May 2022 arXiv papers — page 144

Charles Shaw, Silvio Vanadia

How should we think of the preferences of citizens? Whereas self-optimal policy is relatively straightforward to produce, socially optimal policy often requires a more detailed examination. In this paper, we identify an issue that has received far too little attention in welfarist modelling of public policy, which we name the "hidden assumptions" pro

Gilles Carron, Ilaria Mondello, David Tewodrose

We prove that metric measure spaces obtained as limits of closed Riemannian manifolds with Ricci curvature satisfying a uniform Kato bound are rectifiable. In the case of a non-collapsing assumption and a strong Kato bound, we additionally show that for any $α\in (0,1)$ the regular part of the space lies in an open set with the structure of a $\mathcal{C}^α$

Linh Anh Nguyen

Simulations and bisimulations between two fuzzy automata over a complete residuated lattice were defined by Ćirić et al. (2012) as fuzzy relations between the sets of states of the automata. However, they act as a crisp relationship between the automata. In particular, if there exists a (forward) bisimulation between two fuzzy automata, then the fuzzy langua

Ryoma Sato

Word embeddings are one of the most fundamental technologies used in natural language processing. Existing word embeddings are high-dimensional and consume considerable computational resources. In this study, we propose WordTour, unsupervised one-dimensional word embeddings. To achieve the challenging goal, we propose a decomposition of the desiderata of wor

Seyed Hamed Hashemi, Jouni Mattila

This paper examines the global convergence problem of SLAM algorithms, an issue that faces topological obstructions. This is because the state-space of attitude dynamics is defined on a non-contractible manifold: the special orthogonal group of order three SO(3). Therefore, this paper presents a novel, gradient-based hybrid observer to overcome these topolog

Bishwas Mandal, George Amariucai, Shuangqing Wei

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the cat

Yin Yang, Jindi Wang, Yanping Chen, Hong-lin Liao

The convergence of variable-step L1 scheme is studied for the time-fractional molecular beam epitaxy (MBE) model with slope selection.A novel asymptotically compatible $L^2$ norm error estimate of the variable-step L1 scheme is established under a convergence-solvability-stability (CSS)-consistent time-step constraint. The CSS-consistent condition means that

Goran Vasiljevic, Tamara Petrovic, Barbara Arbanas, Stjepan Bogdan

In this paper, we present a dynamic median consensus protocol for multi-agent systems using acoustic communication. The motivating target scenario is a multi-agent system consisting of underwater robots acting as intelligent sensors, applied to continuous monitoring of the state of a marine environment. The proposed protocol allows each agent to track the me

Rakshit S. Kothari, Reynold J. Bailey, Christopher Kanan, Jeff B. Pelz

The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extract

Xiaodong Liu, Chen Li, Pengxin Liu, Qilong Guo

The generation mechanism of wall heat flux is one of the fundamental problems in supersonic/hypersonic turbulent boundary layers. A novel heat decomposition formula under the curvilinear coordinate was proposed in this paper. The new formula has wider application scope and can be applied in the configurations with grid deformed. The wall heat flux of an inte

Yang Cai, Jaime Llorca, Antonia M. Tulino, Andreas F. Molisch

Emerging Metaverse applications, designed to deliver highly interactive and immersive experiences that seamlessly blend physical reality and digital virtuality, are accelerating the need for distributed compute platforms with unprecedented storage, computation, and communication requirements. To this end, the integrated evolution of next-generation networks

I. Ceyhun Andaç, Benoît Cerutti, Guillaume Dubus, K. Yavuz Ekşi

Pulsars show irregularities in their pulsed radio emission that originate from propagation effects and the intrinsic activity of the source. In this work, we investigate the role played by magnetic reconnection and the formation of plasmoids in the pulsar wind current sheet as a possible source of intrinsic pulse-to-pulse variability in the incoherent, high-

Zhiyong Wu, Wei Bi, Xiang Li, Lingpeng Kong

We propose knowledge internalization (KI), which aims to complement the lexical knowledge into neural dialog models. Instead of further conditioning the knowledge-grounded dialog (KGD) models on externally retrieved knowledge, we seek to integrate knowledge about each input token internally into the model's parameters. To tackle the challenge due to the

