December 2024 arXiv papers — page 49

Junyan Ye, Honglin Lin, Leyan Ou, Dairong Chen

Cross-view geo-localization identifies the locations of street-view images by matching them with geo-tagged satellite images or OSM. However, most existing studies focus on image-to-image retrieval, with fewer addressing text-guided retrieval, a task vital for applications like pedestrian navigation and emergency response. In this work, we introduce a novel

William Farlessyost, Shweta Singh

Industrial ecosystems are coupled with natural systems through utilization of feedstocks and waste disposal. To ensure resilience in production of industrial systems under the threat of climate change scenarios, it is necessary to evaluate the impact of this coupling on productivity and waste generation. In this work, we present a novel methodology for model

T. H. Mohamed Ahadu Shareef, Irfan Navabshan, M Mohamed Divan Masood, T. Eswara Yuvaraj

This study examines the phytochemical characteristics of Ayurvedic products. An analysis was performed on Kottakkal Ayurveda Triphala (T), Kottakkal Ayurveda Hinguvachadi Churnam (H), and Kottakkal Ayurveda Jirakadyarishtam (J) using GC-MS and LC-MS techniques to determine their bioactive constituents, while also assessing their antimicrobial, docking, antic

Hariprasath Ganesan, Stefan Sandfeld

Progress in the emerging fields of atomic and close-to-atomic scale manufacturing is underpinned by enhanced precision and optimization of laser-controlled nanostructuring. Understanding thin films' crystallographic orientations and microstructure effects becomes crucial for optimizing the laser-metallic thin film interactions; however, these effects remain

Roni Con

In this work, we study the performance of Reed-Solomon codes against an adversary that first permutes the symbols of the codeword and then performs insertions and deletions. This adversarial model is motivated by the recent interest in fully anonymous secret-sharing schemes [EBG+24],[BGI+24]. A fully anonymous secret-sharing scheme has two key properties: (1

Ezzat Elokda, Andrea Censi, Saverio Bolognani, Florian Dörfler

The large-scale allocation of public resources (e.g., transportation, energy) is among the core challenges of future Cyber-Physical-Human Systems (CPHS). In order to guarantee that these systems are efficient and fair, recent works have investigated non-monetary resource allocation schemes, including schemes that employ karma. Karma is a non-tradable token t

Van Truong Vo, Samad Noeiaghdam, Denis Sidorov, Aliona Dreglea

Nonlinear differential equations and systems play a crucial role in modeling systems where time-dependent factors exhibit nonlinear characteristics. Due to their nonlinear nature, solving such systems often presents significant difficulties and challenges. In this study, we propose a method utilizing Physics-Informed Neural Networks (PINNs) to solve the nonl

Wei Jiang, Dong Liu, Yufeng Pei, Kaiming Zhao

This paper investigates the structure of Verma modules over the N=1 BMS superalgebra. We provide a detailed classification of singular vectors, establish necessary and sufficient conditions for the existence of subsingular vectors, uncover the structure of maximal submodules, present the composition series of Verma modules, and derive character formulas for

F. Colombo, F. Mantovani, S. Pinton, P. Schlosser

Superoscillatory functions represent a counterintuitive phenomenon in physics but also in mathematics, where a band-limited function oscillates faster than its highest Fourier component. They appear in various contexts, including quantum mechanics, as a result of a weak measurement introduced by Y. Aharonov and collaborators. These functions can be extended

Jingyu Yao, Ruihan Zhang, Sheng Zhang, Haohao Sheng

The magnetic topological phases attract much interest, such as the axion insulator, higher-order topology, Weyl semimetals, and the quantum anomalous Hall effect (QAHE). Here, we predict that the axion insulator phase, magnetic Weyl points, and QAHE can be achieved in Eu3In2As4. Recently, the single-crystal Eu3In2As4 has been successfully synthesized, which

Ke Ma, Siwei Liu, Mengjie Qin, Stefan Goetz

Motor-evoked potentials (MEPs) are among the few directly observable responses to external brain stimulation and serve a variety of applications, often in the form of input-output (IO) curves. Previous statistical models with two variability sources inherently consider the small MEPs at the low-side plateau as part of the neural recruitment properties. Howev

Yue Cao, Shaoshi Yang, Zhiyong Feng

Parametric message passing (MP) is a promising technique that provides reliable marginal probability distributions for distributed cooperative positioning (DCP) based on factor graphs (FG), while maintaining minimal computational complexity. However, conventional parametric MP-based DCP methods may fail to converge in dense wireless networks due to numerous

Antonio Alcántara, Pablo Diaz-Cachinero, Alberto Sánchez-González, Carlos Ruiz

Concentrating Solar Power Tower (CSPT) plants rely on heliostat fields to focus sunlight onto a central receiver. Although simple aiming strategies, such as directing all heliostats to the receivers equator, can maximize energy collection, they often result in uneven flux distributions that lead to hotspots, thermal stresses, and reduced receiver lifetimes.

