November 2022 arXiv papers — page 87

Fabio Ancona, Roberta Bianchini, Charlotte Perrin

This note is concerned with the rigorous justification of the so-called hard congestion limit from a compressible system with singular pressure towards a mixed compressible-incompressible system modeling partially congested dynamics, for small data in the framework of BV solutions. We present a first convergence result for perturbations of a reference state

O Chernoyarov, S Dachian, C Farinetto, Yu Kutoyants

It is considered the problem of localization on the plane of two radioactive sources by K detectors. Each detector records a realization of inhomogeneous Poisson process and the intensity function of this process is a sum of a signal arriving from the sources and the constant Poisson noise of known intensity. The time of the beginning of emissions of two sou

Fabien Gibert, Dimitri Edouart, Paul Monnier, Claire Cénac

A ground-based mobile 3D lidar observatory has been developed for simultaneous measurements of wind speed, temperature, water vapor and carbon dioxide absorption in the atmosphere. The present paper reports details of the instruments, assesses the current performances and gives some examples of measurements for different geophysical applications.

Priscila Romagnoli, Ruvi Lecamwasam, Shilu Tian, James Downes

Researchers seek methods to levitate matter for a wide variety of purposes, ranging from exploring fundamental problems in science, through to developing new sensors and mechanical actuators. Many levitation techniques require active driving and most can only be applied to objects smaller than a few micrometers. Diamagnetic levitation has the strong advantag

Atul Dixit, Rahul Kumar

A modular relation of the form $F(\alpha, w)=F(\beta, iw)$, where $i=\sqrt{-1}$ and $\alpha\beta=1$, is obtained. It involves the generalized digamma function $\psi_w(a)$ which was recently studied by the authors in their work on developing the theory of the generalized Hurwitz zeta function $\zeta_w(s, a)$. The limiting case $w\to0$ of this modular relation

Pin-Ju Tsai, Lun-Ping Yuan, Ying-Cheng Chen

Electromagnetically-induced-transparency (EIT) spectroscopy has been used as a sensitive sensor in quantum metrology applications. The sensitivity of a sensor strongly depends on the measurement precision of EIT spectrum. In this work, we present a theoretical study of the spectral lineshape measurement on a three-level $\Lambda$-type EIT media based on Fish

Junwoo Cho, Seungtae Nam, Hyunmo Yang, Seok-Bae Yun

Physics-informed neural networks (PINNs) have emerged as new data-driven PDE solvers for both forward and inverse problems. While promising, the expensive computational costs to obtain solutions often restrict their broader applicability. We demonstrate that the computations in automatic differentiation (AD) can be significantly reduced by leveraging forward

Yihang Gao, Ka Chun Cheung, Michael K. Ng

Physics-informed neural networks (PINNs) have attracted significant attention for solving partial differential equations (PDEs) in recent years because they alleviate the curse of dimensionality that appears in traditional methods. However, the most disadvantage of PINNs is that one neural network corresponds to one PDE. In practice, we usually need to solve

Hiroyasu Koizumi

Superconductivity is reformulated as a phenomenon in which a stable velocity field is created by a $U(1)$ phase neglected by Dirac in the Schroedinger representation of quantum mechanics. The neglected phase gives rise to a $U(1)$ gauge field expressed as the Berry connection from many-body wave functions. The inclusion of this gauge field transforms the sta

V. Lanabere, P. Démoulin, S. Dasso

Magnetic clouds (MCs) are observed insitu by spacecraft. The rotation of their magnetic field is typically interpreted as the crossing of a twisted magnetic flux tube, or flux rope, which was launched from the solar corona. The detailed magnetic measurements across MCs permit us to infer the flux rope characteristics. Still, the precise spatial distribution

Vu Nguyen Ha, Zaid Abdullah, Geoffrey Eappen, Juan Carlos Merlano Duncan

This paper jointly designs linear precoding (LP) and codebook-based beamforming implemented in a satellite with massive multiple-input multiple-output (mMIMO) antenna technology. The codebook of beamforming weights is built using the columns of the discrete Fourier transform (DFT) matrix, and the resulting joint design maximizes the achievable throughput und

Xuechun Yang, Zhenzhen Yang, Baode Li

We obtain some new characterizations of a variable version of Lipschitz spaces in terms of the boundedness of commutators of sharp maximal functions, fractional maximal functions or fractional maximal commutators in the context of the variable Lebesgue spaces, where the symbols of the commutators belong to the variable Lipschitz space. A useful tool is that

