August 2022 arXiv papers — page 133

Bing Han, Zhengyang Chen, Zhikai Zhou, Yanmin Qian

This paper presents the SJTU system for both text-dependent and text-independent tasks in short-duration speaker verification (SdSV) challenge 2021. In this challenge, we explored different strong embedding extractors to extract robust speaker embedding. For text-independent task, language-dependent adaptive snorm is explored to improve the system performanc

Shao-Wen Wei, Yu-Xiao Liu, Robert B. Mann

In this work, employing the generalized off-shell free energy, we treat black hole solutions as defects in the thermodynamic parameter space. The results show that the positive and negative winding numbers corresponding to the defects indicate the local thermodynamical stable and unstable black hole solutions, respectively. The topological number $W$ defined

Oded Shor, Felix Benninger, Andrei Khrennikov

Quantum mechanics (QM) is derived based on a universe composed solely of events, for example, outcomes of observables. Such an event universe is represented by a dendrogram (a finite tree) and in the limit of infinitely many events by the p-adic tree. The trees are endowed with an ultrametric expressing hierarchical relationships between events. All events a

Jaemo Park, Hyeonjoon Shin

According to the covariant open superstring description of 1/2-BPS D-branes in the AdS$_5\times$S$^5$ background, there are two kinds of purely instantonic D-branes. One is the well known D(-1)-brane or D-instanton, and another is the D1-brane spanning a two dimensional subspace inside S$^5$. We identify the actual 1/2-BPS instantonic D1-brane configurations

Xiaoyu Weng, Yu Miao, Yang Li, Xiangmei Dong

Fractional-order vector vortex beams are recently demonstrated to be new carriers of fractional-strength optical vortices. However, why can those new vortex beams formed by the combination of both unstable states propagate stably in free space? Here, we solve this scientific problem by revealing the physical essence of propagable fractional-strength optical

Bing Han, Zhengyang Chen, Yanmin Qian

For self-supervised speaker verification, the quality of pseudo labels decides the upper bound of the system due to the massive unreliable labels. In this work, we propose dynamic loss-gate and label correction (DLG-LC) to alleviate the performance degradation caused by unreliable estimated labels. In DLG, we adopt Gaussian Mixture Model (GMM) to dynamically

Yanping Lu

In this paper, we consider an inference problem for the first order autoregressive process with non-zero mean driven by a long memory stationary Gaussian process. Suppose that the covariance function of the noise can be expressed as $|k|^{2H-2}$ times a positive constant when $k$ tends to infinity, and the fractional Gaussian noise and the fractional ARIMA m

Vladimir I. Trofimov

A graph $\Gamma$ is called locally finite if, for each vertex $v$ of $\Gamma$, the set $\Gamma(v)$ of all neighbors of $v$ in $\Gamma$ is finite. For any locally finite graph $\Gamma$ with vertex set $V(\Gamma)$ and for any field $F$, let $F^{V(\Gamma)}$ be the vector space over $F$ of all functions $V(\Gamma) \to F$ (with natural componentwise operations) a

Xiangrui Zhao, Sheng Yang, Tianxin Huang, Jun Chen

Poles and building edges are frequently observable objects on urban roads, conveying reliable hints for various computer vision tasks. To repetitively extract them as features and perform association between discrete LiDAR frames for registration, we propose the first learning-based feature segmentation and description model for 3D lines in LiDAR point cloud

Kwanyong Park, Sanghyun Woo, Seoung Wug Oh, In So Kweon

Recently, memory-based approaches show promising results on semi-supervised video object segmentation. These methods predict object masks frame-by-frame with the help of frequently updated memory of the previous mask. Different from this per-frame inference, we investigate an alternative perspective by treating video object segmentation as clip-wise mask pro

Zhou Yue, Liu ZhiGang, Yuan Ye

With the development of blockchain technology, the cryptocurrency based on blockchain technology is becoming more and more popular. This gave birth to a huge cryptocurrency transaction network has received widespread attention. Link prediction learning structure of network is helpful to understand the mechanism of network, so it is also widely studied in cry

Ying Chen, Tian-Yu Ye

In this paper, a semiquantum secret sharing (SQSS) protocol based on x-type states is proposed, which can accomplish the goal that only when two classical communicants cooperate together can they extract the shared secret key of a quantum communicant. Detailed security analysis turns out that this protocol is completely robust against an eavesdropper. This p