Adrian Dumitrescu

The Gale-Berlekamp switching game is played on the following device: $G_n=\{1,2,\ldots,n\} \times \{1,2,\ldots,n\}$ is an $n \times n$ array of lights is controlled by $2n$ switches, one for each row or column. Given an (arbitrary) initial configuration of the board, the objective is to have as many lights on as possible. Denoting the maximum difference (dis

Torgunn Karoline Moe, Nils Peder Astrup Toft

In this paper we present new results about arrangements of lines and osculating curves associated to the Fermat curves in the projective plane. We first consider the sextactic points on the Fermat curves and show that they are distributed on three grids. The grid lines constitute new line arrangements and examples of free curves associated with the Fermat cu

Soumya Das, Goutam Paul, Anindya Banerji

Quantum teleportation efficiently transfers quantum information between distant locations by utilizing a pre-established composite system. Assessing the effectiveness of teleportation hinges on its fidelity, representing the similarity between input and output states. This fidelity, in turn, relies on a singlet fraction, quantifying the resemblance of the co

Khalifa Es-Sebaiy

In this paper we provide a new explicit bound on the total variation distance between a standardized partial sum of random variables belonging to a finite sum of Wiener chaoses and a standard normal random variable. We apply our result to derive an upper bound for the Kolmogorov distance between a ratio of multiple stochastic integrals and a Gaussian random

Samuel Marschall, Kira Maag

Deep neural networks have shown outstanding performance in computer vision tasks such as semantic segmentation and have defined the state-of-the-art. However, these segmentation models are trained on a closed and predefined set of semantic classes, which leads to significant prediction failures in open-world scenarios on unknown objects. As this behavior pre

Yan Liu, Jie Jiang, Bing Sun

In General Relativity, gravity around a black hole is universally regarded as an attractive force. However, quantum effects may significantly alter this classical picture and thus affect the dynamics of test particles. In this work, we investigate geodesic motion in a recently proposed covariant effective quantum black hole model, the ZLMY type I model in \h

Mohammadreza Doostmohammadian, Themistoklis Charalambous

This paper considers target tracking based on a beacon signal's time-difference-of-arrival (TDOA) to a group of cooperating sensors. The sensors receive a reflected signal from the target where the time-of-arrival (TOA) renders the distance information. The existing approaches include: (i) classic centralized solutions which gather and process the target dat

Adam Schwimmer, Stefan Theisen

Using an unambiguous characterization of Trace Anomalies a general proof of matching for Type A and B anomalies in the broken phases of Conformal Field Theories is given. The general constraints on amplitudes of energy-momentum tensors and dilatons in the broken phase, which follow from matching, are analyzed.

Jiangnan Yang, Shuangli Liu, Jingjun Wu, Xinyu Su

These recent years have witnessed that convolutional neural network (CNN)-based methods for detecting infrared small targets have achieved outstanding performance. However, these methods typically employ standard convolutions, neglecting to consider the spatial characteristics of the pixel distribution of infrared small targets. Therefore, we propose a novel

Xiulong Yuan, Xu Yan, Wenting Shen, Xiafei Qiu

Recent deep learning workloads exhibit dynamic characteristics, leading to the rising adoption of dynamic shape compilers. These compilers can generate efficient kernels for dynamic shape graphs characterized by a fixed graph topology and uncertain tensor shapes. However, memory optimization, although particularly crucial in this large model era, remains rel

Zijian Zhang, Shuchang Liu, Ziru Liu, Rui Zhong

User simulators can rapidly generate a large volume of timely user behavior data, providing a testing platform for reinforcement learning-based recommender systems, thus accelerating their iteration and optimization. However, prevalent user simulators generally suffer from significant limitations, including the opacity of user preference modeling and the inc