Mandeep Khatoniar, Nicholas Yama, Areg Ghazaryan, Sriram Guddala

Coherent control and manipulation of quantum degrees of freedom such as spins forms the basis of emerging quantum technologies. In this context, the robust valley degree of freedom and the associated valley pseudospin found in two-dimensional transition metal dichalcogenides is a highly attractive platform. Valley polarization and coherent superposition of v

Hriday Bavle, Jose Luis Sanchez-Lopez, Muhammad Shaheer, Javier Civera

Mobile robots extract information from its environment to understand their current situation to enable intelligent decision making and autonomous task execution. In our previous work, we introduced the concept of Situation Graphs (S-Graphs) which combines in a single optimizable graph, the robot keyframes and the representation of the environment with geomet

Uttaran Ghosh, Sarbari Guha

In this paper, we study the gravitational collapse of a fluid ball undergoing dissipation in the form of heat flux, in the framework of $f(R,T)$ gravity. To apply the junction conditions in order to match the interior and the exterior spacetimes and analyze the dynamics of collapse, it is necessary to know the exterior metric for the radiating fireball, whic

Ashkan Goharfar, Jaber Babaki, Mehdi Rasti, Pedro H. J. Nardelli

With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we propose a novel deep learning approach

Tatsuro Kawakami

We prove that the Kawamata-Viehweg vanishing theorem holds for a log Calabi-Yau surface $(X, B)$ over an algebraically closed field of large characteristic when $B$ has standard coefficients.

A. V. Tsiganov

Affine transformations in Euclidean space generates a correspondence between integrable systems on cotangent bundles to the sphere, ellipsoid and hyperboloid embedded in $R^n$. Using this correspondence and the suitable coupling constant transformations we can get real integrals of motion in the hyperboloid case starting with real integrals of motion in the

Avinash Bhardwaj, Vishnu Narayanan, Hrishikesh Venkataraman

Lonely Runner Conjecture, proposed by J\"{o}rg M. Wills and so nomenclatured by Luis Goddyn, has been an object of interest since it was first conceived in 1967 : Given positive integers $k$ and $n_1,n_2,\ldots,n_k$ there exists a positive real number $t$ such that the distance of $t\cdot n_j$ to the nearest integer is at least $\frac{1}{k+1}$, $\forall~~1\l

Yicheng Hsu, Yonghan Lee, Mingsian R. Bai

Personalized speech enhancement has been a field of active research for suppression of speechlike interferers such as competing speakers or TV dialogues. Compared with single channel approaches, multichannel PSE systems can be more effective in adverse acoustic conditions by leveraging the spatial information in microphone signals. However, the implementatio

Jialong Xu, Tze-Yang Tung, Bo Ai, Wei Chen

Semantic communications is considered as a promising technology to increase the efficiency of next-generation communication systems, particularly targeting human-machine and machine-type communications. In contrast to the source-agnostic approach of conventional wireless communication systems, semantic communication seeks to ensure that only the relevant inf

Travis Scrimshaw

We describe various diagram algebras and their representation theory using cellular algebras of Graham and Lehrer and the decomposition into half diagrams. In particular, we show the diagram algebras surveyed here are all cellular algebras and parameterize their cell modules. We give a new construction to build new cellular algebras from a general cellular a

Evan S. Gawlik, François Gay-Balmaz

Respecting the laws of thermodynamics is crucial for ensuring that numerical simulations of dynamical systems deliver physically relevant results. In this paper, we construct a structure-preserving and thermodynamically consistent finite element method and time-stepping scheme for heat conducting viscous fluids, with general state equations. The method is de

Dale Frymark, Constanze Liaw

We apply both the theory of boundary triples and perturbation theory to the setting of semi-bounded Sturm-Liouville operators with two limit-circle endpoints. For general boundary conditions we obtain refined and new results about their eigenvalues and eigenfunctions. In the boundary triple setup, we obtain simple criteria for identifying which self-adjoint

Yuqi Li, Yuting He, Yihang Zhou, Zirui Gong

In the field of planting fruit trees, pre-harvest estimation of fruit yield is important for fruit storage and price evaluation. However, considering the cost, the yield of each tree cannot be assessed by directly picking the immature fruit. Therefore, the problem is a very difficult task. In this paper, a fruit counting and yield assessment method based on

