October 2022 arXiv papers — page 19

Elodie Millan, Maxime Lavaud, Yacine Amarouchene, Thomas Salez

Brownian motion is a central scientific paradigm. Recently, due to increasing efforts and interests towards miniaturization and small-scale physics or biology, the effects of confinement on such a motion have become a key topic of investigation. Essentially, when confined near a wall, a particle moves much slower than in the bulk due to friction at the bound

Alexandre Perez-Lebel, Marine Le Morvan, Gaël Varoquaux

The ability to ensure that a classifier gives reliable confidence scores is essential to ensure informed decision-making. To this end, recent work has focused on miscalibration, i.e., the over or under confidence of model scores. Yet calibration is not enough: even a perfectly calibrated classifier with the best possible accuracy can have confidence scores t

Ilja Baumann, Dominik Wagner, Franziska Braun, Sebastian P. Bayerl

Recent findings show that pre-trained wav2vec 2.0 models are reliable feature extractors for various speaker characteristics classification tasks. We show that latent representations extracted at different layers of a pre-trained wav2vec 2.0 system can be used as features for binary classification to distinguish between children with Cleft Lip and Palate (CL

Guillaume Lauga, Elisa Riccietti, Nelly Pustelnik, Paulo Gonçalves

This paper presents a multilevel FISTA algorithm, based on the use of the Moreau envelope to build the correction brought by the coarse models, which is easy to compute when the explicit form of the proximal operator of the considered functions is known. This approach is supported by strong theoretical guarantees: we prove both the rate of convergence and th

Tianze Wu, Shaoshan Liu, Bo Yu, Sa Wang

Infrastructure-Vehicle Cooperative Autonomous Driving (IVCAD) is a new paradigm of autonomous driving, which relies on the cooperation between intelligent roads and autonomous vehicles. This paradigm has been shown to be safer and more efficient compared to the on-vehicle-only autonomous driving paradigm. Our real-world deployment data indicates that the eff

Lorenzo Gentilini, Michelangelo Bin, Lorenzo Marconi

This paper deals with the problem of adaptive output regulation for multivariable nonlinear systems by presenting a learning-based adaptive internal model-based design strategy. The approach builds on the recently proposed adaptive internal model design techniques based on the theory of nonlinear Luenberger observers, and the adaptation side is approached as

Atsushi Ando, Ryo Masumura, Akihiko Takashima, Satoshi Suzuki

This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA). Although the effectiveness of pre-trained encoders in various fields has been reported, conventional MSA methods employ them for only linguistic modality, and their application has not been investigated.

Zhengyang Chen, Yao Qian, Bing Han, Yanmin Qian

In real application scenarios, it is often challenging to obtain a large amount of labeled data for speaker representation learning due to speaker privacy concerns. Self-supervised learning with no labels has become a more and more promising way to solve it. Compared with contrastive learning, self-distilled approaches use only positive samples in the loss f

Stephen R. Green, Stefan Hollands, Laura Sberna, Vahid Toomani

We introduce a bilinear form for Weyl scalar perturbations of Kerr. The form is symmetric and conserved, and we show that, when combined with a suitable renormalization prescription involving complex r integration contours, quasinormal modes are orthogonal in the bilinear form for different (l, m, n). These properties are apparently not evident consequences

Ioana Boier

Multiresolution analysis has applications across many disciplines in the study of complex systems and their dynamics. Financial markets are among the most complex entities in our environment, yet mainstream quantitative models operate at predetermined scale, rely on linear correlation measures, and struggle to recognize non-linear or causal structures. In th

Baian Chen, Lipeng Gu, Xin Zhuang, Yiyang Shen

We propose PSFormer, an effective point transformer model for 3D salient object detection. PSFormer is an encoder-decoder network that takes full advantage of transformers to model the contextual information in both multi-scale point- and scene-wise manners. In the encoder, we develop a Point Context Transformer (PCT) module to capture region contextual feat

Xi Chen, James D. Lewis

Assuming the Kunneth decomposition of the Chow groups of products of general Kummer surfaces, we prove that the Hodge-${\mathcal D}$-conjecture fails for the real regulator $r_{k,1}$ on a product of $n$ general elliptic curves for $2n\ge 3k-1\ge 8$.

