February 2024 arXiv papers — page 34

Shiwen Ni, Min Yang, Ruifeng Xu, Chengming Li

Among the various pre-trained neural language models that are popular today, dropout is already an indispensable regularization technique. To solve the inconsistency between training and inference caused by the randomness of dropout, some studies use consistency training to regularize dropout at the output layer. In this paper, we propose a novel Layer-wise

Kosuzu Hamaoka, Keiichi Kato, Shun Takizawa

In this paper we give an estimate for the solution to the Schr\"odinger equation with sub-quadratic potentials in modulation spaces by the norm of the initial functions in Wiener-Amalgum spaces.

Masatoshi Uehara, Yulai Zhao, Kevin Black, Ehsan Hajiramezanali

Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules. However, in many cases, our goal is to model parts of the distribution that maximize certain properties: for example, we may want to generate images with high aesthetic quality, or molecules with high bioactivity. It is natural to frame th

Yiding Sun, Feng Wang, Yutao Zhu, Wayne Xin Zhao

The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data. In order to improve data quality, researchers and practitioners often have to manually curate datasets from difference sources and develop dedicated data cleansing pipeline for each data repository. Lacking a unified data processing framework, this

Haruto Hori, Masanari Kida

In their recent paper, Rosen, Takeyama, Tasaka, and Yamamoto constructed recurrent sequences providing a decomposition law of primes in a Galois extension. In this paper, we reconstruct their sequences via representation theory of finite groups and obtain an explicit description of the sequences.

Daichi Haraguchi, Brian Kenji Iwana, Seiichi Uchida

This study analyzes the relationship between non-verbal information (e.g., genres) and text design (e.g., font style, character color, etc.) through the classification of book genres using text design on book covers. Text images have both semantic information about the word itself and other information (non-semantic information or visual design), such as fon

Md. Shohanur Islam Sobuj, Md. Imran Hossen, Md. Foysal Mahmud, Mahbub Ul Islam Khan

Rice disease classification is a critical task in agricultural research, and in this study, we rigorously evaluate the impact of integrating feature extraction methodologies within pre-trained convolutional neural networks (CNNs). Initial investigations into baseline models, devoid of feature extraction, revealed commendable performance with ResNet-50 and Re

Chenyu Wu, Shaoguang Zhang, Yufei Zhang

This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent efforts have focused on data-driven methods such as field inversion and machine learning (FIML) to correct this issue by

Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris

We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes Large Language Models (LLMs) to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segme

Sitan Chen, Jerry Li, Allen Liu

There has been significant interest in understanding how practical constraints on contemporary quantum devices impact the complexity of quantum learning. For the classic question of tomography, recent work tightly characterized the copy complexity for any protocol that can only measure one copy of the unknown state at a time, showing it is polynomially worse

Zimu Lu, Aojun Zhou, Houxing Ren, Ke Wang

Large language models (LLMs) have exhibited great potential in mathematical reasoning. However, there remains a performance gap in this area between existing open-source models and closed-source models such as GPT-4. In this paper, we introduce MathGenie, a novel method for generating diverse and reliable math problems from a small-scale problem-solution dat

Kento Nakamine, Yuto Yokoyama, William Kai Alexander Worby, Masakazu Muto

This study systematically investigates the flow birefringence of cellulose nanocrystal (CNC) suspensions. The aim is to clarify the importance of the stress component along the camera's optical axis in the stress-optic law (SOL), which describes the relationship between birefringence, the retardation of transmitted polarized light, and the stress field. More

Yugo Kubota, Daichi Haraguchi, Seiichi Uchida

Fonts convey different impressions to readers. These impressions often come from the font shapes. However, the correlation between fonts and their impression is weak and unstable because impressions are subjective. To capture such weak and unstable cross-modal correlation between font shapes and their impressions, we propose Impression-CLIP, which is a novel

Tianjiao Luo, Tim Pearce, Huayu Chen, Jianfei Chen

Generative Adversarial Imitation Learning (GAIL) trains a generative policy to mimic a demonstrator. It uses on-policy Reinforcement Learning (RL) to optimize a reward signal derived from a GAN-like discriminator. A major drawback of GAIL is its training instability - it inherits the complex training dynamics of GANs, and the distribution shift introduced by