Anaelia Ovalle, Sunipa Dev, Jieyu Zhao, Majid Sarrafzadeh

Auditing machine learning-based (ML) healthcare tools for bias is critical to preventing patient harm, especially in communities that disproportionately face health inequities. General frameworks are becoming increasingly available to measure ML fairness gaps between groups. However, ML for health (ML4H) auditing principles call for a contextual, patient-cen

Shinto Eguchi

This paper aims at presenting a new application of information geometry to reinforcement learning focusing on dynamic treatment resumes. In a standard framework of reinforcement learning, a Q-function is defined as the conditional expectation of a reward given a state and an action for a single-stage situation. We introduce an equivalence relation, called th

Faran Ahmed, Kemal Kilic

Linguistic labels are effective means of expressing qualitative assessments because they account for the uncertain nature of human preferences. However, to perform computations with linguistic labels, they must first be converted to numbers using a scale function. Within the context of the Analytic Hierarchy Process (AHP), the most popular scale used to repr

Paweł Przybyłowicz, Verena Schwarz, Michaela Szölgyenyi

We present the first higher-order approximation scheme for solutions of jump-diffusion stochastic differential equations with discontinuous drift. For this transformation-based jump-adapted quasi-Milstein scheme we prove $L^p$-convergence order 3/4. To obtain this result, we prove that under slightly stronger assumptions (but still weaker than anything known

Qiang Liu

Combinatorial optimizations are usually complex and inefficient, which limits their applications in large-scale networks with billions of links. We introduce a distributed computational method for solving a node-covering problem at the scale of factual scenarios. We first construct a genetic algorithm and then design a two-step strategy to initialize the can

He-Liang Huang, Xiao-Yue Xu, Chu Guo, Guojing Tian

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer.

Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen

Visual entailment (VE) is to recognize whether the semantics of a hypothesis text can be inferred from the given premise image, which is one special task among recent emerged vision and language understanding tasks. Currently, most of the existing VE approaches are derived from the methods of visual question answering. They recognize visual entailment by qua

Satej Soman, Emily Aiken, Esther Rolf, Joshua Blumenstock

Machine learning-based estimates of poverty and wealth are increasingly being used to guide the targeting of humanitarian aid and the allocation of social assistance. However, the ground truth labels used to train these models are typically borrowed from existing surveys that were designed to produce national statistics -- not to train machine learning model

Wenbin Luo

In this article, we prove the boundedness of minimal slopes of adelic line bundles over function fields of characteristic 0. This can be applied to prove the equidistribution of generic and small points with respect to a big and semipositive adelic line bundle. Our methods can be applied to the finite places of number fields as well. We also show the continu

Alexander Calvert, Wesley Chan, Tin Tran, Sara Sheikholeslami

Robots must move legibly around people for safety reasons, especially for tasks where physical contact is possible. One such task is handovers, which requires implicit communication on where and when physical contact (object transfer) occurs. In this work, we study whether the trajectory model used by a robot during the reaching phase affects the subjective

Jinghan Sun, Dong Wei, Liansheng Wang, Yefeng Zheng

Medical images are widely used in clinical practice for diagnosis. Automatically generating interpretable medical reports can reduce radiologists' burden and facilitate timely care. However, most existing approaches to automatic report generation require sufficient labeled data for training. In addition, the learned model can only generate reports for the tr

Juwon Oh, Hung-Tzu Chang, Christopher T. Chen, Shaul Aloni

Carrier and phonon dynamics in a multilayer WSe2 film are captured by extreme ultraviolet (XUV) transient absorption (TA) spectroscopy at the W N6,7, W O2,3, and Se M4,5 edges (30-60 eV). After the broadband optical pump pulse, the XUV probe directly reports on occupations of optically excited holes and phonon-induced band renormalizations. By comparing with

Naoya Tajima, Yoshitaka Kawasugi, Takao Morinari, Ryuhei Oka

Theoretical and experimental studies have supported that the electronic structure of $\alpha$-(BEDT-TTF)$_2$I$_3$ under pressure is described by two-dimensional Dirac fermions. When the interlayer tunneling is coherent, the electronic structure of the system becomes three-dimensional, and we expect the peak structure to appear in the interlayer resistivity u

