October 2024 arXiv papers — page 90

Hao-Tang Tsui, Yu-Rou Tuan, Jia-You Chen

This work is a portable MetaVerse implementation, and we use 3D pose estimation with AI to make virtual avatars do synchronized actions and interact with the environment. The motivation is that we find it inconvenient to use joysticks and sensors when playing with fitness rings. In order to replace joysticks and reduce costs, we developed a platform that can

Yulin Song, Guorui Sang, Jing Yu, Chuangbai Xiao

Singing voice synthesis (SVS) system is expected to generate high-fidelity singing voice from given music scores (lyrics, duration and pitch). Recently, diffusion models have performed well in this field. However, sacrificing inference speed to exchange with high-quality sample generation limits its application scenarios. In order to obtain high quality synt

Hao-Tang Tsui, Yu-Rou Tuan, Hong-Han Shuai

It is a common problem in robotics to specify the position of each joint of the robot so that the endpoint reaches a certain target in space. This can be solved in two ways, forward kinematics method and inverse kinematics method. However, inverse kinematics cannot be solved by an algorithm. The common method is the Jacobian inverse technique, and some peopl

Yujiro Kawamata

The derived McKay correspondence conjecture says that there is an equivalence of triangulated categories between the bounded derived categories of commutative and non-commutative crepant resolutions of a Gorenstein singularity. We will prove that this derived equivalence extends between the semi-universal non-commutative deformations of the commutative and t

Fu-Lai Wang, Si-Qiang Luo, Ri-Qing Qian, Xiang Liu

Inspired by recent advances in the study of the $K^{(*)} \bar K^{(*)}$ molecular tetraquarks and the $H$-dibaryon, we focus on the spectroscopic properties of the $\bar K^{(*)} \bar K^{(*)}$ systems, which exhibit exotic flavor quantum number of $ss\bar q \bar q$. A dynamical analysis is performed using the one-boson-exchange model to describe the effective

Timoteo Carletti, Lorenzo Giambagli, Riccardo Muolo, Ginestra Bianconi

Synchronization is a fundamental dynamical state of interacting oscillators, observed in natural biological rhythms and in the brain. Global synchronization which occurs when non-linear or chaotic oscillators placed on the nodes of a network display the same dynamics as received great attention in network theory. Here we propose and investigate Global Topolo

Yu. N. Uzikov, M. N. Platonova

The effect of violation of $T$-invariance, provided that $P$-parity is preserved, is given by the total cross section of the interaction of a vector-polarized particle with a tensor-polarized target. A formalism for calculating this effect developed previously and based on the spin-dependent Glauber theory of elastic $pd$ scattering is used here to calculate

Yann Lanoiselée, Gianni Pagnini, Agnieszka Wyłomańska

The molecular motion in heterogeneous media displays anomalous diffusion by the mean-squared displacement $\langle X^2(t) \rangle = 2 D t^\alpha$. Motivated by experiments reporting populations of the anomalous diffusion parameters $\alpha$ and $D$, we aim to disentangle their respective contributions to the observed variability when this last is due to a tr

Zhekun Shi, Longlin Yu, Tianyu Xie, Cheng Zhang

Recent success of diffusion models has inspired a surge of interest in developing sampling techniques using reverse diffusion processes. However, accurately estimating the drift term in the reverse stochastic differential equation (SDE) solely from the unnormalized target density poses significant challenges, hindering existing methods from achieving state-o

Ali Kahe, Hamed Kebriaei

This paper proposes a novel distributed approach for solving a cooperative Constrained Multi-agent Reinforcement Learning (CMARL) problem, where agents seek to minimize a global objective function subject to shared constraints. Unlike existing methods that rely on centralized training or coordination, our approach enables fully decentralized online learning,

Songtao Jiang, Yan Zhang, Ruizhe Chen, Tianxiang Hu

Multimodal large language models (MLLMs) have achieved remarkable success across various tasks. However, separate training of visual and textual encoders often results in a misalignment of the modality. Such misalignment may lead models to generate content that is absent from the input image, a phenomenon referred to as hallucination. These inaccuracies seve

Dongting Yang, HongLi Liu, Tie Liu, Anandmayee Tej

In this paper, we present a detailed analysis of the IRS 17 filament within the intermediate-mass protocluster IRAS 08448-4343 (of $\sim\,10^3\,\rm L_{\odot}$), using ALMA data from the ATOMS 3-mm and QUARKS 1.3-mm surveys. The IRS 17 filament, which spans $\sim$54000 au ($0.26\,\rm pc$) in length and $\sim$4000 au ($0.02\,\rm pc$) in width, exhibits a compl

Junhao Hu, Wenrui Huang, Weidong Wang, Haoyi Wang

Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become more complex. Context caching improves serving performance by reusing Key-Value (KV) vectors, the intermediate representations of tokens that are repeated across requests. However, ex

Dharmendra Kumar Singh

The aim of this paper is to derive a solution for a generalized free electron laser equation in terms of the incomplete Mittag-Leffler function and in terms of the incomplete Wright function.