Kazuma Yonezu, Yutaro Enomoto, Takato Yoshida, Shuntaro Takeda

We demonstrate universal and programmable three-mode linear optical operations in the time domain by realizing a scalable dual-loop optical circuit suitable for universal quantum information processing (QIP). The programmability, validity, and deterministic operation of our circuit are demonstrated by performing nine different three-mode operations on squeez

Duy Nam Bui, Thi Thanh Van Nguyen, Manh Duong Phung

This paper presents a new Lyapunov-based nonlinear model predictive controller (LNMPC) for the attitude control problem of unmanned aerial vehicles (UAVs), which is essential for their functioning operation. The controller is designed based on a quadratic cost function integrating UAV dynamics and system constraints. An additional contraction constraint is t

Junfan Lin, Jianlong Chang, Lingbo Liu, Guanbin Li

Text-to-motion generation is an emerging and challenging problem, which aims to synthesize motion with the same semantics as the input text. However, due to the lack of diverse labeled training data, most approaches either limit to specific types of text annotations or require online optimizations to cater to the texts during inference at the cost of efficie

Gehui Xu, Guanpu Chen, Zhaoyang Cheng, Yiguang Hong

There has been significant recent interest in leader-follower security games, where the leader dominates the decision process with the Stackelberg equilibrium (SE) strategy. However, such a leader-follower scheme may become invalid in practice due to subjective or objective factors, and then the Nash equilibrium (NE) strategy may be an alternative option. In

Roberto Bramati, Matteo Dalla Riva, Paolo Luzzini, Paolo Musolino

We lay down the preliminary work to apply the Functional Analytic Approach to quasi-periodic boundary value problems for the Helmholtz equation. This consists in introducing a quasi-periodic fundamental solution and the related layer potentials, showing how they are used to construct the solutions of quasi-periodic boundary value problems, and how they behav

Hamid Fsian, Vahid Mohammadi, Pierre Gouton, Saeid Minaei

Stereo Matching is one of the classical problems in computer vision for the extraction of 3D information but still controversial for accuracy and processing costs. The use of matching techniques and cost functions is crucial in the development of the disparity map. This paper presents a comparative study of six different stereo matching algorithms including

Qiang Gao, Xinzhu Zhou, Kunpeng Zhang, Li Huang

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns. Recently, researchers have developed various (recurrent) neural network-based methods to tackle this problem. Without exceptions, they primarily leverage historical market volatility to enhance th

Shengling Wang, Lina Shi, Hongwei Shi, Yifang Zhang

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism

Seidon Alsaody

We study structurable algebras and their associated Freudenthal triple systems over commutative rings. The automorphism groups of these triple systems are exceptional groups of type $\mathrm{E}_7$, and we realize groups of type $\mathrm{E}_6$ as centralizers. When 6 is invertible, we further give a geometric description of homogeneous spaces of type $\mathrm

Naishadh Parmar, Raunak Shah, Tushar Goswamy, Vatsalya Tandon

The identification and control of human factors in climate change is a rapidly growing concern and robust, real-time air-quality monitoring and forecasting plays a critical role in allowing effective policy formulation and implementation. This paper presents DELFI, a novel deep learning-based mixture model to make effective long-term predictions of Particula

Andreas Burger, Leong Chuan Kwek, Dario Poletti

Digital quantum computers have the potential to simulate complex quantum systems. The spin-boson model is one of such systems, used in disparate physical domains. Importantly, in a number of setups, the spin-boson model is open, i.e. the system is in contact with an external environment which can, for instance, cause the decay of the spin state. Here we stud

Mourad Choulli, Abdelmalek Metidji, Éric Soccorsi

This article deals with the uniqueness and stability issues in the inverse problem of determining the unbounded potential of the Schr\"odinger operator in a bounded domain of dimension 3 or greater, endowed with Robin boundary condition, from knowledge of its boundary spectral data. These data are defined by the pairs formed by the eigenvalues and either ful

Ming-Yang Zhou, Manuel Sebastian Mariani, Hao Liao, Rui Mao

The functions of complex networks are usually determined by a small set of vital nodes. Finding the best set of vital nodes (eigenshield nodes) is critical to the network's robustness against rumor spreading and cascading failures, which makes it one of the fundamental problems in network science. The problem is challenging as it requires to maximize the inf

