December 2024 arXiv papers — page 4

Izzatjon Allayarov, Vladimir R. Tuz, Antonio Calà Lesina, Andrey B. Evlyukhin

A general analytical approach to the study of electromagnetic resonances of metasurfaces consisting of meta-atoms with anisotropic electric and magnetic dipole polarizabilities and irradiated with obliquely incident light is developed in the direct dipole-moment representation. The presented approach allows us to clearly trace and explain the features of the

Weiqiang He, Yingchun Zhang

We construct a cluster algebra structure within the quantum cohomology ring of a quiver variety associated with an $A$-type quiver. Specifically, let $Fl:=Fl(N_1,\ldots,N_{n+1})$ denote a partial flag variety of length $n$, and $QH_S^*(Fl)[t]:=QH_S^*(Fl)\otimes \mathbb C[t]$ be its equivariant quantum cohomology ring extended by a formal variable $t$, regard

Evgeniy Petrov, Ruslan Salimov

The class of quasisymmetric mappings on the real axis was first introduced by A. Beurling and L. V. Ahlfors in 1956. In 1980 P. Tukia and J. V\"{a}is\"{a}l\"{a} considered these mappings between general metric spaces. In our paper we generalize the concept of quasisymmetric mappings to the case of general semimetric spaces and study some properties of these

Ravindra K. Bisht, Evgen O. Petrov

This paper advances a line of research in fixed point theory initiated by M. Bessenyei and Z. P\'ales, building on their introduction of the triangle function concept in [J. Nonlinear Convex Anal, Vol 18 (3), 515-524 (2017)]. By applying this concept, the study revises several well-known fixed point theorems in metric spaces, extending their applicability to

Ravindra K. Bisht, Evgeniy Petrov

In this paper, we introduce a three-point analogue of \'Ciri\'c-Reich-Rus type mappings, termed as generalized \'Ciri\'c-Reich-Rus type mappings. We demonstrate that these mappings generally exhibit discontinuity within their domain of definition but necessitate continuity at their fixed points. We showcase the existence and non-uniqueness of fixed points fo

Raphael Schlattmann, Malte Vogl

We explore local vs. global evolution of knowledge systems through the framework of socio-epistemic networks (SEN), applying two complementary methods to a corpus of scientific texts. The framework comprises three interconnected layers-social, semiotic (material), and semantic-proposing a multilayered approach to understanding structural developments of know

Roy Steinberg, Kiril Solovey

In their seminal work, Gauci et al. (2014) studied the fundamental task of aggregation, wherein multiple robots need to gather without an a priori agreed-upon meeting location, using minimal hardware. That paper considered differential-drive robots that are memoryless and unable to compute. Moreover, the robots cannot communicate with one another and are onl

Oluwatosin Akande, Patrick Dondl, Kanan Gupta, Akwum Onwunta

We study the momentum-based minimization of a diffuse perimeter functional on Euclidean spaces and on graphs with applications to semi-supervised classification tasks in machine learning. While the gradient flow in the task at hand is a parabolic partial differential equation, the momentum method corresponds to a damped hyperbolic PDE, leading to qualitative

Miaomiao Zhao, Wei-Wei Yang, Yin Zhong

This pedagogic review aims to give a gentle introduction to an exactly solvable model, the Hatsugai-Kohmoto (HK) model, which has infinite-ranged interaction but conserves the center of mass. Although this model is invented in 1992, intensive studies on its properties ranging from unconventional superconductivity, topological ordered states to non-Fermi liqu

Jameel-Un Nabi, Tuncay Bayram, Mahmut Boyukata, Asim Ullah

We reexamine the nuclear structure properties of waiting point nuclei around A70 using the interacting boson model 1 (IBM 1) and the relativistic mean field (RMF) model. Effective density dependent meson exchange functional (DD ME2) and density dependent point coupling functional (DD PC1) were used for the RMF calculations. We calculated the energy levels, t

Khushbu Saradva, Meeta Matnani, Tapankumar Trivedi

The modular multilevel converter (MMC) is a promising converter technology for various highvoltage high-power applications. The reason to that is low-distortion output quantities can be achieved with low average switching frequencies per switch and without output filters. Modular multilevel converter pulse width modulation based control approaches are evalua

G. J. Fu

In this work, I present closed-form formulas for the norm and many-body density matrices between general wave functions with exact particle numbers in pairing theory, using properties of the generalized Kronecker delta. These formulas, expressed as sums of minors and Pfaffians, apply to both even and odd particle-number systems and accommodate pair condensat