Mingjiao Yan, Yang Yang, Chao Su, Zongliang Zhang

In this study, we derived a three-dimensional scaled boundary finite element formulation for heat conduction problems. By incorporating Wachspress shape functions, a polyhedral scaled boundary finite element method (PSBFEM) was proposed to address heat conduction challenges in complex geometries. To address the complexity of traditional methods, this work in

Xia Chen, Guoquan Lv, Xinwei Zhuang, Carlos Duarte

Symbolic neural networks, such as Kolmogorov-Arnold Networks (KAN), offer a promising approach for integrating prior knowledge with data-driven methods, making them valuable for addressing inverse problems in scientific and engineering domains. This study explores the application of KAN in building physics, focusing on predictive modeling, knowledge discover

István Z. Kiss, Christian Bick, Péter L. Simon

Complex contagion models that involve contagion along higher-order structures, such as simplicial complexes and hypergraphs, yield new classes of mean-field models. Interestingly, the differential equations arising from many such models often exhibit a similar form, resulting in qualitatively comparable global bifurcation patterns. Motivated by this observat

Angel Y. He, Mark Holmes

We present a computer assisted proof for a result concerning a three player betting game, introduced by Angel and Holmes. The three players start with initial capital $x, y, z > 0$ respectively. At each step of this game two players are selected at random to bet on the outcome of a fair coin toss, with the size of the bet being the largest possible, namely t

Gábor Czédli

The block count of an equivalence $\mu\in$ Equ$(A)$ is the number blnum$(\mu)$ of blocks of (the partition corresponding to) $\mu$. We say that $X=\{\mu_1,\mu_2,\mu_3,\mu_4\}$ is a four-element generating set of Equ$(A)$ with consecutive block counts if $X$ generates Equ$(A)$ and blnum$(\mu_{i+1})$ = blnum$(\mu_{1})+i$ for $i\in\{1,2,3\}$. We prove that if t

Liang-Wen Ji, Si-Qi Wu, Bai-Zhuo Li, Wu-Zhang Yang

The antiferromagnetism in transition metal compounds is mostly mediated by the bridging anions through a so-called superexchange mechanism. However, in materials like normal spinels $AB_2X_4$ with local moments only at the $A$ site, such an anion-mediated superexchange needs to be modified. Here we report a new spinel compound Co$_{1+x}$Ir$_{2-x}$S$_4$ ($x$

Hsiu-Yuan Huang, Yutong Yang, Zhaoxi Zhang, Sanwoo Lee

As large language models (LLMs) continue to evolve, understanding and quantifying the uncertainty in their predictions is critical for enhancing application credibility. However, the existing literature relevant to LLM uncertainty estimation often relies on heuristic approaches, lacking systematic classification of the methods. In this survey, we clarify the

Aditya Tamar, Daniel C. M. Palumbo

The near-horizon region of a black hole impacts linear (LP) and circular polarization (CP) through strong lensing of photons, adding large-scale symmetries and anti-symmetries to the polarized image. To probe the signature of lensing in polarimetry, we utilise a geometric model of concentric, Gaussian rings of equal radius to investigate the transition in th

Sergey G. Fedosin

Based on the electron-ion model, parameters of ball and bead lightning are calculated. The model allows us to estimate maximum size of ball lightning, its energy content, electric charge and magnetic field, to determine equilibrium conditions between positively charged ions located inside and outer shell containing rapidly moving electrons. An explanation is

Ilija S. Milutin, Thomas Mannel, K. Keri Vos

The Heavy Quark Expansion (HQE) is the major tool to perform calculations for inclusive semileptonic $B\to X_cl\bar{\nu}$ and, consequently, for precision determinations of the CKM matrix element $V_{cb}$. To further improve precision, we pushed the expansion to $1/m_b^5$ to include even higher order terms in the HQE. Notably, at $1/m_b^5$, ``intrinsic charm