M. S. Nicholas Tey, Xiaoye Chen, Anjan Soumyanarayanan, Pin Ho

The realization of chiral spin textures - comprising myriad distinct, nanoscale arrangements of spins with topological properties - has established pathways for engineering robust, energy-efficient and scalable elements for non-volatile nanoelectronics. Particularly, current-induced manipulation of spin textures in nanowire racetracks and tunnel junction bas

Ruoyi Zhang, Haibo Yuan

We have obtained accurate dust reddening from far-ultraviolet (UV) to the mid-infrared (IR) for up to 5 million stars by the star-pair algorithm based on LAMOST stellar parameters along with GALEX, Pan-STARRS 1, Gaia, SDSS, 2MASS, and WISE photometric data. The typical errors are between 0.01-0.03 mag for most colors. We derived the empirical reddening coeff

Yanqing Xu, Bo Wang, Enbin Song, Qingjiang Shi

The traditional centralized baseband processing architecture is faced with the bottlenecks of high computation complexity and excessive fronthaul communication, especially when the number of antennas at the base station (BS) is large. To cope with these two challenges, the decentralized baseband processing (DPB) architecture has been proposed, where the BS a

Toshihiko Masuda

We apply Evans-Kishimoto's intertwining argument to the classification of actions of discrete amenable groups into the normalizer of a full group of an ergodic transformation. Our proof does not depend on the types of ergodic transformations.

Daniel Echeverri, Nemanja Jovanovic, Jacques-Robert Delorme, Yinzi Xin

The Keck Planet Imager and Characterizer (KPIC) is a series of upgrades for the Keck II Adaptive Optics (AO) system and the NIRSPEC spectrograph to enable diffraction-limited, high-resolution ($R>30,000$) spectroscopy of exoplanets and low-mass companions in the K and L bands. Phase I consisted of single-mode fiber injection/extraction units (FIU/FEU) used i

Philipp Koch, Viktor Stojkoski, César A. Hidalgo

Did migrants make Paris a Mecca for the arts and Vienna a beacon of classical music? Or was their rise a pure consequence of local actors? Here, we use data on more than 22,000 historical individuals born between the years 1000 and 2000 to estimate the contribution of famous immigrants, emigrants, and locals to the knowledge specializations of European regio

Zhaowei Chen, Peng Li, Zeyong Wei, Honghua Chen

We propose GeoGCN, a novel geometric dual-domain graph convolution network for point cloud denoising (PCD). Beyond the traditional wisdom of PCD, to fully exploit the geometric information of point clouds, we define two kinds of surface normals, one is called Real Normal (RN), and the other is Virtual Normal (VN). RN preserves the local details of noisy poin

Guillaume Cabanac

Research misconduct and frauds pollute the scientific literature. Honest errors and malevolent data fabrication, image manipulation, journal hijacking, and plagiarism passed peer review unnoticed. Problematic papers deceive readers, authors citing them, and AI-powered literature-based discovery. Flagship publishers accepted hundreds flawed papers despite cla

Beomseok Kang, Minah Lee, Harshit Kumar, Saibal Mukhopadhyay

Large multi-agent systems are often driven by locally defined agent interactions, which is referred to as self-organization. Our primary objective is to determine when the propagation of such local interactions will reach a specific agent of interest. Although conventional approaches that reconstruct all agent states can be used, they may entail unnecessary

Jiashu Wu, Yang Wang, Binhui Xie, Shuang Li

In this paper, we propose a Joint Semantic Transfer Network (JSTN) towards effective intrusion detection for large-scale scarcely labelled IoT domain. As a multi-source heterogeneous domain adaptation (MS-HDA) method, the JSTN integrates a knowledge rich network intrusion (NI) domain and another small-scale IoT intrusion (II) domain as source domains, and pr

Daniel Echeverri, Garreth Ruane, Nemanja Jovanovic, Jacques-Robert Delorme

As the number of confirmed exoplanets continues to grow, there is an increased push to spectrally characterize them to determine their atmospheric composition, formation paths, rotation rates, and habitability. However, there is a large population of known exoplanets that either do not transit their star or have been detected via the radial velocity (RV) met

Jogendra Nath Kundu, Suvaansh Bhambri, Akshay Kulkarni, Hiran Sarkar

Universal Domain Adaptation (UniDA) deals with the problem of knowledge transfer between two datasets with domain-shift as well as category-shift. The goal is to categorize unlabeled target samples, either into one of the "known" categories or into a single "unknown" category. A major problem in UniDA is negative transfer, i.e. misalignment of "known" and "u