Manuel K. -H. Müller, Nils R. Scheithauer

The transformation behaviour of the vector valued theta function of a positive-definite even lattice under the metaplectic group $\mathrm{Mp}_2(\mathbb{Z})$ is described by the Weil representation. We show that the invariants of this representation are induced from $5$ fundamental invariants. As an application we give simple generating sets for Jacobi forms

Runzhe Xu, Yunhe Bai, Jingsong Zhou, Jiaheng Li

Ultrathin films of intrinsic magnetic topological insulator MnBi2Te4 exhibit fascinating quantum properties such as quantum anomalous Hall effect and axion insulator state. In this work, we systematically investigate the evolution of the electronic structure of MnBi2Te4 thin films. With increasing film thickness, the electronic structure changes from an insu

Sicheng Zhang, Jiarun Yu, Zhida Bao, Shiwen Mao

Artificial intelligence (AI) technology has provided a potential solution for automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are vulnerable to adversarial examples, which seriously threatens the efficient, secure and trusted application of AI in AMC. This issue has attracted the attention of researchers. Various studies on adversa

Daniele Brambilla, Davide Maria Giacomini, Luca Muscarnera, Andrea Mazzoleni

New powerful tools for tackling life science problems have been created by recent advances in machine learning. The purpose of the paper is to discuss the potential advantages of gene recommendation performed by artificial intelligence (AI). Indeed, gene recommendation engines try to solve this problem: if the user is interested in a set of genes, which othe

Mireille Fares, Michele Grimaldi, Catherine Pelachaud, Nicolas Obin

Modeling virtual agents with behavior style is one factor for personalizing human agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs zero shot multimodal style transfer drive

MyeongAh Cho, Tae-young Chung, Hyeongmin Lee, Sangyoun Lee

The region proposal task is to generate a set of candidate regions that contain an object. In this task, it is most important to propose as many candidates of ground-truth as possible in a fixed number of proposals. In a typical image, however, there are too few hard negative examples compared to the vast number of easy negatives, so region proposal networks

Bo-Yong Chen, Yuanpu Xiong

The $p-$Bergman kernel $K_p(\cdot)$ is shown to be of $C^{1,1/2}$ for $1<p<\infty$. An unexpected relation between the off-diagonal $p-$Bergman kernel $K_p(\cdot,z)$ and certain weighted $L^2$ Bergman kernel is given for $1\le p\le 2$. As applications, we show that for each $1\le p\le 2$, $K_p(\cdot,z)\in L^q(\Omega)$ for $q< \frac{2pn}{2n-\alpha(\Omega)}$ a

Nicola Apollonio, Paolo G. Franciosa, Daniele Santoni

Homophily is the principle whereby "similarity breeds connections". We give a quantitative formulation of this principle within networks. Given a network and a labeled partition of its vertices, the vector indexed by each class of the partition, whose entries are the number of edges of the subgraphs induced by the corresponding classes, is viewed as the obse

Penglei Gao, Xi Yang, Rui Zhang, Ping Guo

While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in the present continuous methods. The dynamical systems of multivariate time series could be modelled with complex unknown partial differential equations (PDEs) which play a prominent

Jens-Michalis Papaioannou, Paul Grundmann, Betty van Aken, Athanasios Samaras

Clinical phenotyping enables the automatic extraction of clinical conditions from patient records, which can be beneficial to doctors and clinics worldwide. However, current state-of-the-art models are mostly applicable to clinical notes written in English. We therefore investigate cross-lingual knowledge transfer strategies to execute this task for clinics

Dennis Soldin

IceCube is a cubic-kilometer Cherenkov detector in the deep ice at the geographic South Pole. The dominant event yield in the deep ice detector consists of penetrating atmospheric muons with energies above approximately 300 GeV, produced in cosmic ray air showers. In addition, the surface array, IceTop, measures the electromagnetic component and GeV muons of

Zdravko Marinov, David Schneider, Alina Roitberg, Rainer Stiefelhagen

Domain shifts, such as appearance changes, are a key challenge in real-world applications of activity recognition models, which range from assistive robotics and smart homes to driver observation in intelligent vehicles. For example, while simulations are an excellent way of economical data collection, a Synthetic-to-Real domain shift leads to a > 60% drop i

Xingchen Li, Long Chen, Jian Shao, Shaoning Xiao

Current Scene Graph Generation (SGG) methods tend to predict frequent predicate categories and fail to recognize rare ones due to the severe imbalanced distribution of predicates. To improve the robustness of SGG models on different predicate categories, recent research has focused on unbiased SGG and adopted mean Recall@K (mR@K) as the main evaluation metri