M. Moranges, C. Rouby, M. Plantevit, M. Bensafi

Measuring emotions is a real challenge for fundamental and applied research, especially in ecological contexts. de Wijk and Noldus propose combining two types of measures-explicit to characterize a specific food, and implicit-physiological-to capture the whole experience of a meal in real-life situations. This raises several challenges including development

Jialun Cao, Meiziniu Li, Xiao Chen, Ming Wen

As Deep Learning (DL) systems are widely deployed for mission-critical applications, debugging such systems becomes essential. Most existing works identify and repair suspicious neurons on the trained Deep Neural Network (DNN), which, unfortunately, might be a detour. Specifically, several existing studies have reported that many unsatisfactory behaviors are

Clémentin Boittiaux, Ricard Marxer, Claire Dune, Aurélien Arnaubec

Some recent visual-based relocalization algorithms rely on deep learning methods to perform camera pose regression from image data. This paper focuses on the loss functions that embed the error between two poses to perform deep learning based camera pose regression. Existing loss functions are either difficult-to-tune multi-objective functions or present uns

Ph. Wilina, M. Shubhakanta Singh, N. Nimai Singh

The impact of renormalization group equations(RGEs) on neutrino masses and mixings at high energy scales in Minimal Supersymmetric Standard Model(MSSM) is studied using two different mixing patterns such as Tri-Bimaximal(TBM) mixing and Golden Ratio(GR) mixing in consistent with cosmological bound of the sum of three neutrino masses, $\sum _{i}|m_{i}|$. Magn

Anton Artemyev, Victor Reville, Ivan Zimovets, Yukitoshi Nishimura

Magnetic field line reconnection is a universal plasma process responsible for the magnetic field topology change and magnetic field energy dissipation into charged particle heating and acceleration. In many systems, the conditions leading to the magnetic reconnection are determined by the pre-reconnection configuration of a thin layer with intense currents

Si Suo, Chongpu Zhai, Minglong Xu, Marc Kamlah

We present a theoretical prediction on random close packing factor ϕ_RCP^b of binary granular packings based on the hard-sphere fluid theory. An unexplored regime is unravelled, where the packing fraction ϕ_RCP^b is smaller than that of the mono-sized one ϕ_RCP^m, i.e., the so-called loose jamming state. This is against our common perception that binary pack

Simon Fernandez, Maciej Korczyński, Andrzej Duda

Spam domains are sources of unsolicited mails and one of the primary vehicles for fraud and malicious activities such as phishing campaigns or malware distribution. Spam domain detection is a race: as soon as the spam mails are sent, taking down the domain or blacklisting it is of relative use, as spammers have to register a new domain for their next campaig

Do Thu Ha, Nguyen Xuan Hoang, Nguyen Viet Hoang, Nguyen Huu Du

Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, theref

Taekyun Kim, Dae San Kim

The aim of this paper is to investigate some properties, recurrence relations and identities involving degenerate Stirling numbers of both kinds associated with degenerate hyperharmonic numbers and also with degenerate Bernolli, degenerate Euler, degenerate Bell and degenerate Fubini polynomials.

Guillaume Cébron, Antoine Dahlqvist, Franck Gabriel

The asymptotic freeness of independent unitarily invariant $N\times N$ random matrices holds in expectation up to $O(N^{-2})$. An already known consequence is the infinitesimal freeness in expectation. We put in evidence another consequence for unitarily invariant random matrices: the almost sure asymptotic freeness of type $B$. As byproducts, we recover the

T. Barak, Y. Loewenstein

Video prediction models often combine three components: an encoder from pixel space to a small latent space, a latent space prediction model, and a generative model back to pixel space. However, the large and unpredictable pixel space makes training such models difficult, requiring many training examples. We argue that finding a predictive latent variable an

Agnibha De Sarkar, Nayantara Gupta

Recent observations by the Large High Altitude Air Shower Observatory (LHAASO) have paved the way for the observational detection of PeVatrons in the Milky Way Galaxy, thus revolutionizing the field of $γ$-ray astrophysics. In this paper, we study one such detected source, LHAASO J1908+0621, and explore the origin of multi-TeV $γ$-ray emission from this sour

Yunfeng Xiong, Yong Zhang, Sihong Shao

Performance evaluations on the deterministic algorithms for 6-D problems are rarely found in literatures except some recent advances in the Vlasov and Boltzmann community [Dimarco et al. (2018), Kormann et al. (2019)], due to the extremely high complexity. Thus a detailed comparison among various techniques shall be useful to the researchers in the related f