Yiming Luo, Zixuan Zhuang, Neng Pan, Chen Feng

This paper tackles the challenge of autonomous target search using unmanned aerial vehicles (UAVs) in complex unknown environments. To fill the gap in systematic approaches for this task, we introduce Star-Searcher, an aerial system featuring specialized sensor suites, mapping, and planning modules to optimize searching. Path planning challenges due to incre

Haoyang Li, Jing Zhang, Hanbing Liu, Ju Fan

Language models have shown promising performance on the task of translating natural language questions into SQL queries (Text-to-SQL). However, most of the state-of-the-art (SOTA) approaches rely on powerful yet closed-source large language models (LLMs), such as ChatGPT and GPT-4, which may have the limitations of unclear model architectures, data privacy r

Chaolong Ying, Xinjian Zhao, Tianshu Yu

Recently, there has been an emerging trend to integrate persistent homology (PH) into graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH features into GNN layers always results in marginal improvement with low interpretability. In this paper, we investigate a novel mechanism for injecting global topological invariance into

Nina Badulina, Dmitry Shatilovich, Mikhail Zhitlukhin

We propose a dynamic model of a prediction market in which agents predict the values of a sequence of random vectors. The main result shows that if there are agents who make correct (or asymptotically correct) next-period forecasts, then the aggregated market forecasts converge to the next-period conditional expectations of the random vectors.

Xin-Shuai Yan, Liang-Hui Zhang, Qin Chang, Ya-Dong Yang

Kinematics restrict the ability of rare charm decays to explore the charged Lepton Flavor Violation processes mediated by the quark-level $c\to u \ell \tau$ transition. To fill the gap, we propose exploring new physics (NP) through the quasielastic scattering process $e^-p\to \tau^-\Lambda_c$ and the polarization of the $\tau$ lepton. As analyzing modes for

Yiwei Li, Boyu Tian, Mingyu Gao

Hybrid main memory systems combine both performance and capacity advantages from heterogeneous memory technologies. With larger capacities, higher associativities, and finer granularities, hybrid memory systems currently exhibit significant metadata storage and lookup overheads for flexibly remapping data blocks between the two memory tiers. To alleviate the

Somrita Banerjee, Edward Balaban, Mark Shirley, Kevin Bradner

This work focuses on autonomous contingency planning for scientific missions by enabling rapid policy computation from any off-nominal point in the state space in the event of a delay or deviation from the nominal mission plan. Successful contingency planning involves managing risks and rewards, often probabilistically associated with actions, in stochastic

Maximilian Balthasar Mansky, Jonas Nüßlein, David Bucher, Daniëlle Schuman

Due to the advances in the manufacturing of quantum hardware in the recent years, significant research efforts have been directed towards employing quantum methods to solving problems in various areas of interest. Thus a plethora of novel quantum methods have been developed in recent years. In this paper, we provide a survey of quantum sampling methods along

Asghar Daneshvar, Hajar Kiamehr, Malihe Yousofzadeh

Representation theory of Lie (super)algebras has attracted significant research interest for many years, especially due to its applications in theoretical physics; in this regard, the representation theory of affine Lie (super)algebras is of central importance. To characterize simple modules over affine Lie (super)algebras, it is necessary to study the cases

Pianpian Qin, Sibo Wang, Hui Tong, Qiang Zhao

A relativistic microscopic optical model potential for nucleon-nucleus scattering is developed based on the \emph{ab initio} relativistic Brueckner-Hartree-Fock (RBHF) theory with the improved local density approximation, which is abbreviated as the RBOM potential. Both real and imaginary parts of the single-particle potentials in symmetric and asymmetric nu

Li Zhang, Youwei Liang, Ruiyi Zhang, Amirhosein Javadi

The Segment Anything Model (SAM), a foundation model pretrained on millions of images and segmentation masks, has significantly advanced semantic segmentation, a fundamental task in computer vision. Despite its strengths, SAM encounters two major challenges. Firstly, it struggles with segmenting specific objects autonomously, as it relies on users to manuall

Anusree Rajan, Pavankumar Tallapragada

This paper deals with event-triggered parameterized control (ETPC) of nonlinear systems with external disturbances. In this control method, between two successive events, each control input to the plant is a linear combination of a set of linearly independent scalar functions. At each event, the controller updates the coefficients of the parameterized contro