Euisung Park, Saerom Sim

This paper studies the geometric structure of the locus $\Phi_3 (X)$ of rank $3$ quadratic equations of the Veronese variety $X = \nu_d (\mathbb{P}^n)$. Specifically, we investigate the minimal irreducible decomposition of $\Phi_3 (X)$ of rank $3$ quadratic equations and analyze the geometric properties of the irreducible components of $\Phi_3 (X)$ such as t

Ronghui Li, Youliang Zhang, Yachao Zhang, Yuxiang Zhang

Humans perform a variety of interactive motions, among which duet dance is one of the most challenging interactions. However, in terms of human motion generative models, existing works are still unable to generate high-quality interactive motions, especially in the field of duet dance. On the one hand, it is due to the lack of large-scale high-quality datase

Yan Gu, Jiao Wang

Quantum dynamics of a damped harmonic oscillator has been extensively studied since the sixties of the last century. Here, with a distinct tool termed the ``group-theoretical characteristic function" (GCF), we investigate analytically how a harmonic oscillator immersed in a thermal environment would relax to its equilibrium state. We assume that the oscillat

Peter J. Rousseeuw

Multiple linear regression is a basic statistical tool, yielding a prediction formula with the input variables, slopes, and an intercept. But is it really easy to see which terms have the largest effect, or to explain why the prediction of a specific case is unusually high or low? To assist with this the so-called predictions plot is proposed. Its simplicity

Nguyen Hoang Vu

We generalize the concept of holography for the color superconductivity (CSC) phase by considering $d$-dimensional Anti de Sitter (AdS) space instead of the traditional 6 dimensions. The corresponding dual field theory is an arbitrary confining gauge theory with $SU(N_c)$ symmetry, like quantum chromodynamics (QCD) CSC. We then use a holographic model based

Shuaikai Shi, Ruiyuan Kang, Panos Liatsis

Electrical impedance tomography (EIT) is a non-invasive imaging technique, capable of reconstructing images of the electrical conductivity of tissues and materials. It is popular in diverse application areas, from medical imaging to industrial process monitoring and tactile sensing, due to its low cost, real-time capabilities and non-ionizing nature. EIT vis

Jeongho Kim, Hoiyeong Jin, Sunghyun Park, Jaegul Choo

Recent virtual try-on approaches have advanced by finetuning pre-trained text-to-image diffusion models to leverage their powerful generative ability. However, the use of text prompts in virtual try-on remains underexplored. This paper tackles a text-editable virtual try-on task that modifies the clothing based on the provided clothing image while editing th

Hermann G. Matthies

Systems may depend on parameters which one may control, or which serve to optimise the system, or are imposed externally, or they could be uncertain. This last case is taken as the ``Leitmotiv'' for the following. A reduced order model is produced from the full order model by some kind of projection onto a relatively low-dimensional manifold or subspace. The

Ye-Xin Lu, Hui-Peng Du, Zheng-Yan Sheng, Yang Ai

This paper proposes an Incremental Disentanglement-based Environment-Aware zero-shot text-to-speech (TTS) method, dubbed IDEA-TTS, that can synthesize speech for unseen speakers while preserving the acoustic characteristics of a given environment reference speech. IDEA-TTS adopts VITS as the TTS backbone. To effectively disentangle the environment, speaker,

Tzu-Chieh Chen, Wen-Yang Lin

Named Entity Recognition has traditionally been a key task in natural language processing, aiming to identify and extract important terms from unstructured text data. However, a notable challenge for contemporary deep-learning NER models has been identifying discontinuous entities, which are often fragmented within the text. To date, methods to address Disco

Alireza Alizadeh, Byungju Lim, Mai Vu

As next generation cellular networks become denser, associating users with the optimal base stations at each time while ensuring no base station is overloaded becomes critical for achieving stable and high network performance. We propose multi-agent online Q-learning (QL) algorithms for performing real-time load balancing user association and handover in den

Shaul Katznelson, Noam Kasten, Offek Tziperman, Avner Shultzman

Hanbury Brown and Twiss (HBT) interferometry is a milestone experiment that transformed our understanding of the nature of light. The concept was demonstrated in 1956 to measure the radii of stars through photon coincidence detection. This form of coincidence detection later became a cornerstone of modern quantum optics. Here we connect HBT interferometry to

Alexander von Recum, Christoph Schnabl, Gabor Hollbeck, Silas Alberti

Refusals - instances where large language models (LLMs) decline or fail to fully execute user instructions - are crucial for both AI safety and AI capabilities and the reduction of hallucinations in particular. These behaviors are learned during post-training, especially in instruction fine-tuning (IFT) and reinforcement learning from human feedback (RLHF).

