October 2023 arXiv papers — page 92

Sinclaire E. Jones, Gudmundur Stefansson, Kento Masuda, Jessica E. Libby-Roberts

We report the discovery of a close-in ($P_{\mathrm{orb}} = 3.349\:\mathrm{days}$) warm Neptune with clear transit timing variations (TTVs) orbiting the nearby ($d=47.3\:\mathrm{pc}$) active M4 star, TOI-2015. We characterize the planet's properties using TESS photometry, precise near-infrared radial velocities (RV) with the Habitable-zone Planet Finder (HP)

Jiale Huang, Xiangjian Qian, Mingpu Qin

We revisit the issue about the magnetization of the $120^\circ$ order in the spin-1/2 triangular lattice Heisenberg model (TLHM) with Density Matrix Renormalization Group (DMRG). The accurate determination of the magnetization of this model is challenging for numerical methods and its value exhibits substantial disparities across various methods. We perform

M. Pancaldi, F. Guzzi, C. S. Bevis, M. Manfredda

Electromagnetic waves possessing orbital angular momentum (OAM) are powerful tools for applications in optical communications, new quantum technologies and optical tweezers. Recently, they have attracted growing interest since they can be harnessed to detect peculiar helical dichroic effects in chiral molecular media and in magnetic nanostructures. In this w

Turker Tuncer, Sengul Dogan, Mehmet Baygin, Prabal Datta Barua

The generative pre-trained transformer (GPT)-based chatbot software ChatGPT possesses excellent natural language processing capabilities but is inadequate for solving arithmetic problems, especially multiplication. Its GPT structure uses a computational graph for multiplication, which has limited accuracy beyond simple multiplication operations. We developed

Hai Yu, Chong Deng, Qinglin Zhang, Jiaqing Liu

Topic segmentation is critical for obtaining structured documents and improving downstream tasks such as information retrieval. Due to its ability of automatically exploring clues of topic shift from abundant labeled data, recent supervised neural models have greatly promoted the development of long document topic segmentation, but leaving the deeper relatio

Damien Ackerer, Julien Hugonnier, Urban Jermann

Perpetual futures are contracts without expiration date in which the anchoring of the futures price to the spot price is ensured by periodic funding payments from long to short. We derive explicit expressions for the no-arbitrage price of various perpetual contracts, including linear, inverse, and quantos futures in both discrete and continuous-time. In part

Ricardo Britto, Timothy Murphy, Massimo Iovene, Leif Jonsson

The rapid advancements in Artificial Intelligence (AI), particularly in generative AI and foundational models (FMs), have ushered in transformative changes across various industries. Large language models (LLMs), a type of FM, have demonstrated their prowess in natural language processing tasks and content generation, revolutionizing how we interact with sof

Felix Stollenwerk, Niklas Fastlund, Anna Nyqvist, Joey Öhman

We have trained a named entity recognition (NER) model that screens Swedish job ads for different kinds of useful information (e.g. skills required from a job seeker). It was obtained by fine-tuning KB-BERT. The biggest challenge we faced was the creation of a labelled dataset, which required manual annotation. This paper gives an overview of the methods we

Oana Padurariu

We prove that there are no Shimura curves $X_0^D(N)$ of squarefree level and of genus at least two admitting a smooth plane model.

Yanyu Ye, Zhenxi Zhang, Wei Wei, Chunna Tian

Source-free test-time adaptation for medical image segmentation aims to enhance the adaptability of segmentation models to diverse and previously unseen test sets of the target domain, which contributes to the generalizability and robustness of medical image segmentation models without access to the source domain. Ensuring consistency between target edges an

Shin-ei Fujii, Tetsufumi Hirano

We derive equations of motion of hydrodynamic fluctuations performing perturbative expansion of the energy-momentum conservation equations around the boost invariant solution in one-dimensional expanding system. In the course of derivation, we do not assume any specific forms of constitutive equations for shear stress tensor $\pi^{\mu \nu}$ and bulk pressure

Dingjie Lu, Zhao Wang, Jun Liu, Yangfan Li

We proposed a general quantum-computing-based algorithm that harnesses the exponential power of noisy intermediate-scale quantum (NISQ) devices in solving partial differential equations (PDE). This variational quantum eigensolver (VQE)-inspired approach transcends previous idealized model demonstrations constrained by strict and simplistic boundary condition