Dongtao Cao, Shenghong Gu

Stellar coronal mass ejections (CMEs) have recently attracted much attention for their impacts on stellar evolution and surrounding exoplanets. RS CVn-type stars could produce large flares, and therefore may have frequent CMEs. Here we report the capture of a possible CME or chromospheric condensation on the RS CVn-type star II Pegasi (II Peg) using high-res

Jingming Zhu, Jiawen Zhang

In this paper, we introduce a notion of transfinite nuclear dimension for $C^*$-algebras, which coincides with the nuclear dimension when taking values in natural numbers. We use it to characterise a stronger form of having nuclear dimension at most $\omega$ and moreover, we show that the transfinite nuclear dimension of a uniform Roe algebra is bounded by t

Ron Cherny, Matthew Satriano, Yohan Song

Gerstenhaber proved in 1961 that the unital algebra generated by a pair of commuting $d\times d$ matrices over a field has dimension at most $d$. It is an open problem whether the analogous statement is true for triples of matrices which pairwise commute. We answer this question for special classes of triples of matrices arising from combinatorial data.

Pengyu Zhang, Yingbo Zhou, Ming Hu, Junxian Feng

Federated Instruction Tuning (FIT) has shown the ability to achieve collaborative model instruction tuning among massive data owners without sharing private data. However, it still faces two key challenges, i.e., data and resource heterogeneity. Due to the varying data distribution and preferences among data owners, FIT cannot adapt to the personalized data

Xinyi Mou, Zhongyu Wei, Xuanjing Huang

Social media has emerged as a cornerstone of social movements, wielding significant influence in driving societal change. Simulating the response of the public and forecasting the potential impact has become increasingly important. However, existing methods for simulating such phenomena encounter challenges concerning their efficacy and efficiency in capturi

Xiao Shen

The study of transversal fluctuation of the optimal path has been a crucial aspect of the Kadar-Parisi-Zhang (KPZ) universality class. In this paper, we establish a new probability lower bound, with optimal exponential order, for the rare event in which a given level of the optimal path has a large transversal fluctuation. We present our results in both zero

Hiroto Saito, Takashi Koretsune

Magnetocrystalline anisotropy, a crucial factor in magnetic properties and applications like magnetoresistive random-access memory, often requires extensive $k$-point mesh in first-principles calculations. In this study, we develop a Wannier orbital tight-binding model incorporating crystal and spin symmetries and utilize time-reversal symmetry to divide mag

Paul C. Lou, Ravindra G. Bhardwaj, Anand Katailiha, W. P. Beyermann

The magnetoelectronic coupling can be defined as cross-domain coupling between electronic and magnetic properties, where modulation in magnetic properties changes the electronic properties. In this letter, an explicit experimental evidence of magnetoelectronic coupling is presented, which is uncovered from oscillatory Hall effect response in Hall measurement

Maximilian Balthasar Mansky, Santiago Londoño Castillo, Miguel Armayor-Martínez, Alejandro Bravo de la Serna

We show that quantum circuits restricted by a symmetry inherit the properties of the whole special unitary group $SU(2^n)$, in particular composition, algebraic and topological closedness and connectedness. It extends prior work on symmetric states to the operators and shows that the operator space follows the same structure as the state space. The well-beha

Zhouxiang Zhao, Zhaohui Yang, Mingzhe Chen, Zhaoyang Zhang

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized data before transmitting them to a multi-antenna base station (BS). Our model r

Wonbin Kweon, SeongKu Kang, Junyoung Hwang, Hwanjo Yu

Recent recommender systems started to use rating elicitation, which asks new users to rate a small seed itemset for inferring their preferences, to improve the quality of initial recommendations. The key challenge of the rating elicitation is to choose the seed items which can best infer the new users' preference. This paper proposes a novel end-to-end Deep

Agniva Chowdhury, Pradeep Ramuhalli

In statistics and machine learning, logistic regression is a widely-used supervised learning technique primarily employed for binary classification tasks. When the number of observations greatly exceeds the number of predictor variables, we present a simple, randomized sampling-based algorithm for logistic regression problem that guarantees high-quality appr

Wonbin Kweon

Despite the importance of having a measure of confidence in recommendation results, it has been surprisingly overlooked in the literature compared to the accuracy of the recommendation. In this dissertation, I propose a model calibration framework for recommender systems for estimating accurate confidence in recommendation results based on the learned rankin