Rui Xia, Yanhua Cheng, Yongxiang Tang, Xiaocheng Liu

Recovering user preferences from user-item interaction matrices is a key challenge in recommender systems. While diffusion models can sample and reconstruct preferences from latent distributions, they often fail to capture similar users' collective preferences effectively. Additionally, latent variables degrade into pure Gaussian noise during the forward pro

Qiwei Zhu, Kai Li, Guojing Zhang, Xiaoying Wang

In recent years, deep neural networks, including Convolutional Neural Networks, Transformers, and State Space Models, have achieved significant progress in Remote Sensing Image (RSI) Super-Resolution (SR). However, existing SR methods typically overlook the complementary relationship between global and local dependencies. These methods either focus on captur

Xingyu Bruce Liu, Shitao Fang, Weiyan Shi, Chien-Sheng Wu

One of the long-standing aspirations in conversational AI is to allow them to autonomously take initiatives in conversations, i.e., being proactive. This is especially challenging for multi-party conversations. Prior NLP research focused mainly on predicting the next speaker from contexts like preceding conversations. In this paper, we demonstrate the limita

Philipp Bach, Victor Chernozhukov, Sven Klaassen, Martin Spindler

This paper advances empirical demand analysis by integrating multimodal product representations derived from artificial intelligence (AI). Using a detailed dataset of toy cars on textit{Amazon.com}, we combine text descriptions, images, and tabular covariates to represent each product using transformer-based embedding models. These embeddings capture nuanced

Ondrej Bajgar, Sid William Gould, Rohan Narayan Langford Mitta, Jonathon Liu

As AI systems become increasingly autonomous, aligning their decision-making to human preferences is essential. In domains like autonomous driving or robotics, it is impossible to write down the reward function representing these preferences by hand. Inverse reinforcement learning (IRL) offers a promising approach to infer the unknown reward from demonstrati

Guanwen Fang, Yao Dai, Zesen Lin, Chichun Zhou

In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rot

Sijing Xie, Dingzhu Wen, Xiaonan Liu, Changsheng You

Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round late

Wenhao Dong, Yueyang Li, Weiming Zeng, Lei Chen

Many existing methods that use functional magnetic resonance imaging (fMRI) classify brain disorders, such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), often overlook the integration of spatial and temporal dependencies of the blood oxygen level-dependent (BOLD) signals, which may lead to inaccurate or imprecise clas

David Maltese, Chokri Ogabi

In this paper, we study some anisotropic singular perturbations for a class of linear elliptic problems. We show a global asymptotic expansion of the solution in certain functional space.

Fanwei Meng, Xiaopeng Wang, André Antunes, Jie Zhao

Remote sensing observations and Mars rover missions have recorded the presence of beaches, salt lakes, and wind erosion landforms in Martian sediments. All these observations indicate that Mars was hydrated in its early history. There used to be oceans on Mars, but they have now dried up. Therefore, signs of previous life on Mars could be preserved in the ev

Alexey Golovnev, Sofia Klimova, A. N. Semenova, V. P. Vandeev

We analyse linearised field equations around the Minkowski metric with its standard flat parallel transport in models of Newer GR, that is quadratic actions in terms of nonmetricity tensor. We show that half of the freedom in choosing the model parameters is immediately fixed by asking for reasonable properties of tensors and vectors, defined with respect to

Evelyn Zhang, Bang Xiao, Jiayi Tang, Qianli Ma

Stable Diffusion has achieved remarkable success in the field of text-to-image generation, with its powerful generative capabilities and diverse generation results making a lasting impact. However, its iterative denoising introduces high computational costs and slows generation speed, limiting broader adoption. The community has made numerous efforts to redu

Lingxiao Wang

In this proceeding, we introduce deep learning technologies for studying hadron-hadron interactions. To extract parameterized hadron interaction potentials from collision experiments, we employ a supervised learning approach using Femtoscopy data. The deep neural networks (DNNs) are trained to learn the inverse mapping from observations to potentials. To lin

V. Hocdé, A. Matter, N. Nardetto, A. Gallenne

The circumstellar envelopes (CSE) of Cepheids are still not well characterized despite their potential impact on distance determination via both the period-luminosity relation and the parallax-of-pulsation method. This paper aims to investigate Galactic Cepheids across the instability strip in the mid-infrared with MATISSE/VLTI in order to constrain the geom