Takashi Koide, Naoki Fukushi, Hiroki Nakano, Daiki Chiba

Domain squatting is a technique used by attackers to create domain names for phishing sites. In recent phishing attempts, we have observed many domain names that use multiple techniques to evade existing methods for domain squatting. These domain names, which we call generated squatting domains (GSDs), are quite different in appearance from legitimate domain

Minjie Cheng, Hongteng Xu

When learning graph neural networks (GNNs) in node-level prediction tasks, most existing loss functions are applied for each node independently, even if node embeddings and their labels are non-i.i.d. because of their graph structures. To eliminate such inconsistency, in this study we propose a novel Quasi-Wasserstein (QW) loss with the help of the optimal t

Ruihao Shui, Yixin Cao, Xiang Wang, Tat-Seng Chua

Large language models (LLMs) have demonstrated great potential for domain-specific applications, such as the law domain. However, recent disputes over GPT-4's law evaluation raise questions concerning their performance in real-world legal tasks. To systematically investigate their competency in the law, we design practical baseline solutions based on LLMs an

Fabio D'Agostino, Marco Gallo, Matteo Saviozzi, Federico Silvestro

This work introduces an efficient power management approach for shipboard microgrids that integrates diesel generators, a fuel cell, and battery energy storage system. This strategy addresses both unit commitment and power dispatch, considering the zero-emission capability of the ship, as well as optimizing the ship's speed. The optimization is done through

Jonathan Vacher, Pascal Mamassian

Perception is often viewed as a process that transforms physical variables, external to an observer, into internal psychological variables. Such a process can be modeled by a function coined perceptual scale. The perceptual scale can be deduced from psychophysical measurements that consist in comparing the relative differences between stimuli (i.e. differenc

Zong-Wei Hong, Yu-Chen Lin, Hsuan-Tung Liu, Yi-Ren Yeh

Although face anti-spoofing (FAS) methods have achieved remarkable performance on specific domains or attack types, few studies have focused on the simultaneous presence of domain changes and unknown attacks, which is closer to real application scenarios. To handle domain-generalized unknown attacks, we introduce a new method, DGUA-FAS, which consists of a T

Minghao Zhang, Pinghui Huang, Ruobing Dong

Planets can excite density waves and open annular gas gaps in protoplanetary disks. The depth of gaps is influenced by the evolving angular momentum carried by density waves. While the impact of radiative cooling on the evolution of density waves has been studied, a quantitative correlation to connect gap depth with the cooling timescale is lacking. To addre

Ruoxue Liu, Liang Ding, Wenjia Wang, Lu Zou

Nested simulation encompasses the estimation of functionals linked to conditional expectations through simulation techniques. In this paper, we treat conditional expectation as a function of the multidimensional conditioning variable and provide asymptotic analyses of general Least Squared Estimators on sieve, without imposing specific assumptions on the fun

Songyan Zhang, Xinyu Sun, Hao Chen, Bo Li

Finding corresponding pixels within a pair of images is a fundamental computer vision task with various applications. Due to the specific requirements of different tasks like optical flow estimation and local feature matching, previous works are primarily categorized into dense matching and sparse feature matching focusing on specialized architectures along

Senjie Zhu, Xiao Huang, Lei Xia, Aiqiang Guo

Charm quark production and its hadronization in ep and eA collisions at the future Electron-Ion Collider in China (EicC) will help us understand the quark/gluon fragmentation processes and the hadronization mechanisms in the nuclear medium, especially within a poorly constrained kinematic region ($x<0.1$). In this paper, we report a study on the production o

Naoki Wake, Atsushi Kanehira, Kazuhiro Sasabuchi, Jun Takamatsu

This technical report explores the ability of ChatGPT in recognizing emotions from text, which can be the basis of various applications like interactive chatbots, data annotation, and mental health analysis. While prior research has shown ChatGPT's basic ability in sentiment analysis, its performance in more nuanced emotion recognition is not yet explored. H

Panos Gourgiotis, Gianluca Rizzi, Peter Lewintan, Davide Bernardini

We derive the Green's functions (concentrated force and couple in an infinite space) for the isotropic planar relaxed micromorphic model. Since the relaxed micromorphic model particularises into the microstretch, Cosserat (micropolar), couple-stress, and linear elasticity model for certain choices of material parameters, we recover the fundamental solutions