Jiashuo Jiang, Yinyu Ye

We consider the reinforcement learning problem for the constrained Markov decision process (CMDP), which plays a central role in satisfying safety or resource constraints in sequential learning and decision-making. In this problem, we are given finite resources and a MDP with unknown transition probabilities. At each stage, we take an action, collecting a re

Haitao Wang, Jie Xue

Given in the plane a set of points and a set of halfplanes, we consider the problem of computing a smallest subset of halfplanes whose union covers all points. In this paper, we present an $O(n^{4/3}\log^{5/3}n\log^{O(1)}\log n)$-time algorithm for the problem, where $n$ is the total number of all points and halfplanes. This improves the previously best algo

Yuichi Kitamura, Louise Laage

In the standard stochastic block model for networks, the probability of a connection between two nodes, often referred to as the edge probability, depends on the unobserved communities each of these nodes belongs to. We consider a flexible framework in which each edge probability, together with the probability of community assignment, are also impacted by ob

Szu-Wei Fu, Kuo-Hsuan Hung, Yu Tsao, Yu-Chiang Frank Wang

Speech quality estimation has recently undergone a paradigm shift from human-hearing expert designs to machine-learning models. However, current models rely mainly on supervised learning, which is time-consuming and expensive for label collection. To solve this problem, we propose VQScore, a self-supervised metric for evaluating speech based on the quantizat

Baris Donmez, Gunes Karabulut Kurt

This paper focuses on FSO-based wireless power transmission (WPT) from Earth-Moon Lagrangian Point-2 (EMLP-2) to a receiver optical antenna equipped with solar cells that can be located anywhere on the lunar far side (LFS). Different solar-powered satellite (SPS) configurations which are EMLP-2 located single stable satellite and EMLP-2 halo orbit revolving

Runyu Peng, Yunhua Zhou, Qipeng Guo, Yang Gao

Large Language Models (LLMs) are reshaping the research landscape in artificial intelligence, particularly as model parameters scale up significantly, unlocking remarkable capabilities across various domains. Nevertheless, the scalability of model parameters faces constraints due to limitations in GPU memory and computational speed. To address these constrai

Hao Wang, Shengda Luo, Guosheng Hu, Jianguo Zhang

Multimodal learning with incomplete input data (missing modality) is practical and challenging. In this work, we conduct an in-depth analysis of this challenge and find that modality dominance has a significant negative impact on the model training, greatly degrading the missing modality performance. Motivated by Grad-CAM, we introduce a novel indicator, gra

Hong-Bin Zhang, Deng-Ping Tang

In a large population, the agents temporally form group of the Public Goods Game (PGG) one after another, and size of one group is randomly distributed at $g\in [g_l,g_h]$. Players in it have two strategies to be chosen to cooperate, or to defect for playing the PGG. Based on this structure we investigate the evolution of cooperation in PGG as a function of

Gabriele Farina, Charilaos Pipis

It is a well-known fact that correlated equilibria can be computed in polynomial time in a large class of concisely represented games using the celebrated Ellipsoid Against Hope algorithm (Papadimitriou and Roughgarden, 2008; Jiang and Leyton-Brown, 2015). However, the landscape of efficiently computable equilibria in sequential (extensive-form) games remain

Jeonghwan Kim, Heng Ji

Recent advances in instruction-tuned Large Vision-Language Models (LVLMs) have imbued the models with the ability to generate high-level, image-grounded explanations with ease. While such capability is largely attributed to the rich world knowledge contained within the Large Language Models (LLMs), our work reveals their shortcomings in fine-grained visual c

Jiaxin Song, Hongfei Fu, Charles Zhang

Bit-vectors, which are integers in a finite number of bits, are ubiquitous in software and hardware systems. In this work, we consider the satisfiability modulo theories (SMT) of bit-vectors. Unlike normal integers, the arithmetics of bit-vectors are modular upon integer overflow. Therefore, the SMT solving of bit-vectors needs to resolve the underlying modu

Lin Ai, Zheng Hui, Zizhou Liu, Julia Hirschberg

To address the challenges of out-of-control generation in generative models for machine reading comprehension (MRC), we introduce the Question-Attended Span Extraction (QASE) module. Integrated during the fine-tuning of pre-trained generative language models (PLMs), QASE enables these PLMs to match SOTA extractive methods and outperform leading LLMs like GPT