Bryan Bliewert, Jenny List, Dimitris Ntounis, Junping Tian

The double Higgs-strahlungs process $e^+e^- \rightarrow ZHH$ allows to access the Higgs self-coupling at center-of-mass energies above $450$ GeV. Its cross-section exhibits a very different behavior as a function of the value of the self-coupling than fusion-type processes like gluon-gluon fusion at LHC (and future hadron colliders) and $WW$ / $ZZ$ fusion at

Haoye Chai, Shiyuan Zhang, Xiaoqian Qi, Baohua Qiu

Mobile traffic forecasting allows operators to anticipate network dynamics and performance in advance, offering substantial potential for enhancing service quality and improving user experience. However, existing models are often task-oriented and are trained with tailored data, which limits their effectiveness in diverse mobile network tasks of Base Station

Animesh Singh, Jason Hillyer, Fariba Ariaei, Hossein Jula

This paper presents a comprehensive design process for the integration of a robotic arm into a quadcopter, emphasizing the physical modeling, system integration, and controller development. Utilizing SolidWorks for mechanical design and MATLAB Simscape for simulation and control, this study addresses the challenges encountered in integrating the robotic arm

Paul E. Chang, Nasrulloh Loka, Daolang Huang, Ulpu Remes

Amortized meta-learning methods based on pre-training have propelled fields like natural language processing and vision. Transformer-based neural processes and their variants are leading models for probabilistic meta-learning with a tractable objective. Often trained on synthetic data, these models implicitly capture essential latent information in the data-

Anpeng Wu, Kun Kuang, Minqin Zhu, Yingrong Wang

Recent breakthroughs in artificial intelligence have driven a paradigm shift, where large language models (LLMs) with billions or trillions of parameters are trained on vast datasets, achieving unprecedented success across a series of language tasks. However, despite these successes, LLMs still rely on probabilistic modeling, which often captures spurious co

Tianyi Xu, Patrick Zheng, Shiyan Liu, Sicheng Lyu

Artificial Neural Networks (ANNs) suffer from catastrophic forgetting, where the learning of new tasks causes the catastrophic forgetting of old tasks. Existing Machine Learning (ML) algorithms, including those using Stochastic Gradient Descent (SGD) and Hebbian Learning typically update their weights linearly with experience i.e., independently of their cur

Ryosuke Shimizu

In the spirit of the ground-breaking result of Bourgain--Brezis--Mironescu, we establish some characterizations of Sobolev functions in metric measure spaces including fractals like the Vicsek set, the Sierpi\'{n}ski gasket and the Sierpi\'{n}ski carpet. As corollaries of our characterizations, we present equivalent norms on the Korevaar--Schoen--Sobolev spa

Alan Dao, Dinh Bach Vu, Huy Hoang Ha

Large Language Models (LLMs) have revolutionized natural language processing, but their application to speech-based tasks remains challenging due to the complexities of integrating audio and text modalities. This paper introduces Ichigo, a mixed-modal model that seamlessly processes interleaved sequences of speech and text. Utilizing a tokenized early-fusion

Yusuke Hosoya, Masanori Suganuma, Takayuki Okatani

Open-vocabulary object detection (OVD), detecting specific classes of objects using only their linguistic descriptions (e.g., class names) without any image samples, has garnered significant attention. However, in real-world applications, the target class concepts is often hard to describe in text and the only way to specify target objects is to provide thei

Samarth Garg, Vivek Hruday Kavuri, Gargi Shroff, Rahul Mishra

The constant shifts in social and political contexts, driven by emerging social movements and political events, lead to new forms of hate content and previously unrecognized hate patterns that machine learning models may not have captured. Some recent literature proposes data augmentation-based techniques to enrich existing hate datasets by incorporating sam

Yu Zhao, Hao Fei, Xiangtai Li, Libo Qin

In the visual spatial understanding (VSU) area, spatial image-to-text (SI2T) and spatial text-to-image (ST2I) are two fundamental tasks that appear in dual form. Existing methods for standalone SI2T or ST2I perform imperfectly in spatial understanding, due to the difficulty of 3D-wise spatial feature modeling. In this work, we consider modeling the SI2T and

Ruiqi Dong, Zhixuan Liao, Guangwei Lai, Yuhan Ma

Large Language Models (LLMs) are pivotal AI agents in complex tasks but still face challenges in open decision-making problems within complex scenarios. To address this, we use the language logic game ``Who is Undercover?'' (WIU) as an experimental platform to propose the Multi-Perspective Team Tactic (MPTT) framework. MPTT aims to cultivate LLMs' human-like