Pham Q. Viet, Daniel Romero

Autonomous uncrewed aerial vehicles (UAVs) can be utilized as aerial relays to serve users far from terrestrial infrastructure. Unfortunately, existing algorithms for aerial relay path planning cannot accommodate general flight constraints or channel models. This is required in practice due to connectivity constraints, the presence of obstacles (e.g., buildi

Giuseppe Russo, Manoel Horta Ribeiro, Robert West

Fringe communities promoting conspiracy theories and extremist ideologies have thrived on mainstream platforms, raising questions about the mechanisms driving their growth. Here, we hypothesize and study a possible mechanism: new members may be recruited through fringe-interactions: the exchange of comments between members and non-members of fringe communiti

Kexin Chen, Chao Huang, Jianwei Huang

Information Elicitation Without Verification (IEWV) refers to the problem of eliciting high-accuracy solutions from crowd members when the ground truth is unverifiable. A high-accuracy team solution (aggregated from members' solutions) requires members' effort exertion, which should be incentivized properly. Previous research on IEWV mainly focused on scenar

Tra Huong Thi Le, Yan Kyaw Tun

One of the primary objectives for future wireless communication networks is to facilitate the provision of ultra-reliable and low-latency communication services while simultaneously ensuring the capability for vast connection. In order to achieve this objective, we examine a hybrid multi-access scheme inside the finite blocklength (FBL) regime. This system c

M. Yunus Seker, Oliver Kroemer

Robots need to estimate the material and dynamic properties of objects from observations in order to simulate them accurately. We present a Bayesian optimization approach to identifying the material property parameters of objects based on a set of observations. Our focus is on estimating these properties based on observations of scenes with different sets of

Mithun Das, Animesh Mukherjee

The dramatic increase in the use of social media platforms for information sharing has also fueled a steep growth in online abuse. A simple yet effective way of abusing individuals or communities is by creating memes, which often integrate an image with a short piece of text layered on top of it. Such harmful elements are in rampant use and are a threat to o

Barron Han, Oron Sabag, Victoria Kostina, Babak Hassibi

This paper investigates the problem of zero-delay joint source-channel coding of a vector Gauss-Markov source over a multiple-input multiple-output (MIMO) additive white Gaussian noise (AWGN) channel with feedback. In contrast to the classical problem of causal estimation using noisy observations, we examine a system where the source can be encoded before tr

Xianghong Kong, Chuanjie Hu, Xingsi Liu, Chunqi Zheng

While conventional surface waves propagate along the surface and decay perpendicularly from the interface, the ghost surface polaritons show oblique propagation direction with respect to the interface. Here, we have discovered topologically protected ghost surface polaritons by applying the lattice transformation optics method to gyromagnetic photonic crysta

Yuanjie Lei, Shuangqian Liu, Qinghua Xiao, Huijiang Zhao

The Vlasov-Maxwell-Landau (VML) system and the Vlasov-Maxwell-Boltzmann (VMB) system are fundamental models in dilute collisional plasmas. In this paper, we are concerned with the hydrodynamic limits of both the VML and the non-cutoff VMB systems in the entire space. Our primary objective is to rigorously prove that, within the framework of Hilbert expansion

Jitendra Pal, Sourav Roychowdhury

In this paper we study integrability and non-integrability for type-IIA supergravity background dual to deformed plane wave matrix model. From the bulk perspective, we estimate various chaos indicators that clearly shows chaotic string dynamics in the limit of small value of the parameter $L$ present in the theory. On the other hand, the string dynamics exhi

Pengfei Wang, Moyu Chen, Yongqin Xie, Chen Pan

Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information. Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the reservoir and the readout layer of reservoir computing system. Two-dimensional moire material, with an ar

Songheng Zhang, Haotian Li, Huamin Qu, Yong Wang

Automated visualization recommendation facilitates the rapid creation of effective visualizations, which is especially beneficial for users with limited time and limited knowledge of data visualization. There is an increasing trend in leveraging machine learning (ML) techniques to achieve an end-to-end visualization recommendation. However, existing ML-based

Maria De Iorio, Willem van den Boom, Alexandros Beskos, Ajay Jasra

High-dimensional data analysis typically focuses on low-dimensional structure, often to aid interpretation and computational efficiency. Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data by representing variables as nodes and dependencies as edges. Inference is often focused on individual

Yan Cheng, Xi Yang

A linearly implicit conservative difference scheme is applied to discretize the attractive coupled nonlinear Schr\"odinger equations with fractional Laplacian. Complex symmetric linear systems can be obtained, and the system matrices are indefinite and Toeplitz-plus-diagonal. Neither efficient preconditioned iteration method nor fast direct method is availab

