February 2024 arXiv papers — page 25

Jean-François Delmas, Dylan Dronnier, Pierre-André Zitt

We discuss transformations on matrices that preserve the effective spectrum and/or the effective spectral radius.

Javier Gutiérrez García, Ulrich Höhle

Unitally nondistributive quantales are unital quantales such that the unit is approximable by the totally below relation and does not meet-distribute over arbitrary joins. It is shown that the underlying nondistributive complete lattice contains at least $7$ elements. Moreover, under mild conditions, every quantale has an extension to a unitally nondistribut

Norbert A'Campo, Athanase Papadopoulos

We present a variety of geometrical and combinatorial tools that are used in the study of geometric structures on surfaces: volume, contact, symplectic, complex and almost complex structures. We start with a series of local rigidity results for such structures. Higher-dimensional analogues are also discussed. Some constructions with Riemann surfaces lead, by

C. Guidorzi, M. Sartori, R. Maccary, A. Tsvetkova

The variety of long duration gamma-ray burst (LGRB) light curves (LCs) encode a wealth of information on how LGRB engines release energy following the collapse of the progenitor star. Attempts to characterise GRB LCs focused on a number of properties, such as the minimum variability timescale, power density spectra (both ensemble average and individual), or

Alexandre Defossez, Samuel Alleaume, Marc Montadert, Dino Ienco

The Black Grouse (Lyrurus tetrix) is an emblematic alpine species with high conservation importance. The population size of these mountain bird tends to decline on the reference sites and shows differences according to changes in local landscape characteristics. Habitat changes are at the centre of the identified pressures impacting part or all of its life c

Ming Ye, Xiao Liang, Cunhua Pan, Yinfei Xu

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve ultra-high spectral efficiency for future 6G communications. The mixed line-of-sight (LoS) and non-line-of-sight (NLoS) XL-MIMO near-field channel model is adopted to describe the XL-MIMO near-field channel accurately. In this paper, a generative adversarial ne

Cheol Hun Yeom, Beom Hyun Kim, Moon Jip Park

Topologically ordered phases have robust degenerate ground states against the local perturbations, providing a promising platform for fault-tolerant quantum computation. Despite of the non-local feature of the topological order, we find that local non-Hermitian perturbations can induce the transition between the topologically ordered ground states. In this w

Yiyan Xu, Wenjie Wang, Fuli Feng, Yunshan Ma

Outfit Recommendation (OR) in the fashion domain has evolved through two stages: Pre-defined Outfit Recommendation and Personalized Outfit Composition. However, both stages are constrained by existing fashion products, limiting their effectiveness in addressing users' diverse fashion needs. Recently, the advent of AI-generated content provides the opportunit

Debdyuti Roy, Vincent Chaleix, Atul N. Parikh, Niki Baccile

Myelin figures (MFs) -- cylindrical lyotropic liquid crystalline structures consisting of concentric arrays of bilayers and aqueous media -- arise from the hydration of the bulk lamellar phase of many common amphiphiles. Prior efforts have concentrated on the formation, structure, and dynamics of myelin produced by phosphatidylcholine (PC)-based amphiphiles.

Youssef Azouzi, Youssef Nasri

We persist in our investigation of the sup-completion of a Dedekind complete Riesz space, extending to the broader context of Riesz spaces. some results initially obtained by Feng, Li, Shen, and also by Erd\"os, and R\'enyi.

Junshuo Liu, Yunlong Huang, Wei Yang, Zhe Li

Human activity recognition (HAR) holds significant importance in smart homes, security, and healthcare. Existing systems face limitations because of the insufficient spatial diversity provided by a limited number of antennas. Furthermore, inefficiencies in noise reduction and feature extraction from sensing data pose challenges to recognition performance. Th

Juan Mei, Zhiwei Chen, Zhibo Jiang, Sheng Zheng

We propose a new method for estimating the distances of molecular clouds traced by CO line emission. Stars from 2MASS and Gaia EDR3 are selected as on-cloud stars when they are projected on a cloud. The background on-cloud stars have redder colors on average than the foreground stars. Instead of searching for stars projected away from the cloud, we employed