Ashvin Swaminathan

We determine the mean number of 2-torsion elements in class groups of cubic orders, when such orders are enumerated by discriminant. Specifically, we prove that when isomorphism classes of totally real (resp., complex) cubic orders are enumerated by discriminant, the average $2$-torsion in the class group is $1 + \frac{1}{4} \times \frac{\zeta(2)}{\zeta(4)}$

Yin-Dong Zheng, Guo Chen, Jiahao Wang, Tong Lu

Capturing the state changes of interacting objects is a key technology for understanding human-object interactions. This technical report describes our method using heterogeneous backbones for the Ego4D Object State Change Classification and PNR Temporal Localization Challenge. In the challenge, we used the heterogeneous video understanding backbones, namely

Xuefeng Jiang, Shixiong Yin, Huanan Li, Jiamin Quan

Non-Hermitian wave engineering has attracted a surge of interest in photonics in recent years. One of the prominent phenomena is coherent perfect absorption (CPA), in which the annihilation of electromagnetic scattering occurs by destructive interference of multiple incident waves. This concept has been implemented in various platforms to demonstrate real-ti

Hayato Futami, Emiru Tsunoo, Kentaro Shibata, Yosuke Kashiwagi

Disfluency detection has mainly been solved in a pipeline approach, as post-processing of speech recognition. In this study, we propose Transformer-based encoder-decoder models that jointly solve speech recognition and disfluency detection, which work in a streaming manner. Compared to pipeline approaches, the joint models can leverage acoustic information t

Z. Araghi Rostami, M. Parvizi, P. Niroomand

The Bogomolov multiplier of a group $G$ introduced by Bogomolov in $1988$. After that in $2012$, Moravec introduced an equivalent definition of the Bogomolov multiplier. In this paper we generalized the Bogomolov multiplier with respect to a variety of groups. Then we give some new results on this topic.

Yanbo Yuan, Hua Zhong, Haixiong Li, Xiao cheng

It is believed that human vision system (HVS) consists of pre-attentive process and attention process when performing salient object detection (SOD). Based on this fact, we propose a four-stage framework for SOD, in which the first two stages match the \textbf{P}re-\textbf{A}ttentive process consisting of general feature extraction (GFE) and feature preproce

Hayate Iso, Xiaolan Wang, Yoshi Suhara

Opinion summarization research has primarily focused on generating summaries reflecting important opinions from customer reviews without paying much attention to the writing style. In this paper, we propose the stylized opinion summarization task, which aims to generate a summary of customer reviews in the desired (e.g., professional) writing style. To tackl

MingCai Chen, Yu Zhao, Bing He, Zongbo Han

Learning with Noisy Labels (LNL) has attracted significant attention from the research community. Many recent LNL methods rely on the assumption that clean samples tend to have "small loss". However, this assumption always fails to generalize to some real-world cases with imbalanced subpopulations, i.e., training subpopulations varying in sample size or reco

Bui Thi Ha Giang

A measurement of Z production cross-section in $\gamma e^{-}$ collision at Large Hadron-electron Collider (LHeC) is presented to compare to that at International Linear Collider (ILC). The total cross-section depends strongly on the polarization of the initial and final $e^{-}$ beams, the electron beam energy $E_{e}$ with the energy of the proton beam taken

Jin Zhang

The infrared divergent scalar three-point integrals are evaluated by the loop regularization method. Three kinds of infrared divergent integrals, i.e., massless triangle diagram, triangle diagrams with one and two massive internal lines, are systematically evaluated by loop regularization, analytic results are obtained. According the method, the infrared div

Davide Racco, Antonio Riotto

We show that the large-scale perturbations in the dark matter generated by the freeze-in mechanism are only of adiabatic nature. The freeze-in mechanism is not at odds with the current stringent constraints on isocurvature perturbations.