Cosimo Sguanci, Anastasios Sidiropoulos

The Lightning Network (LN) has enjoyed rapid growth over recent years, and has become the most popular scaling solution for the Bitcoin blockchain. The security of the LN relies on the ability of the nodes to close a channel by settling their balances, which requires confirming a transaction on the Bitcoin blockchain within a pre-agreed time period. We study

Atsushi Suzuki

A new hybrid algorithm for LDU-factorization for large sparse matrix combining iterative solver, which can keep the same accuracy as the classical factorization, is proposed. The last Schur complement will be generated by iterative solver for multiple right-hand sides using block GCR method with the factorization in lower precision as a preconditioner, which

Anders Dyhr Sandgaard, Noam Shemesh, Sune Nørhøj Jespersen, Valerij G. Kiselev

Estimating magnetic susceptibility using MRI depends on inverting a forward relationship between the susceptibility and measured Larmor frequency. However, an often-overlooked constraint in susceptibility fitting is that the Larmor frequency is only measured inside the sample, and after background field removal, susceptibility sources should only reside insi

Sunkyu Yu, Namkyoo Park

Developing hardware for high-dimensional unitary operators plays a vital role in implementing quantum computations and deep learning accelerations. Programmable photonic circuits are singularly promising candidates for universal unitaries owing to intrinsic unitarity, ultrafast tunability, and energy efficiency of photonic platforms. Nonetheless, when the sc

Yuli Sun, Lin Lei, Dongdong Guan, Gangyao Kuang

This is the second part of the paper that provides a new strategy for the heterogeneous change detection (HCD) problem, that is, solving HCD from the perspective of graph signal processing (GSP). We construct a graph to represent the structure of each image, and treat each image as a graph signal defined on the graph. In this way, we can convert the HCD prob

Heejun Yang, Chaebin Kim, Ysun Choi, Jun Han Lee

Kitaev physics has recently attracted attention in condensed matter for its anticipated novel quantum spin liquid state. The thermal transport measurement is crucial for probing the novel features of charge-neutral quasiparticles. In this letter, we report a significant thermal Hall effect in $Na_2Co_2TeO_6$ (NCTO), a Kitaev quantum spin liquid candidate, wh

Jože M Rožanec, Bojan Nemec

One of the most important challenges in robotics is producing accurate trajectories and controlling their dynamic parameters so that the robots can perform different tasks. The ability to provide such motion control is closely related to how such movements are encoded. Advances on deep learning have had a strong repercussion in the development of novel appro

Sebastian Lorek, Anders Johansen

We explore the growth of planetary embryos by planetesimal accretion up to and beyond the point where pebble accretion becomes efficient at the so-called Hill-transition mass. Both the transition mass and the characteristic mass of planetesimals formed by the streaming instability increase with increasing distance from the star. We developed a model for the

Siyuan Wang, Qiong Wu, Qiang Fan, Pingyi Fan

Vehicular networks enable vehicles support real-time vehicular applications through training data. Due to the limited computing capability, vehicles usually transmit data to a road side unit (RSU) at the network edge to process data. However, vehicles are usually reluctant to share data with each other due to the privacy issue. For the traditional federated

S. Anukumar Kathirvel, Peter J. Cameron, T. Tamizh Chelvam

Let G be a finite group with identity e and H \neq \{e\} be a subgroup of G. The generalized non-coprime graph GAmma_{G,H} of G with respect to H is the simple undirected graph with G - \{e \}\) as the vertex set and two distinct vertices a and b are adjacent if and only if \gcd(|a|,|b|) \neq 1 and either a \in H or b \in H, where |a| is the order of a\in G.