Yijie Zhang

Bayesian inference provides a principled probabilistic framework for quantifying uncertainty by updating beliefs based on prior knowledge and observed data through Bayes' theorem. In Bayesian deep learning, neural network weights are treated as random variables with prior distributions, allowing for a probabilistic interpretation and quantification of predic

Mayank Dixit, Timir Hajari, Bhalachandra Laxmanrao Tembe

Magnesium sulfate (${ \rm MgSO_4 }$) is used as an additive to reduce capacity fading in rechargeable zinc-ion batteries. This study investigates the ion pairing and solvation structure of ${ \rm Zn^{2+}-SO_4^{2-} }$ and ${ \rm Mg^{2+}-SO_4^{2-} }$ in mixtures of ${ \rm [ZnSO_4]{2M} + [MgSO_4]{0M} }$, ${ \rm [ZnSO_4]{1M} + [MgSO_4]{1M} }$, and ${ \rm [ZnSO_4

Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Maram Hasanain, Sahinur Rahman Laskar

Large Language Models (LLMs) have demonstrated remarkable success as general-purpose task solvers across various fields. However, their capabilities remain limited when addressing domain-specific problems, particularly in downstream NLP tasks. Research has shown that models fine-tuned on instruction-based downstream NLP datasets outperform those that are not

Jirô Akahori, Ryuya Namba, Atsuhito Watanabe

In the present paper, we first revisit the volatility estimation approach proposed by N. Kunitomo and S. Sato, and second, we show that the volatility estimator proposed by P. Malliavin and M.E. Mancino can be understood in a unified way by the approach. Third, we introduce an alternative estimator that might overcome the inconsistency caused by the microstr

Lei Wang, Liang Du, Peng Zhou, Peng Wu

A symmetric nonnegative matrix factorization algorithm based on self-paced learning was proposed to improve the clustering performance of the model. It could make the model better distinguish normal samples from abnormal samples in an error-driven way. A weight variable that could measure the degree of difficulty to all samples was assigned in this method, a

Geeta Rangwal, Aman Arya, Annapurni Subramaniam, Kulinder Pal Singh

Open clusters (OCs) in the Galaxy are excellent probes for tracing the structure and evolution of the Galactic disk. We present an updated catalog of parameters for 1,145 OCs, estimated using the Gaia DR3 data earlier listed in Cantat-Gaudin et al. (2020). This sample is complemented by 3,677 OCs from the catalog by Hunt & Reffert (2023). Using the Galaxy po

Liang Du, Xin Ren, Haiying Zhang, Peng Zhou

Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables, which makes it vulnerable to the noise and outliers within multiple candidate kernels. This paper first presents the clustering method via kernelized local regression (CKLR). It captures t

Khunanon Thongkham, Anthony H. Gonzalez, Mark Brodwin, Ariane Trudeau

We present the second data release of the Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2). We expand from the equatorial first data release to most of the Dark Energy Camera Legacy Survey area, covering a total area of 6498 deg^2. The catalog consists of 133,036 S/N $\geq5$ galaxy cluster candidates at $0.1\leq z \leq2$, including 6790 candidates at

Wenchao Teng, Louis J. Durlofsky

Data assimilation will be essential for the management and expansion of geological carbon storage operations. In traditional data assimilation approaches a fixed set of geological hyperparameters, such as mean and standard deviation of log-permeability, is often assumed. Such hyperparameters, however, may be highly uncertain in practical CO2 storage applicat

Aditya Khurmi

An elliptic pair $(X, C)$ is a generalization of a rational elliptic fibration $X \to \mathbb{P}^1$ with fiber $C,$ introduced in \cite{jenia_blowup}. Here, $X$ is a projective rational surface with log terminal singularities, and $C$ is an irreducible curve contained in the smooth locus of $X,$ with $p_a(C)=1$ and $C^2=0.$ These naturally arise as blowups $

K. Arjun, A M Vinodkumar, Vishnu Mayya Bannur, Munshi G. Mustafa

The running coupling constant is calculated using the imaginary time formalism (ITF) of thermal field theory under the self-energy approximation. In the process, each Feynman diagram in thermal field theory is rewritten as the summation of non-thermal diagrams with coefficients that are functions of mass and temperature. By employing the same mass scale and

Cunliang Pan, Chengxuan Li, Yu Liu, Yonggang Zheng

The automatic differentiation (AD) in the vanilla physics-informed neural networks (PINNs) is the computational bottleneck for the high-efficiency analysis. The concept of derivative discretization in smoothed particle hydrodynamics (SPH) can provide an accelerated training method for PINNs. In this paper, smoothing kernel physics-informed neural networks (S

