Algorithmic Reduction of Biological Networks With Multiple Time Scales

We present a symbolic algorithmic approach that allows to compute invariantmanifolds and corresponding reduced systems for differential equations modelingbiological networks . Multiple time scales of a given network are obtained by scaling, based on tropical geometry . Theexistence of invariant manifolds is subject to hyperbolicity conditions, whichwe test algorithmically using Hurwitz criteria .…

Entropy stable discontinuous Galerkin methods for nonlinear conservation laws on networks and multi dimensional domains

We present a high-order entropy stable discontinuous Galerkin (ESDG) method for nonlinear conservation laws . These methods utilizetreatments of multi-dimensional interfaces and network junctions retain entropy stability when coupling together entropy stable discretizations . Numerical experiments verify the stability of the proposed schemes, and .comparisons…

An Umbrella Converse for Data Exchange Applied to Caching Computing Shuffling Rebalancing

The problem of data exchange between multiple nodes with (not necessarily uniform) storage and communication capabilities models several current communication problems like Coded Caching, Data shuffling, CodedComputing, etc. The expression of the converse depends only on the number of bits to be moved between differentsubsets of nodes, and does not assume anything further specific about the parameters in the problem .…

Evaluation of Logic Programs with Built Ins and Aggregation A Calculus for Bag Relations

We present a scheme for translating logic programs into algebraic expressions that denote bag relations overground terms of the Herbrand universe . We develop an operational semantics based on term rewriting of thealgebraic expressions . This approach can exploit arithmetic identities and can exploit a range of useful strategies, including lazy strategies that defer work until it becomes possible or necessary .…

Identification of deep breath while moving forward based on multiple body regions and graph signal analysis

Existingnon-contact breath assessments achieve satisfactory results under restricted conditions when human body stays relatively still . When someone moves forward, breath signals detected by depth camera are hidden within signals of trunkdisplacement and deformation . The proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2% .…

Bayesian Attention Modules

Attention modules, as simple and effective tools, have enabled deepneural networks to achieve state-of-the-art results in many domains . Most current models use deterministicattention modules due to their simplicity and ease of optimization . Stochastic counterparts, on the other hand, are less popular despite their potential benefits .…

CoRT Complementary Rankings from Transformers

CoRT is a framework andneural first-stage ranking model that leverages contextual representations fromtransformer-based language models to complement candidates from term-based ranking functions while causing no significant delay . CoRTa representation-focused retrieval at web-scale can be realized with latenciesas low as BM25.…

Advantages of Bilinear Koopman Realizations for the Modeling and Control of Systems with Unknown Dynamics

This paper describes how the Koopman operator can be used to generate approximate linear, bilinear, and nonlinear modelrealizations from data . The paper argues in favor of bilinare realizations forcharacterizing systems with unknown dynamics . It argues that every control-affine system admits an infinite-dimensionalbilinearrealization, but does not necessarily admit a linear one .…

The Role of Robotics in Infectious Disease Crises

The recent coronavirus pandemic has highlighted the many challenges faced by healthcare, public safety, and economic systems when confronted with asurge in patients that require intensive treatment and a population that must be quarantined or shelter in place . Robotic technologies areinherently programmable, and robotic systems have been adapted and deployed, tosome extent, in the current crisis for such purposes as transport, logistics,and disinfection .…

Investigating Cross Domain Losses for Speech Enhancement

Recent years have seen a surge in the number of available frameworks for speech enhancement (SE) and recognition . These frameworks often rely in isolation on either time-domainsignals or time-frequency (TF) representations of speech data . In this study, we investigate the advantages of each set of approaches by examining their impact on speech intelligibility and quality .…

BIRD Big Impulse Response Dataset

This paper introduces BIRD, the Big Impulse Response Dataset . This opendataset consists of 100,000 multichannel room impulse responses (RIRs) BIRD is the largest open dataset currently available . These RIRs can be used to perform online data augmentation for scenarios that involve two microphones and multiple sound sources .…

Assessment of SE specific Sentiment Analysis Tools An Extended Replication Study

Sentiment analysis methods have become popular for investigating human communication, including discussions related to software projects . We find different SE-specific sentiment analysis tools might lead to contradictory results at a fine-grain level, when used ‘off-the-shelf’ Conversely, platform-specific tuning or retraining might be needed to take into account differences in platform conventions, jargon, or document lengths .…

Bernstein polynomial based transcription method for solving optimal trajectory generation problems

This paper presents a method and an open-source implementation,Bernstein/B\’ezier Optimal Trajectories (BeBOT) for the generation of trajectories for autonomous system operations . The proposed method is based oninfinite dimensional optimal control formulations of trajectory generation problems . By approximating the trajectories using Bernstein polynomials, these problems can be transcribed as nonlinear programming problems, which can then be solved using off-the-shelf solvers .…