Tian Xu, Ziniu Li, Yang Yu, Zhi-Quan Luo

Imitation learning learns a policy from expert trajectories. While the expert data is believed to be crucial for imitation quality, it was found that a kind of imitation learning approach, adversarial imitation learning (AIL), can have exceptional performance. With as little as only one expert trajectory, AIL can match the expert performance even in a long h

Yixin Fei, Zhongkai Zhao, Siwei Yang, Bingchen Zhao

We address the problem of generalized category discovery (GCD) in this paper, i.e. clustering the unlabeled images leveraging the information from a set of seen classes, where the unlabeled images could contain both seen classes and unseen classes. The seen classes can be seen as an implicit criterion of classes, which makes this setting different from unsup

Mei Chee Leong, Haosong Zhang, Hui Li Tan, Liyuan Li

Fine-grained action recognition is a challenging task in computer vision. As fine-grained datasets have small inter-class variations in spatial and temporal space, fine-grained action recognition model requires good temporal reasoning and discrimination of attribute action semantics. Leveraging on CNN's ability in capturing high level spatial-temporal featur

Anjun Chu, Asier Piñeiro Orioli, Diego Barberena, James K. Thompson

We propose a new direction in quantum simulation that uses multilevel atoms in an optical cavity as a toolbox to engineer new types of bosonic models featuring correlated hopping processes in a synthetic ladder spanned by atomic ground states. The underlying mechanisms responsible for correlated hopping are collective cavity-mediated interactions that dress

Katja Tuma, Romy Van Der Lee

Cybersecurity threat and risk analysis (RA) approaches are used to identify and mitigate security risks early-on in the software development life-cycle. Existing approaches automate only parts of the analysis procedure, leaving key decisions in identification, feasibility and risk analysis, and quality assessment to be determined by expert judgement. Therefo

M. Juvela, E. Mannfors, T. Liu, L. V. Toth

Study of pre-stellar cloud evolution requires observations with high sensitivity and resolution, and regions of high-mass star formation are particularly challenging. We wish to quantify, to what accuracy the physical conditions within a massive star-forming cloud can be determined from observations. We are particularly interested in the possibilities offere

Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, Bernhard Schölkopf

Normalizing flows are tractable density models that can approximate complicated target distributions, e.g. Boltzmann distributions of physical systems. However, current methods for training flows either suffer from mode-seeking behavior, use samples from the target generated beforehand by expensive MCMC methods, or use stochastic losses that have high varian

C. Piselli, A. Fronzetti Colladon, L. Segneri, A. L. Pisello

The implementation of energy communities represents a cross-disciplinary phenomenon that has the potential to support the energy transition while fostering citizens' participation throughout the energy system and their exploitation of renewables. An important role is played by online information sources in engaging people in this process and increasing their

Yasuyuki Hatsuda, Kazumi Okuyama

Recently Dorigoni, Green and Wen conjectured a remarkable exact formula for an integrated correlator of four superconformal primary operators in $\mathcal{N}=4$ supersymmetric Yang-Mills theory. In this work, we investigate its large $N$ limit in detail. We show that the formula of Dorigoni, Green and Wen can be recast into the sum over the contributions of

Qiong Wu, Xiaobo Wang, Qiang Fan, Pingyi Fan

Federated edge learning (FEEL) technology for vehicular networks is considered as a promising technology to reduce the computation workload while keeping the privacy of users. In the FEEL system, vehicles upload data to the edge servers, which train the vehicles' data to update local models and then return the result to vehicles to avoid sharing the original

Jiarui Jin, Xianyu Chen, Weinan Zhang, Yuanbo Chen

Modelling the user's multiple behaviors is an essential part of modern e-commerce, whose widely adopted application is to jointly optimize click-through rate (CTR) and conversion rate (CVR) predictions. Most of existing methods overlook the effect of two key characteristics of the user's behaviors: for each item list, (i) contextual dependence refers to that

Sharon E. Meidt

Molecular gas disks are generally Toomre stable ($Q_T>$1) and yet clearly gravitationally unstable to structure formation as evidenced by the existence of molecular clouds and ongoing star formation. This paper adopts a 3D perspective to obtain a general picture of instabilities in flattened rotating disks, using the 3D dispersion relation to describe how di

Abdul Halim, Abdur Rohim, B. V. Rathish Kumar, Ripan Saha

In this paper, we propose a fourth order PDE model for image inpainting based on a variant of the famous Mumford-Shah (MS) image segmentation model. Convexity splitting is used to discrtised the time and we replace the Laplacian by its fractional counterpart in the time discretised scheme. Fourier spectral method is used for space discretization. Consistency

Majid Rafiei, Gamal Elkoumy, Wil M. P. van der Aalst

Process mining employs event data extracted from different types of information systems to discover and analyze actual processes. Event data often contain highly sensitive information about the people who carry out activities or the people for whom activities are performed. Therefore, privacy concerns in process mining are receiving increasing attention. To

Bernard Mans, Min Sha, Igor E. Shparlinski, Daniel Sutantyo

We study functional graphs generated by several quadratic polynomials, acting simultaneously on a finite field of odd characteristic. We obtain several results about the number of leaves in such graphs. In particular, in the case of graphs generated by three polynomials, we relate the distribution of leaves to the Sato-Tate distribution of Frobenius traces o

Wojciech Florkowski, Avdhesh Kumar, Aleksas Mazeliauskas, Radoslaw Ryblewski

We briefly review the thermal model predictions related to longitudinal spin polarization of $\Lambda-$hyperons emitted from a hot and rotating hadronic medium produced in the non-central relativistic heavy ion collisions.

