October 2020 arXiv papers — page 42

Yue Zhou, Xinwei Feng, Jiongmin Yong

For Hamilton-Jacobi-Bellman (HJB) equations, with the standard definitions of viscosity super-solution and sub-solution, it is known that there is a comparison between any (viscosity) super-solutions and sub-solutions. This should be the same for HJB type quasi-variational inequalities (QVIs) arising from optimal impulse control problems. However, according

Ji Hyun Nam, Eric Brandt, Sebastian Bauer, Xiaochun Liu

Non-Line-of-Sight (NLOS) imaging aims at recovering the 3D geometry of objects that are hidden from the direct line of sight. In the past, this method has suffered from the weak available multibounce signal limiting scene size, capture speed, and reconstruction quality. While algorithms capable of reconstructing scenes at several frames per second have been

Khanh Q. Nguyen

This research is to assess cryptocurrencies with the conditional beta, compared with prior studies based on unconditional beta or fixed beta. It is a new approach to building a pricing model for cryptocurrencies. Therefore, we expect that the use of conditional beta will increase the explanatory ability of factors in previous pricing models. Besides, this re

Vladimir Rabinovich

We consider operators of boundary value problems for 3D- Dirac operators in unbounded domains with the uniformly regular boundary. We give effective conditions of self-adjointness of operators under consideration and a description of their essential spectra. We also give applications to operators of the MIT bag problems for unbounded domains

Henry Zhou, Alexei Baevski, Michael Auli

Neural latent variable models enable the discovery of interesting structure in speech audio data. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. Our study compares the representations learned by vq-vae and vq-wav2vec in terms of sub-word unit discovery an

Wenshao Zhong, Chen Chen, Xingbo Wu, Song Jiang

LSM-tree based key-value (KV) stores organize data in a multi-level structure for high-speed writes. Range queries on traditional LSM-trees must seek and sort-merge data from multiple table files on the fly, which is expensive and often leads to mediocre read performance. To improve range query efficiency on LSM-trees, we introduce a space-efficient KV index

Hang Li, Wenbiao Ding, Zhongqin Wu, Zitao Liu

Speech emotion recognition is a challenging task because the emotion expression is complex, multimodal and fine-grained. In this paper, we propose a novel multimodal deep learning approach to perform fine-grained emotion recognition from real-life speeches. We design a temporal alignment mean-max pooling mechanism to capture the subtle and fine-grained emoti

Sen Dai, Sunil A. Bhave, Renyuan Wang

We have designed, fabricated, and characterized magnetostatic wave (MSW) resonators on a chip. The resonators are fabricated by patterning single-crystal yttrium iron garnet (YIG) film on a gadolinium gallium garnet (GGG) substrate and excited by loop-inductor transducers. We achieved this technology breakthrough by developing a YIG film etching process and

Weiqing Wang, Danwei Cai, Xiaoyi Qin, Ming Li

In this paper, we present the system submission for the VoxCeleb Speaker Recognition Challenge 2020 (VoxSRC-20) by the DKU-DukeECE team. For track 1, we explore various kinds of state-of-the-art front-end extractors with different pooling layers and objective loss functions. For track 3, we employ an iterative framework for self-supervised speaker representa

Gustavo Aguilar, Bryan McCann, Tong Niu, Nazneen Rajani

Byte-pair encoding (BPE) is a ubiquitous algorithm in the subword tokenization process of language models as it provides multiple benefits. However, this process is solely based on pre-training data statistics, making it hard for the tokenizer to handle infrequent spellings. On the other hand, though robust to misspellings, pure character-level models often

Adina Williams, Tristan Thrush, Douwe Kiela

We perform an in-depth error analysis of Adversarial NLI (ANLI), a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected over multiple rounds. We propose a fine-grained annotation scheme of the different aspects of inference that are responsible for the gold classification labels, and use it to hand-code all

Ying Mao, Weifeng Yan, Yun Song, Yue Zeng

With the prevalence of big-data-driven applications, such as face recognition on smartphones and tailored recommendations from Google Ads, we are on the road to a lifestyle with significantly more intelligence than ever before. Various neural network powered models are running at the back end of their intelligence to enable quick responses to users. Supporti