Igor Bogush, Dmitri Gal'tsov, Galin Gyulchev, Kirill Kobialko

In this article, we conduct a sequential study of possible observable images of black hole simulators described by two recently obtained rotating geometries in Einstein gravity, minimally coupled to a scalar field. One of them, "Kerr-like" (KL), can be seen as a legitimate alternative to the rotating Fisher-Janis-Newman-Winicour (FJNW) solution, and

Hyun-Tak Kim

Emerging devices such as a neuromorphic device and a qubit can use the Mott transition phenomenon, but in particular, the diverging mechanism of the phenomenon remains to be clarified. The diverging-effective mass near Mott insulators was measured in strongly correlated Mott systems such as a fermion $^3$He and a Si metal-oxide-semiconductor-field-effect tra

Han-Jia Ye, Su Lu, De-Chuan Zhan

The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and integrates it with the target model (a.k.a. the "student"), which expands the student's knowledge and improves its learning efficacy. Instead of enforcing the tea

Sebastian Fuchs, Marco Tschimpke

The underlying dependence structure between two random variables can be described in manifold ways. This includes the examination of certain dependence properties such as lower tail decreasingness (LTD), stochastic increasingness (SI) or total positivity of order 2, the latter usually considered for a copula (TP2) or (if existent) its density (d-TP2). In the

Keisuke Yazawa, John Mangum, Prashun Gorai, Geoff L. Brennecka

Ferroelectricity enables key integrated technologies from non-volatile memory to precision ultrasound. Wurtzite ferroelectric Al1-xScxN has recently attracted attention because of its robust ferroelectricity and Si process compatibility in addition to being the first known ferroelectric wurtzite. However, the origin and control of ferroelectricity in wurtzit

Peter Marvin Müller, Jose Pinzon, Thomas Rung, Martin Siebenborn

This work develops an algorithm for PDE-constrained shape optimization based on Lipschitz transformations. Building on previous work in this field, the $p$-Laplace operator is utilized to approximate a descent method for Lipschitz shapes. In particular, it is shown how geometric constraints are algorithmically incorporated avoiding penalty terms by assigning

Kseniya Gagelgans, Alexander Shlapunov

We consider a family of initial problems for the Navier-Stokes type equations generated by the de Rham complex in ${\mathbb R}^n \times [0,T]$, $n\geq 2$, with a positive time $T$ over a scale weighted anisotropic Hölder spaces. As the weights control the order of zero at the infinity with respect to the space variables for vectors fields under the considera

Liyan Xu, Jinho D. Choi

We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way. Especially, we address the two-way interaction between COREF and RE that has not been the focus by previous

Sungyeon Kim, Dongwon Kim, Minsu Cho, Suha Kwak

We present a novel self-taught framework for unsupervised metric learning, which alternates between predicting class-equivalence relations between data through a moving average of an embedding model and learning the model with the predicted relations as pseudo labels. At the heart of our framework lies an algorithm that investigates contexts of data on the e

Rujun Han, Hong Chen, Yufei Tian, Nanyun Peng

Stories or narratives are comprised of a sequence of events. To compose interesting stories, professional writers often leverage a creative writing technique called flashback that inserts past events into current storylines as we commonly observe in novels and plays. However, it is challenging for machines to generate flashback as it requires a solid underst

Yi-Dong Song, Shou-Shan Bao

The flavor-changing neutral current interactions in the standard model are suppressed seriously and such interactions can be used to search the new physics beyond SM. The top quark and Higgs bosons are heavier than the other particles in SM, we can expect the new physics plays a more important role in their interactions. In this work, we study the flavor-cha

Hyunsoo Cho, JiSun Huh, Hayan Nam, Jaebum Sohn

Simultaneous bar-cores, core shifted Young diagrams (or CSYDs), and doubled distinct cores have been studied since Morris and Yaseen introduced the concept of bar-cores. In this paper, our goal is to give a formula for the number of these core partitions on $(s,t)$-cores and $(s,s+d,s+2d)$-cores for the remaining cases that are not covered yet. In order to a