Konstantin Sorokin, Anton Ayzenberg, Konstantin Anokhin, Vladimir Sotskov

In present paper we discuss several approaches to reconstructing the topology of the physical space from neural activity data of CA1 fields in mice hippocampus, in particular, having Cognitome theory of brain function in mind. In our experiments, animals were placed in different new environments and discovered these moving freely while their physical and neu

Sharif Amit Kamran, Khondker Fariha Hossain, Alireza Tavakkoli, George Bebis

Incorporating various mass shapes and sizes in training deep learning architectures has made breast mass segmentation challenging. Moreover, manual segmentation of masses of irregular shapes is time-consuming and error-prone. Though Deep Neural Network has shown outstanding performance in breast mass segmentation, it fails in segmenting micro-masses. In this

Frederik Broucke, Gregory Debruyne

We study the distribution of zeros of zeta functions associated to Beurling generalized prime number systems whose integers are distributed as $N(x) = Ax + O(x^{\theta})$. We obtain in particular \[ N(\alpha, T) \ll T^{\frac{c(1-\alpha)}{1-\theta}}\log^{9} T, \] for a constant $c$ arbitrarily close to $4$, improving significantly the current state of the art

Seokjin Lee, Minhan Kim, Seunghyeon Shin, Daeho Lee

Deep generative models for audio synthesis have recently been significantly improved. However, the task of modeling raw-waveforms remains a difficult problem, especially for audio waveforms and music signals. Recently, the realtime audio variational autoencoder (RAVE) method was developed for high-quality audio waveform synthesis. The RAVE method is based on

Richard Yuanzhe Pang, Vishakh Padmakumar, Thibault Sellam, Ankur P. Parikh

To align conditional text generation model outputs with desired behaviors, there has been an increasing focus on training the model using reinforcement learning (RL) with reward functions learned from human annotations. Under this framework, we identify three common cases where high rewards are incorrectly assigned to undesirable patterns: noise-induced spur

Joseph Atchison, Ralf Rapp

The determination of transport coefficients plays a central role in characterizing hot and dense nuclear matter. Currently, there are significant discrepancies between various calculations of the electric conductivity of hot hadronic matter. In the present work we calculate the electric conductivity of hot pion matter by extracting it from the electromagneti

Hailin Yu, Youji Feng, Weicai Ye, Mingxuan Jiang

Feature matching is crucial in visual localization, where 2D-3D correspondence plays a major role in determining the accuracy of camera pose. A sufficient number of well-distributed 2D-3D correspondences is essential for accurate pose estimation due to noise. However, existing 2D-3D feature matching methods rely on finding nearest neighbors in the feature sp

Aviad Rubinstein, Junyao Zhao

Motivated by large-market applications such as crowdsourcing, we revisit the problem of budget-feasible mechanism design under a "small-bidder assumption". Anari, Goel, and Nikzad (2018) gave a mechanism that has optimal competitive ratio $1-1/e$ on worst-case instances. However, we observe that on many realistic instances, their mechanism is significantly o

P. Zhang, S. Mudi, M. Pendharkar, J. S. Lee

Topological superconductivity is expected to spur Majorana zero modes -- exotic states that are also considered a quantum technology asset. Fractional Josephson effect is their manifestation in electronic transport measurements, often under microwave irradiation. A fraction of induced resonances, known as Shapiro steps, should vanish, in a pattern that signi

Dipankar Chakrabarti, Raj Kishore, Asmita Mukherjee, Sangem Rajesh

We calculate azimuthal asymmetries in back-to-back production of $J/\psi$ and a photon in electron-proton scattering process at the future electron-ion collider (EIC) using TMD factorization framework. We consider the cases where the proton is unpolarized or transversely polarized. For the formation of $J/\psi$, non-relativistic QCD (NRQCD) is used. We find

Jiahao Wang, Guo Chen, Yin-Dong Zheng, Tong Lu

We provide the technical report for Ego4D audio-only diarization challenge in ECCV 2022. Speaker diarization takes the audio streams as input and outputs the homogeneous segments according to the speaker's identity. It aims to solve the problem of "Who spoke when." In this paper, we explore a Detection-based method to tackle the audio-only speaker diarizatio

Cătălin Paşcu Moca, Doru Sticlet, Balázs Dóra, Gergely Zaránd

We study the dissipative dynamics of a one-dimensional bosonic system described in terms of the bipartite Bose-Hubbard model with alternating gain and loss. This model exhibits the $\mathcal{PT}$ symmetry under some specific conditions and features a $\mathcal{PT}$-symmetry phase transition. It is characterized by an order parameter corresponding to the popu

Avriti Chauhan, Mohammad Afzal, Hrishikesh Karmarkar, Yizhak Elboher

Deep Neural Networks (DNNs) are everywhere, frequently performing a fairly complex task that used to be unimaginable for machines to carry out. In doing so, they do a lot of decision making which, depending on the application, may be disastrous if gone wrong. This necessitates a formal argument that the underlying neural networks satisfy certain desirable pr

