GMACO P GPU assisted Preemptive MACO algorithm for enabling Smart Transportation

A GPU assistedPreemptive MACO (GMACO-P) algorithm has been proposed to minimize the total travel time of the commuters . Inefficient traffic signals and routing mechanisms are the major factors that contribute to the increase of road congestion . For smoother traffic movement and reducingcongestion on the roads, the waiting time at intersections must be reduced and an optimal path should be chosen simultaneously .…

Spiking Neural Networks Part II Detecting Spatio Temporal Patterns

Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals . Examples include logs of time stamps, e.g., of tweets, and outputs of neuralprostheses and neuromorphic sensors . In this paper, we review models and training algorithms for the dominant approach that considers SNNs as a Recurrent Neural Network (RNN) and adapt learning rules based on backpropagation through time to therequirements of SNN .…

Abstracting Gradual Typing Moving Forward Precise and Space Efficient Technical Report

Abstracting Gradual Typing (AGT) is a systematic approach to designinggradually-typed languages . Languages developed using AGT automatically satisfy the formal semantic criteria for gradual languages identified by Siek et al. Nonetheless vanilla AGT semantics can still have importantshortcomings . The default operational semantics of AGT may fail to enforcesome invariants in surprising ways .…

Unsupervised Domain Adaptation for Visual Navigation

Advances in visual navigation methods have led to intelligent embodiednavigation agents capable of learning meaningful representations from raw RGBimages . However, most learning-based navigation policies are trained and tested in simulation environments . In order for these policies to be useful, they need to be transferred to the real-world .…

Perception for Autonomous Systems PAZ

Perception for Autonomous Systems (PAZ) is a hierarchical perception library that allow users tomanipulate multiple levels of abstraction in accordance to their requirements . PAZ is divided into three hierarchical levels which we refer to as pipelines, processors, and backends .…

Neural Architecture Search of SPD Manifold Networks

In this paper, we propose a new neural architecture search (NAS) problem ofSymmetric Positive Definite (SPD) manifold networks . Unlike the conventionalNAS problem, our problem requires to search for a unique computational cell called the SPD cell . This SPD cell serves as a basic building block of SPDneural architectures .…

On computation of coupled advection diffusion reaction problems by Schwarz waveform relaxation methods

A study is presented on the computation of coupledadvection-diffusion-reaction equations by Schwarz waveform relaxation methods . An optimal interface condition for Schwarzwaveform relaxation is also obtained, which leads to “perfect convergence”, that is, convergence within two times of iteration . The methodsand analyses are extended to the coupling of the viscous Burgers equations .…

Leveraging speaker attribute information using multi task learning for speaker verification and diarization

Deep speaker embeddings have become the leading method for encoding speakeridentity in speaker recognition tasks . The embedding space should ideally capture the variations between all possible speakers, encoding the multiple aspects that make up speaker identity . We show that by leveraging additional speaker attributeinformation in a multi task learning setting, deep speaker embeddingperformance can be increased for verification and diarization tasks, achieving a relative improvement of 17.8% in DER and 8.9% in EER for Supreme Court audiocompared to omitting the auxiliary task .…

Cascaded encoders for unifying streaming and non streaming ASR

End-to-end (E2E) automatic speech recognition (ASR) models, by now, haveshown competitive performance on several benchmarks . This workpresents cascaded encoders for building a single E2E ASR model that can operate in both modes simultaneously . Results show that this model achieves similar word error rates (WER) as a standalone streaming modelwhen operating in streaming mode, and obtains 10% — 27% relative improvement when operating in non-streaming mode .…

Linear Lavrent ev Integral Equation for the Numerical Solution of a Nonlinear Coefficient Inverse Problem

For the first time, we develop a convergent numerical method for the llinearintegral equation derived by M.M. Lavrent’ev in 1964 with the goal to solve acoefficient inverse problem for a wave-like equation in 3D . The data are nonoverdetermined. Nevertheless, numerical results forthat equation, which use the data generated for that coefficient inverseproblem, show a good reconstruction accuracy .…

Meta MgNet Meta Multigrid Networks for Solving Parameterized Partial Differential Equations

This paper studies numerical solutions for parameterized partial differentialequations (P-PDEs) with deep learning (DL) P-PDE arise in many important application areas and computational cost using traditional numerical schemes can be exorbitant . If we directly apply existing supervised learning models, the models need to be constantly fine-tuned or retrained when the parameters change .…

An Experimental Validation of Accurate and Precise GNSS Time Synchronization in Vehicular Networks

Global Navigation Satellite System(GNSS) is getting increasing attention for effective use of its timedissemination to vehicular networks . In urban road scenarios, blockage of satellite signals affects precise positing due to erroneous geometry . With the recent development of Multi-GNSS receivers, signal receptionno longer relies on a single GNSS constellation but all the existing four GNSSsystems .…

Upsampling artifacts in neural audio synthesis

A number of recent advances in audio synthesis rely on neural upsamplers, which can introduce undesired artifacts . In computer vision, upsampling artifacts have been studied and are known as checkerboard artifacts . Their effect has been overlooked so far in audio processing.…