Allan L. Alinea

The set of polarizing force-identification (PFI) questions in the FCI consists of six items all basically asking only one question: the set of forces acting on a given body. Although it may sound trivial, these questions are among the most challenging in the FCI. In this work involving 163 students, we investigate the correlation between student performance

Karishma Patnaik, Shatadal Mishra, Seyed Mostafa Rezayat Sorkhabadi, Wenlong Zhang

This paper presents the design and control of a novel quadrotor with a variable geometry to physically interact with cluttered environments and fly through narrow gaps and passageways. This compliant quadrotor with passive morphing capabilities is designed using torsional springs at every arm hinge to allow for rotation driven by external forces. We derive t

Peter Shaw, Ming-Wei Chang, Panupong Pasupat, Kristina Toutanova

Sequence-to-sequence models excel at handling natural language variation, but have been shown to struggle with out-of-distribution compositional generalization. This has motivated new specialized architectures with stronger compositional biases, but most of these approaches have only been evaluated on synthetically-generated datasets, which are not represent

Yang Zuo, Javier E. Lopez, Thomas W. Smith, Cameron C. Foster

Myocardial blood flow (MBF) and flow reserve are usually quantified in the clinic with positron emission tomography (PET) using a perfusion-specific radiotracer (e.g. 82Rbchloride). However, the clinical accessibility of existing perfusion tracers remains limited. Meanwhile, 18F-fluorodeoxyglucose (FDG) is a commonly used radiotracer for PET metabolic imagin

Yuning Mao, Xiang Ren, Heng Ji, Jiawei Han

Despite significant progress, state-of-the-art abstractive summarization methods are still prone to hallucinate content inconsistent with the source document. In this paper, we propose Constrained Abstractive Summarization (CAS), a general setup that preserves the factual consistency of abstractive summarization by specifying tokens as constraints that must

David Arnas, Richard Linares

This work introduces a new set of orbital elements to fully represent the zonal harmonics problem around an oblate celestial body. This new set of orbital elements allows to obtain a complete linear system for the unperturbed problem and, in addition, a complete polynomial system when considering the perturbation produced by the zonal harmonics from the grav

Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur

Ensembling is now recognized as an effective approach for increasing the predictive performance and calibration of deep networks. We introduce a new approach, Parameter Ensembling by Perturbation (PEP), that constructs an ensemble of parameter values as random perturbations of the optimal parameter set from training by a Gaussian with a single variance param

Mattheus Aguiar, Pavel Zalesski

Let $(\mathcal{G},\Gamma)$ be an abstract graph of finite groups. If $\Gamma$ is finite, we can construct a profinite graph of groups in a natural way $(\hat{\mathcal{G}},\Gamma)$, where $\hat{\mathcal{G}}(m)$ is the profinite completion of $\mathcal{G}(m)$ for all $m \in \Gamma$. The main reason for this is that $\Gamma$ is finite, so it is already profinit

Falcon Z. Dai

Being inspired by the success of \texttt{word2vec} \citep{mikolov2013distributed} in capturing analogies, we study the conjecture that analogical relations can be represented by vector spaces. Unlike many previous works that focus on the distributional semantic aspect of \texttt{word2vec}, we study the purely \emph{representational} question: can \emph{all}

Tanmay Gangwani, Yuan Zhou, Jian Peng

Long-term temporal credit assignment is an important challenge in deep reinforcement learning (RL). It refers to the ability of the agent to attribute actions to consequences that may occur after a long time interval. Existing policy-gradient and Q-learning algorithms typically rely on dense environmental rewards that provide rich short-term supervision and

Isura Nirmal, Abdelwahed Khamis, Mahbub Hassan, Wen Hu

While decade-long research has clearly demonstrated the vast potential of radio frequency (RF) for many human sensing tasks, scaling this technology to large scenarios remained problematic with conventional approaches. Recently, researchers have successfully applied deep learning to take radio-based sensing to a new level. Many different types of deep learni

Luis E. Padilla, Tanja Rindler-Daller, Paul R. Shapiro, Tonatiuh Matos

Scalar-field dark matter (SFDM) halos exhibit a core-envelope structure with soliton-like cores and CDM-like envelopes. Simulations without self-interaction (free-field case) report a core-halo mass relation $M_c\propto M_{h}^{\beta}$, with either $\beta=1/3$ or $\beta=5/9$, which can be understood if core and halo obey certain energy or velocity scalings. W