Hansam Cho, Jonghyun Lee, Seunggyu Chang, Yonghyun Jeong

While GAN-based models have been successful in image stylization tasks, they often struggle with structure preservation while stylizing a wide range of input images. Recently, diffusion models have been adopted for image stylization but still lack the capability to maintain the original quality of input images. Building on this, we propose OSASIS: a novel on

Yajun Liu, Beth Andrews

A binomial time series describes binary behaviors of individuals within a group, which depend on group behaviors in the past. Binomial time series data is widely applied in fields such as infection tracking and behavior analysis. In this paper, we introduce a generalized Binomial AR($p$) model with exogenous variables based on Generalized Linear Model (GLM),

Seong-Ho Son, Kwang-Jae Lee, Won-Kwang Park

The problem of the real-time microwave imaging of small, moving objects from a scattering matrix, whose elements are measured scattering parameters, without diagonal elements is considered herein. An imaging algorithm based on a Kirchhoff migration operated at single frequency is designed, and its mathematical structure is investigated by establishing a rela

Satoru Odake

For the isospectral Darboux transformations of the discrete quantum mechanics with real shifts, there are two methods: type I and type II constructions. Based on the type I construction, the type I multi-indexed little $q$-Jacobi and little $q$-Laguerre orthogonal polynomials were presented in J. Phys. {\bf A50} (2017) 165204. Based on the type II constructi

Ruo-Si Lu, Rui Qiao, Ke Gong, Wen-Xi Peng

The Silicon Charge Detector (SCD) is a subdetector of the High Energy Cosmic Radiation Detection payload. The dynamic range of the silicon microstrip detector can be extended by the capacitive coupling effect, which is related to the interstrip capacitance and the coupling capacitance. A detector prototype with several sets of parameters was designed and tes

Yushan Han, Kaer Huang

Efficiently modeling spatio-temporal relations of objects is a key challenge in visual object tracking (VOT). Existing methods track by appearance-based similarity or long-term relation modeling, resulting in rich temporal contexts between consecutive frames being easily overlooked. Moreover, training trackers from scratch or fine-tuning large pre-trained mo

Chunjiang Mu, Hao Guo, Yang Chen, Chen Shen

The study of cooperation within social dilemmas has long been a fundamental topic across various disciplines, including computer science and social science. Recent advancements in Artificial Intelligence (AI) have significantly reshaped this field, offering fresh insights into understanding and enhancing cooperation. This survey examines three key areas at t

Cam-Van Thi Nguyen, Cao-Bach Nguyen, Quang-Thuy Ha, Duc-Trong Le

Emotion recognition in conversation (ERC) is a crucial task in natural language processing and affective computing. This paper proposes MultiDAG+CL, a novel approach for Multimodal Emotion Recognition in Conversation (ERC) that employs Directed Acyclic Graph (DAG) to integrate textual, acoustic, and visual features within a unified framework. The model is en

Jongga Lee, Jaeseung Yim, Seohee Park, Changwon Lim

Text classification is the task of assigning a document to a predefined class. However, it is expensive to acquire enough labeled documents or to label them. In this paper, we study the regularization methods' effects on various classification models when only a few labeled data are available. We compare a simple word embedding-based model, which is simple b

Sergey V. Gusev

A monoid is aperiodic if all its subgroups are trivial. We completely classify all varieties of aperiodic monoids whose subvariety lattice is distributive.

Hong Cheng, Huan Luo, Zhi Liu, Wei Sun

Active distribution networks (ADNs) incorporating massive photovoltaic (PV) devices encounter challenges of rapid voltage fluctuations and potential violations. Due to the fluctuation and intermittency of PV generation, the state gap, arising from time-inconsistent states and exacerbated by imprecisely known system delays, significantly impacts the accuracy

Manoj Kumar, Bernhard Ersfeld, Jaeho Lee, Dohyun Park

A novel method is presented for generating radiation using the beat wave associated with a bi-frequency laser pulse, to excite plasma oscillations in a plasma slab with a density gradient. By resonantly exciting a plasma wave, it can be localised and transformed into a plasma oscillator that produces a beam of radially polarised terahertz radiation. Particle

Qiyun Tang, Yan He

We investigate the appearance of mobility edges in a one-dimensional non-Hermitian tight-banding model with alternating hopping constants and slowly varying quasi-periodic on-site potentials. Due to the presence of slowly varying exponent, the parity-time (PT) symmetry of this model is broken and its spectra is complex. It is found that the spectrum of this

