Tele operative Robotic Lung Ultrasound Scanning Platform for Triage of COVID 19 Patients

Lung ultrasound (LUS) has emerged as a rapid non-invasive imaging tool for the diagnosis of COVID-19 infected patients . Concerns surrounding LUS include the disparity of infected patients and healthcare providers, relatively small number of physicians and sonographers . A 2-dimensional (2D) tele-operative robotic platform capable of performing LUS may be of significant benefit .…

TAMPC A Controller for Escaping Traps in Novel Environments

Trap-Aware Model Predictive Control (TAMPC) is a two-level hierarchical control algorithm that reasons about traps and non-nominal dynamics to decide between goal-seeking and recovery policies . We evaluate our method on simulated planar pushing andpeg-in-hole problems against adaptive controland reinforcement learning baselines .…

Ordinal analysis of partial combinatory algebras

For every partial combinatory algebra (pca), we define a hierarchy ofextensionality relations using ordinals . We investigate the closure ordinals ofpca’s, i.e.\ the smallest ordinals where these relations become equal . We calculate the exactcomplexities of the extensionality relations in Kleene’s first model, showingthat they exhaust the hyperarithmetical hierarchy .…

Automatic Repair of Vulnerable Regular Expressions

A regular expression is called vulnerable if there exist input strings onwhich the usual backtracking-based matching algorithm runs super linear time . Software containing vulnerable regular expressions are prone to algorithmic-complexity denial of service attack in which the malicious user provides input strings exhibiting the bad behavior .…

Kinodynamic Planning for an Energy Efficient Autonomous Ornithopter

This paper presents a novel algorithm to plan energy-efficient trajectoriesfor autonomous ornithopters . We propose an efficient approach to plan ornithopter trajectoriesthat minimize energy consumption by combining gliding and flapping maneuvers . The algorithm builds a tree of dynamically feasible trajectories and appliesheuristic search for efficient online planning, using reference curves to guidethe search and prune states .…

A Study of Transfer Learning in Music Source Separation

Supervised deep learning methods for performing audio source separation can be very effective in domains where there is a large amount of training data . While some music domains have enough data suitable for training a separationsystem, many musical domains do not, such as classical music, choral music, and non-Western music traditions .…

Training Noisy Single Channel Speech Separation With Noisy Oracle Sources A Large Gap and A Small Step

When training deeplearning separation models, a need for ground truth leads to training on synthetic mixtures . Training in noisy conditions requires either usingnoise synthetically added to clean speech, or training using mixtures of noisy speech . We demonstrate therelative inseparability of noise and that this noisy speech paradigm leads to significant degradation of system performance .…

The IDLAB VoxCeleb Speaker Recognition Challenge 2020 System Description

IDLab top-scoring submissions for VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) in the supervisedand unsupervised speaker verification tracks . For the supervised verificationtracks we trained 6 state-of-the-art ECAPA-TDNN systems and 4 Resnet34 basedsystems with architectural variations . On all models we apply a large marginfine-tuning strategy, which enables the training procedure to use higher marginpenalties by using longer training utterances .…

Speech Activity Detection Based on Multilingual Speech Recognition System

This paper leverages a multi-lingualAutomatic Speech Recognition (ASR) system to perform Speech Activity Detection(SAD) In out-of-domain datasets, the proposed SAD model shows significantly better performance w.r.t. baselinemodels. In the Ester2 dataset, without using any in-domain data, this modeloutperforms the WebRTC, phoneme recognizer based VAD (Phn\_Rec), and Pyannote baselines (respectively 7.1, 1.7, and 2.7% absolutely) in Detection Error Rate(DetER) metrics.…

Speech enhancement aided end to end multi task learning for voice activity detection

Robust voice activity detection (VAD) is a challenging task in lowsignal-to-noise (SNR) environments . Recent studies show that speech enhancementis helpful to VAD, but the performance improvement is limited . Here we propose a new joint optimizationobjective—VAD-masked scale-invariant source to noise ratio (mSI-SDR) The model has two decoders, one for speech enhancement and the other for VAD .…

Adjoint Reactive GUI

Functional Reactive Programming (FRP) allows GUIs to be designed in a declarative fashion . Most FRP languages are synchronous and continually check for new data . This means that an FRP-style GUIs will “wake up” on each program cycle . This isproblematic for applications like text editors and browsers, where oftennothing happens for extended periods of time, and we want the implementation tosleep until new data arrives .…

Clustering via Information Access in a Network

Researchers have begun to study the differing access to information of individuals within a network . This previous work suggests that informationaccess is itself a potential aspect of privilege based on network position . Instead of using standard groupingmethods for graph clustering, we design and explore a clustering that incorporates models of how information flows on a network.…