Jagadeesh Balam, Jocelyn Huang, Vitaly Lavrukhin, Slyne Deng

We present our experiments in training robust to noise an end-to-end automatic speech recognition (ASR) model using intensive data augmentation. We explore the efficacy of fine-tuning a pre-trained model to improve noise robustness, and we find it to be a very efficient way to train for various noisy conditions, especially when the conditions in which the mo

A. C. Aguilar, M. N. Ferreira, J. Papavassiliou

We present a novel method for computing the nonperturbative kinetic term of the gluon propagator from an exactly solvable ordinary differential equation, whose origin is the fundamental Slavnov-Taylor identity satisfied by the three-gluon vertex, evaluated in a special kinematic limit. The main ingredients comprising the solution are a well-known projection

Ashutosh Pandey, DeLiang Wang

We propose a dual-path self-attention recurrent neural network (DP-SARNN) for time-domain speech enhancement. We improve dual-path RNN (DP-RNN) by augmenting inter-chunk and intra-chunk RNN with a recently proposed efficient attention mechanism. The combination of inter-chunk and intra-chunk attention improves the attention mechanism for long sequences of sp

Shuguang Chen, Gustavo Aguilar, Leonardo Neves, Thamar Solorio

Multimodal named entity recognition (MNER) requires to bridge the gap between language understanding and visual context. While many multimodal neural techniques have been proposed to incorporate images into the MNER task, the model's ability to leverage multimodal interactions remains poorly understood. In this work, we conduct in-depth analyses of existing

Poorya Mianjy, Raman Arora

We study dropout in two-layer neural networks with rectified linear unit (ReLU) activations. Under mild overparametrization and assuming that the limiting kernel can separate the data distribution with a positive margin, we show that dropout training with logistic loss achieves $\epsilon$-suboptimality in test error in $O(1/\epsilon)$ iterations.

Debajyoti Datta, Maria Phillips, Jennifer Chiu, Ginger S. Watson

Machine learning techniques applied to the Natural Language Processing (NLP) component of conversational agent development show promising results for improved accuracy and quality of feedback that a conversational agent can provide. The effort required to develop an educational scenario specific conversational agent is time consuming as it requires domain ex

Sergio L. L. M. Ramos, Marcos A. Pimenta, Ana Champi

The double-resonance (DR) Raman process is a signature of all sp2 carbon material and provide fundamental information of the electronic structure and phonon dispersion in graphene, carbon nanotubes and different graphite-type materials. We have performed in this work the study of different DR Raman bands of rhombohedral graphite using five different excitati

F. L. Rommel, F. Braga-Ribas, J. Desmars, J. I. B. Camargo

Trans-Neptunian objects (TNOs) and Centaurs are remnants of our planetary system formation, and their physical properties have invaluable information for evolutionary theories. Stellar occultation is a ground-based method for studying these small bodies and has presented exciting results. These observations can provide precise profiles of the involved body,

Dorottya Demszky, Devyani Sharma, Jonathan H. Clark, Vinodkumar Prabhakaran

Building NLP systems that serve everyone requires accounting for dialect differences. But dialects are not monolithic entities: rather, distinctions between and within dialects are captured by the presence, absence, and frequency of dozens of dialect features in speech and text, such as the deletion of the copula in "He {} running". In this paper, we introdu

Deepak. K. Agrawal, Bradford J. Smith, Peter D. Sottile, David J. Albers

The acute respiratory distress syndrome (ARDS) is characterized by the acute development of diffuse alveolar damage (DAD) resulting in increased vascular permeability and decreased alveolar gas exchange. Mechanical ventilation is a potentially lifesaving intervention to improve oxygen exchange but has the potential to cause ventilator-induced lung injury (VI

Mohsen Soltanifar, Michael Escobar, Annie Dupuis, Russell Schachar

The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) as a measurement of the latency of the unobservable stopping process has been modeled with a nonparametric method by Hans Colonius (1990) and with a Bayesian parametric method by Eric-Jan Wagenmakers and colleagues (2012). These methods assume equal impact of the prece

Ashkan Vakil, Farzad Niknia, Ali Mirzaeian, Avesta Sasan

With the outsourcing of design flow, ensuring the security and trustworthiness of integrated circuits has become more challenging. Among the security threats, IC counterfeiting and recycled ICs have received a lot of attention due to their inferior quality, and in turn, their negative impact on the reliability and security of the underlying devices. Detectin