Sriram Narayanan, Dinesh Jayaraman, Manmohan Chandraker

We address key challenges in long-horizon embodied exploration and navigation by proposing a new object transport task and a novel modular framework for temporally extended navigation. Our first contribution is the design of a novel Long-HOT environment focused on deep exploration and long-horizon planning where the agent is required to efficiently find and

Devansh Jalota, Jessica Lazarus, Alexandre Bayen, Marco Pavone

Credit-based congestion pricing (CBCP) has emerged as a mechanism to alleviate the social inequity concerns of road congestion pricing - a promising strategy for traffic congestion mitigation - by providing low-income users with travel credits to offset some of their toll payments. While CBCP offers immense potential for addressing inequity issues that hampe

Lin Guan, Karthik Valmeekam, Subbarao Kambhampati

Generating complex behaviors that satisfy the preferences of non-expert users is a crucial requirement for AI agents. Interactive reward learning from trajectory comparisons (a.k.a. RLHF) is one way to allow non-expert users to convey complex objectives by expressing preferences over short clips of agent behaviors. Even though this parametric method can enco

Jun-Sung Moon, Jounghun Lee

A numerical detection of the radius-dependent spin transition of dark matter halos is reported. Analyzing the data from the IllustrisTNG simulations, we measure the halo spin vectors at several inner radii within the virial boundaries and investigate their orientations in the principal frames of the tidal and velocity shear fields, called the Tweb and Vweb,

Bach Tran, Binh-Son Hua, Anh Tuan Tran, Minh Hoai

Recently, great progress has been made in 3D deep learning with the emergence of deep neural networks specifically designed for 3D point clouds. These networks are often trained from scratch or from pre-trained models learned purely from point cloud data. Inspired by the success of deep learning in the image domain, we devise a novel pre-training technique f

Ruijie Tao, Kong Aik Lee, Zhan Shi, Haizhou Li

Neural network-based speaker recognition has achieved significant improvement in recent years. A robust speaker representation learns meaningful knowledge from both hard and easy samples in the training set to achieve good performance. However, noisy samples (i.e., with wrong labels) in the training set induce confusion and cause the network to learn the inc

Qichao Ying, Hang Zhou, Zhenxing Qian, Sheng Li

Digital images are vulnerable to nefarious tampering attacks such as content addition or removal that severely alter the original meaning. It is somehow like a person without protection that is open to various kinds of viruses. Image immunization (Imuge) is a technology of protecting the images by introducing trivial perturbation, so that the protected image

Zheng Liu, Xiaohan Li, Philip Yu

Health disparities, or inequalities between different patient demographics, are becoming crucial in medical decision-making, especially in Electronic Health Record (EHR) predictive modeling. To ensure the fairness of sensitive attributes, conventional studies mainly adopt calibration or re-weighting methods to balance the performance on among different demog

Yong-Liang Zhao, Xian-Ming Gu

The extended Fisher--Kolmogorov (EFK) equation has been used to describe some phenomena in physical, material and biology systems. In this paper, we propose a full-rank splitting scheme and a rank-adaptive splitting approach for this equation. We first use a finite difference method to approximate the space derivatives. Then, the resulting semi-discrete syst

X. L. Liu, X. Q. Li

Based on the set of nonlinear coupling equations describing the interaction of the high-frequency field, the self-generated magnetic field and the ion-acoustic field, the dispersion relation for the circular magnetic field is obtained. The numerical results indicate that the strength of the magnetic field have influence on the growth rate of modulation insta

Taiki Miyagawa

We derive and solve an ``Equation of Motion'' (EoM) for deep neural networks (DNNs), a differential equation that precisely describes the discrete learning dynamics of DNNs. Differential equations are continuous but have played a prominent role even in the study of discrete optimization (gradient descent (GD) algorithms). However, there still exist gaps betw

Phuoc-Hieu Le, Quynh Le, Rang Nguyen, Binh-Son Hua

High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstruction. Recent attempts at single-image HDR

Yi Shi, Xiaodong Wang

For every $r\in\mathbb{N}_{\geq 2}\cup\{\infty\}$, we prove a $C^r$-orbit connecting lemma for dynamically coherent and plaque expansive partially hyperbolic diffeomorphisms with 1-dimensional orientation preserving center bundle. To be precise, for such a diffeomorphism $f$, if a point $y$ is chain attainable from $x$ through pseudo-orbits, then for any nei