Rishikesh Magar, Yuyang Wang, Amir Barati Farimani

Machine learning (ML) models have been widely successful in the prediction of material properties. However, large labeled datasets required for training accurate ML models are elusive and computationally expensive to generate. Recent advances in Self-Supervised Learning (SSL) frameworks capable of training ML models on unlabeled data have mitigated this prob

Cheng-Yen Yang, Zhongyu Jiang, Shih-Yu Gu, Jenq-Neng Hwang

The Alberta Infant Motor Scale (AIMS) is a well-known assessment scheme that evaluates the gross motor development of infants by recording the number of specific poses achieved. With the aid of the image-based pose recognition model, the AIMS evaluation procedure can be shortened and automated, providing early diagnosis or indicator of potential developmenta

Yun-Mei Li

Magnon spin Hall systems could hardly show experimentally observable particle and thermal transport phenomena intrinsically due to the spin cancellation. Here we demonstrated that the magnon spin Hall systems can exhibit magnon Nernst effect and thermal Hall effect under external magnetic field by considering two typical systems, i.e. the antiferromagnetical

Yun-Zhu Song, Yi-Syuan Chen, Hong-Han Shuai

A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. In this paper, we present an extract-then-abstract Transformer framework to overcome the problem. Specifically, we leverage pre-trained language models to construct a hierarchical extractor for salient sentence selection across documents and an abstractor for

Osamu Hatori

We study $C$-rich spaces, lush spaces, and $C$-extremely regular spaces concerning with the Mazur-Ulam property. We show that a uniform algebra and the real part of a uniform algebra with the supremum norm are $C$-rich spaces, hence lush spaces. We prove that a uniformly closed subalgebra of the algebra of complex-valued continuous functions on a locally com

Anshul Nayak, Azim Eskandarian, Zachary Doerzaph

Past research on pedestrian trajectory forecasting mainly focused on deterministic predictions which provide only point estimates of future states. These future estimates can help an autonomous vehicle plan its trajectory and avoid collision. However, under dynamic traffic scenarios, planning based on deterministic predictions is not trustworthy. Rather, est

Sen Li, Yingzhi Xia, Yu Liu, Qifeng Liao

In this paper we present a Fourier feature based deep domain decomposition method (F-D3M) for partial differential equations (PDEs). Currently, deep neural network based methods are actively developed for solving PDEs, but their efficiency can degenerate for problems with high frequency modes. In this new F-D3M strategy, overlapping domain decomposition is c

Soravit Changpinyo, Doron Kukliansky, Idan Szpektor, Xi Chen

Visual Question Answering (VQA) has benefited from increasingly sophisticated models, but has not enjoyed the same level of engagement in terms of data creation. In this paper, we propose a method that automatically derives VQA examples at volume, by leveraging the abundance of existing image-caption annotations combined with neural models for textual questi

Tian-Yu Ye

How to solve the information leakage problem has become the research focus of quantum dialogue. In this paper, in order to overcome the information leakage problem in quantum dialogue, a novel approach for sharing the initial quantum state privately between communicators, i.e., quantum encryption sharing, is proposed by utilizing the idea of quantum encrypti

Wubing B. Qin

This paper investigates the car-following problem and proposes a nonlinear controller that considers driving comfort, safety concerns, steady-state response and transient response. This controller is designed based on the demands of lower cost, faster response, increased comfort, enhanced safety and elevated extendability from the automotive industry. Design

YI Liang, Shuai Zhao, Bo Cheng, Yuwei Yin

Few-shot relation learning refers to infer facts for relations with a limited number of observed triples. Existing metric-learning methods for this problem mostly neglect entity interactions within and between triples. In this paper, we explore this kind of fine-grained semantic meanings and propose our model TransAM. Specifically, we serialize reference ent

Tian-Yu Ye, Li-Zhen Jiang

In order to avoid the risk of information leakage during the information mutual transmission between two authorized participants, i.e., Alice and Bob, a quantum dialogue protocol based on the entanglement swapping between any two Bell states and the shared secret Bell state is proposed. The proposed protocol integrates the ideas of block transmission, two-st

Xudong Han, Aili Shen, Yitong Li, Lea Frermann

This paper presents fairlib, an open-source framework for assessing and improving classification fairness. It provides a systematic framework for quickly reproducing existing baseline models, developing new methods, evaluating models with different metrics, and visualizing their results. Its modularity and extensibility enable the framework to be used for di