Mingxu Tao, Dongyan Zhao, Yansong Feng

Open-ended question answering requires models to find appropriate evidence to form wellreasoned, comprehensive and helpful answers. In practical applications, models also need to engage in extended discussions on potential scenarios closely relevant to the question. With augmentation of retrieval module, open-source Large Language Models (LLMs) can produce c

Hantao Yang, Xutong Liu, Zhiyong Wang, Hong Xie

We study the problem of federated contextual combinatorial cascading bandits, where $|\mathcal{U}|$ agents collaborate under the coordination of a central server to provide tailored recommendations to the $|\mathcal{U}|$ corresponding users. Existing works consider either a synchronous framework, necessitating full agent participation and global synchronizat

Jingsi Yu, Cunliang Kong, Liner Yang, Meishan Zhang

Sentence Pattern Structure (SPS) parsing is a syntactic analysis method primarily employed in language teaching.Existing SPS parsers rely heavily on textbook corpora for training, lacking cross-domain capability.To overcome this constraint, this paper proposes an innovative approach leveraging large language models (LLMs) within a self-training framework. Pa

Bangchao Deng, Bingqing Qu, Pengyang Wang, Dingqi Yang

Location prediction forecasts a user's location based on historical user mobility traces. To tackle the intrinsic sparsity issue of real-world user mobility traces, spatiotemporal contexts have been shown as significantly useful. Existing solutions mostly incorporate spatiotemporal distances between locations in mobility traces, either by feeding them as add

Yasunori Okumura

This study considers the method to derive a ranking of alternatives by aggregating the rankings submitted by several individuals who may not evaluate all of them. The collection of subsets of alternatives that individuals (can) evaluate is referred to as an evaluability profile. For a given evaluability profile, we define an aggregating ranking function whos

Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao

Designing 3D ligands within a target binding site is a fundamental task in drug discovery. Existing structured-based drug design methods treat all ligand atoms equally, which ignores different roles of atoms in the ligand for drug design and can be less efficient for exploring the large drug-like molecule space. In this paper, inspired by the convention in p

Yizhe Wu, Haitz Sáez de Ocáriz Borde, Jack Collins, Oiwi Parker Jones

3D scene understanding for robotic applications exhibits a unique set of requirements including real-time inference, object-centric latent representation learning, accurate 6D pose estimation and 3D reconstruction of objects. Current methods for scene understanding typically rely on a combination of trained models paired with either an explicit or learnt vol

Miyeon Lee, Sucheol Kim, Minje Kim, Dong-Hyun Jung

Satellite networks are emerging as vital solutions for global connectivity beyond 5G. As companies such as SpaceX, OneWeb, and Amazon are poised to launch a large number of satellites in low Earth orbit, the heightened inter-satellite interference caused by mega-constellations has become a significant concern. To address this challenge, recent works have int

Yubo Shuai

Consider a supercritical birth and death process where the children acquire mutations. We study the mutation rates along the ancestral lineages in a sample of size $n$ from the population at time $T$. The mutation rate is time-inhomogenous and has a natural probabilistic interpretation. We use these results to obtain asymptotic results for the site frequency

Wonbin Kweon, Hwanjo Yu

Recommender systems often suffer from selection bias as users tend to rate their preferred items. The datasets collected under such conditions exhibit entries missing not at random and thus are not randomized-controlled trials representing the target population. To address this challenge, a doubly robust estimator and its enhanced variants have been proposed

Xuantong Liu, Tianyang Hu, Wenjia Wang, Kenji Kawaguchi

As a dominant force in text-to-image generation tasks, Diffusion Probabilistic Models (DPMs) face a critical challenge in controllability, struggling to adhere strictly to complex, multi-faceted instructions. In this work, we aim to address this alignment challenge for conditional generation tasks. First, we provide an alternative view of state-of-the-art DP

Wonbin Kweon, SeongKu Kang, Sanghwan Jang, Hwanjo Yu

The conventional top-K recommendation, which presents the top-K items with the highest ranking scores, is a common practice for generating personalized ranking lists. However, is this fixed-size top-K recommendation the optimal approach for every user's satisfaction? Not necessarily. We point out that providing fixed-size recommendations without taking into