Krishnan Raghavan, Prasanna Balaprakash, Alessandro Lovato, Noemi Rocco

A microscopic description of the interaction of atomic nuclei with external electroweak probes is required for elucidating aspects of short-range nuclear dynamics and for the correct interpretation of neutrino oscillation experiments. Nuclear quantum Monte Carlo methods infer the nuclear electroweak response functions from their Laplace transforms. Inverting

Juan Carlos Seck-Tuoh-Mora, Nayeli J. Escamilla-Serna, Joselito Medina-Marin, Norberto Hernandez-Romero

The Flexible Job Shop Scheduling Problem (FJSP) is a combinatorial problem that continues to be studied extensively due to its practical implications in manufacturing systems and emerging new variants, in order to model and optimize more complex situations that reflect the current needs of the industry better. This work presents a new meta-heuristic algorith

Sara C. Billey, Joshua P. Swanson

In earlier work, Billey--Konvalinka--Swanson studied the asymptotic distribution of the coefficients of Stanley's $q$-hook length formula, or equivalently the major index on standard tableaux of straight shape and certain skew shapes. We extend those investigations to Stanley's $q$-hook-content formula related to semistandard tableaux and $q$-hook length for

Jiajie Chen

It is conjectured that the generalization of the Constantin-Lax-Majda model (gCLM) $\omega_t + a u\omega_x = u_x \omega$ due to Okamoto, Sakajo and Wunsch can develop a finite time singularity from smooth initial data for $a < 1$. For the endpoint case where $a$ is close to and less than $1$, we prove finite time asymptotically self-similar blowup of gCLM on

Stefan Grünewald, Annemarie Friedrich, Jonas Kuhn

The introduction of pre-trained transformer-based contextualized word embeddings has led to considerable improvements in the accuracy of graph-based parsers for frameworks such as Universal Dependencies (UD). However, previous works differ in various dimensions, including their choice of pre-trained language models and whether they use LSTM layers. With the

Gideon Stein, Andrey Filchenkov, Arip Asadulaev

Since the publication of the original Transformer architecture (Vaswani et al. 2017), Transformers revolutionized the field of Natural Language Processing. This, mainly due to their ability to understand timely dependencies better than competing RNN-based architectures. Surprisingly, this architecture change does not affect the field of Reinforcement Learnin

Vivek Miglani, Narine Kokhlikyan, Bilal Alsallakh, Miguel Martin

Integrated Gradients has become a popular method for post-hoc model interpretability. De-spite its popularity, the composition and relative impact of different regions of the integral path are not well understood. We explore these effects and find that gradients in saturated regions of this path, where model output changes minimally, contribute disproportion

Xiaotian Zheng, Athanasios Kottas, Bruno Sansó

Mixture transition distribution time series models build high-order dependence through a weighted combination of first-order transition densities for each one of a specified number of lags. We present a framework to construct stationary transition mixture distribution models that extend beyond linear, Gaussian dynamics. We study conditions for first-order st

Leif Andersen, Michael Ballantyne, Matthias Felleisen

Many programming problems call for turning geometrical thoughts into code: tables, hierarchical structures, nests of objects, trees, forests, graphs, and so on. Linear text does not do justice to such thoughts. But, it has been the dominant programming medium for the past and will remain so for the foreseeable future. This paper proposes a novel mechanism fo

Sayali Kulkarni, Sheide Chammas, Wan Zhu, Fei Sha

Summarization is the task of compressing source document(s) into coherent and succinct passages. This is a valuable tool to present users with concise and accurate sketch of the top ranked documents related to their queries. Query-based multi-document summarization (qMDS) addresses this pervasive need, but the research is severely limited due to lack of trai

Nadezhda Chirkova

Source code processing heavily relies on the methods widely used in natural language processing (NLP), but involves specifics that need to be taken into account to achieve higher quality. An example of this specificity is that the semantics of a variable is defined not only by its name but also by the contexts in which the variable occurs. In this work, we d