Xinyu Zhou, Chang Liu, Jun Zhao

The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. Due to privacy concerns and the limited

Sicheng Mo, Fangzhou Mu, Yin Li

This report describes Badgers@UW-Madison, our submission to the Ego4D Natural Language Queries (NLQ) Challenge. Our solution inherits the point-based event representation from our prior work on temporal action localization, and develops a Transformer-based model for video grounding. Further, our solution integrates several strong video features including Slo

Yongjie Chen, Tieru Wu

Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of videos with fast scene switching, these VSR

Jaeyeon Kim, Binh-Son Hua, Duc Thanh Nguyen, Sai-Kit Yeung

In this paper, we propose a new method for mapping a 3D point cloud to the latent space of a 3D generative adversarial network. Our generative model for 3D point clouds is based on SP-GAN, a state-of-the-art sphere-guided 3D point cloud generator. We derive an efficient way to encode an input 3D point cloud to the latent space of the SP-GAN. Our point cloud

Masha Itkina, Mykel J. Kochenderfer

Although neural networks have seen tremendous success as predictive models in a variety of domains, they can be overly confident in their predictions on out-of-distribution (OOD) data. To be viable for safety-critical applications, like autonomous vehicles, neural networks must accurately estimate their epistemic or model uncertainty, achieving a level of sy

Jiadong Yu, Ahmad Alhilal, Pan Hui, Danny H. K. Tsang

The Metaverse has emerged to extend our lifestyle beyond physical limitations. As essential components in the Metaverse, digital twins (DTs) are the real-time digital replicas of physical items. Multi-access edge computing (MEC) provides responsive services to the end users, ensuring an immersive and interactive Metaverse experience. While the digital repres

Xingcheng Xu

We introduce a novel model called GAMMT (Generative Ambiguity Models using Multiple Transformers) for sequential data that is based on sets of probabilities. Unlike conventional models, our approach acknowledges that the data generation process of a sequence is not deterministic, but rather ambiguous and influenced by a set of probabilities. To capture this

Azizollah Azad, Nasim Karimi

Let G be a finite group with a generating set A. By the (symmetric) diameter of G with respect to A we mean the maximum over g in G of the length of the shortest word in (A union A inverse)A expressing g.By the (symmetric) diameter of G we mean the maximum of (symmetric) diameter over all generating sets of G. Let n greater than or equal to 1, by G power n w

Samuel S. H. Ng, Jeremiah Croshaw, Marcel Walter, Robert Wille

Recent research interest in emerging logic systems based on quantum dots has been sparked by the experimental demonstration of nanometer-scale logic devices composed of atomically sized quantum dots made of silicon dangling bonds (SiDBs), along with the availability of SiQAD, a computer-aided design tool designed for this technology. Latest design automation

Hanbo Cai, Pengcheng Zhang, Hai Dong, Yan Xiao

Keyword spotting (KWS) has been widely used in various speech control scenarios. The training of KWS is usually based on deep neural networks and requires a large amount of data. Manufacturers often use third-party data to train KWS. However, deep neural networks are not sufficiently interpretable to manufacturers, and attackers can manipulate third-party tr

JinHua Fei

In this paper, We use the Fourier series expansion of real variables function, We give a formula to calculate the Dirichlet character sum, and four special examples are given.

Yibin Xu, Tijs Slaats, Boris Düdder, Søren Debois

We provide a practical translation from the Dynamic Condition Response (DCR) process modelling language to the Transaction Execution Approval Language (TEAL) used by the Algorand blockchain. Compared to earlier implementations of business process notations on blockchains, particularly Ethereum, the present implementation is four orders of magnitude cheaper.

Kazuya Shinjo, Shigetoshi Sota, Seiji Yunoki, Takami Tohyama

Owning to their high controllability, laser pulses have contributed greatly to our understanding of strongly correlated electron systems. However, typical multicycle pulses do not control the symmetry of systems that plays an important role in the emergence of novel quantum phases. Here, we demonstrate that subcycle pulses whose oscillation is less than one

Youfa Li, Shengli Fan, Deguang Han

While traditionally the computerized tomography of a function $f\in L^{2}(\mathbb{R}^{2})$ depends on the samples of its Radon transform at multiple angles, the real-time imaging sometimes requires the reconstruction of $f$ by the samples of its Radon transform $\mathcal{R}_{\emph{\textbf{p}}}f$ at a single angle $\theta$, where $\emph{\textbf{p}}=(\cos\thet