Adam Larios, Isabel Safarik

This note investigates the explicit convergence rates of nonlocal peridynamic operators to their classical (local) counterparts in $L^q$-norm. Previous results used Fourier series and hence were restricted to showing convergence in $L^2$. Moreover, convergence rates were not explicit due to the use of the Lebesgue Dominated Convergence Theorem. Some previous

Yijing Liu, Chao Du, Tianyu Pang, Chongxuan Li

Recent research has made significant progress in optimizing diffusion models for downstream objectives, which is an important pursuit in fields such as graph generation for drug design. However, directly applying these models to graph presents challenges, resulting in suboptimal performance. This paper introduces graph diffusion policy optimization (GDPO), a

Subhajit Acharya, Biman Bagchi

The celebrated Stokes Law (SL) of hydrodynamics predicts that the velocity of a particle pulled through a liquid by an external force, Fex, is directly proportional to the force and inversely proportional to the friction {\zeta} acted by the medium on the particle. We investigate the range of validity of Stokes Law at molecular length scales by employing com

Ka Man Lo, Yiming Liang, Wenyu Du, Yuantao Fan

Modular neural architectures are gaining attention for their powerful generalization and efficient adaptation to new domains. However, training these models poses challenges due to optimization difficulties arising from intrinsic sparse connectivity. Leveraging knowledge from monolithic models through techniques like knowledge distillation can facilitate tra

Anna Sokol, Nuno Moniz, Nitesh Chawla

Should prediction models always deliver a prediction? In the pursuit of maximum predictive performance, critical considerations of reliability and fairness are often overshadowed, particularly when it comes to the role of uncertainty. Selective regression, also known as the "reject option," allows models to abstain from predictions in cases of considerable u

Chaoguang Luo, Liuying Wen, Yong Qin, Liangwei Yang

Recommender systems serve a dual purpose for users: sifting out inappropriate or mismatched information while accurately identifying items that align with their preferences. Numerous recommendation algorithms are designed to provide users with a personalized array of information tailored to their preferences. Nevertheless, excessive personalization can confi

Sushmita Sarker, Prithul Sarker, George Bebis, Alireza Tavakkoli

Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammography exam, leveraging the inherent correlations in these views to effectively detect tumors. Acknowledging the significance of multi-view analy

Jiahao Wang, Sikun Yang, Heinz Koeppl, Xiuzhen Cheng

Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to estimate uncertainties, and thus are especially suitable for dealing with \emph{noisy} and \emph{incomplete} count data. Among these models, Poisson-Gamma Dynamical Systems (PGDSs)

Juan Orendain

The length of a double category is a numerical invariant measuring the 'work' it takes to reconstruct the double category from its globular data. The smallest possible length of a double category is 1. It is conjectured that framed bicategories are of length 1. In this paper we prove this conjecture for a particular class of framed bicategories, namely for t

Zeqian Li

This paper considers an $n$-particle jump-diffusion system with mean filed interaction, where the coefficients are locally Lipschitz continuous. We address the convergence as $n\to\infty$ of the empirical measure of the jump-diffusions to the solution of a deterministic McKean-Vlasov equation. The strong well-posedness of the associated McKean-Vlasov equatio

Xiao Liu, Mingyuan Li, Xu Wang, Guangsheng Yu

Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all inherited models complicates the process

Jiewei Huang, Zhenyu Zhang, Minyong Guo, Bin Chen

In this study, we develop a numerical method to generate images on an observer's screen, formed by radiation from hotspots on any timelike orbits outside a black hole. This method uses the calculation of fractional numbers, enabling us not only to produce the overall image but also to distinguish between primary, secondary, and higher-order images. Building

Francisco Ponce-Carrión, Seth Sullivant

We establish a bijection between marginal independence models on $n$ random variables and split closed order ideals in the poset of partial set partitions. We also establish that every discrete marginal independence model is toric in cdf coordinates. This generalizes results of Boege, Petrovic, and Sturmfels and Drton and Richardson, and provides a unified f

Yunusa Haruna, Shiyin Qin, Abdulrahman Hamman Adama Chukkol, Isah Bello

Detecting objects across varying scales is still a challenge in computer vision, particularly in agricultural applications like Rice Leaf Disease (RLD) detection, where objects exhibit significant scale variations (SV). Conventional object detection (OD) like Faster R-CNN, SSD, and YOLO methods often fail to effectively address SV, leading to reduced accurac