Emir Syahreza Fadhilla, Ardian Nata Atmaja, Bobby Eka Gunara

In this paper, we elucidate the problem of gravitating Skyrmion governed by field equations of the Einstein-Skyrme system with no potential term in the Bondi coordinate. The spherical symmetry has to be assumed and both the metric functions and Skyrme ansatz depend on radial and retarded time coordinates which implies that the system is dynamic. We show that

Sovanlal Mondal, Madhumita Roy, Máté Wierdl

An increasing sequence $(a_n)$ of positive integers which satisfies $\frac{a_{n+1}}{a_n}>1+\eta$ for some positive $\eta$ is called a lacunary sequence. It has been known for over twenty years that every lacunary sequence is strong sweeping out which means that in every aperiodic dynamical system we can find a set $E$ of arbitrary small measure so that $\lim

Weiyan Shi, Emily Dinan, Kurt Shuster, Jason Weston

Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. However, humans may not always provide explicit signals when the chatbot makes mistakes during interactions. In this work, we propose Juicer, a framework to make use of both binary and free-form textual human feedback. It works by: (i) extending sparse

Xingzhuo Chen, Lifan Wang, Lei Hu, Peter J. Brown

Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses (Chen et al. 2020), we train a set of deep neural networks based on the one-dimensional radiative transfer code TARDIS (Kerzendorf & Sim 2014) to simulate the optical spectra of Type Ia supernovae (SNe Ia) between 10 and 40 days after the explosion. The ne

B. E. Grinyuk, D. V. Piatnytskyi, V. S. Vasilevsky

We study the ground and the first excited $0^{+}$ states of two mirror nuclei $^{14}$C and $^{14}$O within a five-cluster model (three alpha-particles and two extra nucleons) with the use of high accuracy variational approach with Gaussian bases. The first excited $0^{+}$ state of these nuclei is shown to be connected with a change of the structure of the tw

Namitha Anna Koshi, Anup Kumar Mandia, Bhaskaran Muralidharan, Seung-Cheol Lee

The Hall scattering factor of Sc2CF2, Sc2CO2 and Sc2C(OH)2 is calculated using Rode's iterative approach by solving the Boltzmann transport equation. This is carried out in conjunction with calculations based on density functional theory. The electrical transport in Sc2CF2, Sc2CO2, and Sc2C(OH)2 is modelled by accounting for both elastic (acoustic and piezoe

Wenguan Wang, Yi Yang, Fei Wu

Neural-symbolic computing (NeSy), which pursues the integration of the symbolic and statistical paradigms of cognition, has been an active research area of Artificial Intelligence (AI) for many years. As NeSy shows promise of reconciling the advantages of reasoning and interpretability of symbolic representation and robust learning in neural networks, it may

Richard Qiu, Plamen Krastev, Kiranjyot Gill, Edo Berger

The Laser Interferometer Gravitational-Wave Observatory (LIGO) and Virgo Interferometer Collaborations have now detected all three classes of compact binary mergers: binary black hole (BBH), binary neutron star (BNS), and neutron star-black hole (NSBH). For coalescences involving neutron stars, the simultaneous observation of gravitational and electromagneti

Reo Yoneyama, Ryuichi Yamamoto, Kentaro Tachibana

Neural audio super-resolution models are typically trained on low- and high-resolution audio signal pairs. Although these methods achieve highly accurate super-resolution if the acoustic characteristics of the input data are similar to those of the training data, challenges remain: the models suffer from quality degradation for out-of-domain data, and paired

Eric Bahuaud, Christine Guenther, James Isenberg, Rafe Mazzeo

We present simple conditions which ensure that a strongly elliptic operator $L$ generates an analytic semigroup on H\"older spaces on an arbitrary complete manifold of bounded geometry. This is done by establishing the equivalent property that $L$ is "sectorial", a condition that specifies the decay of the resolvent $(\lambda I - L)^{-1}$ as $\lambda$ diverg

Balsam Alkouz, Amani Abusafia, Abdallah Lakhdari, Athman Bouguettaya

We propose a novel framework for swarm-based drone delivery services with in-flight energy recharging. The framework aims to enhance the delivery time of multiple packages by reducing the number of stops and recharging times at intermediate stations. The proposed framework considers various intrinsic and extrinsic delivery constraints. We propose to use supp