Yuanfei Dai, Wenzhong Guo, Carsten Eickhoff

Research on knowledge graph embedding (KGE) has emerged as an active field in which most existing KGE approaches mainly focus on static structural data and ignore the influence of temporal variation involved in time-aware triples. In order to deal with this issue, several temporal knowledge graph embedding (TKGE) approaches have been proposed to integrate te

Yongzhen Wang, Xuefeng Yan, Fu Lee Wang, Haoran Xie

While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to leverage unpaired real-world hazy and cle

Guillaume Payeur, Étienne Artigau, Laurence Perreault-Levasseur, René Doyon

We present a new procedure rooted in deep learning to construct science images from data cubes collected by astronomical instruments using HxRG detectors in low-flux regimes. It improves on the drawbacks of the conventional algorithms to construct 2D images from multiple readouts by using the readout scheme of the detectors to reduce the impact of correlated

M. T. Burkey, G. Savard, A. T. Gallant, N. D. Scielzo

The electroweak interaction in the Standard Model (SM) is described by a pure vector-axial-vector structure, though any Lorentz-invariant component could contribute. In this work, we present the most precise measurement of tensor currents in the low-energy regime by examining the $β$-$\barν$ correlation of trapped $^{8}$Li ions with the Beta-decay Paul Trap.

Jim Agler, John E. McCarthy

We study the algebra $\mathcal{A}$ generated by the Hardy operator $H$ and the operator $M_x$ of multiplication by $x$ on $L^2[0,1]$. We call $\mathcal{A}$ the Hardy-Weyl algebra. We show that its quotient by the compact operators is isomorphic to the algebra of functions that are continuous on $Λ$ and analytic on the interior of $Λ$ for a planar set $Λ$ = $

Nian Shao, Wenbin Chen

In this paper, we consider the Newton-Schur method in Hilbert space and obtain quadratic convergence. For the symmetric elliptic eigenvalue problem discretized by the standard finite element method and non-overlapping domain decomposition method, we use the Steklov-Poincaré operator to reduce the eigenvalue problem on the domain $Ω$ into the nonlinear eigenv

Petr Kabáth, Priyanka Chaturvedi, Phillip J. MacQueen, Marek Skarka

We present the confirmation and characterization of three hot Jupiters, TOI-1181b, TOI-1516b, and TOI-2046b, discovered by the TESS space mission. The reported hot Jupiters have orbital periods between 1.4 and 2.05 days. The masses of the three planets are $1.18\pm0.14$ M$_{\mathrm{J}}$, $3.16\pm0.12$\, M$_{\mathrm{J}}$, and 2.30 $\pm 0.28$ M$_{\mathrm{J}}$,

Yi Li, Shaohua Wang, Tien N. Nguyen

The existing deep learning (DL)-based automated program repair (APR) models are limited in fixing general software defects. % We present {\tool}, a DL-based approach that supports fixing for the general bugs that require dependent changes at once to one or multiple consecutive statements in one or multiple hunks of code. % We first design a novel fault local

Jim Agler, John E. McCarthy

We study monomial operators on $ L^2[0,1]$, that is bounded linear operators that map each monomial $x^n$ to a multiple of $x^{p_n}$ for some $p_n$. We show that they are all unitarily equivalent to weighted composition operators on a Hardy space. We characterize what sequences $p_n$ can arise. In the case that $p_n$ is a fixed translation of $n$, we give a

Jim Agler, John E. McCarthy

The Hardy operator has all the monomial functions as eigenvectors. We study bounded operators on L^2 that take monomial functions to multiples of other monomials, with a shifted exponent. We prove that they all leave the space of functions vanishing on [0,s] invariant. We prove an asymptotic Muntz-Szasz theorem, characterizing the set of functions that are l

N. Menon, B. A. Lidbury, A. M. Richardson

Pathology tests are central to modern healthcare in terms of diagnosis and patient management. Aggregated pathology results provide opportunities for research into fundamental and applied questions in health and medicine, but data analytic challenges appear since test profiles vary between medical practitioners, resulting in missing data. In this study we pr