Daniela Pagnani, Łukasz H. Kocewiak, Jesper Hjerrild, Frede Blaabjerg

Power systems are currently experiencing a transition towards decarbonisation of electrical generation through large-scale deployment of renewable energy sources. These are gradually replacing conventional thermal power plants which today are the main providers of black start (BS) services. Consequently, in case of a total/partial blackout, conventional blac

Shigeo S. Kimura, Kenji Toma, Hirofumi Noda, Kazuhiro Hada

Supermassive black holes in active galactic nuclei launch relativistic jets, as indicated by observed superluminal radio blobs. The energy source of these jets is widely discussed in the theoretical framework of Blandford-Znajek process, the electromagnetic energy extraction from rotating black holes (BHs), while formation mechanism of the radio blobs in the

Yuli Sun, Lin Lei, Dongdong Guan, Gangyao Kuang

This paper provides a new strategy for the Heterogeneous Change Detection (HCD) problem: solving HCD from the perspective of Graph Signal Processing (GSP). We construct a graph for each image to capture the structure information, and treat each image as the graph signal. In this way, we convert the HCD into a GSP problem: a comparison of the responses of the

Yujie Yao, Hao Zhou, Melike Erol-Kantarci

User location is a piece of critical information for network management and control. However, location uncertainty is unavoidable in certain settings leading to localization errors. In this paper, we consider the user location uncertainty in the mmWave networks, and investigate joint vision-aided sensing and communications using deep reinforcement learning-b

Dan Song, Lin-Qing Song, Shu-Yi Kong, Jun He

In this work, we perform a systematic study of possible molecular states composed of two charmed baryons including hidden-charm systems $\Lambda_c\bar{\Lambda}_c$, $\Sigma_c^{(*)}\bar{\Sigma}_c^{(*)}$, and $\Lambda_c\bar{\Sigma}_c^{(*)}$, and corresponding double-charm systems $\Lambda_c\Lambda_c$, $\Sigma_c^{(*)}\Sigma_c^{(*)}$, and $\Lambda_c\Sigma_c^{(*)}

N. Sinthuja, S. Rajasekar, M. Senthilvelan

We compute the instability rate for single- and double-periodic wave solutions of a fourth-order nonlinear Schr\"odinger equation. The single- and double-periodic solutions of a fourth-order nonlinear Schr\"odinger equation are derived in terms of Jacobian elliptic functions such as $dn$, $cn$, and $sn$. From the spectral problem, we compute Lax and stabilit

Willem Fouche, Safari Mukeru

In this paper we study the local times of Brownian motion from the point of view of algorithmic randomness. We introduce the notion of effective local time and show that any path which is Martin-L\"of random with respect to the Wiener measure has continuous effective local times at every computable point. Finally we obtain a new simple representation of clas

Md Shahriar Tasjid, Ahmed Al Marouf

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillan

Avi Shmidman, Joshua Guedalia, Shaltiel Shmidman, Cheyn Shmuel Shmidman

We present a new pre-trained language model (PLM) for Rabbinic Hebrew, termed Berel (BERT Embeddings for Rabbinic-Encoded Language). Whilst other PLMs exist for processing Hebrew texts (e.g., HeBERT, AlephBert), they are all trained on modern Hebrew texts, which diverges substantially from Rabbinic Hebrew in terms of its lexicographical, morphological, synta

Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu

Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historical impacts. While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means th

Sean McCurdy

In arXiv:0908.1031, Varvaruca and Weiss eliminate the existence of cusps for a free-boundary problem for two-dimensional water waves under assumptions that hold for solutions such that $\{u>0\}$ is a "strip-like" domain in the sense of arXiv:0708.4371. In this paper it is proven that cusps do not exists in the natural setting for these free-boundary problems