Arnab Roy, Basudeb Dasgupta, Monoranjan Guchait

We reappraise the viability of asymmetric dark matter (ADM) realized as a Dirac fermion coupling dominantly to the Standard Model fermions. Treating the interactions of such a DM particle with quarks/leptons in an effective-interactions framework, we derive updated constraints using mono-jet searches from the Large Hadron Collider (LHC) and mono-photon searc

Jingyi Xu, Junyi Ma, Qi Wu, Zijie Zhou

Fusion-based place recognition is an emerging technique jointly utilizing multi-modal perception data, to recognize previously visited places in GPS-denied scenarios for robots and autonomous vehicles. Recent fusion-based place recognition methods combine multi-modal features in implicit manners. While achieving remarkable results, they do not explicitly con

Dongmin Park

In this dissertation, we propose a systemic framework that prioritizes informative features and examples to enhance each stage of the development process. Specifically, we prioritize informative features and examples and improve the performance of feature learning, data labeling, and data selection. We first propose an approach to extract only informative fe

Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang

Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase. Low-rank adaptation (LoRA) is based on the idea that the adaptation process is intrinsically low-dimensional, i.e., significant model changes can be represented with relatively

S. Alex Yang, Angela Huyue Zhang

The rapid advancement of generative AI is poised to disrupt the creative industry. Amidst the immense excitement for this new technology, its future development and applications in the creative industry hinge crucially upon two copyright issues: 1) the compensation to creators whose content has been used to train generative AI models (the fair use standard);

Zhenhong Zhou, Jiuyang Xiang, Haopeng Chen, Quan Liu

Large Language Models (LLMs) have been demonstrated to generate illegal or unethical responses, particularly when subjected to "jailbreak." Research on jailbreak has highlighted the safety issues of LLMs. However, prior studies have predominantly focused on single-turn dialogue, ignoring the potential complexities and risks presented by multi-turn dialogue,

Azim Fitri Zainul Abidin, Ikutaro Hamada

We present a density functional theory study of the oxygen reduction reaction (ORR) on a single atom catalyst embedded in graphene, namely, TM-N$_{4}$-C (TM = Fe and Co), using the effective screening medium method combined with the reference interaction site model (ESM-RISM). It was found that Fe-N$_{4}$-C and Co-N$_{4}$-C show comparable ORR activities fro

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We present GFlaT, a new algorithm that uses a graph-neural-network to determine the flavor of neutral $B$ mesons produced in $\Upsilon(4S)$ decays. It improves previous algorithms by using the information from all charged final-state particles and the relations between them. We evaluate its performance using $B$ decays to flavor-specific hadronic final state

Liwen Tan, Yin Cao, Yi Zhou

Modality discrepancies have perpetually posed significant challenges within the realm of Automated Audio Captioning (AAC) and across all multi-modal domains. Facilitating models in comprehending text information plays a pivotal role in establishing a seamless connection between the two modalities of text and audio. While recent research has focused on closin

Rajeeva Laxman Karandikar, Bhamidi V Rao

Stochastic Approximation (SA) was introduced in the early 1950's and has been an active area of research for several decades. While the initial focus was on statistical questions, it was seen to have applications to signal processing, convex optimisation. %Over the last decade, there has been a revival of interest in SA as In later years SA has found applica

Jie Cheng, Gang Xiong, Xingyuan Dai, Qinghai Miao

Preference-based Reinforcement Learning (PbRL) circumvents the need for reward engineering by harnessing human preferences as the reward signal. However, current PbRL methods excessively depend on high-quality feedback from domain experts, which results in a lack of robustness. In this paper, we present RIME, a robust PbRL algorithm for effective reward lear

Pei Wang, Keqing He, Yejie Wang, Xiaoshuai Song

Out-of-domain (OOD) intent detection aims to examine whether the user's query falls outside the predefined domain of the system, which is crucial for the proper functioning of task-oriented dialogue (TOD) systems. Previous methods address it by fine-tuning discriminative models. Recently, some studies have been exploring the application of large language mod