Saurabh Kataria, Shi-Xiong Zhang, Dong Yu

To improve speaker verification in real scenarios with interference speakers, noise, and reverberation, we propose to bring together advancements made in multi-channel speech features. Specifically, we combine spectral, spatial, and directional features, which includes inter-channel phase difference, multi-channel sinc convolutions, directional power ratio f

Debasish Chakroborti

In this thesis first we propose an intermediate data management scheme for a SWfMS. In our second attempt, we explored the possibilities and introduced an automatic recommendation technique for a SWfMS from real-world workflow data (i.e Galaxy [1] workflows) where our investigations show that the proposed technique can facilitate 51% of workflow building in

Wenrui Zhang, Peng Li

As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two problems. 1. Due to a lack of architectural guidance, random recurrent connectivity is often adopted, which does

Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour

In this paper, we introduce a generalization of graphlets to heterogeneous networks called typed graphlets. Informally, typed graphlets are small typed induced subgraphs. Typed graphlets generalize graphlets to rich heterogeneous networks as they explicitly capture the higher-order typed connectivity patterns in such networks. To address this problem, we des

Jiawei Yang, Jeffrey M. Hausdorff

Physiologic signals have properties across multiple spatial and temporal scales, which can be shown by the complexity-analysis of the coarse-grained physiologic signals by scaling techniques such as the multiscale. Unfortunately, the results obtained from the coarse-grained signals by the multiscale may not fully reflect the properties of the original signal

Ugo Dal Lago, Claudia Faggian, Simona Ronchi Della Rocca

Randomized higher-order computation can be seen as being captured by a lambda calculus endowed with a single algebraic operation, namely a construct for binary probabilistic choice. What matters about such computations is the probability of obtaining any given result, rather than the possibility or the necessity of obtaining it, like in (non)deterministic co

Oshin Agarwal, Heming Ge, Siamak Shakeri, Rami Al-Rfou

Prior work on Data-To-Text Generation, the task of converting knowledge graph (KG) triples into natural text, focused on domain-specific benchmark datasets. In this paper, however, we verbalize the entire English Wikidata KG, and discuss the unique challenges associated with a broad, open-domain, large-scale verbalization. We further show that verbalizing a

Bijan Mazaheri, Siddharth Jain, Jehoshua Bruck

Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this often rely on estimating the ratio of training and target probability density functions. These techniques require parameter tuning and can be unstable across different datasets. We pr

František Farka, Aleksandar Nanevski, Anindya Banerjee, Germán Andrés Delbianco

Concurrent separation logic is distinguished by transfer of state ownership upon parallel composition and framing. The algebraic structure that underpins ownership transfer is that of partial commutative monoids (PCMs). Extant research considers ownership transfer primarily from the logical perspective while comparatively less attention is drawn to the algeb

Aritra De, Rafid Mahbub

We present a complete numerical treatment of inflationary dynamics under the influence of stochastic corrections from sub-Hubble modes. We discuss how to exactly model the stochastic noise terms arising from the sub-Hubble quantum modes that give rise to the coarse-grained inflaton dynamics in the form of stochastic differential equations. The stochastic dif

Valentin Hofmann, Janet B. Pierrehumbert, Hinrich Schütze

Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and extralinguistic

Jyun-Yu Jiang, Chenyan Xiong, Chia-Jung Lee, Wei Wang

The computing cost of transformer self-attention often necessitates breaking long documents to fit in pretrained models in document ranking tasks. In this paper, we design Query-Directed Sparse attention that induces IR-axiomatic structures in transformer self-attention. Our model, QDS-Transformer, enforces the principle properties desired in ranking: local

Mehmet Aygün, Zorah Lähner, Daniel Cremers

In this work, we propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework. Instead of depending on ground-truth correspondences or the computationally expensive geodesic distances, we use heat kernels. These can be computed quickly during training as the supervisor signal. Moreover, we prop

Natraj Raman, Armineh Nourbakhsh, Sameena Shah, Manuela Veloso

Task specific fine-tuning of a pre-trained neural language model using a custom softmax output layer is the de facto approach of late when dealing with document classification problems. This technique is not adequate when labeled examples are not available at training time and when the metadata artifacts in a document must be exploited. We address these chal

Luca Riz, Francesco Pederiva, Diego Lonardoni, Stefano Gandolfi

The spin susceptibility in pure neutron matter is computed from auxiliary field diffusion Monte Carlo calculations over a wide range of densities. The calculations are performed for different spin asymmetries, while using twist-averaged boundary conditions to reduce finite-size effects. The employed nuclear interactions include both the phenomenological Argo