Roberto Pegurri, Francesco Linsalata, Eugenio Moro, Jakob Hoydis

The increasing complexity of 6G systems demands innovative tools for network management, simulation, and optimization. This work introduces the integration of ns-3 with Sionna RT, establishing the foundation for the first open source full-stack Digital Network Twin (DNT) capable of supporting multi-RAT. By incorporating a deterministic ray tracer for precise

Derya Malak

Our work addresses the well-known open problem of distributed computing of bilinear functions of two correlated sources ${\bf A}$ and ${\bf B}$. In a setting with two nodes, with the first node having access to ${\bf A}$ and the second to ${\bf B}$, we establish bounds on the optimal sum rate that allows a receiver to compute an important class of non-linear

Yi Wang, Wenji Wu, Hui Zhou

For the discrete-time dynamical system generated by the Poincare map T of a time-periodic closed-loop negative feedback system, we present an amenable condition which enables us to obtain the global convergence of the orbits. This yields the global convergence to the harmonic periodic solutions of the corresponding time-periodic systems with negative feedbac

Tsunehide Kuroki, Yuki Sato

We study the $N \times N$ Hermitian one-matrix model modified by the double-trace interaction. It is known that the coupling for the double-trace interaction can control the weight for the microscopic wormholes if interpreting the matrix model as the lattice model of random surface; tuning the coupling to its critical value, the effect of wormholes become su

Ahmet Astar, Ozan Nurcan, Erk Demirel, Emir Ozen

Soft growing robots are novel devices that mimic plant-like growth for navigation in cluttered or dangerous environments. Their ability to adapt to surroundings, combined with advancements in actuation and manufacturing technologies, allows them to perform specialized manipulation tasks. This work presents an approach for design optimization of soft growing

Yifan Tian, Yixin Liu, Yi Bu, Jiqun Liu

This paper investigates the performance of several representative large models in the tasks of literature recommendation and explores potential biases in research exposure. The results indicate that not only LLMs' overall recommendation accuracy remains limited but also the models tend to recommend literature with greater citation counts, later publication d

Kalyani Bhukya, Shahid Aamir Sheikh, Radha Krishna Ganti

The evolution of 5G New Radio (NR) has brought significant improvements in signal strength and service quality for users. By integrating Multiple Input Multiple Output (MIMO) systems into communications, multiple data streams can be transmitted simultaneously across multiple antennas. Additionally, the incorporation of precoding in MIMO systems enables enhan

Menglin Yang, Jialin Chen, Jinkai Tao, Yifei Zhang

The rapid advancement of foundation modelslarge-scale neural networks trained on diverse, extensive datasetshas revolutionized artificial intelligence, enabling unprecedented advancements across domains such as natural language processing, computer vision, and scientific discovery. However, the substantial parameter count of these models, often reaching bill

Ruslan Salimov, Bogdan Klishchuk

We consider the class of ring $Q$-homeomorphisms with respect to $p$-modulus in $\mathbb{R}^{n}$ with $p > n$, and obtain lower bounds for limsups of the distance distortions under such mappings. These estimates can be treated as H\"{o}lder's continuity of the inverses near the origin. The sharpness is illustrated by example

Leo Ardon, Daniel Furelos-Blanco, Roko Parac, Alessandra Russo

Reward machines (RMs) are an effective approach for addressing non-Markovian rewards in reinforcement learning (RL) through finite-state machines. Traditional RMs, which label edges with propositional logic formulae, inherit the limited expressivity of propositional logic. This limitation hinders the learnability and transferability of RMs since complex task

Xiaoning Dong, Peilin Xin, Jia Li, Wei Xu

Privacy computing receives increasing attention but writing privacy computing code remains challenging for developers due to limited library functions, necessitating function implementation from scratch, and data-oblivious requirement, contradicting intuitive thinking and usual practices of programmers. Automating the generation of privacy computing code wit

Antonio Di Crescenzo, Barbara Martinucci, Julio Mulero

Distorted distributions were introduced in the context of actuarial science for several variety of insurance problems. In this paper we consider the quantile-based probabilistic mean value theorem given in Di Crescenzo et al. [4] and provide some applications based on distorted random variables. Specifically, we consider the cases when the underlying random

Bofeng Wu, En-Wei Liang, Xiao Zhang

In the static and infalling spherical-shell models of optically thin accretion on Schwarzschild black hole, the formulas for the integrated intensities observed by a distant observer are derived, and by taking the monochromatic emission pattern with a $1/r^{2}$ radial profile as example, the black hole images for the spherical shell with different boundaries