J. Pascal Gollin, Kevin Hendrey, Sang-il Oum, Bruce Reed

One of the fundamental results in graph minor theory is that for every planar graph $H$, there is a minimum integer $f(H)$ such that graphs with no minor isomorphic to $H$ have treewidth at most $f(H)$. A lower bound for ${f(H)}$ can be obtained by considering the maximum integer $k$ such that $H$ contains $k$ vertex-disjoint cycles. There exists a graph of

J. H. Cook, S. Balu, B. Greig, C. M. Trott

The cosmological 21cm signal offers a potential probe of the early Universe and the first ionizing sources. Current experiments probe the spatially-dependent variance (Gaussianity) of the signal through the power spectrum (PS). The signal however is expected to be highly non-Gaussian due to the complex topology of reionization and X-ray heating. We investiga

Yuxin Dong, Hezi Lin, Lingen Lu

We prove the Heisenberg-Pauli-Weyl inequality, Hardy-Sobolev inequality, and Caffarelli-Kohn-Nirenberg (CKN) inequality on manifolds with nonnegative Ricci curvature and Euclidean volume growth, of dimension n>=3.

Thomas-Otavio Peulen, Katherina Hemmen, Annemarie Greife, Benjamin M. Webb

We introduce software for reading, writing and processing fluorescence single-molecule and image spectroscopy data and developing analysis pipelines that unifies various spectroscopic analysis tools. Our software can be used for processing multiple experiment types, e.g., for time-resolved single-molecule (sm) spectroscopy, laser scanning microscopy, fluores

Yun Li, Zhe Liu, Hang Chen, Lina Yao

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen attribute-object pairs based on a limited set of observed examples. Current CZSL methodologies, despite their advancements, tend to neglect the distinct specificity levels present in attributes. For instance, given images of sliced strawberries, they may fail to prioritize `Sliced-Strawberry' o

Avrajit Bandyopadhyay, Timothy C Beers, Rana Ezzeddine, Thirupathi Sivarani

We present a comprehensive analysis of the detailed chemical abundances for a sample of 11 metal-poor, very metal-poor and extremely metal-poor stars ([Fe/H] = -1.65 to [Fe/H] = -3.0) as part of the HESP-GOMPA (Galactic survey Of Metal Poor stArs) survey. The abundance determinations encompass a range of elements, including C, Na, Mg, Al, Si, Ca, Sc, Ti, Cr,

Tosin Ige, Christopher Kiekintveld, Aritran Piplai

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable t

Ryan T. Sandberg, Remi Lehe, Chad E. Mitchell, Marco Garten

Particle accelerator modeling is an important field of research and development, essential to investigating, designing and operating some of the most complex scientific devices ever built. Kinetic simulations of relativistic, charged particle beams and advanced plasma accelerator elements are often performed with high-fidelity particle-in-cell simulations, s

Yao Li, Chengpu Yu, Hao Fang, Jie Chen

This paper addresses the inverse optimal control for the linear quadratic tracking problem with a fixed but unknown target state, which aims to estimate the possible triplets comprising the target state, the state weight matrix, and the input weight matrix from observed optimal control input and the corresponding state trajectories. Sufficient conditions hav

Meng Lou, Hanning Ying, Xiaoqing Liu, Hong-Yu Zhou

Automated classification of liver lesions in multi-phase CT and MR scans is of clinical significance but challenging. This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts. The proposed SDR-Former utilizes a st

Daiqing Li, Aleks Kamko, Ehsan Akhgari, Ali Sabet

In this work, we share three insights for achieving state-of-the-art aesthetic quality in text-to-image generative models. We focus on three critical aspects for model improvement: enhancing color and contrast, improving generation across multiple aspect ratios, and improving human-centric fine details. First, we delve into the significance of the noise sche

Jiajian Shen, Paige A. Taylor, Carlos E. Vargas, Minglei Kang

A survey was designed to inquire about the practice of proton SBRT treatment for prostate cancer. The survey was distributed to all 30 proton therapy centers in the United States that participate in the National Clinical Trial Network in Feb. 2023. The survey focused on usage, patient selection criteria, prescriptions, target contours, dose constraints, trea

Pavel Petrovič, Fedir Agarshev

Robotics sets have been successfully used in elementary and secondary schools in conformance with the 'learning through play' philosophy fostered by LEGO Education, while utilizing the Constructionism didactic approach. Learners discover and acquire knowledge through first-hand tangible experiences, building their own representations in a constructivist lear

