## An introduction to distributed training of deep neural networks for segmentation tasks with large seismic datasets

Deep learning applications are drastically progressing in seismic processing and interpretation tasks . The majority of approaches subsample data and restrict model sizes to minimise computational requirements . Subsampling the data risks losing vital spatio-temporal information which couldaid training whilst restricting model sizes can impact model performance .…

## Distributionally Robust Federated Averaging

In this paper, we study communication efficient distributed algorithms fordistributionally robust federated learning via periodic averaging with adaptivesampling . We propose a Distributionally Robust Federated Federated Averaging(DRFA) algorithm that employs a novel snapshotting scheme to approximate theaccumulation of history gradients of the mixing parameter .…

## ISALT Inference based schemes adaptive to large time stepping for locally Lipschitz ergodic systems

Efficient simulation of SDEs is essential in many applications, particularly for ergodic systems that demand efficient simulation of both short-timedynamics and large-time statistics . We introduce a framework to constructinference-based schemes adaptive to large time-steps (ISALT) from data . We explore the use of numerical schemes (such as theEuler-Maruyama, a hybrid RK4, and an implicit scheme) to derive informed basisfunctions, leading to a parameter inference problem .…

## New Singly and Doubly Even Binary 72 36 12 Self Dual Codes from M_2 R G Group Matrix Rings

In this work, we present a number of generator matrices of the form $[I_{2n}\ | \ \tau_k(v)],$ where $I_{kn}$ is the $kn \times kn$ identity matrix, $v$ is an element in the group matrix ring $M_2(R)G$ and where $R$ is a finitecommutative Frobenius ring .…

## LET Linguistic Knowledge Enhanced Graph Transformer for Chinese Short Text Matching

Chinese short text matching is a fundamental task in natural language processing . Existing approaches usually take Chinese characters or words as tokens . We introduce HowNet as an externalknowledge base and propose a Linguistic knowledge Enhanced graph Transformer . We adopt the word latticegraph as input to maintain multi-granularity information .…

## BGK models for inert mixtures comparison and applications

Consistent BGK models for inert mixtures are compared to hydrodynamic limits that can be derived from indifferent collision-dominated regimes . Application to the plane shock wave in a binary mixture of noble gases is also presented . The comparison is carried out bothanalytically and numerically, for the latter using an asymptotic preservingsemi-Lagrangian scheme .…

## Batched Neural Bandits

The BatchNeuralUCB algorithm combines neuralnetworks with optimism to address the exploration-exploitation tradeoff while keeping the total number of batches limited . We prove that it achieves the same regret as the fully sequential version while reducing the number of policy updates considerably .…

## Diffusion Earth Mover s Distance and Distribution Embeddings

Diffusion Earth Mover’s Distance(EMD) is more accurate than similarly fast algorithms such astree-based EMDs . Diffusion EMD is fully differentiable, making itenable to future uses in gradient-descent frameworks such as deep neuralnetworks . The method is applicable to all datasets that are massivelycollected in parallel in many medical and biological systems .…

## Survival of the Fittest Numerical Modeling of Supernova 2014C

Initially classified as a supernova (SN) type Ib, 100 days after the explosion SN\,2014C made a transition to a SN type II . This has been interpreted as evidence of interaction between the supernova shock wave and a massive shell previously ejected from the progenitor star .…

## On a Network SIS Epidemic Model with Cooperative and Antagonistic Opinion Dynamics

We propose a mathematical model to study coupled epidemic and opiniondynamics in a network of communities . Our model captures SIS epidemic dynamics whose evolution is dependent on the opinions of the communities toward the epidemic . We propose an Opinion-DependentReproduction Number to characterize the mutual influence between epidemicspreading and opinion dissemination over the networks .…

## Do Input Gradients Highlight Discriminative Features

In this work, we introduce an evaluation framework to study this hypothesis for benchmark image classification tasks . We make two surprising observations on CIFAR-10 and Imagenet-10 datasets . We introduce a synthetic testbed and theoretically justify our counter-intuitive empirical findings .…

## Retrieval Augmentation to Improve Robustness and Interpretability of Deep Neural Networks

Deep neural network models have achieved state-of-the-art results in varioustasks related to vision and/or language . Most models are trained by iterating over single input-output pairs . We exploit the training data to improve the robustness andinterpretability of deep neural networks .…