Noemie Globus, Anatoli Fedynitch, Roger D. Blandford

The origin of Ultra High Energy Cosmic Rays is a 60-year old mystery. We show that with more events at the highest energies (above 150~EeV) it may be possible to limit the character of the sources and learn about the intervening magnetic fields. Individual sources become more prominent, relative to the background, as the horizon diminishes. An event-by-event

Gustavo Magallanes-Guijón, Sergio Mendoza

Using multifrequency observations, from radio to gamma-rays of the blazar Mrk~501, we constructed their corresponding light curves and built periodograms using RobPer and Lomb-Scargle algorithms. Long-term variability was also studied using the power density spectrum and the detrended function analysis. Using the software VARTOOLS, we also computed the analy

Hadi Hadizadeh, Ivan V. Bajić

End-to-end learning-based video compression has made steady progress over the last several years. However, unlike learning-based image coding, which has already surpassed its handcrafted counterparts, learning-based video coding still has some ways to go. In this paper we present learned conditional coding modes for video coding (LCCM-VC), a video coding mod

Byung-Hak Kim, Zhongfen Deng, Philip S. Yu, Varun Ganapathi

The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods. However, most previous SOTA works based on deep learning are still in early stages in terms of providing textual

Tomohiro Tetsumoto, Antoine Rolland

A Kerr microresonator frequency comb has enabled the generation of low-phase-noise millimeter- and terahertz-waves in conjunction with an ultrafast photodiode. It is intriguing to employ the new light source in wireless communication at above 100 GHz band, where a carrier signal with a high signal-to-noise ratio is desired to achieve higher data rates. In th

Dongyang Wang, Biao Yang, Mudi Wang, Ruo-Yang Zhang

Nodal links are special configurations of band degeneracies in the momentum space, where nodal line branches encircle each other. In PT symmetric systems, nodal lines can be topologically characterized using the eigenvector frame rotations along an encircling loop and the linking structure can be described with non-Abelian frame charges interacting among adj

Haiguang Liao, Vinay Patil, Xuliang Dong, Devika Shanbhag

We present an automatic multilayer power plane generation method to accelerate the design of printed circuit boards (PCB). In PCB design, while automatic solvers have been developed to predict important indicators such as the IR-drop, power integrity, and signal integrity, the generation of the power plane itself still largely relies on laborious manual meth

Zhounan Chen, Daihui Yang, Jinglin Liang, Xinwu Liu

Many important tasks such as forensic signature verification, calligraphy synthesis, etc, rely on handwriting trajectory recovery of which, however, even an appropriate evaluation metric is still missing. Indeed, existing metrics only focus on the writing orders but overlook the fidelity of glyphs. Taking both facets into account, we come up with two new met

Bowen Ma, Rudong An, Wei Zhang, Yu Ding

As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e.g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation. Thus a long-standing challenge of FAU analysis arises from the data scarcity of manual annotations, limiting the generalization abili

Dhawal Buaria, Katepalli R. Sreenivasan

We study the properties of various Eulerian contributions to fluid particle acceleration by using well-resolved direct numerical simulations of isotropic turbulence, with the grid resolution as high as $12288^3$ and the Taylor-scale Reynolds number $R_\lambda$ in the range between 140 and 1300. The variance of convective acceleration, when normalized by Kolm

Yist Y. Lin, Tao Han, Haihua Xu, Van Tung Pham

One of limitations in end-to-end automatic speech recognition (ASR) framework is its performance would be compromised if train-test utterance lengths are mismatched. In this paper, we propose an on-the-fly random utterance concatenation (RUC) based data augmentation method to alleviate train-test utterance length mismatch issue for short-video ASR task. Spec

Michael Eastwood

We linearise the Einstein vacuum equations with a cosmological constant via the Calabi operator from projective differential geometry.