Yuxiang Wang, Shuzhan Ye, Xiaoliang Xu, Yuxia Geng

Given an attributed graph $G$ and a query node $q$, \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and attribute-cohesive subgraph from $G$ that contains $q$. Although CS-AG has been widely studied, they still face three challenges. (1) Exact methods based on graph traversal are time-

Yiyu Zhang, Tianyi Liu, Yueyang Wang, Yun Qi

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over the past decades, numerous attempts have been made to lower the overhead of DTA. Unfortunately, the reductions they achiev

Jiawei He, Xiaogang Li

Let $G$ be a finite group acting faithfully on a finite set $\Omega$. For a positive integer $k$, $G$ acts naturally on the Catesian product $\Omega^k := \Omega \times ...\times \Omega$. In this paper, we prove that finite nilpotent group $G$ with $2\nmid |G|$ is a totally $k$-closed group if and only if $G$ is abelian with $n(G)\leq k-1$ or cyclic, where $n

Shu Wang, Xiaodian Chen

The interstellar extinction law is important for interpreting observations and inferring the properties of interstellar dust grains. Based on the 993 prism/CLEAR spectra from the James Webb Space Telescope (JWST), we investigate $0.6-5.3\,{\rm \mu m}$ interstellar dust extinction law. We propose a pair method to obtain the reddening curves based only on JWST

Zhen Yang, Ming Ding, Tinglin Huang, Yukuo Cen

Negative sampling has swiftly risen to prominence as a focal point of research, with wide-ranging applications spanning machine learning, computer vision, natural language processing, data mining, and recommender systems. This growing interest raises several critical questions: Does negative sampling really matter? Is there a general framework that can incor

Madhukrishna Chakraborty, Subenoy Chakraborty

The paper deals with the Raychaudhuri equation (RE) which is a non-linear ordinary differential equation in $\Theta$, the expansion scalar corresponding to a geodesic flow. Focusing theorem which follows as a consequence of the RE has been restated in terms of the cosmic parameter $q$ (deceleration parameter) both for Einstein gravity and for modified gravit

Wanqing Cui, Rui Cheng, Jiafeng Guo, Xueqi Cheng

Existing two-stream models, such as CLIP, encode images and text through independent representations, showing good performance while ensuring retrieval speed, have attracted attention from industry and academia. However, the single representation often struggles to capture complex content fully. Such models may ignore fine-grained information during matching

Zhang Xiong, Haoxuan Li, Zhuang Liu, Zhuofan Chen

Personalized education, tailored to individual student needs, leverages educational technology and artificial intelligence (AI) in the digital age to enhance learning effectiveness. The integration of AI in educational platforms provides insights into academic performance, learning preferences, and behaviors, optimizing the personal learning process. Driven

Jincheng Mei, Zixin Zhong, Bo Dai, Alekh Agarwal

We show that the \emph{stochastic gradient} bandit algorithm converges to a \emph{globally optimal} policy at an $O(1/t)$ rate, even with a \emph{constant} step size. Remarkably, global convergence of the stochastic gradient bandit algorithm has not been previously established, even though it is an old algorithm known to be applicable to bandits. The new res

A. Yu. Zakharov

A comparative analysis of two concepts aimed at microscopic substantiation of thermodynamics and kinetics has been performed. The first concept is based on the idea of microscopic reversibility of the dynamics of a system of particles, while macroscopic irreversibility is of statistical origin. The second concept is based on the idea of the initial microscop

Bob Junyi Zou, Matthew E. Levine, Dessi P. Zaharieva, Ramesh Johari

Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning). The incorporation of mechanistic models also provi

Qiao Zhuang, Chris Ziyi Yao, Zhongqiang Zhang, George Em Karniadakis

We propose a two-scale neural network method for solving partial differential equations (PDEs) with small parameters using physics-informed neural networks (PINNs). We directly incorporate the small parameters into the architecture of neural networks. The proposed method enables solving PDEs with small parameters in a simple fashion, without adding Fourier f

Debrup Das, Debopriyo Banerjee, Somak Aditya, Ashish Kulkarni

Tool-augmented Large Language Models (TALMs) are known to enhance the skillset of large language models (LLMs), thereby, leading to their improved reasoning abilities across many tasks. While, TALMs have been successfully employed in different question-answering benchmarks, their efficacy on complex mathematical reasoning benchmarks, and the potential comple