Emanuele Polino, Luis Villegas-Aguilar, Davide Poderini, Nathan Walk

The certification of randomness is essential for both fundamental science and information technologies. Unlike traditional random number generators, randomness obtained from nonlocal correlations is fundamentally guaranteed to be unpredictable. However, it is also highly susceptible to noise. Here, we show that extending the conventional bipartite Bell scena

Ryo Taniuchi

The properties of the neutron-rich isotope $^{78}$Ni, long postulated to be doubly magic, have been extensively explored through recent experimental and theoretical studies. Confirmations of robust shell closures at $Z=28$ and $N=50$ as well as hints of competing deformations in neighboring isotopes have been obtained. Innovations of a thick liquid hydrogen

Elie Antoine, Frédéric Béchet, Philippe Langlais

This study investigates the behavior of model-integrated routers in Mixture of Experts (MoE) models, focusing on how tokens are routed based on their linguistic features, specifically Part-of-Speech (POS) tags. The goal is to explore across different MoE architectures whether experts specialize in processing tokens with similar linguistic traits. By analyzin

Dennis J. N. J. Soemers, Spyridon Samothrakis, Kurt Driessens, Mark H. M. Winands

It is common practice in reinforcement learning (RL) research to train and deploy agents in bespoke simulators, typically implemented by engineers directly in general-purpose programming languages or hardware acceleration frameworks such as CUDA or JAX. This means that programming and engineering expertise is not only required to develop RL algorithms, but i

Chunxu Zhang, Guodong Long, Hongkuan Guo, Zhaojie Liu

Multifaceted user modeling aims to uncover fine-grained patterns and learn representations from user data, revealing their diverse interests and characteristics, such as profile, preference, and personality. Recent studies on foundation model-based recommendation have emphasized the Transformer architecture's remarkable ability to capture complex, non-linear

Jianfeng Lu, Ying Zhang, Riheng Jia, Shuqin Cao

Federated Learning (FL) mitigates privacy leakage in decentralized machine learning by allowing multiple clients to train collaboratively locally. However, dynamic mobile networks with high mobility, intermittent connectivity, and bandwidth limitation severely hinder model updates to the cloud server. Although previous studies have typically addressed user m

Paula Hähndel, Rebecca Waldecker

This article revisits earlier work by the second author together with Kay Magaard. We correct several little results and we briefly discuss why, fortunately, the errors hardly affect our main theorems and in particular do not affect the classification of simple groups that act with fixity 2. As an addition to the submitted article, this version also contains

Keiichi Shigechi

We study the relation between a coefficient of an element of the Jones--Wenzl projection in the Temperley--Lieb algebra of type $D$ and an enumeration of Dyck tilings. The coefficient can be non-recursively expressed as an enumerative generating function of Dyck tilings by considering the generalized Hermite histories, which we call bi-colored vertical Hermi

Muzamil Shah, Muhammad Sabieh Anwar

Monolayer-jacutingaite (Pt2HgSe3) has been predicted to be the first large-gap Kane-Mele quantum spin Hall insulator. Materials in the jacutingaite family undergo topological phase transitions (TPTs), i.e., from a topologically non-trivial to a semimetallic phase and further to the normal insulating phase when exposed to electric fields and off-resonance, hi

Huanqia Cai, Yijun Yang, Zhifeng Li

Solving complex mathematical problems via system-2 reasoning is a natural human skill, yet it remains a significant challenge for current large language models (LLMs). We identify the scarcity of deliberate multi-step reasoning data as a primary limiting factor. To this end, we introduce Enriched Instruction Tuning (EIT), a method that enriches existing huma

Fanshuang Kong, Richong Zhang, Ziqiao Wang

Hierarchical text classification (HTC) aims to assign one or more labels in the hierarchy for each text. Many methods represent this structure as a global hierarchy, leading to redundant graph structures. To address this, incorporating a text-specific local hierarchy is essential. However, existing approaches often model this local hierarchy as a sequence, f

Zuguang Gu

The 2x2 space-filling curve is a type of generalized space-filling curve characterized by a basic unit is in a "U-shape" that traverses a 2x2 grid. In this work, we propose a universal framework for constructing general 2x2 curves where self-similarity is not strictly required. The construction is based on a novel set of grammars that define the expa