Xuefeng Yang, Li Liu, Wenju Zhou, Jing Shi

This paper focuses on discrete-time wireless sensor networks with privacy-preservation. In practical applications, information exchange between sensors is subject to attacks. For the information leakage caused by the attack during the information transmission process, privacy-preservation is introduced for system states. To make communication resources more

William Berquist, Danylo Lykov, Minzhao Liu, Yuri Alexeev

Noisy quantum simulation is challenging since one has to take into account the stochastic nature of the process. The dominating method for it is the density matrix approach. In this paper, we evaluate conditions for which this method is inferior to a substantially simpler way of simulation. Our approach uses stochastic ensembles of quantum circuits, where ra

Tomohito Amano, Tamio Yamazaki, Ryosuke Akashi, Terumasa Tadano

We calculate the lattice dielectric function of strongly anharmonic rutile $\mathrm{TiO}_2$ from ab initio anharmonic lattice dynamics methods. Since an accurate calculation of the $\Gamma$ point phonons is essential for determining optical properties, we employ the modified self-consistent approach, including third-order anharmonicity as well as fourth-orde

Jianfeng Wu, Yi Su, Wenhui Zhu, Negar Jalili Mallak

Background: Beta-amyloid (A$\beta$) plaques and tau protein tangles in the brain are the defining 'A' and 'T' hallmarks of Alzheimer's disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative ('N') biomarkers comprise the ''ATN framework'' of AD. Current methods to detect A$

Wu Haiwei, Zhou Jiantao, Zhang Shile, Tian Jinyu

With the continuous research on Deepfake forensics, recent studies have attempted to provide the fine-grained localization of forgeries, in addition to the coarse classification at the video-level. However, the detection and localization performance of existing Deepfake forensic methods still have plenty of room for further improvement. In this work, we prop

Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang

We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features. There are different ways to understand the dynamic emphasis of a language expression, especially when interacting with the image. However, the lea

Ryotaro Shimizu, Masanari Kimura, Masayuki Goto

Several techniques to map various types of components, such as words, attributes, and images, into the embedded space have been studied. Most of them estimate the embedded representation of target entity as a point in the projective space. Some models, such as Word2Gauss, assume a probability distribution behind the embedded representation, which enables the

Federico Bianchi, Stefanie Anja Hills, Patricia Rossini, Dirk Hovy

Well-annotated data is a prerequisite for good Natural Language Processing models. Too often, though, annotation decisions are governed by optimizing time or annotator agreement. We make a case for nuanced efforts in an interdisciplinary setting for annotating offensive online speech. Detecting offensive content is rapidly becoming one of the most important

Tingting Huang

Interval-valued data receives much attention due to its wide applications in the fields of finance, econometrics, meteorology and medicine. However, most regression models developed for interval-valued data assume observations are mutually independent, not adapted to the scenario that individuals are spatially correlated. We propose a new linear model to acc

Nobuyuki Morioka, Heiga Zen, Nanxin Chen, Yu Zhang

Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model. However, serving hundreds of fine-tuned neural TTS models is expensive as each of them requires significant footprint and separate computational resources (e.g., accelerators, memory).

Gaofeng Ding, Hongliang Wo, Rui Leonard Luo, Yimeng Gu

The rare-earth triangular-lattice chalcogenide is a great platform for exploring both spin liquids and novel magnetic orders with anisotropic spin interactions and magnetic frustrations. Here, we report the thermodynamic and neutron scattering measurements of rare-earth triangular-lattice chalcogenide KErSe$_{2}$, using single-crystal samples. Our experiment

S. Giraud, J. C. Zamora, R. Zegers, D. Bazin

For the first time, the $(d,{}^2\text{He})$ reaction was successfully used in inverse kinematics to extract the Gamow-Teller transition strength in the $\beta^{+}$ direction from an unstable nucleus. The nucleus studied was $^{14}$O, and the Gamow-Teller transition strength to $^{14}$N was extracted up to an excitation energy of 22 MeV. The measurement of th

Chang Liu, Yuwen Yang, Xun Cai, Yue Ding

Federated learning (FL) faces three major difficulties: cross-domain, heterogeneous models, and non-i.i.d. labels scenarios. Existing FL methods fail to handle the above three constraints at the same time, and the level of privacy protection needs to be lowered (e.g., the model architecture and data category distribution can be shared). In this work, we prop

Junya Endo, Hiroyasu Matsuura, Masao Ogata

We study the properties of the upper critical field of superconductivity in nodal-line semimetals in a continuous model, which has a nodal-line on the $k_{z} = 0$ plane. Using the semiclassical Green's function method, we calculate the upper critical field for the two limiting cases: the dirty limit with many impurities and the clean limit with few impuritie