Lun Wang, Om Thakkar, Rajiv Mathews

It is well-known that neural networks can unintentionally memorize their training examples, causing privacy concerns. However, auditing memorization in large non-auto-regressive automatic speech recognition (ASR) models has been challenging due to the high compute cost of existing methods such as hardness calibration. In this work, we design a simple auditin

Chunmiao Li, Yijun Yu, Haitao Wu, Luca Carlig

Clippy lints are considered as essential tools for Rust developers, as they can be configured as gate-keeping rules for a Rust project during continuous integration. Despite their availability, little was known about practical application and cost-effectiveness of the lints in reducing code quality issues. In this study, we embark on a comprehensive analysis

O. Février, C. K. Tsui, G. Durr-Legoupil-Nicoud, C. Theiler

In recent years, negative triangularity (NT) has emerged as a potential high-confinement L-mode reactor solution. In this work, detachment is investigated using core density ramps in lower single null Ohmic L-mode plasmas across a wide range of upper, lower, and average triangularity (the mean of upper and lower triangularity: $\delta$) in the TCV tokamak. I

Yunlu Chen, Yang Li, Keli Liu, Feng Ruan

We study feature learning in a compositional variant of kernel ridge regression in which the predictor is applied to a learnable linear transformation of the input. When the response depends on the input only through a low-dimensional predictive subspace, we show that all global minimizers of the population objective for the linear transformation annihilate

K. Fong, X. Fu, Q. W. Zheng, T. Au

The LLRF of five of TRIUMF's ISAC-1 accelerator cavities have been replaced by 3 similar FPGA based system with different operating frequencies. These LLRF use internal digital phase locked loops for frequency generation and synchronization, feedback control using Amplitude/Phase regulations. These FPGAs also have internal stepper motor controller for resona

Hamza Chaggara, Abdelhamid Gahami

The Brenke type generating functions are the polynomial generating functions of the form $$\sum_{n=0}^{\infty}{P_n(x )\over n!}t^n=A(t)B(xt), $$ where $A$ and $B$ are two formal power series subject to the conditions $A(0)\;B^{(k)}(0)\neq0,\, k=0,1,2\ldots$.\\ In this work, we determine all Brenke-type polynomials when they are also $2$-orthogonal polynomial

Shiqi Li, Jihua Zhu, Yifan Xie

Point cloud registration plays a crucial role in various computer vision tasks, and usually demands the resolution of partial overlap registration in practice. Most existing methods perform a serial calculation of rotation and translation, while jointly predicting overlap during registration, this coupling tends to degenerate the registration performance. In

Guande He, Peng Cui, Jianfei Chen, Wenbo Hu

Despite the significant progress made in practical applications of aligned language models (LMs), they tend to be overconfident in output answers compared to the corresponding pre-trained LMs. In this work, we systematically evaluate the impact of the alignment process on logit-based uncertainty calibration of LMs under the multiple-choice setting. We first

Jianlan Luo, Perry Dong, Jeffrey Wu, Aviral Kumar

The offline reinforcement learning (RL) paradigm provides a general recipe to convert static behavior datasets into policies that can perform better than the policy that collected the data. While policy constraints, conservatism, and other methods for mitigating distributional shifts have made offline reinforcement learning more effective, the continuous act

Bo Yan, Yang Cao, Haoyu Wang, Wenchuan Yang

The heterogeneous information network (HIN), which contains rich semantics depicted by meta-paths, has emerged as a potent tool for mitigating data sparsity in recommender systems. Existing HIN-based recommender systems operate under the assumption of centralized storage and model training. However, real-world data is often distributed due to privacy concern

Bing Gu

The time-convolutionless master equation provides a general framework to model non-Markovian dynamics of an open quantum system with a time-local generator. A diagrammatic representation is developed and proven for the perturbative expansion of the exact time-local generator for an open quantum system interacting with arbitrary environments. A truncation of

Inmo Yeon, Iljoo Jeong, Seungchul Lee, Jung-Woo Choi

Accurate estimation of indoor space geometries is vital for constructing precise digital twins, whose broad industrial applications include navigation in unfamiliar environments and efficient evacuation planning, particularly in low-light conditions. This study introduces EchoScan, a deep neural network model that utilizes acoustic echoes to perform room geo