Subenoy Chakraborty, Madhukrishna Chakraborty

In this review, we provide a concrete overview of the Raychaudhuri equation, Focusing theorem and Convergence conditions in a plethora of backgrounds and discuss the consequences. We also present various classical and quantum approaches suggested in the literature that could potentially mitigate the initial big-bang singularity and the black-hole singularity

Yu Nong, Mohammed Aldeen, Long Cheng, Hongxin Hu

Security vulnerabilities are increasingly prevalent in modern software and they are widely consequential to our society. Various approaches to defending against these vulnerabilities have been proposed, among which those leveraging deep learning (DL) avoid major barriers with other techniques hence attracting more attention in recent years. However, DL-based

Li Lin, Xinan He, Yan Ju, Xin Wang

Although effective deepfake detection models have been developed in recent years, recent studies have revealed that these models can result in unfair performance disparities among demographic groups, such as race and gender. This can lead to particular groups facing unfair targeting or exclusion from detection, potentially allowing misclassified deepfakes to

Wenhao Tang, Fengtao Zhou, Sheng Huang, Xiang Zhu

Multiple instance learning (MIL) is the most widely used framework in computational pathology, encompassing sub-typing, diagnosis, prognosis, and more. However, the existing MIL paradigm typically requires an offline instance feature extractor, such as a pre-trained ResNet or a foundation model. This approach lacks the capability for feature fine-tuning with

Ziteng Wang, Jianfei Chen, Jun Zhu

Sampling-based algorithms, which eliminate ''unimportant'' computations during forward and/or back propagation (BP), offer potential solutions to accelerate neural network training. However, since sampling introduces approximations to training, such algorithms may not consistently maintain accuracy across various tasks. In this work, we introduce a variance-

Xiaolong Wang, Yile Wang, Yuanchi Zhang, Fuwen Luo

Large Language Models (LLMs) have achieved remarkable performance in objective tasks such as open-domain question answering and mathematical reasoning, which can often be solved through recalling learned factual knowledge or chain-of-thought style reasoning. However, we find that the performance of LLMs in subjective tasks is still unsatisfactory, such as me

Minsoo Jang, Sergey G. Menabde, Fatemeh Kiani, Jacob T. Heiden

Scattering-type scanning near-field optical microscope (s-SNOM) has become an essential tool to study polaritons - quasiparticles of light coupled to collective charge oscillations - via direct probing of their near field with a spatial resolution far beyond the diffraction limit. However, extraction of the polariton complex propagation constant from the nea

Sen-Lin Pang, Zi-Gao Dai

The afterglow of a gamma-ray burst (GRB) has been widely argued to arise from the interaction of a relativistic outflow with its ambient medium. During such an interaction, a pair of shocks are generated: a forward shock that propagates into the medium, and a reverse shock that propagates into the outflow. Extensive studies have been conducted on the emissio

Yiming Jiang, Jiangfan Zhang

Numerous blockchain applications are designed with tasks that naturally have finite durations, and hence, a double-spending attack (DSA) on such blockchain applications leans towards being conducted within a finite timeframe, specifically before the completion of their tasks. Furthermore, existing research suggests that practical attackers typically favor ex

Iasson Karafyllis, Miroslav Krstic, Alexandros Aslanidis

In this paper we extend our recently proposed Deadzone-Adapted Disturbance Suppression (DADS) Control approach from systems with matched uncertainties to general systems in parametric strict feedback form. The DADS approach prevents gain and state drift regardless of the size of the disturbance and unknown parameter and achieves an attenuation of the plant o

James Allen Fill, Daniel Naiman, Ao Sun

For $d\ge2$ and iid $d$-dimensional observations $X^{(1)},X^{(2)},\dots$ with independent Exponential$(1)$ coordinates, we revisit the study by Fill and Naiman (Electron. J. Probab., 2020) of the boundary (relative to the closed positive orthant), or "frontier", $F_n$ of the closed Pareto record-setting (RS) region \[ \mbox{RS}_n:=\{0\le x\in{\mathbb R}^d:x\