Suthee Ruangwises, Tomoki Ono, Yoshiki Abe, Kyosuke Hatsugai

Research in the area of secure multi-party computation with an unconventional method of using a physical deck of playing cards began in 1989 when den Boer proposed a protocol to compute the logical AND function using five cards. Since then, the area has gained interest from many researchers and several card-based protocols to compute various functions have b

Alec Cao, William J. Eckner, Theodor Lukin Yelin, Aaron W. Young

Many-particle entanglement is a key resource for achieving the fundamental precision limits of a quantum sensor. Optical atomic clocks, the current state-of-the-art in frequency precision, are a rapidly emerging area of focus for entanglement-enhanced metrology. Augmenting tweezer-based clocks featuring microscopic control and detection with the high-fidelit

Yiming Du, Hongru Wang, Zhengyi Zhao, Bin Liang

Long-term memory plays a critical role in personal interaction, considering long-term memory can better leverage world knowledge, historical information, and preferences in dialogues. Our research introduces PerLTQA, an innovative QA dataset that combines semantic and episodic memories, including world knowledge, profiles, social relationships, events, and d

Yingyue Hong, Jiayu Huang, Dong H. Zhang

We present a simple and general way to accurately describe long-range interactions between atoms and molecules through combining neural networks with physical models. Demonstrations on the H$_3$, Li$_3$ and 2KRb systems illustrate the exceptional extrapolation capabilities of the trained model, supported by underlying physical models. More importantly, the m

You-Cheng Chou, Chin-Lung Wang, Po-Sheng Wu

We give a complete characterization of the classical Lam\'e equations $y'' = (n(n + 1)\wp(z) + B)y$, $n \in \Bbb R$, $B \in \Bbb C$ on flat tori $E_\tau = \Bbb C/(\Bbb Z + \Bbb Z\,\tau)$ with finite monodromy groups $M$. Beuker--Waall had shown that such $n$ must lie in a finite number of arithmetic progressions $n_i + \Bbb N \subset \Bbb Q$ and they determi

Raheem Karim Hashmani, Emre Akbaş, Melahat Bilge Demirköz

The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The Transition Radiation Detector and Electromagnetic Calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS

Phillip Drake, Matthew J. Patitz, Scott M. Summers, Tyler Tracy

In the abstract Tile Assembly Model, self-assembling systems consisting of tiles of different colors can form structures on which colored patterns are ``painted.'' We explore the complexity, in terms of the numbers of unique tile types required, of assembling various patterns. We first demonstrate how to efficiently self-assemble a set of simple patterns, th

Sahand Tabatabaei, Pritam Priyadarsi, Namanish Singh, Pardis Sahafi

Dynamic nuclear polarization (DNP) has revolutionized the field of NMR spectroscopy, expanding its reach and capabilities to investigate diverse materials, biomolecules, and complex dynamic processes. Bringing high-efficiency DNP to the nanometer scale would open new avenues for studying nanoscale nuclear spin ensembles, such as single biomolecules, virus pa

Wen-Hong Ruan, Zong-Kuan Guo

Coalescing massive black hole binaries (MBHBs) are one of primary sources for space-based gravitational wave (GW) observations. The mergers of these binaries are expected to give rise to detectable electromagnetic (EM) emissions with a narrow time window. The premerger detection of GW signals is vital for follow-up EM observations. The conventional approach

Yanlong Peng, Zhigang Wang, Yisheng Zhang, Pengxu Chang

Task and Motion Planning (TAMP) is essential for robots to interact with the world and accomplish complex tasks. The TAMP problem involves a critical gap: exploring the robot's configuration parameters (such as chassis position and robotic arm joint angles) within continuous space to ensure that task-level global constraints are met while also enhancing the

Yu Ming, Zihao Wu, Jie Yang, Danyi Li

Nucleus instance segmentation from histopathology images suffers from the extremely laborious and expert-dependent annotation of nucleus instances. As a promising solution to this task, annotation-efficient deep learning paradigms have recently attracted much research interest, such as weakly-/semi-supervised learning, generative adversarial learning, etc. I