J. P. Hu, Y. Y. Wang, J. Hu, F. Y. Wang

The cosmological principle is fundamental to the standard cosmological model. It assumes that the Universe is homogeneous and isotropic on very large scales. As the basic assumption, it must stand the test of various observations. In this work, using the region fitting (RF) method, we mapped the all-sky distribution of cosmological parameters ($\Omega_{m}$ a

Martin Bergen, Valentin Walther, Binodbihari Panda, Mariam Harati

We investigate the interaction between highly excited Rydberg excitons and charged impurities in the semiconductor Cuprous Oxide. We find that it is long-ranged and based on charge-induced dipole interactions. We demonstrate that we can utilize this interaction to neutralize impurities throughout the whole crystal - an effect we call purification. Purificati

Nian Liu, Ziyang Luo, Ni Zhang, Junwei Han

While previous CNN-based models have exhibited promising results for salient object detection (SOD), their ability to explore global long-range dependencies is restricted. Our previous work, the Visual Saliency Transformer (VST), addressed this constraint from a transformer-based sequence-to-sequence perspective, to unify RGB and RGB-D SOD. In VST, we develo

Miaoshiqi Liu, Zhou Zhou

The paper considers simultaneous nonparametric inference for a wide class of M-regression models with time-varying coefficients. The covariates and errors of the regression model are tackled as a general class of nonstationary time series and are allowed to be cross-dependent. A novel and easy-to-implement self-convolved bootstrap procedure is proposed. With

Inès Osman, Salvatore F. Pileggi, Sadok Ben Yahia

Data integration is considered a classic research field and a pressing need within the information science community. Ontologies play a critical role in such a process by providing well-consolidated support to link and semantically integrate datasets via interoperability. This paper approaches data integration from an application perspective, looking at tech

Yaxin Fan, Feng Jiang, Benyou Wang, Peifeng Li

Foundation Models (FMs) have the potential to revolutionize the way users self-diagnose through search engines by offering direct and efficient suggestions. Recent studies primarily focused on the quality of FMs evaluated by GPT-4 or their ability to pass medical exams, no studies have quantified the extent of self-diagnostic atomic knowledge stored in FMs'

Caoyun Fan, Jidong Tian, Yitian Li, Wenqing Chen

Chain-of-Thought (CoT) is a technique that guides Large Language Models (LLMs) to decompose complex tasks into multi-step reasoning through intermediate steps in natural language form. Briefly, CoT enables LLMs to think step by step. However, although many Natural Language Understanding (NLU) tasks also require thinking step by step, LLMs perform less well t

Mishio Kawashita, Wakako Kawashita

In this paper, we discuss the role of the shortest distance in time-dependent enclosure method for the inverse problems when the inclusions are embedded in a non-layered or two-layered medium. Furthermore, the regularity assumptions for the boundaries of the inclusions are relaxed.

Andrew Craig, Claudette Robinson

We give a definition of representability for distributive quasi relation algebras (DqRAs). These algebras are a generalisation of relation algebras and were first described by Galatos and Jipsen (2013). Our definition uses a construction that starts with a poset. The algebra is concretely constructed as the lattice of upsets of a partially ordered equivalenc

Josh Kline, Pekka Koskela, Khanh Nguyen

In this paper, we study the behaviour at infinity of $p$-Sobolev functions in the setting of Ahlfors $Q$-regular metric measure spaces supporting a $p$-Poincar\'e inequality. By introducing the notions of sets which are $p$-thin at infinity, we show that functions in the homogeneous space $\dot N^{1,p}(X)$ necessarily have limits at infinity outside of $p$-t

Marijana Smailagic, Jason Xavier Prochaska, Joseph Burchett, Guangtun Zhu

We study ultraviolet HI and metal line transitions in the circumgalactic medium (CGM) of 15 massive, quenched luminous red galaxies (LRGs) at redshift $z\sim 0.5$ and with impact parameters up to 400 kpc. We selected 8 of LRG-CGM systems to study general properties of the CGM around LRGs, while the other 7 are already known to contain cool CGM gas from MgII

Ming Li, Lichang Chen, Jiuhai Chen, Shwai He

Recent advancements in Large Language Models (LLMs) have expanded the horizons of natural language understanding and generation. Notably, the output control and alignment with the input of LLMs can be refined through instruction tuning. However, as highlighted in several studies, low-quality data in the training set are usually detrimental to instruction tun