Melanie Walsh, Anna Preus, Elizabeth Gronski

Generating poetry has become a popular application of LLMs, perhaps especially of OpenAI's widely-used chatbot ChatGPT. What kind of poet is ChatGPT? Does ChatGPT have its own poetic style? Can it successfully produce poems in different styles? To answer these questions, we prompt the GPT-3.5 and GPT-4 models to generate English-language poems in 24 differen

I. V. Leonov

Using \emph{ab initio} band structure and DFT+dynamical mean-field theory methods we examine the effects of electron-electron interactions on the electronic structure, magnetic state, and structural phase stability of the recently discovered double-layer perovskite superconductor La$_3$Ni$_2$O$_7$ (LNO) under pressure. Our results show the emergence of a dou

Jing Yang Lee, Seokhwan Kim, Kartik Mehta, Jiun-Yu Kao

Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in general, predominantly exhibit reactive behavior, lacking the ability to generate proactive responses that actively engage users in sustained conversations. However, existing defin

Xunzhao Yin, Hamza Errahmouni Barkam, Franz Müller, Yuxiao Jiang

Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising a 'neuro' component for feature extraction and a 'symbolic' component for decision-making, neuro-symbolic AI has yet to fully benefit from efficient hardware accelerators. Additio

Leonardo Giani, Rodrigo Von Marttens, Ryan Camilleri

We propose a two parameters extension of the flat $\Lambda$CDM model to capture the impact of matter inhomogeneities on our cosmological inference. Non virialized but non-linearly evolving overdense and underdense regions, whose abundance is quantified using the Press-Schechter formalism, are collectively described by two effective perfect fluids $\rho_{\rm{

Xiaolin Lv, Liang Du, Peng Zhou, Peng Wu

Feature selection technology is a key technology of data dimensionality reduction. Becauseof the lack of label information of collected data samples, unsupervised feature selection has attracted more attention. The universality and stability of many unsupervised feature selection algorithms are very low and greatly affected by the dataset structure. For this

Yi Yang, Richard M. Voyles, Haiyan H. Zhang, Robert A. Nawrocki

Accumulated detailed knowledge about the neuronal activities in human brains has brought more attention to bio-inspired spiking neural networks (SNNs). In contrast to non-spiking deep neural networks (DNNs), SNNs can encode and transmit spatiotemporal information more efficiently by exploiting biologically realistic and low-power event-driven neuromorphic ar

Qun Niu, Jie Yao, Quanchao Song, Humaira Akber

Introducing uniform magnetic order in two-dimensional topological insulators (2D TIs) by constructing heterostructures of TI and magnet is a promising way to realize the high-temperature Quantum Anomalous Hall effect. However, the topological properties of 2D materials are susceptible to several factors that make them difficult to maintain, and whether topol

Shihoko Ishii

We study a pair consisting of a smooth variety over a field of positive characteristic and a multi-ideal with a real exponent. We prove the finiteness of the set of minimal log discrepancies for a fixed exponent if the dimension is less than or equal to three. We also prove that the set of log canonical thresholds (lct for short) of ideals on a smooth variet

Aryan Abbasian, Mahtab Mirmohseni, Masoumeh Nasiri Kenari

Recent experiments have demonstrated the feasibility of storing digital information in macromolecules such as DNA and protein. However, the DNA storage channel is prone to errors such as deletions, insertions, and substitutions. During the synthesis and reading phases of DNA strings, many noisy copies of the original string are generated. The problem of reco

Vincent Cheval, Caroline Fontaine

Computer-aided analysis of security protocols heavily relies on equational theories to model cryptographic primitives. Most automated verifiers for security protocols focus on equational theories that satisfy the Finite Variant Property (FVP), for which solving unification is decidable. However, they either require to prove FVP by hand or at least to provide

Yue Li, Xiao Li, Hao Wu, Yue Zhang

The rapid expansion of software systems and the growing number of reported vulnerabilities have emphasized the importance of accurately identifying vulnerable code segments. Traditional methods for vulnerability localization, such as manual code audits or rule-based tools, are often time-consuming and limited in scope, typically focusing on specific programm

Tian Lan, Wenwei Zhang, Chengqi Lyu, Shuaibin Li

Critique ability, a meta-cognitive capability of humans, presents significant challenges for LLMs to improve. Recent works primarily rely on supervised fine-tuning (SFT) using critiques generated by a single LLM like GPT-4. However, these model-generated critiques often exhibit flaws due to the inherent complexity of the critique. Consequently, fine-tuning L