Dawen Yu, Shunping Ji

Instance segmentation performance in remote sensing images (RSIs) is significantly affected by two issues: how to extract accurate boundaries of objects from remote imaging through the dynamic atmosphere, and how to integrate the mutual information of related object instances scattered over a vast spatial region. In this study, we propose a novel Shape Guide

Zhiting He, Jiayi Su, Li Chen, Tianqi Wang

Visitors to cultural heritage sites often encounter official information, while local people's unofficial stories remain invisible. To explore expression of local narratives, we conducted a workshop with 20 participants utilizing Generative AI (GenAI) to support visual narratives, asking them to use Stable Diffusion to create images of familiar cultural heri

Yue Fan, Xiaojian Ma, Rongpeng Su, Jun Guo

This paper investigates the problem of understanding dynamic 3D scenes from egocentric observations, a key challenge in robotics and embodied AI. Unlike prior studies that explored this as long-form video understanding and utilized egocentric video only, we instead propose an LLM-based agent, Embodied VideoAgent, which constructs scene memory from both egoce

Shuzhen Lv, Philip B. Zhang

In this paper, we extend recent results by Lv and Kitaev by proving 20 (out of 22 possible) joint equidistributions of mesh patterns 123 and 321 with symmetric shadings, as well as all 36 joint equidistributions of these patterns with minus-antipodal shadings. Our results link several joint equidistributions of mesh patterns to various integer sequences, inc

Marlies Pirner

We consider a multi component gas mixture with translational and internal energy degrees of freedom without chemical reactions assuming that the number of particles of each species remains constant. We will illustrate the derived model in the case of two species, but the model can be generalized to multiple species. The two species are allowed to have differ

Ilan Schvartzman, Roei Sarussi, Maor Ashkenazi, Ido kringel

Malicious URL (Uniform Resource Locator) classification is a pivotal aspect of Cybersecurity, offering defense against web-based threats. Despite deep learning's promise in this area, its advancement is hindered by two main challenges: the scarcity of comprehensive, open-source datasets and the limitations of existing models, which either lack real-time capa

Saima Bashir, Muzaffar Qadir Lone, Prince A Ganai

Within quantum information frameworks, managing decoherence stands as a pivotal task. The present work delves into decoherence dynamics of a dressed qubit, represented by a spinless fermion hopping between two lattice sites that are strongly coupled to a collective bosonic bath. To simplify calculations under strong coupling, we adopt the Lang-Firsov transfo

Heng Zhao, Sheng Cen, Yifei Zhu

Large constellations of Earth Observation Low Earth Orbit satellites collect enormous amounts of image data every day. This amount of data needs to be transferred to data centers for processing via ground stations. Ground Station as a Service (GSaaS) emerges as a new cloud service to offer satellite operators easy access to a network of ground stations on a

Wanlong Liu, Junying Chen, Ke Ji, Li Zhou

Retrieval-Augmented Generation (RAG) has emerged as a key paradigm for enhancing large language models (LLMs) by incorporating external knowledge. However, current RAG methods face two limitations: (1) they only cover limited RAG scenarios. (2) They suffer from limited task diversity due to the lack of a general RAG dataset. To address these limitations, we

Runnan Chen, Zhaoqing Wang, Jiepeng Wang, Yuexin Ma

Understanding geometric, semantic, and instance information in 3D scenes from sequential video data is essential for applications in robotics and augmented reality. However, existing Simultaneous Localization and Mapping (SLAM) methods generally focus on either geometric or semantic reconstruction. In this paper, we introduce PanoSLAM, the first SLAM system

Ofir Gorodetsky

Granville and Soundararajan showed that the $k$th moment in the Erd\H{o}s--Kac theorem is equal to the $k$th moment of the standard Gaussian distribution in the range $k=o((\log \log x)^{1/3})$, up to a negligible error term. We show that their range is sharp: when $k/(\log \log x)^{1/3}$ tends to infinity, a different behavior emerges, and odd moments start

Rayees A Mala, Mehboob Rashid, Muzaffar Qadir Lone

Quantum information processing relies on how dynamics unfold in open quantum systems. In this work, we study the non-Markovian dynamics in the single mode spin-boson model at strong couplings. In order to apply perturbation theory, we transform our Hamiltonian to polaron frame, so that the effective system-bath coupling gets reduced. We employ coherence defi