Optimization Based Framework for Excavation Trajectory Generation

Traditional methods on excavationtrajectory generation usually separate the excavation motion into a sequence offixed phases, resulting in limited trajectory searching space . Our frameworkexplores the space of all possible excavation trajectories represented with waypoints interpolated by a polynomial spline . We formulate a generic task specification forexcavation by constraining the instantaneous motion of the bucket and furtheradd a target-oriented constraint, i.e.…

Randomized double and triple Kaczmarz for solving extended normal equations

The randomized Kaczmarz algorithm has received considerable attention recently because of its simplicity, speed, and the ability to approximately solve large-scale linear systems of equations . The proposed algorithms avoidforming $A^\top A$ explicitly and work for $m\geq n$ (full rank or rank deficient) Upper bounds showing exponential convergence in the mean square sense of the proposed algorithms are presented and numerical experiments are given to illustrate the theoretical results .…

Transporter Networks Rearranging the Visual World for Robotic Manipulation

Transporter Network is asimple model architecture that rearranges deep features to infer spatialdisplacements from visual input – which can parameterize robot actions . It is orders of magnitude more efficient than our benchmarked alternatives in learning vision-basedmanipulation tasks . Our method can represent complex multi-modalpolicy distributions and generalizes to multi-step sequential tasks, as well as 6DoF pick-and-place .…

Parallel waveform synthesis based on generative adversarial networks with voicing aware conditional discriminators

This paper proposes voicing-aware conditional discriminators for ParallelWaveGAN-based waveform synthesis systems . We adopt aprojection-based conditioning method that can significantly improve thediscriminator’s performance . As each discriminator learns the distinctive characteristics of theharmonic and noise components, the adversarial training process becomes more efficient, allowing the generator to produce more realistic speechwaveforms .…

ByteCover Cover Song Identification via Multi Loss Training

ByteCover is built based on the classicalResNet model, and two major improvements are designed to further enhance the model for CSI . ByteCover outperformed the best competitive system by 20.9\% . A set of experiments demonstrated the effectiveness and efficiency of ByteCover on multiple datasets, and in theDa-TACOS dataset, ByteCover outranks the best competition system by 20.9 \% .…

Dataset LoED The LoRaWAN at the Edge Dataset

Real-world LoRaWAN datasets are important for repeatable sensor-network and communications research and evaluation . Data is collected from nine gateways over a four month period in a dense urban environment . The dataset contains packet header information and all physicallayer properties reported by gateways such as the CRC, RSSI, SNR and spreadingfactor .…

Dependency Smells in JavaScript Projects

In this paper, we empirically examine evidence of recurring dependency management issues (dependency smells) We look at commit data for a dataset of 1,146 active JavaScript repositories . Practitioners agree that dependency smells bring about many problems including security threats, bugs,dependency breakage, runtime errors, and other maintenance issues .…

Deep generative factorization for speech signal

Various information factors are blended in speech signals, which forms the difficulty for most speech information processing tasks . An intuitiveidea is to factorize speech signal into individual information factors (e.g.,phonetic content and speaker trait), though it turns out to be highlychallenging .…

Recent Developments on ESPnet Toolkit Boosted by Conformer

In this study, we present recent developments on ESPnet: End-to-End SpeechProcessing toolkit, which mainly involves a recently proposed architecture called Conformer, Convolution-augmented Transformer . Our experiments reveal various training tips and significant performance benefits obtained with the Conformer on different tasks .…

Star edge coloring of some special graphs

The star chromatic index is the minimum number of colors needed to properlycolor the edges of $G$ such that no path or cycle of length $4$ is bicolored . In this paper, we study the star edge-coloring of Halin graphs, $k$-powergraphs and the generalized Petersen graphs $P(3n, n)$.…

Mutual Borders and Overlaps

A word is said to be bordered if it contains a non-empty proper prefix that is also a suffix . We give a recurrence for the number of mutually bordered words of length-$n$ over a $k$-letter alphabet . We show that, asymptotically, there are $c\cdotk^{2n}$ mutually-bordered words over a letter alphabet, where $c$ is a constant .…

The p Airy distribution

In this manuscript we consider the set of Dyck paths equipped with the uniform measure . We study the statistical properties of a deformation of the observable “area below the Dyck path” as the size $N$ of the path goes toinfinity .…

Random walks and community detection in hypergraphs

We propose a one parameter family of random walk processes on hypergraphs . A parameter biases the dynamics of the walker towards hyperedges of low or high cardinality . We show that for each value of the parameter the resulting process defines its own hypergraph projection on a weighted network .…

Combining Label Propagation and Simple Models Out performs Graph Neural Networks

Graph Neural Networks (GNNs) are the predominant technique for learning overgraphs . However, there is relatively little understanding of why GNNs are necessary for good performance . Here, we show that for many standard transductive node classificationbenchmarks, we can exceed or match the performance of state-of-the-art GNN models by combining shallow models that ignore the graph structure with two simple post-processing steps that exploit correlation in the label structure .…

Computation of Large Asymptotics of 3 Manifold Quantum Invariants

Quantum topological invariants have played an important role in computationaltopology . They are at the heart of major modern mathematical conjectures . We study the experimental problem of computing large $r$values of Turaev-Viro invariants . We base our approach on anoptimized backtracking algorithm, consisting of enumerating combinatorial data on a triangulation of a 3-manifold .…