Confidence Aware Scheduled Sampling for Neural Machine Translation

Scheduled sampling is an effective method to alleviate the exposure biasproblem of neural machine translation . It simulates the inference scene by replacing ground-truth target input tokens with predicted ones during training . Despite its success, its critical schedule strategies are merelybased on training steps, ignoring the real-time model competence .…

Conservative DG Method for the Micro Macro Decomposition of the Vlasov Poisson Lenard Bernstein Model

The micro-macro (mM) decomposition approach is considered for the numericalsolution of the Vlasov–Poisson–Lenard–Bernstein (VPLB) system . In the mM approach, the kineticdistribution function is decomposed as $f=\mathcal{E}[\boldsymbol{\rho}_{f]]$ and $g$, the microscopicdistribution, is defined such that $g$ is defined . We aim to design numerical methods for the .…

High dimensional expansion implies amplified local testability

In this work we show that high dimensional expansion implies locally testablecode . We define a notion that we callhigh-dimensional-expanding-system (HDE-system) We show that a code that can be modelled over HDE-system islocally testable . This implies that high-dimensional expansion phenomenon solely implies local testability of codes .…

Type based Enforcement of Infinitary Trace Properties for Java

A common approach to improve software quality is to use programming guidelines to avoid common kinds of errors . In this paper, we consider theproblem of enforcing guidelines for Featherweight Java (FJ) We formalize guidelines as sets of finite or infinite execution traces and develop aregion-based type and effect system for FJ that can enforce such guidelines .…

The Traveling Firefighter Problem

The objective is to schedule a destination visit sequence for a traveler of unit speed to minimizethe Minkowski $p$-norm of the resulting vector of visit/service times . For $p =\infty$ the problem becomes a path variant of the TSP . We also study the all-norm-TSP problem [Golovin et al.…

Improving Blockchain Consistency by Assigning Weights to Random Blocks

Blockchains based on the Nakamoto consensus protocol have shown promise in several applications, including cryptocurrencies . However, these blockchains have inherent scalability limits caused by the protocol’s consensusproperties . This paper proposes a novel method, Ironclad,that improves blockchain consistency by assigning different weights to randomlyselected blocks .…

Multi Stream Transformers

Transformer-based encoder-decoder models produce a fused token-wiserepresentation after every encoder layer . We investigate the effects of allowing the encoder to preserve and explore alternative hypotheses, combined at the end of the encoding process . We design and examine a $\textit{Multi-stream Transformer}$ architecture and find that splitting theTransformer encoder into multiple encoder streams and allowing the model to merge multiple representational hypotheses improves performance .…

Abstract Reasoning via Logic guided Generation

Abstract reasoning, i.e., inferring complicated patterns from givenobservations, is a central building block of artificial general intelligence . We propose logic-guided generation (LoGe), a novelgenerative DNN framework that reduces abstract reasoning as an optimization problem in propositional logic . LoGe is composed of three steps: extractpropositional variables from images, reason the answer variables with a logiclayer, and reconstruct the answer image from the variables .…

Typing assumptions improve identification in causal discovery

Under assumptions about the data-generative process, the causal graph can often be identified up to anequivalence class . Proposing new realistic assumptions to circumscribe suchequivalence classes is an active field of research . In this work, we propose anew set of assumptions that constrain possible causal relationships based on the nature of the variables .…

Learning Sparse Fixed Structure Gaussian Bayesian Networks

Gaussian Bayesian networks are widely used to model causal interactions among continuous variables . In this work, we study the problem of learning a fixed-structureGaussianBayesian network up to a bounded error in total variation distance . We show that the commonly used node-wise least squares regression (LeastSquares) has a near-optimal sample complexity .…

Privileged Information for Modeling Affect In The Wild

A key challenge of affective computing research is discovering ways to transfer affect models that are built in the laboratory to real world settings . The existing gap between in vitro and in vivo applications is mainly caused by limitations related to affect sensing including intrusiveness, hardware malfunctions, availability of sensors, privacy and security .…

MFGNet Dynamic Modality Aware Filter Generation for RGB T Tracking

MFGNet aims to boost the message communication between visible and thermaldata by adaptively adjusting the convolutional kernels for various input images . To address issues caused by heavy occlusion, fast motion, and out-of-view, we propose to conduct a joint local and global search byexploiting a new direction-aware target-driven attention mechanism .…

Qanaat A Scalable Multi Enterprise Permissioned Blockchain System with Confidentiality Guarantees

Qanaat is a scalablemulti-enterprise permissioned blockchain system that guaranteesconfidentiality . The experimental results reveal the efficiency of QanaAT in processing multi-shard and multi-enterprisetransactions . QanaAt consists of multiple enterprises where each enterprisepartitions its data into multiple shards and replicates a data shard on a cluster of nodes to provide fault tolerance .…

Fundamental Constructs in Programming Languages

PLanCompS project has developed an initial collection of funcons for translation of functional and imperative languages . Thebehaviour of each funcon is defined, once and for all, using a modular variantof structural operational semantics . The definitions are available online .…