James Allen Fill, Ao Sun

Given a sequence of independent random vectors taking values in ${\mathbb R}^d$ and having common continuous distribution function $F$, say that the $n^{\rm \scriptsize th}$ observation sets a (Pareto) record if it is not dominated (in every coordinate) by any preceding observation. Let $p_n(F) \equiv p_{n, d}(F)$ denote the probability that the $n^{\rm \scr

Hanjie Wu, Qian Yao, Zhenguang Liu, Butian Huang

As an innovative technology for enhancing authenticity, security, and risk management, blockchain is being widely adopted in trade and finance systems. The unique capabilities of blockchain, such as immutability and transparency, enable new business models of distributed data storage, point-to-point transactions, and decentralized autonomous organizations. I

Emelia Hughes, Renee Wang, Prerna Juneja, Tony Li

As more users turn to video-sharing platforms like YouTube as an information source, they may consume misinformation despite their best efforts. In this work, we investigate ways that users can better assess the credibility of videos by first exploring how users currently determine credibility using existing signals on platforms and then by introducing and e

Zijian Guo, Weichao Zhou, Wenchao Li

Offline safe reinforcement learning (RL) aims to train a constraint satisfaction policy from a fixed dataset. Current state-of-the-art approaches are based on supervised learning with a conditioned policy. However, these approaches fall short in real-world applications that involve complex tasks with rich temporal and logical structures. In this paper, we pr

Yifan Zhang, Bo Liu, Yulu Gong, Jiaxin Huang

In recent years, cloud computing has been widely used. Cloud computing refers to the centralized computing resources, users through the access to the centralized resources to complete the calculation, the cloud computing center will return the results of the program processing to the user. Cloud computing is not only for individual users, but also for enterp

Lexing Ying

This note considers the multidimensional unstructured sparse recovery problems. Examples include Fourier inversion and sparse deconvolution. The eigenmatrix is a data-driven construction with desired approximate eigenvalues and eigenvectors proposed for the one-dimensional problems. This note extends the eigenmatrix approach to multidimensional problems. Num

Hao-Yang Peng, Jia-Peng Zhang, Meng-Hao Guo, Yan-Pei Cao

In the field of digital content creation, generating high-quality 3D characters from single images is challenging, especially given the complexities of various body poses and the issues of self-occlusion and pose ambiguity. In this paper, we present CharacterGen, a framework developed to efficiently generate 3D characters. CharacterGen introduces a streamlin

Xiangqing Shen, Fanfan Wang, Siwei Wu, Rui Xia

Visual commonsense plays a vital role in understanding and reasoning about the visual world. While commonsense knowledge bases like ConceptNet provide structured collections of general facts, they lack visually grounded representations. Scene graph datasets like Visual Genome, though rich in object-level descriptions, primarily focus on directly observable i

Sridhar Gajendran, Ing-Guey Jiang, Li-Chin Yeh, Devesh P. Sariya

We report the conditional occurrences between three planetary types: super-Earths (m sin i $<$ 10 M$_\oplus$, P $<$ 100 days), warm Jupiters (m sin i $>$ 95 $M_\oplus$, 10 $<$ P $<$ 100 days), and cold Jupiters (m sin i $>$ 95 M$_\oplus$, P $>$ 400 days) for sun-like stars. We find that while the occurrence of cold Jupiters in systems with super-Earths is $2

Giacomo Torlai, Roger G. Melko

We perform a quantum Monte Carlo simulation of the resonating valence bond wavefunction on a two-dimensional square lattice with periodic boundary conditions. Using two replicas of the system, we calculate the second Renyi entropy on a spatial bipartition with a square geometry. Through a finite-size scaling analysis, we extract the logarithmic correction to

Guobiao Li, Sheng Li, Zicong Luo, Zhenxing Qian

Steganography is the art of hiding secret data into the cover media for covert communication. In recent years, more and more deep neural network (DNN)-based steganographic schemes are proposed to train steganographic networks for secret embedding and recovery, which are shown to be promising. Compared with the handcrafted steganographic tools, steganographic

Hyun-June Jang, Hyou-Arm Joung, Artem Goncharov, Anastasia Gant Kanegusuku

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-bas

Mo Zhou, Jianfeng Lu

We propose an actor-critic framework to solve the time-continuous stochastic optimal control problem. A least square temporal difference method is applied to compute the value function for the critic. The policy gradient method is implemented as policy improvement for the actor. Our key contribution lies in establishing a linear rate of convergence for our p