Power pooling An adaptive pooling function for weakly labelled sound event detection

The proposed power pooling function outperforms the state-of-the-art linear softmaxpooling on both coarsegrained and fine-grained metrics . It improvesthe event-based F1 score (which evaluates the detection of event onsets andoffsets) by 11.4% and 10.2% relative on the two datasets . While this paperfocuses on sound event detection applications, the proposed method can be applied to other domains in other domains, such as engineering and other domains where it can be used to detect sound events with weak labels that only specify the types of events .…

Misleading Repurposing on Twitter

We present the first large scale andprincipled study of account repurposing on Twitter . We find 3,500 repurposed accounts in the Twitter Elections Integrity Datasets . 100,000 accounts that have more than 5,000 followers and were active in the first six months of 2020 using Twitter’s 1% real-timeample .…

The Effect of Spectrogram Reconstruction on Automatic Music Transcription An Alternative Approach to Improve Transcription Accuracy

Most of the state-of-the-art automatic music transcription (AMT) models breakdown the main transcription task into sub-tasks such as onset prediction and offset prediction . We explore the effect that spectrogramreconstruction has on our AMT model . Our proposed model consists of two U-nets: the first U-net transcribes the spectrogram into a posteriorgram, and the secondU-net transforms the posteriorgram back into a spectrogram .…

Is this pofma Analysing public opinion and misinformation in a COVID 19 Telegram group chat

We analyse a Singapore-based COVID-19 Telegram group with more than 10,000 participants . Engagement peaked when the Ministry of Health raised the diseasealert level . We find that government-identified misinformation is rare in the group . Messages discussing misinformation express skepticism, we find that messages discussing these pieces of misinformation are often skeptical of each other .…

Sparse Gaussian Process Variational Autoencoders

Large, multi-dimensional spatio-temporal datasets are omnipresent in modern science and engineering . An effective framework for handling such data are Gaussian process deep generative models (GP-DGMs) The SGP-VAE is evaluated in a variety of experiments where it outperforms alternative approaches including multi-output GPs and structuredVAEs .…

Negotiating Team Formation Using Deep Reinforcement Learning

When autonomous agents interact in the same environment, they must oftencooperate to achieve their goals . One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it . When agents are self-interested, gains from team formation must be allocated appropriately to incentivize agreement .…

UmlsBERT Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus

Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing . However, such models do not take into consideration expert domain knowledge . UmlsBERT can encode clinical domain knowledge into word embeddings and outperform existing domain-specific models on common named-entityrecognition (NER) and clinical natural language inference clinical NLP tasks .…

Extracting Seasonal Gradual Patterns from Temporal Sequence Data Using Periodic Patterns Mining

Mining frequent episodes aims at recovering sequential patterns from temporaldata sequences, which can then be used to predict the occurrence of related events in advance . Gradual patterns captureco-variation of complex attributes in the form of ” when X increases/decreases, Y increases/increase” Such patterns may add knowledge to certain applications, such as e-commerce .…

Graph Fairing Convolutional Networks for Anomaly Detection

Graph convolution is a fundamental building block for many deep neuralnetworks on graph-structured data . In this paper, we introduce a simple, yetvery effective graph convolutional network with skip connections forsemi-supervised anomaly detection . The effectiveness ofour model is demonstrated through extensive experiments on five benchmarkdatasets, achieving better or comparable anomaly detection results against strong baseline methods .…

Provenance Graph Kernel

Provenance is a record that describes how entities, activities, and agents influenced a piece of data . Graph kernels, on the other hand, have been consistently and successfully used to efficiently classify graphs . In thispaper, we introduce a novel graph kernel called \emph{provenance kernel .…

Multi Radar Tracking Optimization for Collaborative Combat

Smart Grids of collaborative netted radars accelerate kill chains through more efficient cross-cueing over centralized command and control . We propose two novel reward-based learning approaches to decentralizednetted radar coordination based on black-box optimization and ReinforcementLearning . To make the RL approach tractable, we use a simplification of the problem that we proved to be equivalent to the initial formulation .…

Neural Approximate Sufficient Statistics for Implicit Models

We consider the fundamental problem of how to automatically construct summarystatistics for implicit generative models . The idea is to frame the task of constructing sufficient statisticsas learning mutual information maximizing representation of the data . Thisrepresentation is computed by a deep neural network trained by a jointstatistic-posterior learning strategy .…

Extracting Procedural Knowledge from Technical Documents

Procedures are an important knowledge component of documents that can beleveraged by cognitive assistants for automation, question-answering or driving a conversation . It is a challenging problem to parse big dense documents likeproduct manuals, user guides to automatically understand which parts are talking about procedures and subsequently extract them .…