Su Ah Lee, Seokjin Oh, Woohwan Jung

Named Entity Recognition (NER) frequently suffers from the problem of insufficient labeled data, particularly in fine-grained NER scenarios. Although $K$-shot learning techniques can be applied, their performance tends to saturate when the number of annotations exceeds several tens of labels. To overcome this problem, we utilize existing coarse-grained datas

Yiyang Su, Ali Vosoughi, Shijian Deng, Yapeng Tian

The audio-visual sound separation field assumes visible sources in videos, but this excludes invisible sounds beyond the camera's view. Current methods struggle with such sounds lacking visible cues. This paper introduces a novel "Audio-Visual Scene-Aware Separation" (AVSA-Sep) framework. It includes a semantic parser for visible and invisible sounds and a s

Tianning Xiao, Zhiyi Li, Zongzheng Zhou, Sheng Fang

Besides its original spin representation, the Ising model is known to have the Fortuin-Kasteleyn (FK) bond and loop representations, of which the former was recently shown to exhibit two upper critical dimensions $(d_c=4,d_p=6)$. Using a lifted worm algorithm, we determine the critical coupling as $K_c = 0.077\,708\,91(4)$ for $d=7$, which significantly impr

Zhi Zhang, Kyle Ritscher, Oscar Hernan Madrid Padilla

This paper investigates risk bounds for quantile additive trend filtering, a method gaining increasing significance in the realms of additive trend filtering and quantile regression. We investigate the constrained version of quantile trend filtering within additive models, considering both fixed and growing input dimensions. In the fixed dimension case, we d

Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim

Aphasia, a language disorder resulting from brain damage, requires accurate identification of specific aphasia types, such as Broca's and Wernicke's aphasia, for effective treatment. However, little attention has been paid to developing methods to detect different types of aphasia. Recognizing the importance of analyzing co-speech gestures for distinguish ap

Zhen Zhang, Bingqiao Luo, Shengliang Lu, Bingsheng He

Numerous studies have been conducted to investigate the properties of large-scale temporal graphs. Despite the ubiquity of these graphs in real-world scenarios, it's usually impractical for us to obtain the whole real-time graphs due to privacy concerns and technical limitations. In this paper, we introduce the concept of {\it Live Graph Lab} for temporal gr

Wei Huang, Fan Gao, Junting Wang, Hao Zhang

Underwater Sound Speed Profile (SSP) distribution has great influence on the propagation mode of acoustic signal, thus the fast and accurate estimation of SSP is of great importance in building underwater observation systems. The state-of-the-art SSP inversion methods include frameworks of matched field processing (MFP), compressive sensing (CS), and feedfor

Markus Fröb, Albert Much, Kyriakos Papadopoulos

We compute the relative entropy between the vacuum and a coherent state for a massive scalar field in de Sitter spacetime, using Tomita-Takesaki modular theory and the Araki-Uhlmann formula for the relative entropy. Embedding de Sitter spacetime as a hyperboloid in the ambient Minkowski space, we can restrict the Minkowski wedge and the corresponding modular

Jatin Chauhan, Xiaoxuan Wang, Wei Wang

We present one of the preliminary NLP works under the challenging setup of Learning from Label Proportions (LLP), where the data is provided in an aggregate form called bags and only the proportion of samples in each class as the ground truth. This setup is inline with the desired characteristics of training models under Privacy settings and Weakly supervisi

Robert J. Joyce, Edward Raff, Charles Nicholas, James Holt

Existing research on malware classification focuses almost exclusively on two tasks: distinguishing between malicious and benign files and classifying malware by family. However, malware can be categorized according to many other types of attributes, and the ability to identify these attributes in newly-emerging malware using machine learning could provide s

Xiaohong Chen, Baoyindureng Wu

Let $G$ be a graph of order $n$. A path decomposition $\mathcal{P}$ of $G$ is a collection of edge-disjoint paths that covers all the edges of $G$. Let $p(G)$ denote the minimum number of paths needed in a path decomposition of $G$. Gallai conjectured that if $G$ is connected, then $p(G)\leq \lceil\frac{n}{2}\rceil$. In this paper, we prove that Gallai's pat

Le Ma, Ran Zhang, Yikun Han, Shirui Yu

As high-dimensional vector data increasingly surpasses the processing capabilities of traditional database management systems, Vector Databases (VDBs) have emerged and become tightly integrated with large language models, being widely applied in modern artificial intelligence systems. However, existing research has primarily focused on underlying technologie