Sebastian Bahamonde, Laur Järv

In symmetric teleparallel gravities, where the independent connection is characterized by nonmetricity while curvature and torsion are zero, it is possible to find a coordinate system whereby the connection vanishes globally and covariant derivatives reduce to partial derivatives -- the coincident gauge. In this paper we derive general transformation rules i

Tryambak Gangopadhyay, Somnath De, Qisai Liu, Achintya Mukhopadhyay

Lean combustion is environment friendly with low NOx emissions and also provides better fuel efficiency in a combustion system. However, approaching towards lean combustion can make engines more susceptible to lean blowout. Lean blowout (LBO) is an undesirable phenomenon that can cause sudden flame extinction leading to sudden loss of power. During the desig

Xiao Zhang, Hao Tan, Xuan Huang, Denghui Zhang

With the development of hardware and algorithms, ASR(Automatic Speech Recognition) systems evolve a lot. As The models get simpler, the difficulty of development and deployment become easier, ASR systems are getting closer to our life. On the one hand, we often use APPs or APIs of ASR to generate subtitles and record meetings. On the other hand, smart speake

Mintaek Oh, Jeonghun Park, Jinseok Choi

To realize ultra-reliable low latency communications with high spectral efficiency and security, we investigate a joint optimization problem for downlink communications with multiple users and eavesdroppers in the finite blocklength (FBL) regime. We formulate a multi-objective optimization problem to maximize a sum secrecy rate by developing a secure precode

Jeremy T. Young, Sean R. Muleady, Michael A. Perlin, Adam M. Kaufman

We propose a new protocol for preparing spin squeezed states in controllable atomic, molecular, and optical systems, with particular relevance to emerging optical clock platforms compatible with Rydberg interactions. By combining a short-ranged, soft-core potential with an external drive, we can transform naturally emerging Ising interactions into an XX spin

Alexander P Browning, Matthew J Simpson

An enduring challenge in computational biology is to balance data quality and quantity with model complexity. Tools such as identifiability analysis and information criterion have been developed to harmonise this juxtaposition, yet cannot always resolve the mismatch between available data and the granularity required in mathematical models to answer importan

Antonio González-Arroyo, Ken-Ichi Ishikawa, Yingbo Ji, Masanori Okawa

We compute the first four perturbative coefficients of the internal energy for the twisted reduced principal chiral model (TRPCM) using numerical stochastic perturbation theory (NSPT). This matrix model has the same large $N$ limit as the ordinary principal chiral model (PCM) at infinite volume. Indeed, we verify that the first three coefficients match the a

Yong Xu, Fan Zhang, Yongshan Liu, Renyou Xu

Recent progress in orbitronics reveals the possibility of using orbit current as an information carrier. The interconversion between orbit currents and charge currents is crucial for orbit information processing. Although orbit currents can be created from charge currents via the orbital Hall effect, the conversion from orbit currents into charge currents ha

Yuchen Liu

In regions that practice common law, relevant historical cases are essential references for sentencing. To help legal practitioners find previous judgement easier, this paper aims to label each court judgement by some tags. These tags are legally important to summarize the judgement and can guide the user to similar judgements. We introduce a heuristic syste

Shota Hamanaka

We study some preservation phenomena for lower bound of total scalar curvatures on a smooth manifold. In particular, we prove that the lower bound of the weighted total scalar curvature (which is known as Perelman's $\mathcal{F}$-functional) on a closed $n$-manifold is preserved under the $W^{1, p}~(p > n^{2}/2)$-convergence of Riemannian metrics and uniform

Dohoon Ryu, Jong Chul Ye

Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal diffusion model that can generate high r

C. Ryan-Anderson, N. C. Brown, M. S. Allman, B. Arkin

We compare two different implementations of fault-tolerant entangling gates on logical qubits. In one instance, a twelve-qubit trapped-ion quantum computer is used to implement a non-transversal logical CNOT gate between two five qubit codes. The operation is evaluated with varying degrees of fault tolerance, which are provided by including quantum error cor

Phillip Karle, Ferenc Török, Maximilian Geisslinger, Markus Lienkamp

Reliably predicting the motion of contestant vehicles surrounding an autonomous racecar is crucial for effective and performant planning. Although highly expressive, deep neural networks are black-box models, making their usage challenging in safety-critical applications, such as autonomous driving. In this paper, we introduce a structured way of forecasting

Alfredo Gurrola, José David Ruiz-Álvarez

We consider a simplified model where a W' boson is added to the standard model with negligible couplings to quarks, but generic couplings to leptons and electroweak bosons. We study the implications of such a model for LHC searches. Consequently, we propose an LHC search through the vector boson fusion topology which would have sensitivity for such a new par