Pierfrancesco Alaimo Di Loro, Fabio Divino, Alessio Farcomeni, Giovanna Jona Lasinio

A novel parametric regression model is proposed to fit incidence data typically collected during epidemics. The proposal is motivated by real-time monitoring and short-term forecasting of the main epidemiological indicators within the first outbreak of COVID-19 in Italy. Accurate short-term predictions, including the potential effect of exogenous or external

Mu-Tao Wang

I shall discuss the Chen-Wang-Yau quasilocal angular momentum, which is defined based on the theory of optimal isometric embedding and quasilocal mass of Wang-Yau, and the limits of which at spatial and null infinity of an isolated gravitating system. This is based on joint work with Po-Ning Chen, Jordan Keller, Ye-Kai Wang, and Shing-Tung Yau.

Aldo Figallo-Orellano, Juan Sebastian Slagter

MV-algebras are an algebraic semantics for Lukasiewicz logic and MV-algebras generated by a finite chain are Heyting algebras where the Godel implication can be written in terms of De Morgan and Moisil's modal operators. In our work, a fragment of trivalent Lukasiewicz logic is studied. The propositional and first-order logic is presented. The maximal consis

Jieyi Lu, Baihong Jin

High-resolution data are desired in many data-driven applications; however, in many cases only data whose resolution is lower than expected are available due to various reasons. It is then a challenge how to obtain as much useful information as possible from the low-resolution data. In this paper, we target interval energy data collected by Advanced Metering

Mu-Tao Wang

The mathematical theory of isometric embedding is applied to study the notion of quasilocal mass in general relativity. In particular, I shall report some recent progress of quasilocal mass with reference to a cosmological spacetime, such as the de Sitter or the Anti-de Sitter spacetime, or a blackhole spacetime, such as the Schwarzschild spacetime. This art

Clarice D. Aiello, D. D. Awschalom, Hannes Bernien, Tina Brower-Thomas

Interest in building dedicated Quantum Information Science and Engineering (QISE) education programs has greatly expanded in recent years. These programs are inherently convergent, complex, often resource intensive and likely require collaboration with a broad variety of stakeholders. In order to address this combination of challenges, we have captured ideas

Chunchuan Lyu, Shay B. Cohen, Ivan Titov

Abstract Meaning Representations (AMR) are a broad-coverage semantic formalism which represents sentence meaning as a directed acyclic graph. To train most AMR parsers, one needs to segment the graph into subgraphs and align each such subgraph to a word in a sentence; this is normally done at preprocessing, relying on hand-crafted rules. In contrast, we trea

Daniel M. Zuckerman, John D. Russo

Despite the importance of non-equilibrium statistical mechanics in modern physics and related fields, the topic is often omitted from undergraduate and core-graduate curricula. Key aspects of non-equilibrium physics, however, can be understood with a minimum of formalism based on a rigorous trajectory picture. The fundamental object is the ensemble of trajec

David Gaddy, Alex Kouzemtchenko, Pavankumar Reddy Muddireddy, Prateek Kolhar

In this paper, we explore how to use a small amount of new data to update a task-oriented semantic parsing model when the desired output for some examples has changed. When making updates in this way, one potential problem that arises is the presence of conflicting data, or out-of-date labels in the original training set. To evaluate the impact of this under

Thomas Schoegje, Chris Kamphuis, Koen Dercksen, Djoerd Hiemstra

We explore how to generate effective queries based on search tasks. Our approach has three main steps: 1) identify search tasks based on research goals, 2) manually classify search queries according to those tasks, and 3) compare three methods to improve search rankings based on the task context. The most promising approach is based on expanding the user's q

Liang Lu, Zhong Meng, Naoyuki Kanda, Jinyu Li

Hybrid Autoregressive Transducer (HAT) is a recently proposed end-to-end acoustic model that extends the standard Recurrent Neural Network Transducer (RNN-T) for the purpose of the external language model (LM) fusion. In HAT, the blank probability and the label probability are estimated using two separate probability distributions, which provides a more accu

Camila Riccio, Jorge Finke, Camilo Rocha

This paper proposes a workflow to identify genes that respond to specific treatments in plants. The workflow takes as input the RNA sequencing read counts and phenotypical data of different genotypes, measured under control and treatment conditions. It outputs a reduced group of genes marked as relevant for treatment response. Technically, the proposed appro

Davoud Hejazi, Renda Tan, Neda Kari Rezapour, Mehrnaz Mojtabavi

Developing techniques for high-quality synthesis of mono and few-layered 2D materials with lowered complexity and cost continues to remain an important goal, both for accelerating fundamental research and for applications development. We present the simplest conceivable technique to synthesize micrometer-scale single-crystal triangular monolayers of MoS2, i.