## Optimising the mitigation of epidemic spreading through targeted adoption of contact tracing apps

The ongoing COVID-19 pandemic is the first epidemic in human history in which digital contact-tracing has been deployed at a global scale . Tracking and quarantining all the contacts of individuals who test positive to a virus can help slowing-down an epidemic .…

## Metal Oxide Sensor Array for Selective Gas Detection in Mixtures

We present a monolithic, microfabricated, metal-oxide semiconductor (MOS) sensor array in conjunction with a machine learning algorithm to determine unique fingerprints of individual gases within homogenous mixtures . The arraycomprises four different metal oxides, and is engineered for independenttemperature control and readout from each individual pixel in a multiplexed fashion .…

## Ensuring Progress for Multiple Mobile Robots via Space Partitioning Motion Rules and Adaptively Centralized Conflict Resolution

In environments where multiple robots must coordinate in a shared space,decentralized approaches allow for decoupled planning at the cost of global guarantees . In this work, we present a framework that ensuresprogress for all robots without assumptions on any robot’s planning strategy .…

## Defining Preferred and Natural Robot Motions in Immersive Telepresence from a First Person Perspective

This paper presents some early work and future plans regarding how the autonomous motions of a telepresence robot affect a person embodied in therobot through a head-mounted display . We consider preferences, comfort, and the perceived naturalness of aspects of parts of piecewise linear paths compared to the same aspects on a smooth path .…

## Stochastic Aggregation in Graph Neural Networks

Graph neural networks (GNNs) manifest pathologies including over-smoothing and limited discriminating power as a result of suboptimally expressiveaggregating mechanisms . We present a unifying framework for stochasticaggregation (STAG) in GNNs . Noise is (adaptively) injected into theaggregation process from the neighborhood to form node embeddings .…

## Mixed Variable Bayesian Optimization with Frequency Modulated Kernels

Bayesian optimization (BO) is often boosted by Gaussian Process (GP) surrogate models . In this paper, we propose the frequency modulated (FM) kernelflexibly modeling dependencies among different types of variables . The FM kernel uses distanceson continuous variables to modulate the graph Fourier spectrum derived fromdiscrete variables .…

## SparseBERT Rethinking the Importance Analysis in Self attention

Transformer-based models are popular for natural language processing (NLP) tasks due to its powerful capacity . As the core component, self-attention module has aroused widespread interests . Attention map visualization of apre-trained model is one direct method for understanding self-assessment .…

## FASA Feature Augmentation and Sampling Adaptation for Long Tailed Instance Segmentation

Feature Augmentation and Sampling Adaptation (FASA) is a fast, generic method that can be easily plugged into standard or long-tailed segmentationframeworks, with consistent performance gains and little added cost . FASAdoes not require any elaborate loss design, and removes the need forinter-class transfer learning that often involves large cost andmanually-defined head/tail class groups .…

## Lie Group integrators for mechanical systems

Lie group integrators have become amethod of choice in many application areas . They include multibody dynamics, shape analysis, data science, image registration and biophysical simulations . The theory is illustrated by applying the methods totwo nontrivial applications in mechanics .…

## A Simulation based End to End Learning Framework for Evidential Occupancy Grid Mapping

Evidential occupancy grid maps are a popular representation of theenvironment of automated vehicles . Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds . Geometric ISMs show alimited performance when estimating states in unobserved but inferable areas .…

## Automatic Story Generation Challenges and Attempts

The scope of this survey paper is to explore the challenges in automaticstory generation . We hope to contribute in the following ways: Explore howprevious research in story generation addressed those challenges . Discuss future research directions and new technologies that may aid more advancements .…

## A flapping feathered wing powered aerial vehicle

An aerial vehicle powered by flapping feathered wings was designed, developed and fabricated . Different from legacy flapping-wing aerial vehicles withmembrane wings, the new design uses authentic bird feathers to fabricate wings . In field tests, a radio-controlled electric-powered aerial vehicle successfully took off, flew up to 63.88 s and landedsafely .…

## On continual single index learning

In this paper, we generalize the problem of single index model to the context of continual learning in which a learner is challenged with a sequence of tasks one by one and the dataset of each task is revealed in an online fashion .…