Manu Canals, Natalia Chepiga, Luca Tagliacozzo

The Lattice Gauge Theory Hilbert space is divided into gauge-invariant sectors selected by the background charges. Such a projector can be directly embedded in a tensor network ansatz for gauge-invariant states as originally discussed in [Phys. Rev. B 83, 115127 (2011)] and in [Phys. Rev. X 4, 041024 (2014)] in the context of PEPS. The original ansatz is bas

Giulio Foggi Rota, Alessandro Chiarini, Marco Edoardo Rosti

We investigate the behaviour of a flexible stem completely submerged under a surface gravity wave of finite amplitude using fully resolved direct numerical simulations. By varying the rigidity of the stem over ten orders of magnitude, we explore its motion in the drag-dominated regime with realistic air and water properties. Our findings reveal two distinct

Hyun Kyu Kim, Zhihao Wang

The ${\rm SL}_n$-skein algebra of a punctured surface $\mathfrak{S}$, studied by Sikora, is an algebra generated by isotopy classes of $n$-webs living in the thickened surface $\mathfrak{S} \times (-1,1)$, where an $n$-web is a union of framed links and framed oriented $n$-valent graphs satisfying certain conditions. For each ideal triangulation $λ$ of $\mat

Yichen Wang, Yuxuan Chou, Ziqi Zhou, Hangtao Zhang

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustn

Cezar Joiţa, Dirk Siersma, Mihai Tibăr

Our study concerns the Euclidean distance function in case of complex plane curves. We decompose the ED discriminant into components which are responsible for three types of behavior of the Morse points. Besides the traditional focal component, which is non--linear; the other components are lines. In particular we shed light on the ``atypical discriminant''

Haowei Zhu, Fangyuan Zhang, Rui Qin, Tianxiang Pan

As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance compared to full fine-tuning. However, indiscriminately applying prompts to every layer without considering their inherent correlations, can cause significant disturbances, leading to

Ziqi Zhou, Bowen Li, Yufei Song, Zhifei Yu

With the advancement of deep learning, object detectors (ODs) with various architectures have achieved significant success in complex scenarios like autonomous driving. Previous adversarial attacks against ODs have been focused on designing customized attacks targeting their specific structures (e.g., NMS and RPN), yielding some results but simultaneously co

Wenqiang Liu, Lihua Dong, Yun Guo

Using a hybrid approach based on a phenomenological heavy-quark potential model and the background field effective theory, we assess the influence of a nontrivial Polyakov loop on the in-medium properties of the heavy quarkonium states. Without resorting to any temperature-dependent parameter, the lattice simulations on the complex heavy-quark potential are

Jundong Xu, Hao Fei, Meng Luo, Qian Liu

In the context of large language models (LLMs), current advanced reasoning methods have made impressive strides in various reasoning tasks. However, when it comes to logical reasoning tasks, major challenges remain in both efficacy and efficiency. This is rooted in the fact that these systems fail to fully leverage the inherent structure of logical tasks thr

Ramkrishna Jyoti Samanta, Somabha Mukherjee, Jiang Zhang

The Glauber dynamics for the classical $2$-spin Curie-Weiss model on $N$ nodes with inverse temperature $\beta$ and zero external field is known to mix in time $\Theta(N\log N)$ for $\beta < \frac{1}{2}$, in time $\Theta(N^{3/2})$ at $\beta = \frac{1}{2}$, and in time $\exp(\Omega(N))$ for $\beta >\frac{1}{2}$. In this paper, we consider the $p$-spin general

Ariel Kelman, Umberto Borla, Patrick Emonts, Erez Zohar

Lattice gauge theory is an important framework for studying gauge theories that arise in the Standard Model and condensed matter physics. Yet many systems (or regimes of those systems) are difficult to study using conventional techniques, such as action-based Monte Carlo sampling. In this paper, we demonstrate the use of gauged Gaussian projected entangled p

L. Scharenberg, J. Alozy, W. Billereau, F. Brunbauer

Combining gaseous detectors with a high-granularity pixelated charge readout enables experimental applications which otherwise could not be achieved. This includes high-resolution tracking of low-energetic particles, requiring ultra-low material budget, X-ray polarimetry at low energies ($\lessapprox$ 2 keV) or rare-event searches which profit from event sel

Shize Cao, Cuiwei Zhang, Yueshan Xu, Jianzhou Zhao

Flat electronic bands in condensed matter provide a rich avenue for exploring novel quantum phenomena. Here, we report an optical spectroscopy study of a topological hourglass semimetal Nb3SiTe6 with the electric field of the incident light parallel to its crystalline ab-plane. The ab-plane optical conductivity spectra of Nb3SiTe6 single crystals exhibit a r