M. H. Ding, H. Y. Liu, G. H. Zheng

This paper investigates the shape reconstructions of sub-wavelength objects from near-field measurements in transverse electromagnetic scattering. This geometric inverse problem is notoriously ill-posed and challenging. We develop a novel reconstruction scheme using plasmon resonances with significantly enhanced sensitivity and resolution. First, by spectral

Yue Ying, Zhuo-Zhi Zhang, Joel Moser, Zi-Jia Su

The motion of a vibrating object is determined by the way it is held. This simple observation has long inspired string instrument makers to create new sounds by devising elegant string clamping mechanisms, whereby the distance between the clamping points is modulated as the string vibrates. At the nanoscale, the simplest way to emulate this principle would b

Makoto Morishita, Jun Suzuki, Masaaki Nagata

Although a machine translation model trained with a large in-domain parallel corpus achieves remarkable results, it still works poorly when no in-domain data are available. This situation restricts the applicability of machine translation when the target domain's data are limited. However, there is great demand for high-quality domain-specific machine transl

Surbhi Khetrapal

We study the out-of-time-ordered correlator (OTOC) in a zero temperature two dimensional conformal field theory (CFT) under evolution by a Liouvillian composed of the Virasoro generators. A bound was conjectured in arXiv:1812.08657 on the growth of the OTOC set by the Krylov complexity which is a measure of operator growth. The latter grows as an exponential

Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum

Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvements compared to standard LMs. One such approach, the $k$NN-LM, interpolates any existing LM's predictions with the output of a $k$-nearest neighbors model and requires no additional

Xingrui Yang, Hai Li, Hongjia Zhai, Yuhang Ming

In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods. Our approach is inspired by the recently developed implicit mapping and positioning system and further extends the idea so that it can be freely applied to practical scenarios. Speci

M. M. González, D. Avila Rojas, A. Pratts, S Hernández-Cadena

Gamma-ray bursts (GRBs) have been considered as potential very high-energy photon emitters due to the large amount of energy released as well as the strong magnetic fields involved in their jets. However, the detection of TeV photons is not expected from bursts beyond a redshift of $z\gtrsim 0.1$ due to their attenuation with the extragalactic background lig

Jintai Liang, Yueming Zhou, Wei-Chao Jiang, Min Li

The magnetic-field effect of the laser pulse is investigated in laser-assisted XUV ionization. By numerically solving the three-dimensional time-dependent Schr\"odinger equation, we find that the photoelectron momentum distribution is distorted by the magnetic-field effect of the IR streaking field. It results in a transverse-momentum- and time-delay-depende

Khai Doan, Wesley Araujo, Evangelos Kranakis, Ioannis Lambadaris

The recent drastic increase in mobile data traffic has pushed the mobile edge computing systems to the limit of their capacity. A promising solution to this problem is the task migration provided by unmanned aerial vehicles (UAV). Key factors to be taken into account in the design of UAV offloading schemes must include the number of tasks waiting in the syst

Zhaowei Liu, Andrew McBride, Abhishek Ghosh, Luca Heltai

The inflation of hyperelastic thin shells is an important and highly nonlinear problem that arises in multiple engineering applications involving severe kinematic and constitutive nonlinearities in addition to various instabilities. We present an isogeometric approach to compute the inflation of hyperelastic thin shells, following the Kirchhoff-Love hypothes

Shulin He, Wei Rao, Jinjiang Liu, Jun Chen

Most neural network speech enhancement models ignore speech production mathematical models by directly mapping Fourier transform spectrums or waveforms. In this work, we propose a neural source filter network for speech enhancement. Specifically, we use homomorphic signal processing and cepstral analysis to obtain noisy speech's excitation and vocal tract. U

Joel Meyer, Allison Pinosky, Thomas Trzpit, Ed Colgate

We present a game benchmark for testing human-swarm control algorithms and interfaces in a real-time, high-cadence scenario. Our benchmark consists of a swarm vs. swarm game in a virtual ROS environment in which the goal of the game is to capture all agents from the opposing swarm; the game's high-cadence is a result of the capture rules, which cause agent t

Shuhao Gu, Yang Feng

The many-to-many multilingual neural machine translation can translate between language pairs unseen during training, i.e., zero-shot translation. Improving zero-shot translation requires the model to learn universal representations and cross-mapping relationships to transfer the knowledge learned on the supervised directions to the zero-shot directions. In