## File fragment recognition based on content and statistical features

The known files are divided into different fragments, and different classificational algorithms are used to solve the problems of file fragment recognition . The proposed recognition algorithm can recognize 6 types of useful files and may distinguish a type of file fragments with higher accuracy thanthe similar works done .…

## Automatic Classification of OSA related Snoring Signals from Nocturnal Audio Recordings

In this study, the development of an automatic algorithm is presented toclassify the nocturnal audio recording of an obstructive sleep apnoea (OSA) patient as OSA related snore, simple snore and other sounds . The algorithm achieved an accuracy of 87% for identifying snore events from the audio recordings and 72% for .…

## SCD A Stacked Carton Dataset for Detection and Segmentation

Carton detection is an important technique in the automatic logistics system . Images are collected from the internet and several warehourses, and objects are labeled using per-instancesegmentation for precise localization . There are totally 250,000 instance masksfrom 16,136 images . The improvement of AP on MS COCO and PASCAL VOC is 1.8% – 2.2% and 3.4% – 4.3% respectively.…

## A Primer on Contrastive Pretraining in Language Processing Methods Lessons Learned and Perspectives

Modern natural language processing (NLP) methods employ self-supervisedpretraining objectives such as masked language modeling to boost the performance of various application tasks . These pretraining methods are frequently extended with recurrence, adversarial or linguistic propertymasking, and more recently with contrastive learning objectives .…

## Graphfool Targeted Label Adversarial Attack on Graph Embedding

Deep learning is effective in graph analysis . It is widely applied in many areas, such as link prediction, node classification, communitydetection, and graph classification etc . Graphfool can achieve an average improvement of 11.44% in attack success rate . To the best of our knowledge, this is the first targeted label attack technique .…

## Railway Anomaly detection model using synthetic defect images generated by CycleGAN

Train companies are facing difficulties in gathering adequate images of defective equipment . Machine-learning models have developed a model using CycleGAN to generate artificial images instead of real images . These generated images play a vital role in enhancing the accuracy of the defect detection models, say researchers .…

## Long term IaaS Provider Selection using Short term Trial Experience

We propose a novel approach to select privacy-sensitive IaaS providers for along-term period . The proposed approach leverages a consumer’s short-term trialexperiences for long-term selection . We design a novel equivalence partitioningbased trial strategy to discover the temporal and unknown QoS performancevariability of an IAAS provider .…

## Designing zonal based flexible bus services under stochatic demand

In this paper, we develop a zonal-based flexible bus services (ZBFBS) by considering both passenger demands spatial (origin-destination or OD) and volume stochastic variations . Service requests are grouped by zonal OD pairsand number of passengers per request, and aggregated into demand categories .…

## Frequency Dynamics with Grid Forming Inverters A New Stability Paradigm

Traditional power system frequency dynamics are driven by Newtonian physics,where a synchronous generator (SG), the historical primary source of power,follows a deceleration frequency trajectory upon power imbalances . Subsequent to a disturbance, an SG will modifypre-converter, mechanical power as a function of frequency .…

## Research on False Data Injection Attacks in VSC HVDC Systems

The false data injection (FDI) attack is a crucial form of cyber-physical security problems facing cyber power systems . There is noresearch revealing the problem of FDI attacks facing voltage source converterbased high voltage direct current transmission (VSC-HVDC) systems . And finally, the modified IEEE-14 bus system is used to demonstrate that attackers are capable of disrupting the operation security of converter stations in VSC- HVDC systems by FDI attack strategies .…

## Lossless Compression of Efficient Private Local Randomizers

Locally Differentially Private (LDP) Reports are commonly used for collection of statistics and machine learning in the federated setting . LDP reports are known to have relatively little information about the user’s data due to randomization . Several schemes are known that exploit this fact to design low-communication versions of LDP algorithms but all of them do so at the expense of a significant loss in utility .…

## The rich still get richer Empirical comparison of preferential attachment via linking statistics in Bitcoin and Ethereum

Bitcoin and Ethereum transactions present one of the largest real-world complex networks that are publicly available for study . Preference attachment continues to be a key factor in the evolution of both the Bitcoin and . Ethereum, the second most important cryptocurrency, continues to evolve, authors say .…

## Malicious and Low Credibility URLs on Twitter during COVID 19

This study provides an in-depth analysis of a Twitter dataset around astraZeneca COVID vaccine development released as a part of Grand Challenge,North American Social Network Conference, 2021 . We show the presence of malicious and low credibility information sources shared on Twittermessages in multiple languages .…