Guangjin Pan, Heng Zhang, Shugong Xu, Shunqing Zhang

The high computational complexity and high energy consumption of artificial intelligence (AI) algorithms hinder their application in augmented reality (AR) systems. This paper considers the scene of completing video-based AI inference tasks in the mobile edge computing (MEC) system. We use multiply-and-accumulate operations (MACs) for problem analysis and op

Giulia C. Cinquegrana, Meridith Joyce, Amanda I. Karakas

One--dimensional stellar structure and evolution programs are built using different physical prescriptions and algorithms, which means there can be variations between models' predictions even when using identical input physics. This leads to questions about whether such deviations are physical or numerical; code validation studies are important and necessary

Aaro Järvinen, Tünde Fülöp, Eero Hirvijoki, Mathias Hoppe

Plasma-terminating disruptions in future fusion reactors may result in conversion of the initial current to a relativistic runaway electron beam. Validated predictive tools are required to optimize the scenarios and mitigation actuators to avoid the excessive damage that can be caused by such events. Many of the simulation tools applied in fusion energy rese

Jingfeng Wu, Difan Zou, Vladimir Braverman, Quanquan Gu

We study linear regression under covariate shift, where the marginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across the two domains. We investigate a transfer learning approach with pretraining on the source data and finetuning

Zhaoyu Wang, Kai Xu, Engui Fan

In this paper, we study large-time asymptotics for the complex modified Korteveg-de Vries equation \begin{equation} u_t + \frac{1}{2}u_{xxx}+3|u|^2 u_x=0, \end{equation} with the step-like initial data \begin{equation} u(x,0)=u_0(x)= \begin{cases} 0, & {x \ge 0,}\\ A e^{iBx}, &{x < 0.} \end{cases} \end{equation} It is shown that the step-like initial problem

Belle Collaboration, Y. -T. Lai, I. Adachi, H. Aihara

We study $B^{+}\to \pi^{+}\pi^{0}\pi^{0}$ using 711 $\rm{fb}^{-1}$ of data collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^{+}e^{-}$ collider. We measure an inclusive branching fraction of $(19.0\pm 1.5\pm 1.4)\times 10^{-6}$ and an inclusive \textit{CP} asymmetry of $(9.2 \pm 6.8 \pm 0.7)\%$, where the firs

Honghao Luo, Rang Liu, Ming Li, Yang Liu

Integrated sensing and communication (ISAC) has been envisioned as a promising technology to tackle the spectrum congestion problem for future networks. In this correspondence, we investigate to deploy a reconfigurable intelligent surface (RIS) in an ISAC system for achieving better performance. In particular, a multi-antenna base station (BS) simultaneously

Bharat Runwal, Vivek, Sandeep Kumar

Graph neural network (GNN) is achieving remarkable performances in a variety of application domains. However, GNN is vulnerable to noise and adversarial attacks in input data. Making GNN robust against noises and adversarial attacks is an important problem. The existing defense methods for GNNs are computationally demanding and are not scalable. In this pape

Sangbum Cho, Jung Hoon Lee

Given a Heegaard splitting of the $3$-sphere, the primitive disk complex is defined to be the full subcomplex of the disk complex for one of the handlebodies of the splitting. It is an open question whether the primitive disk complex is connected or not when the genus of the splitting is greater than three. In this note, we prove that a quotient of the primi

Ki-Hoon Hong, Ulugbek Yakhshiev, Hyun-Chul Kim

We investigate the mass spectrum and electromagnetic transitions of charmonia, emphasizing the instanton effects on them. The heavy-quark potential consists of the Coulomb-like potential from one-gluon exchange and the linear confining potential. We introduce the nonperturbative heavy-quark potential derived from the instanton vacuum. We also consider the sc

Jonathan Schwartz, Chris Harris, Jacob Pietryga, Huihuo Zheng Prashant Kumar

The demand for high-throughput electron tomography is rapidly increasing in biological and material sciences. However, this 3D imaging technique is computationally bottlenecked by alignment and reconstruction which runs from hours to days. We demonstrate real-time tomography with dynamic 3D tomographic visualization to enable rapid interpretation of specimen