Xiangtian Li, Xiaobo Wang, Zhen Qi, Han Cao

Dynamic texture synthesis aims to generate sequences that are visually similar to a reference video texture and exhibit specific stationary properties in time. In this paper, we introduce a spatiotemporal generative adversarial network (DTSGAN) that can learn from a single dynamic texture by capturing its motion and content distribution. With the pipeline of

Hanfang Liang, Jinming Hu, Xiaohuan Ling, Bing Wang

The increasing deployment of small drones as tools of conflict and disruption has amplified their threat, highlighting the urgent need for effective anti-drone measures. However, the compact size of most drones presents a significant challenge, as traditional supervised point cloud or image-based object detection methods often fail to identify such small obj

Yuanda Hu, Xing Liu, Meiying Li, Yate Ge

It is significantly challenging to recognize daily human actions in homes due to the diversity and dynamic changes in unconstrained home environments. It spurs the need to continually adapt to various users and scenes. Fine-tuning current video understanding models on newly encountered domains often leads to catastrophic forgetting, where the models lose the

Jianwei Xu

The Kirkwood-Dirac (KD) distribution is a quantum state representation that relies on two chosen fixed orthonormal bases, or alternatively, on the transition matrix of these two bases. In recent years, it has been discovered that the KD distribution has numerous applications in quantum information science. The presence of negative or nonreal KD distributions

Xu Wang, Shengeng Tang, Peipei Song, Shuo Wang

Sign Language Production (SLP) aims to generate sign videos corresponding to spoken language sentences, where the conversion of sign Glosses to Poses (G2P) is the key step. Due to the cross-modal semantic gap and the lack of word-action correspondence labels for strong supervision alignment, the SLP suffers huge challenges in linguistics-vision consistency.

Ekai Hashimoto, Mikio Nakano, Takayoshi Sakurai, Shun Shiramatsu

This study aims to improve the efficiency and quality of career interviews conducted by nursing managers. To this end, we have been developing a slot-filling dialogue system that engages in pre-interviews to collect information on staff careers as a preparatory step before the actual interviews. Conventional slot-filling-based interview dialogue systems have

Prajwal Singh, Gautam Vashishtha, Indra Deep Mastan, Shanmuganathan Raman

The success of deep learning in supervised fine-grained recognition for domain-specific tasks relies heavily on expert annotations. The Open-Set for fine-grained Self-Supervised Learning (SSL) problem aims to enhance performance on downstream tasks by strategically sampling a subset of images (the Core-Set) from a large pool of unlabeled data (the Open-Set).

Y. H. Chen, Thomas Y. He, Y. Hu, Y. X. Xie

Recently, Andrews considered the partitions with parts separated by parity, in which parts of a given parity are all smaller than those of the other parity. Inspired from the partitions with parts separated by parity, we investigate the overpartitions with separated overlined parts and non-overlined parts, in which the sizes of overlined parts (resp. non-ove

Martha Liliana Cortes

Our understanding of the structure of atomic nuclei largely derives from the nuclear shell model, which has proven widely successful. Further test to our interpretation of the nuclear properties is provided by the study of shell evolution. Increasing experimental information has shown that the nuclear energy shells change when going towards the most exotic n

Wenhao Shen, Mingliang Zhou, Yu Chen, Xuekai Wei

Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abductive counterfactual inference to investigate the causal rel

Yishen Ji, Zhiqi Li, Tong Lu

Track Mapless demands models to process multi-view images and Standard-Definition (SD) maps, outputting lane and traffic element perceptions along with their topological relationships. We propose a novel architecture that integrates SD map priors to improve lane line and area detection performance. Inspired by TopoMLP, our model employs a two-stage structure

Jiajun Ding, Beiyao Zhu, Wenjie Wang, Shurong Zhang

With the rapid development of deep learning and computer vision technologies, medical image segmentation plays a crucial role in the early diagnosis of breast cancer. However, due to the characteristics of breast ultrasound images, such as low contrast, speckle noise, and the highly diverse morphology of tumors, existing segmentation methods exhibit signific

Zhongjian Hu, Peng Yang, Bing Li, Fengyuan Liu

Recently, Large Language Models (LLMs) have been used for knowledge-based Visual Question Answering (VQA). Despite the encouraging results of previous studies, prior methods prompt LLMs to predict answers directly, neglecting intermediate thought processes. We argue that prior methods do not sufficiently activate the capacities of LLMs. We propose a framewor