## Automated Fuzzing of Automotive Control Units

Modern vehicles are governed by a network of Electronic Control Units (ECUs) ECUs are programmed to sense inputs from the driver and the environment, toprocess these inputs, and to control actuators that, e.g., regulate the engineor even control the steering system .…

## Adversarial Robustness with Non uniform Perturbations

Robustness of machine learning models is critical for security related applications . We propose using characteristics of the empirical datadistribution, both on correlations between the features and the importance of the features themselves . The key idea of our proposed approach is to enable non-uniformperturbations that can adequately represent these feature dependencies during training .…

## The non positive circuit weight problem in parametric graphs a fast solution based on dioid theory

In this paper, we design an algorithm thatsolves the Non-positive Circuit weight Problem (NCP) on this class ofparametric graphs . The proposed algorithm isbased on max-plus algebra and formal languages and runs faster than otherexisting approaches . It achieves strongly polynomial time complexity$\mathcal{O}(n^4)$ (where $n$ is the number of nodes in the graph) The proposed algorithms are based on max plus algebra, and run faster than existing approaches .…

## Multiplicative Reweighting for Robust Neural Network Optimization

Deep neural networks suffer from degraded performance in the presence of noisy labels at traintime or adversarial examples during inference . Multiplicative weights updates (MW) updates were recently shown to be robust to moderate adversarial corruptions . MW improves network’s accuracy in the .…

## Scaling Distributed Ledgers and Privacy Preserving Applications

This thesis proposes techniques aiming to make blockchain technologies andsmart contract platforms practical by improving their scalability, latency, andprivacy . This thesis starts by presenting the design and implementation ofChainspace, a distributed ledger that supports user defined smart contracts and executes user-supplied transactions .…

## Attestation Infrastructures for Private Wallets

In this paper we focus on one part of the trust infrastructures needed for the future virtual assets industry . Our focus is on regulated private wallets utilizing trusted hardware, and the capability of the wallet toyield attestation evidence suitable to address requirements in severaluse-cases, such as asset insurance and regulatory compliance .…

## Decentralized conjugate gradients with finite step convergence

The decentralized solution of linear systems of equations arises as asubproblem in optimization over networks . Typical examples include the KKTsystem corresponding to equality constrained quadratic programs in distributedoptimization algorithms or in active set methods . This note presents a tailoredstructure-exploiting decentralized variant of the conjugate gradient method .…

## Estimation and Distributed Eradication of SIR Epidemics on Networks

This work examines the discrete-time networked SIR(susceptible-infected-recovered) epidemic model . The infection and recovery parameters may be time-varying . We provide a sufficient condition for the SIR model to converge to the set of healthy states exponentially . We illustrate the results via simulations over northern Indiana, USA.…

## Investigating Moral Foundations from Web Trending Topics

Moral foundations theory helps understand differences in morality across cultures . In this paper, we propose a model to predict moral foundations (MF) from social media trending topics . We also investigate whether differences inMF influence emotional traits .…

## Robust SleepNets

State-of-the-art convolutional neural networks excel in machine learning tasks such as face recognition, but suffers significantly when adversarial attacks are present . In this study, we investigate eyeclosedness detection to prevent vehicle accidents related to driverdisengagements and driver drowsiness . We develop two models to detect eye closedness:first model on eye images and a second model on face images .…

## Computing Differential Privacy Guarantees for Heterogeneous Compositions Using FFT

Fast Fourier Transform (FFT)-based accountant forevaluating privacy guarantees using theprivacy loss distribution formalism has been shown to give tighter bounds than R\’enyi accountants when applied to compositions of homogeneous mechanisms . This approach is also applicable to certain discretemechanisms that cannot be analysed with R\’ENI accountants .…

## No Regret Algorithms for Private Gaussian Process Bandit Optimization

The widespread proliferation of data-driven decision-making has ushered in interest in the design of privacy-preserving algorithms . We propose a solution for differentially private GP bandit optimization that combines a uniform kernelapproximator with random perturbations . For twospecific DP settings – joint and local differential privacy, we provide algorithms based on efficient quadrature Fourier feature approximators that are computationally efficient and provably no-regret for popular stationary kernel functions .…