Yiming Meng, Jun Liu

In this paper, we focus on discrete-time stochastic systems modelled by nonlinear stochastic difference equations and propose robust abstractions for verifying probabilistic linear temporal specifications. The current literature focuses on developing sound abstraction techniques for stochastic dynamics without perturbations. However, soundness thus far has o

Ahsan Ali, Syed Zawad, Paarijaat Aditya, Istemi Ekin Akkus

In today's production machine learning (ML) systems, models are continuously trained, improved, and deployed. ML design and training are becoming a continuous workflow of various tasks that have dynamic resource demands. Serverless computing is an emerging cloud paradigm that provides transparent resource management and scaling for users and has the pote

Erina Takeshita, Asahi Sakaguchi, Daisuke Hisano, Yoshiaki Inoue

Communication in extreme environments is an important research topic for various use cases including environmental monitoring. A typical example is underwater acoustic communication for 6G mobile networks. The major challenges in such environments are extremely high-latency and high-error rate. They make real-time image transmission difficult using existing

Chenyu Zhang, Benjamin Van Durme, Zhuowan Li, Elias Stengel-Eskin

Our commonsense knowledge about objects includes their typical visual attributes; we know that bananas are typically yellow or green, and not purple. Text and image corpora, being subject to reporting bias, represent this world-knowledge to varying degrees of faithfulness. In this paper, we investigate to what degree unimodal (language-only) and multimodal (

Zhen-Yi Cai, Mattia Negrello, Gianfranco De Zotti

The results of the recently published spectroscopically complete survey of dusty star-forming galaxies detected by the South Pole Telescope (SPT) over 2500 deg^2 proved to be challenging for galaxy formation models that generally underpredict the observed abundance of high-z galaxies. In this paper we interpret these results in the light of a physically grou

Xue-Qing Yu, Hang Yan, Li-Xuan Wei, Ze-Xian Deng

Electron-doped infinite-layer Sr$_{1-x}$Eu$_{x}$CuO$_{2+y}$ films over a wide doping range have been prepared epitaxially on SrTiO$_3$(001) using reactive molecular beam epitaxy. In-plane transport measurements of the single crystalline samples reveal a dome-shaped nodeless superconducting phase centered at $x$ $\sim$ 0.15, a Fermi-liquid behavior and pronou

Jia-Qi Fan, Xue-Qing Yu, Fang-Jun Cheng, Heng Wang

The microscopic understanding of high-temperature superconductivity in cuprates has been hindered by the apparent complexity of crystal structures in these materials. We used scanning tunneling microscopy and spectroscopy to study an electron-doped copper oxide compound Sr$_{1-x}$Nd$_x$CuO$_2$ that has only bare cations separating the CuO$_2$ planes and thus

Shaiful Alam Chowdhury, Gias Uddin, Reid Holmes

Code metrics have been widely used to estimate software maintenance effort. Metrics have generally been used to guide developer effort to reduce or avoid future maintenance burdens. Size is the simplest and most widely deployed metric. The size metric is pervasive because size correlates with many other common metrics (e.g., McCabe complexity, readability, e

Jinhao Jiang, Kun Zhou, Wayne Xin Zhao, Ji-Rong Wen

Commonsense reasoning in natural language is a desired ability of artificial intelligent systems. For solving complex commonsense reasoning tasks, a typical solution is to enhance pre-trained language models~(PTMs) with a knowledge-aware graph neural network~(GNN) encoder that models a commonsense knowledge graph~(CSKG). Despite the effectiveness, these appr

Jeffry Wicaksana, Zengqiang Yan, Dong Zhang, Xijie Huang

The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that the training set for each local client is annotated in a similar fashion and thus follows the same image superv

Jan Grebík, Zoltán Vidnyánszky

We construct bounded degree acyclic Borel graphs with large Borel chromatic number using a graph arising from Ramsey theory and limits of expander sequences.