Te Li, Mengze Zhang, Yan Zhou

Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introdu

Yuchen Wang, Shangxin Guo, Chee Wei Tan

The advancements in cloud-based Large Languages Models (LLMs) have revolutionized AI-assisted programming. However, their integration into certain local development environments like ones within the Apple software ecosystem (e.g., iOS apps, macOS) remains challenging due to computational demands and sandboxed constraints. This paper presents CAMP, a multi-mo

Felix Tian, Ajay Byadgi, Daniel Kim, Daochen Zha

Current large language models (LLMs) have proven useful for analyzing financial data, but most existing models, such as BloombergGPT and FinGPT, lack customization for specific user needs. In this paper, we address this gap by developing FinGPT Search Agents tailored for two types of users: individuals and institutions. For individuals, we leverage Retrieval

Shirong Zheng, Shaobo Liu, Zhenhong Zhang, Dian Gu

With the advancement of global climate change and sustainable development goals, urban building energy consumption optimization and carbon emission reduction have become the focus of research. Traditional energy consumption prediction methods often lack accuracy and adaptability due to their inability to fully consider complex energy consumption patterns, es

Shihoko Ishii, Ken-ichi Yoshida

We show the vanishing of the first direct image of the structure sheaf of a normal scheme $X$ which is mapped properly and birationally over a regular scheme of any dimension. On the other hand, for any dimension greater than two, we show examples of a proper birational morphism from a normal and Cohen-Macaulay scheme to a regular scheme such that the second

Can Cui, Yunsheng Ma, Sung-Yeon Park, Zichong Yang

With the broader adoption and highly successful development of Large Language Models (LLMs), there has been growing interest and demand for applying LLMs to autonomous driving technology. Driven by their natural language understanding and reasoning capabilities, LLMs have the potential to enhance various aspects of autonomous driving systems, from perception

John Hurwitz, Charles Nicholas, Edward Raff

It is generally well understood that predictive classification and compression are intrinsically related concepts in information theory. Indeed, many deep learning methods are explained as learning a kind of compression, and that better compression leads to better performance. We interrogate this hypothesis via the Normalized Compression Distance (NCD), whic

Ning Wang, Yun Xiao, Xiaopeng Peng, Xiaojun Chang

Temporal action detection (TAD), which locates and recognizes action segments, remains a challenging task in video understanding due to variable segment lengths and ambiguous boundaries. Existing methods treat neighboring contexts of an action segment indiscriminately, leading to imprecise boundary predictions. We introduce a single-stage ContextDet framewor

Yuankai Li, Jia-Chen Gu, Di Wu, Kai-Wei Chang

Retrieval-augmented generation (RAG) can supplement large language models (LLMs) by integrating external knowledge. However, as the number of retrieved documents increases, the input length to LLMs grows linearly, causing a dramatic increase in latency and a degradation in long-context understanding. This is particularly serious for multi-hop questions that

Eason Chen, Xinyi Tang, Zimo Xiao, Chuangji Li

The vision of Web3 is to improve user control over data and assets, but one challenge that complicates this vision is the prevalence of non-transparent, scam-prone applications and vulnerable smart contracts that put Web3 users at risk. While code audits are one solution to this problem, the lack of smart contracts source code on many blockchain platforms, s

Paul Goyes-Peñafiel, Umair bin Waheed, Henry Arguello

The global demand for unconventional energy sources such as geothermal energy and white hydrogen requires new exploration techniques for precise subsurface structure characterization and potential reservoir identification. The Magnetotelluric (MT) method is crucial for these tasks, providing critical information on the distribution of subsurface electrical r

Jiaxing Yu, Tieyao Zhang, Songruoyao Wu, Xinda Wu

Participation in music activities has many benefits, but often requires music theory knowledge and aural skills, which can be challenging for beginners. To help them engage more easily, it's crucial to adopt teaching strategies that lower these barriers. Informed by formative investigation and inspired by LEGO, we introduce ArchiTone, a gamified system that

Jiayang Niu, Jie Li, Ke Deng, Mark Sanderson

Quantum annealers offer a promising hardware platform for solving combinatorial optimization problems, especially those formulated as Quadratic Unconstrained Binary Optimization (QUBO). In this work, we propose PDQUBO (Performance-Driven Quadratic Unconstrained Binary Optimization), a QUBO-based feature selection method that is directly executable on quantum