Yuzhe Han, Qimin Cheng, Wenjin Wu, Ziyang Huang

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical,

Daniel V. Mathews, Orion Zymaris

Descartes' circle theorem relates the curvatures of four mutually externally tangent circles, three "petal" circles around the exterior of a central circle, forming a "$3$-flower" configuration. We generalise this theorem to the case of an "$n$-flower", consisting of $n$ tangent circles around the exterior of a central circle, and give an explicit equation s

Tomohiro Suzuki, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

In many sports, player re-identification is crucial for automatic video processing and analysis. However, most of the current studies on player re-identification in multi- or single-view sports videos focus on re-identification in the closed-world setting using labeled image dataset, and player re-identification in the open-world setting for automatic video

Jing Bi, Nguyen Manh Nguyen, Ali Vosoughi, Chenliang Xu

Augmented reality (AR) requires the seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction. While auditory and visual inputs facilitate real-time and contextual user guidance, the potential of large language models (LLMs) in this landscape remains largely untapped. Our study introduces an innovative method

Yingjie Qi, Jianlei Yang, Ao Zhou, Tong Qiao

Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN computation intensifies, a variety of programming abstractions designed for optimizing GNN Aggregation have emerged to facilitate acceleration. However, there is no comprehensive evalu

Gerardo González Robert, Mumtaz Hussain, Nikita Shulga

Given $b=-A\pm i$ with $A$ being a positive integer, we can represent any complex number as a power series in $b$ with coefficients in $\mathcal A=\{0,1,\ldots, A^2\}$. We prove that, for any real $\tau\geq 2$ and any non-empty proper subset $J(b)$ of $\mathcal A$, there are uncountably many complex numbers (including transcendental numbers) that can be expr

Kaito Kimura

Let $R$ be a commutative noetherian ring, $I$ an ideal of $R$, and $M$ a finitely generated $R$-module. We consider the asymptotic injective dimensions, projective dimensions, Bass numbers, and Betti numbers of localizations of $M/I^n M$ at prime ideals of $R$ and prove that these invariants are stable or have polynomial growth for large integers $n$ that do

BahaaEddin AlAila, Zahra Jandaghi, Abolfazl Farahani, Mohammad Ziad Al-Saad

The problem of image-to-image translation is one that is intruiging and challenging at the same time, for the impact potential it can have on a wide variety of other computer vision applications like colorization, inpainting, segmentation and others. Given the high-level of sophistication needed to extract patterns from one domain and successfully applying t

Chenyangguang Zhang, Guanlong Jiao, Yan Di, Gu Wang

Previous works concerning single-view hand-held object reconstruction typically rely on supervision from 3D ground-truth models, which are hard to collect in real world. In contrast, readily accessible hand-object videos offer a promising training data source, but they only give heavily occluded object observations. In this paper, we present a novel syntheti

Jingzhen Li, Yi Cai, Xuanke Zeng, Xiaowei Lu

Atomic time scale imaging, opening a new era for studying dynamics in microcosmos, is presently attracting immense research interesting on the global level due to its powerful ability. On the atom level, physics, chemistry, and biology are identical for researching atom motion and atomic state change. The light possesses twoness, the information carrier and

Qi Chen, Shijin Ding, Zhilin Lin, Zhifei Zhang

In this paper, we study the nonlinear stability for the 3-D plane Poiseuille flow $(1-y^2,0,0)$ at high Reynolds number $Re$ in a finite channel $\mathbb{T}\times [-1,1 ]\times \mathbb{T}$ with non-slip boundary condition. We prove that if the initial velocity $v_0$ satisfies $\|v_0-(1-y^2,0,0)\|_{H^{4}}\leq c_0 Re^{-\frac{7}{4}}$ for some $c_0>0$ independen

Jianzhi Xv, Gang Li, Tianbao Yang

While deep AUC maximization (DAM) has shown remarkable success on imbalanced medical tasks, e.g., chest X-rays classification and skin lesions classification, it could suffer from severe overfitting when applied to small datasets due to its aggressive nature of pushing prediction scores of positive data away from that of negative data. This paper studies how

Lili Shen, Jun Xian, Cheng Cheng

The random sampling on graph signals is one of the fundamental topics in graph signal processing. In this letter, we consider the random sampling of k-bandlimited signals from the local measurements and show that no more than O(klogk) measurements with replacement are sufficient for the accurate and stable recovery of any k-bandlimited graph signals. We prop