Improving Polyphonic Sound Event Detection on Multichannel Recordings with the Sørensen Dice Coefficient Loss and Transfer Learning

The S.rensen–Dice Coefficient has recently seen rising popularity as aloss function (also known as Dice loss) due to its robustness in tasks where the number of negative samples significantly exceeds that of positive samples . Conventional training of polyphonic sound event detection systemswith binary cross-entropy loss often results in suboptimal detectionperformance as the training is often overwhelmed by updates from negativesamples .…

Evaluation of contextual embeddings on less resourced languages

The current dominance of deep neural networks in natural language processing is based on contextual embeddings such as ELMo, BERT, and BERT derivatives . Most existing work focuses on English; in contrast, we present here the firstmultilingual empirical comparison of two ELMo and several monolingual and multilingual BERT models using 14 tasks in nine languages .…

CNN based Realized Covariance Matrix Forecasting

Most of the models available in the literaturedepend on strong structural assumptions and they often suffer from the curse ofdimensionality . We propose an end-to-end trainable model built on the CNN andConvolutional LSTM (ConvLSTM) The proposed model focuses on local structures andspatiotemporal correlations .…

Reproducibility of COVID 19 pre prints

To examine reproducibility of COVID-19 research, we create a dataset of pre-prints posted to arXiv, bioRxiv, medRxv, and SocArXiv . We extract the text from these pre-prints and parse them looking for keywords signalling theavailability of the data and code underpinning the pre-printed .…

Fourier Reflexive Partitions Induced by Poset Metric

Let $H$ be the cartesian product of a family of finite abeliangroups indexed by a finite set . A given poset (i.e., partially orderedset) gives rise to a posetmetric on a given set . We prove that if$P} is Fourier-reflexive, then its dualpartition $Lambda$ coincides with the partition of the dual poset of $\mathbf{H$ .…

Fourier growth of structured mathbb F _2 polynomials and applications

We analyze the Fourier growth of various well-studied classes of “structured”$\mathbb{F}_2$-polynomials . This study is motivated by applications inpseudorandomness, in particular recent results and conjectures due to[CHHL19,CHLT19,CGLSS20] We show that any symmetric degree-$d$ $p$ has $L_1$ Fourier weight at level $k$ and this is tight for any constant $k$.…

DeltaCharger Charging Robot with Inverted Delta Mechanism and CNN driven High Fidelity Tactile Perception for Precise 3D Positioning

DeltaCharger is a novel charging robot with an Inverted Delta structure for 3D positioning of electrodes to achieve robust and safe transferring energy . The embedded high-fidelity tactile sensors allow toestimate the angular, vertical and horizontal misalignments between electrodeson the charger mechanism and electrodes on the target robot using pressure data .…

How many Fourier coefficients are needed

We are looking at families of functions or measures on the torus (indimension one and two) which are specified by a finite number of parameters$N$. The task, for a given family, is to look at a small number of Fouriercoefficients of the object, at a set of locations that is predetermined and maydepend only on $N$ and determine the object .…

LES3 Learning based Exact Set Similarity Search

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search . Past approaches on setsimilarity search utilize either heavy indexing structures, incurring largesearch costs or indexes that produce large candidate sets .…

MPIs Language Bindings are Holding MPI Back

C++ has been adopted as a major HPC language, displacing C to a large extent, and Fortran to some degree as well . MPIs syntax and semantics are defined and extended with C and F91 interfaces that align with the capabilities and limitations of C89 and F77 .…

Shedding some light on Light Up with Artificial Intelligence

The Light-Up puzzle, also known as the AKARI puzzle, has never been solved using modern artificial intelligence (AI) methods . This project is an effort to apply new AI techniques for solving the Light-up puzzle faster and more computationallyefficient . The algorithms explored for producing optimal solutions include hillclimbing, simulated annealing, feed-forward neural network (FNN), and CNN, and an evolutionary theory algorithm .…

Reproducibility of COVID 19 pre prints

To examine reproducibility of COVID-19 research, we create a dataset of pre-prints posted to arXiv, bioRxiv, medRxv, and SocArXiv . We extract the text from these pre-prints and parse them looking for keywords signalling theavailability of the data and code underpinning the pre-printed .…

Randomized Online Algorithms for Adwords

The general adwords problem has remained largely unresolved . We define asubcase called $k-TYPICAL, $k \in \Zplus$ as follows: the total budgetof all the bidders is sufficient to buy $k$ bids for each bidder . We also giverandomized online algorithms for other special cases of adwords .…

Dynamic Cantor Derivative Logic

$d-logics have not previously been studied in the framework of dynamical systems, which are pairs of a topological space $X$ equipped with a continuous function$f\colon X\to X$. We introduce the logics $wK4C$ and $GLC$ and show that they all have the finite Kripke model property and are sound and complete withrespect to the $d$-semantics in this dynamical setting .…