Muhammad Aadil Khan, Sai Thatipamula, Simona Onori

Real-life batteries tend to experience a range of operating conditions, and undergo degradation due to a combination of both calendar and cycling aging. Onboard health estimation models typically use cycling aging data only, and account for at most one operating condition e.g., temperature, which can limit the accuracy of the models for state-of-health (SOH)

Shiyu Hu, Xuchen Li, Xuzhao Li, Jing Zhang

Despite rapid progress in large vision-language models (LVLMs), existing video caption benchmarks remain limited in evaluating their alignment with human understanding. Most rely on a single annotation per video and lexical similarity-based metrics, failing to capture the variability in human perception and the cognitive importance of events. These limitatio

Lior Shamir

The asymmetry in the large-scale distribution of the directions towards spiral galaxies rotate has been observed by multiple telescopes, all show a consistent asymmetry in the distribution of galaxy spin directions as observed from Earth. Here, galaxies with redshift from HSC DR3 are annotated by their direction of rotation, and their distribution is analyze

Bo Pan, Zhen Xiong, Guanchen Wu, Zheng Zhang

Graph representation learning has garnered significant attention due to its broad applications in various domains, such as recommendation systems and social network analysis. Despite advancements in graph learning methods, challenges still remain in explainability when graphs are associated with semantic features. In this paper, we present GraphNarrator, the

Haiwen Diao, Ying Zhang, Shang Gao, Jiawen Zhu

Cross-modal metric learning is a prominent research topic that bridges the semantic heterogeneity between vision and language. Existing methods frequently utilize simple cosine or complex distance metrics to transform the pairwise features into a similarity score, which suffers from an inadequate or inefficient capability for distance measurements. Consequen

Affan Khadir, Ayush Pandhi, Sebastian Hutschenreuter, Bryan Gaensler

The line-of-sight structure of the Galactic magnetic field (GMF) can be studied using Faraday rotation measure (RM) grids. We analyze how the choice of interpolation kernel can affect the accuracy and reliability of reconstructed RM maps. We test the following kernels: inverse distance weighting (IDW), natural neighbour interpolation (NNI), inverse multiquad

Raiyan Abdul Baten, Ali Sarosh Bangash, Krish Veera, Gourab Ghoshal

Can peer recommendation engines elevate people's creative performances in self-organizing social networks? Answering this question requires resolving challenges in data collection (e.g., tracing inspiration links and psycho-social attributes of nodes) and intervention design (e.g., balancing idea stimulation and redundancy in evolving information environment

Jonathan Hus, Antonios Anastasopoulos

Machine translation systems for high resource languages perform exceptionally well and produce high quality translations. Unfortunately, the vast majority of languages are not considered high resource and lack the quantity of parallel sentences needed to train such systems. These under-represented languages are not without resources, however, and bilingual d

Weichao Zhou, Jiaxin Zhang, Hilaf Hasson, Anu Singh

In retrieval-augmented systems, context ranking techniques are commonly employed to reorder the retrieved contexts based on their relevance to a user query. A standard approach is to measure this relevance through the similarity between contexts and queries in the embedding space. However, such similarity often fails to capture the relevance. Alternatively,

Fang Zhang, Tao Feng, Yurong Ruan, Xiaoyuan Ye

Magnetic frustration has been recognized as pivotal to investigating new phases of matter in correlation-driven Kondo breakdown quantum phase transitions that are not clearly associated with broken symmetry. The nature of these new phases, however, remains underexplored. Here, we report quantum criticalities emerging from a cluster spin-glass in the heavy-fe

Xiaoyu Ma, Xiaozheng He

Most research on within-day dynamic traffic equilibrium with information provision implicitly considers travel time information, often assuming information to be perfect or imperfect based on travelers' perception error. However, lacking explicit formulation of information limits insightful analysis of information impact on dynamic traffic equilibrium and th

Hassan Nadeem, Diwakar Shukla

Efficient sampling in biomolecular simulations is critical for accurately capturing the complex dynamical behaviors of biological systems. Adaptive sampling techniques aim to improve efficiency by focusing computational resources on the most relevant regions of phase space. In this work, we present a framework for identifying the optimal sampling policy thro

Menglan Liao

A degenerate wave equation with time-varying delay in the boundary control input is considered. The well-posedness of the system is established by applying the semigroup theory. The boundary stabilization of the degenerate wave equation is concerned and the uniform exponential decay of solutions is obtained by combining the energy estimates with suitable Lya