The Impact Of Noise And Topology On Opinion Dynamics In Social Networks

We investigate the impact of noise and topology on opinion diversity insocial networks . We find that opinion diversity decreases as communities and clusters are broken down . We test our predictions against data describingempirical influence networks between major news outlets and find that incorporating our measure in linear models for the sentiment expressed by suchsources on a variety of topics yields a notable improvement in terms of explanatory power .…

Enriching Under Represented Named Entities To Improve Speech Recognition Performance

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to insufficient instances and poor contextual coverage in the training data to learn reliable estimates andrepresentations . We propose approaches to enriching UR-NEs to improve speech recognition performance . We first enrich the representations in a pre-trained recurrent neural network LM (RNNLM) by borrowing theembedding representations of the rich-represented NEs .…

A Simple Approach for Handling Out of Vocabulary Identifiers in Deep Learning for Source Code

There is an emerging interest in the application of deep learning models to source code processing tasks . We propose a simple yet effective method based on identifier anonymization to handle out-of-vocabulary(OOV) identifiers . We show that the proposed OOVanonymization method significantly improves the performance of the Transformer in two code processing task: code completion and bug fixing .…

An analysis of the SIGMOD 2014 Programming Contest Complex queries on the LDBC social network graph

This report contains an analysis of the queries defined in the SIGMOD 2014Programming Contest . We first describe the data set, then present the queries, providing graphical illustrations for them and pointing out their caveats . We also demonstrate the influence of this contest by listing followup works which used these queries as inspiration to design better algorithms or to define interesting graph queries .…

The fragility of opinion formation in a complex world

With vast amounts of high-quality information at our fingertips, how is it possible that many people believe that the Earth is flat and vaccinationharmful? We quantify the implications of an opinionformation mechanism whereby an uninformed observer gradually forms opinions about a world composed of subjects interrelated by a signed network of mutual trust and distrust .…

Don t shoot butterfly with rifles Multi channel Continuous Speech Separation with Early Exit Transformer

Multi-channel speech separation sometimes doesnot necessarily need such a heavy structure for all time frames . To deal with this problem, we propose an early exit mechanism, which enables the Transformer model to handledifferent cases with adaptive depth . Early exit mechanism accelerates the inference, but also improves the accuracy of the separation task, says the authors .…

Robust Planning and Control for Dynamic Quadrupedal Locomotion with Adaptive Feet

In this paper, we aim to improve the robustness of dynamic quadrupedallocomotion through three aspects: 1) fast model predictive foothold planning, 2) LQR control for robust motion tracking and 3) adaptive feet for terrainadaptation . The specially designed foot with adaptive sole aims at improving thetraversability of rough terrains with rocks, loose gravel and rubble byenlarging the contact surfaces with ground .…

Primal Dual Mesh Convolutional Neural Networks

The method takes features for both edges and faces of a 3D mesh as input and dynamicallyaggregates them using an attention mechanism . At the same time, we introduce apooling operation with a precise geometric interpretation, that allows handlingvariations in the mesh connectivity by clustering mesh faces in a task-driven fashion .…

The Case for Distance Bounded Spatial Approximations

Spatial approximations have been traditionally used in spatial databases to accelerate the processing of complex geometric operations . However, many emerging applications (e.g., visualization tools) requireinteractive responses, while only needing approximate results . Besides, real-world geospatial data is inherently imprecise, which makes exact dataprocessing unnecessary .…

How Phonotactics Affect Multilingual and Zero shot ASR Performance

Transformer encoder-decoder model leverages multilingual data well in IPA transcriptions of languages presented during training . However, the representations it learned were not successful in zero-shot transfer to unseen languages . We show that the gain from modelingcrosslingual phonotactics is limited, and imposing a too strong model can hurt too strong models .…

Geometric Separability using Orthogonal Objects

We study the geometric separability problem whenthe separator is a rectangular annulus of fixed orientation . We give polynomial time algorithms to construct separators of each of the above type that also optimizes a givenparameter . We also give an optimal algorithm that computes a separating orthogonal convex polygon withminimum number of edges, that runs in $O(n\log n) time.…

From Conjunctive Queries to Instance Queries in Ontology Mediated Querying

We consider ontology-mediated queries (OMQs) based on expressive descriptionlogics of the ALC family and (unions) of conjunctive queries . Our results include exact characterizations of when such a rewriting is possible and tightcomplexity bounds for deciding rewritability . We also give a tight complexitybound for the related problem of deciding whether a given MMSNP sentence is equivalent to a CSP .…