Yuhan Song

ELO rating system is proposed by Arpad Elo, a Hungarian-American physics professor. Originally, it was proposed for the ranking system of chess players, but it was soon adapted to many other zero-sum sports fields like football, baseball, basketball , etc. Nowadays, besides the traditional sports games, computer/video games are also playing an important role

Yang Li, Jiting Cao, Yan Xu, Lipeng Zhu

Most existing data-driven power system short-term voltage stability assessment (STVSA) approaches presume class-balanced input data. However, in practical applications, the occurrence of short-term voltage instability following a disturbance is minimal, leading to a significant class imbalance problem and a consequent decline in classifier performance. This

Jiefeng Chen, Jinsung Yoon, Sayna Ebrahimi, Sercan O Arik

Large language models (LLMs) have recently shown great advances in a variety of tasks, including natural language understanding and generation. However, their use in high-stakes decision-making scenarios is still limited due to the potential for errors. Selective prediction is a technique that can be used to improve the reliability of the LLMs by allowing th

S. Amano, Y. Aritomo, S. Ishizaki, M. Okubayashi

Mass-angle distribution (MAD) measurement of heavy and superheavy element fragmentation reactions is one of the powerful tools for investigating the mechanism of fission and fusion process. MAD shows a strong correlation between mass and angle when the quasi-fission event is dominant. It has characteristic that appears diagonal correlation as long as the qua

Kai Li, Chenwei Zhang, Vincent Larivière

Contributorship statements have been effective at recording granular author contributions in research articles and have been broadly used to understand how labor is divided across research teams. However, one major limitation in existing empirical studies is that two classification systems have been adopted, especially from its most important data source, jo

Xin Li, Liping Zhang, Yifen Ke

In this paper, a classical deflation process raised by Dayton, Li and Zeng is realized for the Brent equations, which provides new bounds for local dimensions of the solution set. Originally, this deflation process focuses on isolated solutions. We generalize it to the case of irreducible components and a related conjecture is given. We analyze its realizati

Yichuan Deng, Zhao Song, Kaijun Yuan, Tianyi Zhou

Large transformer models have achieved state-of-the-art results in numerous natural language processing tasks. Among the pivotal components of the transformer architecture, the attention mechanism plays a crucial role in capturing token interactions within sequences through the utilization of softmax function. Conversely, linear attention presents a more com

Bhargav Ganguly, Yang Xu, Vaneet Aggarwal

This paper investigates the potential of quantum acceleration in addressing infinite horizon Markov Decision Processes (MDPs) to enhance average reward outcomes. We introduce an innovative quantum framework for the agent's engagement with an unknown MDP, extending the conventional interaction paradigm. Our approach involves the design of an optimism-driven t

Jing Li

This paper proposes a new non-parametric bootstrap method to quantify the uncertainty of average treatment effect estimate for the treated from matching estimators. More specifically, it seeks to quantify the uncertainty associated with the average treatment effect estimate for the treated by bootstrapping the treatment group only and finding the counterpart

Vivek C. Peddiraju, Pravallika Bandaru, Shourya Dutta-Gupta, Subhradeep Chatterjee

Nanofabrication is an integral part of realization of advanced functional devices ranging from optical displays to memory devices. Focused ion beam (FIB) milling is one of the widely used nanofabrication methods. Conventionally, FIB milling has been carried out for patterning single-phase stable thin films. However, the influence of FIB milling on phase sepa

Kerui Zhu, Jie Huang, Kevin Chen-Chuan Chang

We present a novel system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge. Unlike previous search engines or exploration systems that retrieve unconnected passages, our system organizes descriptive sentences as a relational graph, enabling

M. Hashem Pesaran, Liying Yang

The commonly used two-way fixed effects estimator is biased under correlated heterogeneity and can lead to misleading inference. The mean group estimator proposed by Pesaran and Smith (1995) is robust to correlated heterogeneity but requires the underlying individual estimates to have second-order moments that could fail if the number of estimated coefficien

Saburo Kakei

A class of "elliptic soliton" solutions of the Kadomtsev-Petviashvili hierarchy, which includes a determinantal solution of Li and Zhang, is described in terms of pseudo-differential operator formulation. In our approach, the Li-Zhang solution is obtained by repeatedly applying the Darboux transformation to a stationary solution. Real-valued solutions are di