Zhen Qi, Liwei Ding, Xiangtian Li, Jiacheng Hu

With the continuous advancement of industrial automation, product quality inspection has become increasingly important in the manufacturing process. Traditional inspection methods, which often rely on manual checks or simple machine vision techniques, suffer from low efficiency and insufficient accuracy. In recent years, deep learning technology, especially

Hanrui Zhang, Yu Cheng, Vincent Conitzer

We study equilibrium computation with extensive-form correlation in two-player turn-taking stochastic games. Our main results are two-fold: (1) We give an algorithm for computing a Stackelberg extensive-form correlated equilibrium (SEFCE), which runs in time polynomial in the size of the game, as well as the number of bits required to encode each input numbe

Mahdi Mostajabdaveh, Timothy T. Yu, Samarendra Chandan Bindu Dash, Rindranirina Ramamonjison

In this paper, we introduce and apply Operations Research Question Answering (ORQA), a new benchmark designed to assess the generalization capabilities of Large Language Models (LLMs) in the specialized technical domain of Operations Research (OR). This benchmark evaluates whether LLMs can emulate the knowledge and reasoning skills of OR experts when confron

Kai Zheng, Qingfeng Sun, Can Xu, Peng Yu

This paper explores the use of Large Language Models (LLMs) for sequential recommendation, which predicts users' future interactions based on their past behavior. We introduce a new concept, "Integrating Recommendation Systems as a New Language in Large Models" (RSLLM), which combines the strengths of traditional recommenders and LLMs. RSLLM uses a unique pr

Xingrui Wang, Cuiling Lan, Hanxin Zhu, Zhibo Chen

Modeling and understanding the 3D world is crucial for various applications, from augmented reality to robotic navigation. Recent advancements based on 3D Gaussian Splatting have integrated semantic information from multi-view images into Gaussian primitives. However, these methods typically require costly per-scene optimization from dense calibrated images,

Wenfu Cao, Yang Huang, Hongsheng Zhang

We propose the concept of mutual information for particle pair (MIPP) in curved spacetime, and show that MIPP has potential to be a proper chaos indicator. We tested this method in the Schwarzschild and Kerr spacetime and compared it with the fast Lyapunov indicator. The results show that the MIPP effectively identify orbital states and demonstrates prominen

Bowen Zhang, Yang Huang, Timothy C. Beers, Kai Xiao

The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the

Sergei Gukov, Babak Haghighat, Yihua Liu, Nicolai Reshetikhin

In this paper we construct irregular representations of the affine Kac-Moody algebra $\widehat{sl}(2,\mathbb{C})$. We show how such irregular representations correspond to irregular Gaiotto-Teschner representations of the Virasoro algebra. The intertwiners for such representations satisfy a version of Knizhnik-Zamolodchikov (KZ) equations which we call irreg

Zhenyuan Xiao, Yizhuo Yang, Guili Xu, Xianglong Zeng

The increasing use of compact UAVs has created significant threats to public safety, while traditional drone detection systems are often bulky and costly. To address these challenges, we propose AV-DTEC, a lightweight self-supervised audio-visual fusion-based anti-UAV system. AV-DTEC is trained using self-supervised learning with labels generated by LiDAR, a

Yasmeen Fekery Yaseen El Khodary, Mousa Gowfal Selmey Gowfal Selmey, Elsayed Farrag Elsaid Mohamed Elsayed

This paper investigated the impact of Egypt's accession to the BRICS bloc by studying the participation rate of BRICS countries in Egypt, as well as the exposure rate and the ratio of foreign investment of BRICS countries in Egypt to the total foreign investment in Egypt, as well as Egypt's export opportunities in the BRICS markets and the impact of these va

Jinheon Baek, Sun Jae Lee, Prakhar Gupta, Geunseob Oh

In-Context Learning (ICL) is a technique by which language models make predictions based on examples provided in their input context. Previously, their context window size imposed a limit on the number of examples that can be shown, making example selection techniques crucial for identifying the maximally effective set of examples. However, the recent advent

Nirmalya Thakur, Kesha A. Patel, Audrey Poon, Shuqi Cui

This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily post count, compound sentiment, fine-gr