Mo Zhou, Yiding Yang, Haoxiang Li, Vishal M. Patel

With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection. Yet, it turns into a harmful but inevitable training set bias upon test distributions that shift differently across space and time. Nevertheless, the existing detectors cannot incorporate deployment context prior during the te

Tianshuo Zhou, Wei Yu Tang, Apoorv Malik, David H. Mathews

RNA design aims to find a sequence that folds with highest probability into a designated target structure. However, certain structures are undesignable, meaning no sequence can fold into the target structure under the default (Turner) RNA folding model. Understanding the specific local structures (i.e., "motifs") that contribute to undesignability is crucial

Jianhao Shen, Ye Yuan, Srbuhi Mirzoyan, Ming Zhang

We introduce a new challenge to test the STEM skills of neural models. The problems in the real world often require solutions, combining knowledge from STEM (science, technology, engineering, and math). Unlike existing datasets, our dataset requires the understanding of multimodal vision-language information of STEM. Our dataset features one of the largest a

Majid Memari, Khaled R. Ahmed, Shahram Rahimi, Noorbakhsh Amiri Golilarz

This research addresses a critical challenge in the field of generative models, particularly in the generation and evaluation of synthetic images. Given the inherent complexity of generative models and the absence of a standardized procedure for their comparison, our study introduces a pioneering algorithm to objectively assess the realism of synthetic image

Kazuhisa Shimakawa

We introduce a nonstandard extension of the category of diffeological spaces, and demonstrate its application to the study of generalized functions. Just as diffeological spaces are defined as concrete sheaves on the site of Euclidean open sets, our nonstandard diffeological spaces are defined as concrete sheaves on the site of open subsets of nonstandard Eu

Ziyue Xu, Mingfeng Xu, Tianchi Liao, Zibin Zheng

Recently, the success of large models has demonstrated the importance of scaling up model size. This has spurred interest in exploring collaborative training of large-scale models from federated learning perspective. Due to computational constraints, many institutions struggle to train a large-scale model locally. Thus, training a larger global model using o

Fotis Drakopoulos, Kevin Garner, Christopher Rector, Nikos Chrisochoides

Converting a three-dimensional medical image into a 3D mesh that satisfies both the quality and fidelity constraints of predictive simulations and image-guided surgical procedures remains a critical problem. Presented is an image-to-mesh conversion method called CBC3D. It first discretizes a segmented image by generating an adaptive Body-Centered Cubic (BCC)

Ahmed S. Alahmed, Guido Cavraro, Andrey Bernstein, Lang Tong

We propose an operating envelopes (OEs) aware energy community market mechanism that dynamically charges/rewards its members based on two-part pricing. The OEs are imposed exogenously by a regulated distribution system operator (DSO) on the energy community's revenue meter and is subject to a generalized net energy metering (NEM) tariff design. By formulatin

Qunliang Xing, Mai Xu, Shengxi Li, Xin Deng

Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However, these methods exhibit a pervasive enhancement bias towards the compression domain, inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their

Misa Nozaki, Peter Krüger

A versatile method for angle-resolved photoemission spectra (ARPES) calculations is reported within the one-step model of photoemission. The initial states are obtained from a repeated-slab calculation using the projector-augmented wave (PAW) method. ARPES final states are constructed by matching the repeated-slab eigenstates of positive energy with free ele

Peng Gao, Liangyi Zhao

We apply the method of multiple Dirichlet series to develop $L$-functions ratios conjecture with one shift in both the numerator and denominator in certain ranges for the family of quartic Hecke $L$-functions of prime moduli over the Gaussian field under the generalized Riemann hypothesis. As consequences, we evaluate asymptotically the first moment of centr

Renfei Wang, Xiao Liu, Mengmeng Wu, Yoon Jang Chung

We systematically study the acousto-current of two-dimensional electron systems in the integer and fractional quantum Hall regimes using surface acoustic waves. We are able to separate the co-existing acoustic scattering and drag, when phonons induce drag current and tune the electron conductivity, respectively. At large acoustic power, the drag current is f

Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang

We propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1-minute and 5-minute resolution data. SYMHnet takes, as input, the