a totalsystem belief with spatially dependent resolution and freshness .…

Strategic Mitigation of Agent Inattention in Drivers with Open Quantum Cognition Models

State-of-the-art driver-assist systems have failed to effectively mitigatedriver inattention and had minimal impacts on the ever-growing number of roadmishaps . This is because traditional human-machine interaction settings are modeled in classical and behavioralgame-theoretic domains . We propose a novel equilibrium notion in human-systeminteraction games, where the system maximizes its expected utility and humandecisions can be characterized using any general decision model .…

Into Summarization Techniques for IoT Data Discovery Routing

In this paper, we consider the IoT data discovery problem in very large and growing scale networks . We investigate in depth the routing tablesummarization techniques to support effective and space-efficient IoT datadiscovery routing . Novel summarization algorithms, including alphabeticalbased, hash based, and meaning based summarization, are proposed .…

Filament Plots for Data Visualization

We construct a computationally inexpensive 3D extension of Andrew’s plots . We consider linear isometries from a Euclidean data space to infinite dimensional spaces of 2D curves . Weparametrize the linear isometry that produces (on average) optimally smoothcurves over a given dataset .…

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 .…

On function homophily of microbial Protein Protein Interaction Networks

We present a new method for assessing homophily in networks whose verticeshave categorical attributes, namely when the vertices of networks comepartitioned into classes . We apply this method to Protein- Protein Interactionnetworks, where vertices correspond to proteins, partitioned according to theyfunctional role, and edges represent potential interactions between proteins .…

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…

DOA Estimation for Hybrid Massive MIMO Systems using Mixed ADCs Performance Loss and Energy Efficiency

Due to power consumption and high circuit cost in antenna arrays, thepractical application of massive multiple input multiple-input multiple-output (MIMO) in thesixth generation (6G) and future wireless networks is still challenging . Employing lowresolution analog-to-digital converters (ADCs) and hybrid analogand digital (HAD) structure is two low-cost choice with acceptable performanceloss .…

Multi modal Residual Perceptron Network for Audio Video Emotion Recognition

Emotion recognition is an important research field for Human-ComputerInteraction . Audio-Video Emotion Recognition (AVER) is now attacked withDeep Neural Network (DNN) modeling tools . We hypothesize that for fuzzycategories of emotional events, the higher noise of one modality can amplify the lower noise of the second modality represented indirectly in the parameters of the modeling neural network .…

Music Plagiarism Detection via Bipartite Graph Matching

There is an urgent need for a tool that can detect musicplagiarism automatically . The increasing number of musical pieces have made the problem of music plagiarism prominent . Researchers have proposed various methods to extractlow-level and high-level features of music and compute their similarities .…

Cell Free SUCRe Protocol A User Centric Adaptation

The motivation of this paper is to introduce a cell-free adaptation of the strongest-user collision resolution (SUCRe) protocol . The SUCRe protocol was originally proposed forcellular massive multiple-input multiple- input multiple-output (MIMO) networks . The goal with this adaptation is to show that a cell free network can better handle the attempts of users’ equipment (UEs) due to the augmented macro-diversity brought by the disposition of access points (APs) over the coverage area .…

Audio Captioning Transformer

Audio captioning aims to automatically generate a natural languaged description of an audio clip . Most captioning models follow an encoder-decoderarchitecture, where the decoder predicts words based on the audio featuresextracted by the encoder . Convolutional neural networks (CNNs) and recurrentneural networks (RNNs) are often used as the audio encoder.…

Into Summarization Techniques for IoT Data Discovery Routing

In this paper, we consider the IoT data discovery problem in very large and growing scale networks . We investigate in depth the routing tablesummarization techniques to support effective and space-efficient IoT datadiscovery routing . Novel summarization algorithms, including alphabeticalbased, hash based, and meaning based summarization, are proposed .…

Controlling the Remixing of Separated Dialogue with a Non Intrusive Quality Estimate

Remixing separated audio sources trades off interferer attenuation against the amount of audible deteriorations . This paper proposes a non-intrusive audio quality estimation method for controlling this trade-off in a signal-adaptivemanner . Deep neural networks (DNNs) are trained to estimate the 2f-model intrusively using the reference target(iDNN2f), using the input mix as reference (nDNN) and reference-free using only the separated output signal .…

Conditional Sound Generation Using Neural Discrete Time Frequency Representation Learning

Deep generative models have recently achieved impressive performance inspeech synthesis and music generation . However, the generation of general sounds (such as carhorn, dog barking, and gun shot) has received less attention, despite their potential applications . In this work, we propose to generate sounds conditioned on sound classes via neural discretetime-frequency representation learning .…

CL4AC A Contrastive Loss for Audio Captioning

Automated Audio captioning (AAC) is a cross-modal translation task that aims to use natural language to describe the content of an audio clip . We propose a novel encoder-decoder framework called Contrastive Lossfor Audio Captioning (CL4AC) In CL4AC, the self-supervision signals derivedfrom the original audio-text paired data are used to exploit thecorrespondences between audio and texts by contrasting samples, which can improve the quality of latent representation .…

Global Outliers Detection in Wireless Sensor Networks A Novel Approach Integrating Time Series Analysis Entropy and Random Forest based Classification

Wireless Sensor Networks (WSNs) have recently attracted greater attention due to their practicality in monitoring, communicating, and reportingspecific physical phenomena . The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signalweakness, or intrusion attacks depending on the specific situation .…

Multi Agent Belief Sharing through Autonomous Hierarchical Multi Level Clustering

Coordination in multi-agent systems is challenging for agile robots such as UAVs, where relative agent positions frequentlychange due to unconstrained movement . This work proposes autonomous hierarchical multi-level clustering (MLC), which forms aclustering hierarchy utilizing decentralized methods . Using observation aggregation, compression, and dissemination, agentsshare local observations throughout the hierarchy, giving every agent a totalsystem belief with spatially dependent resolution and freshness .…

Fast and Multiscale Formation of Isogeometric matrices of Microstructured Geometric Models

The matrix formation associated to high-order discretizations is known to benumerically demanding . We design a multiscale assembly procedure to reduce the exorbitant assembly time in the context of isogeometric linear elasticity of complexmicrostructured geometries modeled via spline compositions . The strategy turns out to be of great interest not only to form finite elementoperators but also to compute other quantities in a fast manner as for instancesensitivity analyses commonly used in design optimization .…

Guided Generation of Cause and Effect

We present a conditional text generation framework that posits sententialexpressions of possible causes and effects . This framework depends on a very large-scale collection of English sentences expressing causal patterns CausalBank . We extend prior work in lexically-constrained decoding to support disjunctive positive constraints .…

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 .…