Fei Gao, Hui Zhan

For the time-space fractional degenerate Keller-Segel equation \begin{equation*} \begin{cases} \partial _{t}^{\beta }u=-(-\Delta )^{\frac{\alpha}{2}}(\rho (v)u),& t>0\\ (-\Delta )^{\frac{\alpha}{2}} v+v=u,& t>0 \end{cases} \end{equation*} $x\in\Omega, \Omega \subset \mathbb{R}^{n}, \beta\in (0,1),\alpha\in (1,2)$, we consider for $n\geq 3$ the problem of fin

Neehar Peri, Achal Dave, Deva Ramanan, Shu Kong

Contemporary autonomous vehicle (AV) benchmarks have advanced techniques for training 3D detectors, particularly on large-scale lidar data. Surprisingly, although semantic class labels naturally follow a long-tailed distribution, contemporary benchmarks focus on only a few common classes (e.g., pedestrian and car) and neglect many rare classes in-the-tail (e

Omkar R. Durgada, Vinay Kumar Chapala, S. M. Zafaruddin

Existing research works on reconfigurable intelligent surfaces (RIS) based terahertz (THz) system ignores the effect of phase noise and employ the zero-boresight pointing errors model of the free-space optics channel in performance analysis. In this paper, we analyze the performance of RIS-THz transmission under the combined effect of channel fading, THz poi

Manabu Takeichi, Ryo Furuta, Shuichi Murakami

A nodal-line semimetal is a topological gapless phase containing one-dimensional degeneracies called nodal lines. The nodal lines are deformed by a continuous change of the system such as pressure and they can even change their topology, but it is not systematically understood what kind of changes of topology of nodal lines are possible. In this paper, we cl

Anuj Kumar Upadhyay, Anil Kumar, Sanjib Kumar Agarwalla, Amol Dighe

Atmospheric neutrinos provide a unique avenue to explore the internal structure of Earth based on weak interactions, which is complementary to seismic studies and gravitational measurements. In this work, we demonstrate that the atmospheric neutrino oscillations in the presence of Earth matter can serve as an important tool to locate the core-mantle boundary

Ce Ji, Zhiyuan Wang, Chenglang Yang

Given a tau-function $\tau(t)$ of the BKP hierarchy satisfying $\tau(0)=1$, we discuss the relation between its BKP-affine coordinates on the isotropic Sato Grassmannian and its BKP-wave function. Using this result, we formulate a type of Kac-Schwarz operators for $\tau(t)$ in terms of BKP-affine coordinates. As an example, we compute the affine coordinates

Ofir Moshe, Gil Fidel, Ron Bitton, Asaf Shabtai

State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their wide adoption in mission-critical contexts is hampered by two major weaknesses - their susceptibility to adversarial attacks and their opaqueness. The former raises concerns about the security and generalization of DNNs in real-world conditions

Yasunori Yamada, Masatomo Kobayashi, Kaoru Shinkawa, Miyuki Nemoto

Early diagnosis of dementia, particularly in the prodromal stage (i.e., mild cognitive impairment, or MCI), has become a research and clinical priority but remains challenging. Automated analysis of the drawing process has been studied as a promising means for screening prodromal and clinical dementia, providing multifaceted information encompassing features

Andre He, Nicholas Tomlin, Dan Klein

We present a state-of-the-art neural approach to the unsupervised reconstruction of ancient word forms. Previous work in this domain used expectation-maximization to predict simple phonological changes between ancient word forms and their cognates in modern languages. We extend this work with neural models that can capture more complicated phonological and m

Kousik Bera, Dipankar Chugh, Aditya Bandopadhyay, Hark Hoe Tan

We demonstrate a non-monotonic evolution of thermal conductivity of large-area hexagonal boron nitride films with thickness. Wrinkles and defects/impurities are present in these films. Raman spectroscopy, an optothermal non-contact technique, is employed to probe the temperature and laser power dependence property of the Raman active E2ghigh phonon mode, whi

Wang Qi, Yu-Ping Ruan, Yuan Zuo, Taihao Li

Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research. Previous methods in this field add tunable adapters into MHA or/and FFN of Transformer blocks to enable PLMs achieve transferability. However, as an important pa