Chang Meng, Ziqi Zhao, Wei Guo, Yingxue Zhang

Multi-types of behaviors (e.g., clicking, adding to cart, purchasing, etc.) widely exist in most real-world recommendation scenarios, which are beneficial to learn users' multi-faceted preferences. As dependencies are explicitly exhibited by the multiple types of behaviors, effectively modeling complex behavior dependencies is crucial for multi-behavior pred

Harukuni Ikeda

In this work, we investigate a symmetric deformed random matrix, which is obtained by perturbing the diagonal elements of the Wigner matrix. The eigenvector $\mathbf{x}_{\rm min}$ of the minimal eigenvalue $\lambda_{\rm min}$ of the deformed random matrix tends to condensate at a single site. In certain types of perturbations and in the limit of the large co

Rina Miyajima, Ryutaroh Matsumoto

Secret sharing schemes for classical secrets can be classified into classical secret sharing schemes and quantum secret sharing schemes. Classical secret sharing has been known to be able to distribute some shares before a given secret. On the other hand, quantum mechanics extends the capabilities of secret sharing beyond those of classical secret sharing. W

Satori Tsuzuki

Our recent study suggested that a fully classical mechanical approximation of the two-fluid model of superfluid helium-4 based on smoothed-particle hydrodynamics (SPH) is equivalent to solving a many-body quantum mechanical equation under specific conditions. This study further verifies the existence of this equivalence. First, we derived the SPH form of the

Fan Zhang, Lee Lindblom

Numerical solutions to the Einstein constraint equations are constructed on a selection of compact orientable three-dimensional manifolds with non-trivial topologies. A simple constant mean curvature solution and a somewhat more complicated non-constant mean curvature solution are computed on example manifolds from three of the eight Thursten geometrization

Johnson Vo, Jiabao Xie, Sahil Patel

Deep learning networks have demonstrated high performance in a large variety of applications, such as image classification, speech recognition, and natural language processing. However, there exists a major vulnerability exploited by the use of adversarial attacks. An adversarial attack imputes images by altering the input image very slightly, making it near

Xiao Qi, David J. Foran, John L. Nosher, Ilker Hacihaliloglu

The role of chest X-ray (CXR) imaging, due to being more cost-effective, widely available, and having a faster acquisition time compared to CT, has evolved during the COVID-19 pandemic. To improve the diagnostic performance of CXR imaging a growing number of studies have investigated whether supervised deep learning methods can provide additional support. Ho

Sachith Seneviratne, Damith Senanayake, Sanka Rasnayaka, Rajith Vidanaarachchi

Automatically converting text descriptions into images using transformer architectures has recently received considerable attention. Such advances have implications for many applied design disciplines across fashion, art, architecture, urban planning, landscape design and the future tools available to such disciplines. However, a detailed analysis capturing

Gregory Eskin

We consider a Lorentzian metric in $\mathbb{R}\times\mathbb{R}^n$. We show that if we know the lengths of the space-time geodesics starting at $(0,y,\eta)$ when $t=0$, then we can recover the metric at $y$. We prove the rigidity of Lorentzian metrics. We also prove a variant of the rigidity property for the case of null-geodesics: if two metrics are close an

Wenkai Li, Cheng Feng, Ting Chen, Jun Zhu

Time series anomaly detection (TSAD) is an important data mining task with numerous applications in the IoT era. In recent years, a large number of deep neural network-based methods have been proposed, demonstrating significantly better performance than conventional methods on addressing challenging TSAD problems in a variety of areas. Nevertheless, these de

Shreya Ghosh, Abhinav Dhall, Jarrod Knibbe, Munawar Hayat

Over the past few years, there has been an increasing interest to interpret gaze direction in an unconstrained environment with limited supervision. Owing to data curation and annotation issues, replicating gaze estimation method to other platforms, such as unconstrained outdoor or AR/VR, might lead to significant drop in performance due to insufficient avai

Sudhi P. V., Victorita Dolean, Pierre Jolivet, Brandon Robinson

A nonlinear partial differential equation (PDE) based compartmental model of COVID-19 provides a continuous trace of infection over space and time. Finer resolutions in the spatial discretization, the inclusion of additional model compartments and model stratifications based on clinically relevant categories contribute to an increase in the number of unknown

Hui Su, Yue Ye, Zhiwei Chen, Mingli Song

Weakly supervised object localization is a challenging task which aims to localize objects with coarse annotations such as image categories. Existing deep network approaches are mainly based on class activation map, which focuses on highlighting discriminative local region while ignoring the full object. In addition, the emerging transformer-based techniques