Hailiang Zhao, Xueyan Tang, Peng Chen, Shuiguang Deng

In this paper, we study learning-augmented algorithms for the Bahncard problem. The Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to irrevocably and repeatedly decide between a cheap short-term solution and an expensive long-term one with an unknown future. Even though the problem is canonical, only a primal-dual-base

Nhat A. Nghiem

We describe a simple method for simulating time-independent Hamiltonian $H$ that could be decomposed as $H = \sum_{i=1}^m H_i$ where each $H_i$ can be efficiently simulated. Approaches relying on product formula generally work by splitting the evolution time into segments, and approximate the evolution in each segment by the evolution of composing Hamiltonia

Junyuan Bai, Gaowu Qin, Xueyong Pang, Zhihao Zhao

Aluminum alloys, the most widely utilized lightweight structural materials, predominantly depend on coherent complex-structured nano-plates to enhance their mechanical properties. Despite several decades of research, the atomic-scale nucleation and growth pathways for these complex-structured nano-plates remain elusive, as probing and simulating atomic event

Wen-Shi Tang, Xiang-dong Li, Zhe Cui

The discrepancies between observations and theoretical predictions of cataclysmic variables (CVs) suggest that there exists unknown angular momentum loss mechanism(s) besides magnetic braking and gravitational radiation. Mass loss due to nova eruptions belongs to the most likely candidates. While standard theory assumes that mass is lost in the form of radia

Zhen Yang, J. N. Han, Kan Wu, Ruobing Xie

Large language models have revolutionized data processing in numerous domains, with their ability to handle extended context reasoning receiving notable recognition. To speed up inference, maintaining a key-value (KV) cache memory is essential. Nonetheless, the growing demands for KV cache memory create significant hurdles for efficient implementation. This

Mateus Silva Rêgo, Mário Rocha dos Santos, Marcelo Lopes Pereira Júnior, Eduardo Costa Girão

Graphene is a promising material for the development of applications in nanoelectronic devices, but the lack of a band gap necessitates the search for ways to tune its electronic properties. In addition to doping, defects, and nanoribbons, a more radical alternative is the development of 2D forms with structures that are in clear departure from the honeycomb

Haodong Feng, Peiyan Hu, Yue Wang, Dixia Fan

Control in fluid environments is an important research area with numerous applications across various domains, including underwater robotics, aerospace engineering, and biomedical systems. However, in practice, control methods often face challenges due to sparse or missing observations, stemming from sensor limitations and faults. These issues result in obse

Yucheng Guo, Sergey Nadtochiy, Mykhaylo Shkolnikov

The Stefan problem with surface tension is well known to exhibit discontinuities in the associated moving aggregate (i.e., in the domain occupied by the solid), whose structure has only been understood under translational or radial symmetry so far. In this paper, we derive an auxiliary partial differential equation of second-order hyperbolic type, referred t

Shaokang Cheng, Nada Osman, Shiru Qu, Lamberto Ballan

High-quality spatiotemporal traffic data is crucial for intelligent transportation systems (ITS) and their data-driven applications. Inevitably, the issue of missing data caused by various disturbances threatens the reliability of data acquisition. Recent studies of diffusion probability models have demonstrated the superiority of deep generative models in i

Yujia Wu, Junyi Mo, Elynn Chen, Yuzhou Chen

Graph contrastive learning (GCL) has emerged as a promising approach to enhance graph neural networks' (GNNs) ability to learn rich representations from unlabeled graph-structured data. However, current GCL models face challenges with computational demands and limited feature utilization, often relying only on basic graph properties like node degrees and edg

J. Terroa, M. Tasinkevych, C. S. Dias

Liquid crystals are known for their optical birefringence, a property that gives rise to intricate patterns and colors when viewed in a microscope between crossed polarisers. Resulting images are rich in geometric patterns and serve as valuable fingerprints of the liquid crystal's intrinsic properties. By using machine learning techniques, it is possible to

Ruicheng Ao, Hengyu Fu, David Simchi-levi

We study a two-stage online reusable resource allocation problem over T days involving advance reservations and walk-ins. Each day begins with a reservation stage (Stage I), where reservation requests arrive sequentially. When service starts (Stage II), both reserved and walk-in customers arrive to check in and occupy resources for several days. Reserved cus

A. P. Radünz, L. Portella, R. S. Oliveira, F. M. Bayer

The discrete cosine transform (DCT) is a central tool for image and video coding because it can be related to the Karhunen-Lo\`eve transform (KLT), which is the optimal transform in terms of retained transform coefficients and data decorrelation. In this paper, we introduce 16-, 32-, and 64-point low-complexity DCT approximations by minimizing individually t