Maria Manolopoulou, Ben Hoyle, Robert G. Mann, Martin Sahlen

Galaxy clusters are widely used to constrain cosmological parameters through their properties, such as masses, luminosity and temperature distributions. One should take into account all kind of biases that could affect these analyses in order to obtain reliable constraints. In this work, we study the difference in the properties of clusters residing in diffe

Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Djamila Aouada

We propose 3DBooSTeR, a novel method to recover a textured 3D body mesh from a textured partial 3D scan. With the advent of virtual and augmented reality, there is a demand for creating realistic and high-fidelity digital 3D human representations. However, 3D scanning systems can only capture the 3D human body shape up to some level of defects due to its com

Prasun Roy, Saumik Bhattacharya, Partha Pratim Roy, Umapada Pal

Sign language is a gesture-based symbolic communication medium among speech and hearing impaired people. It also serves as a communication bridge between non-impaired and impaired populations. Unfortunately, in most situations, a non-impaired person is not well conversant in such symbolic languages restricting the natural information flow between these two c

Sean Plummer, Shuang Zhou, Anirban Bhattacharya, David Dunson

Transformation-based methods have been an attractive approach in non-parametric inference for problems such as unconditional and conditional density estimation due to their unique hierarchical structure that models the data as flexible transformation of a set of common latent variables. More recently, transformation-based models have been used in variational

Jaan Parts

We present a tiling of more than 99.985698% of the Euclidean plane with six colors, reducing the previous record for uncovered fraction of the plane by about 12.8%. We also present a tiling of more than 95.99% of the plane with five colors. It is thus shown that any unit-distance graph of order at most 6992 and 24 in the plane can be properly 6-colored and 5

Amy Steele, John Debes, Siyi Xu, Sherry Yeh

Between 30 - 50% of white dwarfs (WDs) show heavy elements in their atmospheres. This "pollution" is thought to arise from the accretion of planetesimals perturbed by outer planet(s) to within the WD's tidal disruption radius. A small fraction of these WDs show either emission or absorption from circumstellar (C-S) gas. The abundances of metals in the photos

Yu. A. Simonov

Nucleon form factors play an especially important role in studying the dynamics of nucleons and explicit structure of the wave functions at arbitrary nucleon velocity. The purpose of the paper is to explain theoretically all four nucleon form factors measured experimentally in the cross section measurements (by the Rosenbluth method), yielding almost equal n

Jaan Parts

We introduce a new graph minimization method, in which it is required to preserve some graph property and there is an effective procedure for checking this property. We applied this method to minimize 5-chromatic unit-distance graphs and obtained a graph with 509 vertices and 2442 edges.

Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues

Generalization error bounds are critical to understanding the performance of machine learning models. In this work, we propose a new information-theoretic based generalization error upper bound applicable to supervised learning scenarios. We show that our general bound can specialize in various previous bounds. We also show that our general bound can be spec

Nadezhda Chirkova, Sergey Troshin

There is an emerging interest in the application of natural language processing models to source code processing tasks. One of the major problems in applying deep learning to software engineering is that source code often contains a lot of rare identifiers, resulting in huge vocabularies. We propose a simple, yet effective method, based on identifier anonymi

Yunjie Zhang, Fei Tao, Xudong Liu, Runze Su

With the rising of short video apps, such as TikTok, Snapchat and Kwai, advertisement in short-term user-generated videos (UGVs) has become a trending form of advertising. Prediction of user behavior without specific user profile is required by advertisers, as they expect to acquire advertisement performance in advance in the scenario of cold start. Current

Jaan Parts

We present a new proof of the known fact that the chromatic number of the plane is at least 5. The main difference of this proof is that it can be verified manually without the help of the computer.

Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta

To guide behavior, the brain extracts relevant features from high-dimensional data streamed by sensory organs. Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals which provide context and task-relevant information. Here, adopting a normative approach, we model these instructive s

Cheng Zhang, Yicheng Sun, Hejia Chen, Jie Wang

This paper presents a novel approach to automatic generation of adequate distractors for a given question-answer pair (QAP) generated from a given article to form an adequate multiple-choice question (MCQ). Our method is a combination of part-of-speech tagging, named-entity tagging, semantic-role labeling, regular expressions, domain knowledge bases, word em

J. B. Lovell, M. C. Wyatt, M. Ansdell, M. Kama

Class III stars are those in star forming regions without large non-photospheric infrared emission, suggesting recent dispersal of their protoplanetary disks. We observed 30 class III stars in the 1-3 Myr Lupus region with ALMA at ${\sim}856\mu$m, resulting in 4 detections that we attribute to circumstellar dust. Inferred dust masses are $0.036{-}0.093M_\opl

Jaan Parts

We give a new, simple proof for the lower bound of the chromatic number of the Euclidean plane with two forbidden distances, based on a graph with only 16 vertices.

Stuart J. Thomson, Matthew Durey, Rodolfo R. Rosales

A discrete and periodic complex Ginzburg-Landau equation, coupled to a discrete mean equation, is systematically derived from a driven and dissipative oscillator model, close to the onset of a supercritical Hopf bifurcation. The oscillator model is inspired by recent experiments exploring active vibrations of quasi-one-dimensional lattices of self-propelled

Ivan Palaia, Igor M. Telles, Alexandre P. dos Santos, Emmanuel Trizac

We study the role of ionic correlations on the electroosmotic flow in planar double-slit channels, without salt. We propose an analytical theory, based on recent advances in the understanding of correlated systems. We compare the theory with mean-field results and validate it by means of dissipative particle dynamics simulations. Interestingly, for some surf

Nithin Rao Koluguri, Jason Li, Vitaly Lavrukhin, Boris Ginsburg

We propose SpeakerNet - a new neural architecture for speaker recognition and speaker verification tasks. It is composed of residual blocks with 1D depth-wise separable convolutions, batch-normalization, and ReLU layers. This architecture uses x-vector based statistics pooling layer to map variable-length utterances to a fixed-length embedding (q-vector). Sp

Fabrice Muhlenbach

Considering the use of artificial intelligence for greater personalization of patient care and better management of human and material resources may seem like an opportunity not to be missed. In order to offer a better humanization of the care pathway, artificial intelligence is a tool that decision-makers in the hospital sector must appropriate by taking ca

Mahdis Mahdieh, Mia Xu Chen, Yuan Cao, Orhan Firat

One challenge of machine translation is how to quickly adapt to unseen domains in face of surging events like COVID-19, in which case timely and accurate translation of in-domain information into multiple languages is critical but little parallel data is available yet. In this paper, we propose an approach that enables rapid domain adaptation from the perspe

Aurélien Alfonsi, Adel Cherchali, Jose Arturo Infante Acevedo

This paper studies the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. This problem arises naturally when considering many stress tests and appears in the calculation of the interest rate module of the standard formula for the SCR. We obtain theoretical convergence results that complements the recent work of Gil

Andreas Bugler, Bryan Pardo, Prem Seetharaman

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 separation system, such as rock and pop genres, many musical domains do not, such as classical music, choral music, and non-Western music tra

Aida Abiad, Gabriel Coutinho, Miquel Angel Fiol, Bruno Nogueira

The $k^{\text{th}}$ power of a graph $G=(V,E)$, $G^k$, is the graph whose vertex set is $V$ and in which two distinct vertices are adjacent if and only if their distance in $G$ is at most $k$. This article proves various eigenvalue bounds for the independence number and chromatic number of $G^k$ which purely depend on the spectrum of $G$, together with a met

Blair Chen, Liu Ziyin, Zihao Wang, Paul Pu Liang

It has been hypothesized that label smoothing can reduce overfitting and improve generalization, and current empirical evidence seems to corroborate these effects. However, there is a lack of mathematical understanding of when and why such empirical improvements occur. In this paper, as a step towards understanding why label smoothing is effective, we propos