Huimin Zhu

For approximate inference in the generalized quadratic equations model, many state-of-the-art algorithms lack any prior knowledge of the target signal structure, exhibits slow convergence, and can not handle any analytic prior knowledge of the target signal structure. So, this paper proposes a new algorithm, Quadratic Message passing (QMP). QMP has a complex

Yukihiro Shiraishi, Ken Kaneiwa

Recently, ontology embeddings representing entities in a low-dimensional space have been proposed for ontology completion. However, the ontology embeddings for concept subsumption prediction do not address the difficulties of similar and isolated entities and fail to extract the global information of annotation axioms from an ontology. In this paper, we prop

Mingxiang Li, Xingwang Xu

We mainly show that for a conformal metric $g=u^{\frac{4}{n-2m}}|dx|^2$ on $\mathbb{R}^n$ with $n\geq 2m+1$, if the higher order Q-curvature $Q^{(2m)}_g$ is positive and has slow decay barrier near infinity, the lower order Q-curvature $Q^{(2)}_g$ and $Q^{(4)}_g$ are both positive if $m$ is at least two.

Dongjoo Kim, Soojin Lee, Hanseok Jung, Dongchan Kim

In this comprehensive study, we investigate $K$-factors ($K=\sigma_{\text{NLO}}/\sigma_{\text{LO}}\equiv 1+\delta K$) for a broad array of Standard Model processes at the 14 TeV LHC, which are pivotal for background assessments in Beyond the Standard Model (BSM) searches. Using MadGraph5_aMC@NLO, we calculate the leading-order and next-to-leading order (NLO)

S. A. Narawade, S. K. Tripathy, Raghunath Patra, B. Mishra

In this paper, we have explored the observed matter-antimatter asymmetry in the Universe to constrain the model parameters in extended symmetric teleparallel gravity (STG) or $f(Q,T)$ gravity, where $Q$ be the nonmetricity and $T$ be the trace of energy momentum tensor. We have considered two functional forms of $f(Q,T)$ to find the baryon asymmetry to entro

Liangqi Liu, Wenqiang Pu, Yingru Li, Bo Jiu

The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics. In this paper, the competition between a frequency agile radar and a Digital Radio Frequency Memory (DRFM)-based intellig

Kazuki Nakazawa, Yasuyuki Kato, Yukitoshi Motome

Co-based shandite Co$_3$Sn$_2$S$_2$ is a ferromagnet hosting Weyl fermions in the layered Co kagome structure. The band topology as well as the magnetism is predicted to vary drastically in the atomically thin films depending on the thickness and surface termination, and as an extreme case, the quantum anomalous Hall state is expected in a monolayer of the C

Miao He, Zhonghua Qin, Diru Wu, Meihang Xu

The Jiangmen Underground Neutrino Observatory is a neutrino experiment that incorporates 20,012 20-inch photomultiplier tubes (PMTs) and 25,600 3-inch PMTs. A dedicated system was designed to protect the PMTs from an implosion chain reaction underwater. As a crucial element of the protection system, over 20,000 acrylic covers were manufactured through inject

Ming-Jia Fu

The biochronometers used to keep time in eukaryotes include short-period biochronometer (SPB) and long-period biochronometer (LPB). Because the circadian clock reflects the biological time rhythm of a day, it is considered as SPB. Telomere shortening, which reflects the decreasing of telomere DNA length of chromosomes with the increase of cell division times

Guang Zhao, Linghui Wu, Francesco Grancagnolo, Nicola De Filippis

Cluster counting in drift chamber is the most promising breakthrough in particle identification (PID) technique in particle physics experiment. Reconstruction algorithm is one of the key challenges in cluster counting. In this paper, a semi-supervised domain adaptation (DA) algorithm is developed and applied on the peak finding problem in cluster counting. T

Segev Wasserkrug, Leonard Boussioux, Dick den Hertog, Farzaneh Mirzazadeh

Significantly simplifying the creation of optimization models for real-world business problems has long been a major goal in applying mathematical optimization more widely to important business and societal decisions. The recent capabilities of Large Language Models (LLMs) present a timely opportunity to achieve this goal. Therefore, we propose research at t

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency. To codify how foundation model developers should provide transparency about the development and deployment of their models, we propose Foundation Model Transparency Reports, drawing upon the transparency reporting practices in social media. While