Ivan S. Maksymov, Bui Quoc Huy Nguyen, Andrey Pototsky, Sergey A. Suslov

Frequency combs (FCs) -- spectra containing equidistant coherent peaks -- have enabled researchers and engineers to measure the frequencies of complex signals with high precision thereby revolutionising the areas of sensing, metrology and communications and also benefiting the fundamental science. Although mostly optical FCs have found widespread application

Angel Daruna, Devleena Das, Sonia Chernova

Learned knowledge graph representations supporting robots contain a wealth of domain knowledge that drives robot behavior. However, there does not exist an inference reconciliation framework that expresses how a knowledge graph representation affects a robot's sequential decision making. We use a pedagogical approach to explain the inferences of a learne

Henry Ehrhard

Gasharov introduced the combinatorial objects known as $P$-arrays to prove $s$-positivity for the chromatic symmetric functions of incomparability graphs of (3+1)-free posets. We define a crystal, a directed colored graph with some additional axioms, on the set of $P$-arrays. The components of the crystal have $s$-positive characters, thereby refining the $s

Chanseul Lee, Tai Hyun Yoon

A theoretical model of an underdamped harmonic oscillator (UHO) driven by periodic short pulses may find plenty of applications in classical, semiclassical, and quantum physics. We present here two different forms of analytical solutions: {\it time-periodic solutions} and {\it harmonic solutions} for one-dimensional classical UHO driven by three different tr

Hanxiang Yang, Jiawei Yan, Zihan Zhu, Haixiao Deng

X-ray free-electron lasers (XFELs) are powerful tools to explore and study nature for achieving remarkable advances. Generally, seeded FELs are ideal sources for supplying full coherent soft x-ray pulses. Benefiting from the high-frequency up-conversion efficiency, the cascading configuration with echo-enabled harmonic generation (EEHG) and high-gain harmoni

Pedro A. Guil Asensio, Ashish K. Srivastava

MacWilliams proved that every finite field has the extension property for Hamming weight which was later extended in a seminal work by Wood who characterized finite Frobenius rings as precisely those rings which satisfy the MacWilliams extension property. In this paper, the question of when is a MacWilliams ring quasi-Frobenius is addressed. It is proved tha

Yuanyuan Lian, Kai Zhang

In this note, we give a simple proof of the pointwise BMO estimate for Poisson's equation. Then the Calderón-Zygmund estimate follows by the interpolation and duality.

James Van Strien, Phred Petersen, Petros Lappas, Leslie Yeo

In addition to topical delivery, nasal sprays offer an alternative drug administration route to the systemic circulation, therefore avoiding the need for painful, invasive delivery techniques. Studies on the efficacy of nasal drug delivery to date have been conflicting despite its potential. In particular computational studies, e.g. Computational Fluid Dynam

James Y. Huang, Bangzheng Li, Jiashu Xu, Muhao Chen

Semantic typing aims at classifying tokens or spans of interest in a textual context into semantic categories such as relations, entity types, and event types. The inferred labels of semantic categories meaningfully interpret how machines understand components of text. In this paper, we present UniST, a unified framework for semantic typing that captures lab

Anirudh Mittal, Yufei Tian, Nanyun Peng

In this paper, we propose a simple yet effective way to generate pun sentences that does not require any training on existing puns. Our approach is inspired by humor theories that ambiguity comes from the context rather than the pun word itself. Given a pair of definitions of a pun word, our model first produces a list of related concepts through a reverse d

Peng Gao, Liangyi Zhao

In this paper, we study central values of the family of quadratic twists of modular $L$-functions of moduli $8p$, with $p$ ranging over odd primes. Assuming the truth of the generalized Riemann hypothesis, we establish a positive proportion non-vanishing result for the corresponding $L$-values.

Sai Peng, Qiyu Deng, Lin Zhou, Tao Huang

In this study, a series of simulations are conducted to investigate the motion of a small cylinder in an expansion tube, focusing on two-dimensional dynamics. These simulations are performed on the FLUENT platform employing the Overset function. The collision between the cylinder and the tube wall is modeled as a positive rigid body collision without losing

Ziyi Yang, Yuwei Fang, Chenguang Zhu, Reid Pryzant

Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview. Most current pretraining methods, however, are limited to one or two modalities. We present i-Code, a self-supervised pretraining framework where users may flexibly combine the modalities of vision, speech, and language into unified and g

Aleksi Julku

Heterobilayer transition metal dichalcogenide (TMDC) moiré systems provide an ideal framework to investigate strongly correlated physics. Here we theoretically study bosonic many-body phases of excitons in moiré TMDCs. By using two moiré models and cluster mean-field theory, we reveal that, due to non-local Coulomb interactions, moiré excitons can feature ex