Yidan Lu, Yinzhao Dong, Ji Ma, Jiahui Zhang

Legged robots have shown promise in locomotion complex environments, but recovery from falls on challenging terrains remains a significant hurdle. This paper presents an Adaptive Fall Recovery (AFR) controller for quadrupedal robots on challenging terrains such as rocky, breams, steep slopes, and irregular stones. We leverage deep reinforcement learning to t

Zhaoxing Zhang, Junda Cheng, Gangwei Xu, Xiaoxiang Wang

Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noisy and inconsistent flow matching, making it difficult to handle challenging scenarios and long-sequence estimation. To overcome these challenges, we introduce Spatio-Temporal Visu

Bohan Jin, Qianyou Sun, Lihua Chen

In the current global economy, supply chain transparency plays a pivotal role in ensuring this security by enabling companies to monitor supplier performance and fostering accountability and responsibility. Despite the advancements in supply chain relationship datasets like Bloomberg and FactSet, supply chain transparency remains a significant challenge in e

Tanayveer Singh Bhatia, Mayukh Panja, Robert Cameron, Sami Solanki

We compute realistic 3D radiative MHD near-surface models of starspots with substantial penumbrae on cool main-sequence stars using the MURaM simulation code. This work is an improvement on the the previous starspot models in a slab geometry. The umbra, penumbra and the quiet star for all starspots are distinct, not only in intensity and temperature, but als

Samir Das, Shishira Mahunta, Nikhil Gupt, Victor Mukherjee

We use Full Counting Statistics to study fluctuations and optimal control in a three-terminal Floquet quantum thermal transistor. We model the setup using three qubits (termed as the emitter, collector and base) coupled to three thermal baths. As shown in Phys. Rev. E 106, 024110 (2022), one can achieve significant change in the emitter and collector current

Xuying Zhang, Yutong Liu, Yangguang Li, Renrui Zhang

We present TAR3D, a novel framework that consists of a 3D-aware Vector Quantized-Variational AutoEncoder (VQ-VAE) and a Generative Pre-trained Transformer (GPT) to generate high-quality 3D assets. The core insight of this work is to migrate the multimodal unification and promising learning capabilities of the next-token prediction paradigm to conditional 3D

Yuhang Gan, Wenjie Xuan, Zhiming Luo, Lei Fang

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream binary change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, such methods primarily emphasize

Hikaru Takeda, Misaki Ishikawa, Masashi Takigawa, Minoru Yamashita

We report the magnetization and the $^{51}$V NMR measurements in the polar magnet GaV$_4$Se$_8$ in which a magnetic skyrmion lattice appears in the structural domain with the polar axis parallel to the magnetic field. Although we successfully separate the $^{51}$V NMR signals in the domain from those in the other structural domains, only the high-frequency r

Badih Ghazi, Charlie Harrison, Arpana Hosabettu, Pritish Kamath

The Privacy Sandbox initiative from Google includes APIs for enabling privacy-preserving advertising functionalities as part of the effort around limiting third-party cookies. In particular, the Private Aggregation API (PAA) and the Attribution Reporting API (ARA) can be used for ad measurement while providing different guardrails for safeguarding user priva

Tianyun Zhong, Chao Liang, Jianwen Jiang, Gaojie Lin

Diffusion-based audio-driven talking avatar methods have recently gained attention for their high-fidelity, vivid, and expressive results. However, their slow inference speed limits practical applications. Despite the development of various distillation techniques for diffusion models, we found that naive diffusion distillation methods do not yield satisfact

Shay Elmalem, Gur Lubin, Michael Wayne, Claudio Bruschini

Temporal photon correlations have been a crucial resource for quantum and quantum-enabled optical science for over half a century. However, attaining non-classical information through these correlations has typically been limited to a single point (or at best, a few points) at-a-time. We perform here a massively multiplexed wide-field photon correlation meas

Yulan Liu, Shaohua Pan, Shujun Bi

This paper concerns the tilt stability of local optimal solutions to a class of nonlinear semidefinite programs, which involves a twice continuously differentiable objective function and a convex feasible set. By leveraging the second subderivative of the extended-valued objective function and imposing a suitable restriction on the multiplier, we derive two

Hal Tasaki

We carefully review the hierarchical construction by Bouch [Bouch2015] of trees on the square lattice that can be grown from its root in $L!/C^L$ distinct ways, where $L$ denotes the number of bonds constituting the tree, and $C>1$ is a constant. (As discussed in Section IV.A of [ParkerCaoAvdoshkinScaffidiAltman2019] and Appendix A.3 of [ShiraishiTasaki2024]