Atsuro Okazawa

Traditional semantic segmentation requires a large labeled image dataset and can only be predicted within predefined classes. To solve this problem, few-shot segmentation, which requires only a handful of annotations for the new target class, is important. However, with few-shot segmentation, the target class data distribution in the feature space is sparse

Dipnarayan Das, Asha Durafe, Vinod Patidar

Active research is going on to securely transmit a secret message or so-called steganography by using data-hiding techniques in digital images. After assessing the state-of-the-art research work, we found, most of the existing solutions are not promising and are ineffective against machine learning-based steganalysis. In this paper, a lightweight steganograp

Alexander Chernyavsky, Leonid Frumin, Andrey Gelash

We consider right and left formulations of the inverse scattering problem for the Zakharov-Shabat system and the corresponding integral Gelfand-Levitan-Marchenko equations. Both formulations are helpful for numerical solving of the inverse scattering problem, which we perform using the previously developed Toeplitz Inner Bordering (TIB) algorithm. First, we

Kang Fu, Jianwei Hu, Seydou Keita, Hang Liu

The stochastic block model is widely used for detecting community structures in network data. However, the research interest of much literature focuses on the study of one sample of stochastic block models. How to detect the difference of the community structures is a less studied issue for stochastic block models. In this article, we propose a novel test st

Hao Wang, Xiwen Chen, Abolfazl Razi, Michael Kozicki

Today's supply chains heavily rely on cyber-physical systems such as intelligent transportation, online shopping, and E-commerce. It is advantageous to track goods in real-time by web-based registration and authentication of products after any substantial change or relocation. Despite recent advantages in technology-based tracking systems, most supply chains

Minh Tung Nguyen, Tien-Son Pham

We first study Clarke's tangent cones at infinity to unbounded subsets of $\mathbb{R}^n.$ We prove that these cones are closed convex and show a characterization of their interiors. We then study subgradients at infinity for extended real value functions on $\mathbb{R}^n$ and derive necessary optimality conditions at infinity for optimization problems. We al

Patrick K. Schelling

The Soret effect is the tendency of fluid mixtures to exhibit concentration gradients in the presence of a temperature gradient. Using molecular-dynamics simulation of two-component Lennard-Jones liquids, it is demonstrated that spatially-sinusoidal heat pulses generate both temperature and pressure gradients. Over short timescales, the dominant effect is th

Hyoukjun Kwon, Krishnakumar Nair, Jamin Seo, Jason Yik

Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference workloads, are emerging for applications areas like extended reality (XR) to support metaverse use cases. These workloads combine user interactivity with computationally complex machine learning (ML) activities. Compared to standard ML applications, these ML workloads pr

Serge Massar, Fabrice Devaux, Eric Lantz

Experimental demonstrations of entangled quantum images produced through parametric downconversion have so far been confined to studying two photon correlations. Here we show that multiphoton correlations between quantum images are accessible experimentally and exhibit many new features including being sensitive to the phase of the bi-photon wavefunction. As

Thomas Harris, Nicholas Geard, Cameron Zachreson

Early estimates of the transmission properties of a newly emerged pathogen are critical to an effective public health response, and are often based on limited outbreak data. Here, we use simulations to investigate a potential source of bias in such estimates, arising from correlations between the viral load of cases in transmission chains. We show that this

Miao Zhang, Rumi Chunara

Satellite imagery is being leveraged for many societally critical tasks across climate, economics, and public health. Yet, because of heterogeneity in landscapes (e.g. how a road looks in different places), models can show disparate performance across geographic areas. Given the important potential of disparities in algorithmic systems used in societal conte

Zhening Li, Gabriel Poesia, Omar Costilla-Reyes, Noah Goodman

Humans tame the complexity of mathematical reasoning by developing hierarchies of abstractions. With proper abstractions, solutions to hard problems can be expressed concisely, thus making them more likely to be found. In this paper, we propose Learning Mathematical Abstractions (LEMMA): an algorithm that implements this idea for reinforcement learning agent

SungGyu Chun, Bingqiang Ji, Zhengyu Yang, Vinit Kumar Malik

The motion of a long gas bubble in a confined capillary tube is ubiquitous in a wide range of engineering and biological applications. While the understanding of the deposited thin viscous film near the tube wall in Newtonian fluids is well developed, the deposition dynamics in commonly encountered non-Newtonian fluids remains much less studied. Here, we inv