Allen Herman, Roghayeh Maleki

Let $\mathbf{B}$ be a basis for an $r$-dimensional algebra $A$ over a field or commutative ring with unity. The semifusions of $\mathbf{B}$ are the partitions of $\mathbf{B}$ whose characteristic functions form the basis of a subalgebra of $A$, and fusions are semifusions that respect a given involution on $A$. In this paper, we give an algorithm for computi

Marco Valentino, André Freitas

A fundamental research goal for Explainable AI (XAI) is to build models that are capable of reasoning through the generation of natural language explanations. However, the methodologies to design and evaluate explanation-based inference models are still poorly informed by theoretical accounts on the nature of explanation. As an attempt to provide an epistemo

Victor Ayala, Adriano Da Silva, Erik Mamani

This paper explicitly computes the unique control set $D$ with non-empty interior of a linear control system on $\mathbb{R}^2$, when the associated matrix has complex eigenvalues. It turns out that the closure of $D$ coincides with the the region delimited by a computable periodic orbit $\mathcal{O}$ of the system.

Jeremy Orosco, William Connacher, James Friend

We find capillary wave turbulence (WT) to span multiple dynamical regimes and geometries -- from weakly to strongly nonlinear WT (SWT) and from shallow to deep domains -- all within a 40uL volume millifluidic system. This study is made viable with recent advances in ultra-high-speed digital holographic microscopy, providing 10-us time and 10-nm spatial resol

Robin Bloomfield, John Rushby

An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security. A natural question is then "how much" confidence does the case provide? We argue that confidence cannot be reduced to a single attribute or measurement. Instead, we suggest it should be based on attributes that draw on t

Pranjal Awasthi, Nishanth Dikkala, Pritish Kamath

Recent investigations in noise contrastive estimation suggest, both empirically as well as theoretically, that while having more "negative samples" in the contrastive loss improves downstream classification performance initially, beyond a threshold, it hurts downstream performance due to a "collision-coverage" trade-off. But is such a phenome

Nicholas Pischke

Accretive and monotone operator theory are central branches of nonlinear functional analysis and constitute the abstract study of set-valued mappings between function spaces. This paper deals with the computational properties of certain large classes of operators, namely accretive and (generalized) monotone set-valued ones. In particular, we develop (and ext

Paul Medvedev

The theoretical analysis of performance has been an important tool in the engineering of algorithms in many application domains. Its goals are to predict the empirical performance of an algorithm and to be a yardstick that drives the design of novel algorithms that perform well in practice. While these goals have been achieved in many instances, they have no

Gaetano Fiore

We point out a rather effective approach for solving the time-dependent harmonic oscillator $\ddot q=-\omega^2 q$ under various regularity assumptions. Where $\omega(t )$ is $C^1$ this is reduced to Hamilton equation for the angle variable $\psi$ {\it alone} (the action variable ${\cal I}$ is obtained \it by quadrature}). The fixed point theorem for the inte

Alice Baird, Panagiotis Tzirakis, Gauthier Gidel, Marco Jiralerspong

The ICML Expressive Vocalization (ExVo) Competition is focused on understanding and generating vocal bursts: laughs, gasps, cries, and other non-verbal vocalizations that are central to emotional expression and communication. ExVo 2022, includes three competition tracks using a large-scale dataset of 59,201 vocalizations from 1,702 speakers. The first, ExVo-

Fabio Bernasconi, Iacopo Brivio, Tatsuro Kawakami, Jakub Witaszek

We prove that every globally $F$-split surface admits an equisingular lifting over the ring of Witt vectors.

Bartosz Malman

It is well-known that for any inner function $θ$ defined in the unit disk $D$ the following two conditons: $(i)$ there exists a sequence of polynomials $\{p_n\}_n$ such that $\lim_{n \to \infty} θ(z) p_n(z) = 1$ for all $z \in D$, and $(ii)$ $\sup_n \| θp_n \|_\infty < \infty$, are incompatible, i.e., cannot be satisfied simultaneously. In this note we discu

Nikhil Cherian Kurian, Amit Lohan, Gregory Verghese, Nimish Dharamshi

Although the U-Net architecture has been extensively used for segmentation of medical images, we address two of its shortcomings in this work. Firstly, the accuracy of vanilla U-Net degrades when the target regions for segmentation exhibit significant variations in shape and size. Even though the U-Net already possesses some capability to analyze features at