Tatsuki Murakami, Koshiro Wada, Soma Kogure, Ryomei Takabayashi

Dissipative Kerr soliton microcombs have inspired various intriguing applications such as spectroscopy, ranging, telecommunication, and high purity microwave generation. Mechanically actuated soliton microcombs provide enhanced controllability and flexibility for Kerr solitons, thus enabling technological progress to be made on such practical applications. H

Qi Zhang, Huamin Wang, Hangchi Shen, Shukai Duan

Recently, it can be noticed that most models based on spiking neural networks (SNNs) only use a same level temporal resolution to deal with speech classification problems, which makes these models cannot learn the information of input data at different temporal scales. Additionally, owing to the different time lengths of the data before and after the sub-mod

Shun Liang, Changchang Li, Wenqing Yu, Zhenzhi Liu

Magnetic-free nonreciprocal optical devices that can prevent backscattering of signals are essential for integrated optical information processing. The achieved nonreciprocal behaviors mostly rely on various dispersive effects in optical media, which give rise to dispersive modulations of the transverse beam profile, such as spatial broadening and discretiza

Xiaolei Wang, Xiaoyang Wang, Huihui Bai, Eng Gee Lim

Existing unsupervised distillation-based methods rely on the differences between encoded and decoded features to locate abnormal regions in test images. However, the decoder trained only on normal samples still reconstructs abnormal patch features well, degrading performance. This issue is particularly pronounced in unsupervised multi-class anomaly detection

Elena Beretta, Maolin Deng, Alberto Gandolfi, Bangti Jin

In this work, we develop a novel neural network (NN) approach to solve the discrete inverse conductivity problem of recovering the conductivity profile on network edges from the discrete Dirichlet-to-Neumann map on a square lattice. The novelty of the approach lies in the fact that the sought-after conductivity is not provided directly as the output of the N

Giuseppe Cannizzaro, Quentin Moulard, Fabio Toninelli

The Stochastic Burgers Equation (SBE) is a singular, non-linear Stochastic Partial Differential Equation (SPDE) that describes, on mesoscopic scales, the fluctuations of stochastic driven diffusive systems with a conserved scalar quantity. In space dimension d = 2, the SBE is critical, being formally scale invariant under diffusive scaling. As such, it falls

Yanhong Li, Karen Livescu, Jiawei Zhou

We introduce Chunk-Distilled Language Modeling (CD-LM), an approach to text generation that addresses two challenges in current large language models (LLMs): the inefficiency of token-level generation, and the difficulty of adapting to new data and knowledge. Our method combines deep network-based LLMs with a straightforward retrieval module, which allows th

Yiwen Wang, Siyuan Chen, Ran Yi

3D Gaussian Splatting is emerging as a state-of-the-art technique in novel view synthesis, recognized for its impressive balance between visual quality, speed, and rendering efficiency. However, reliance on third-degree spherical harmonics for color representation introduces significant storage demands and computational overhead, resulting in a large memory

A. V. Tsvetkova, Ya. I. Rodionov, P. D. Grigoriev

We study the effects of imperfect nesting in a simple 2D tight-binding model on the electronic properties in the density-wave (DW) state. The discussed model reflects the main features of quasi-1D metals, where the DW emerges. We show that an imperfect nesting leads to unusual singularities in the quasi-particle density of states, leading to a strong renorma

Haifeng Zhao, Yuguang Jin, Leilei Ma

Significant advancements have been made in single label incremental learning (SLCIL),yet the more practical and challenging multi label class incremental learning (MLCIL) remains understudied. Recently,visual language models such as CLIP have achieved good results in classification tasks. However,directly using CLIP to solve MLCIL issue can lead to catastrop

Kainan Liu, Yong Zhang, Ning Cheng, Zhitao Li

Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inference cost. While such approaches can improve efficiency, indiscriminate layer pruning often results in significant performance degradation. In this paper, we propose GRASP (Gradient-b

Bin Li, Deng Pan, Ting Qu, Yuliang Jin

Glasses possess complex energy landscapes and exhibit non-equilibrium aging dynamics. Here, we propose a generalized trap model for activated aging based on a key static property of the energy landscape: the distribution of energy barriers. Our theory predicts that, upon cooling, weak ergodicity breaking (WEB) in quenching dynamics occurs prior to strong erg

Mitali Bafna, Dor Minzer

In the almost-everywhere reliable message transmission problem, introduced by [Dwork, Pippenger, Peleg, Upfal'86], the goal is to design a sparse communication network $G$ that supports efficient, fault-tolerant protocols for interactions between all node pairs. By fault-tolerant, we mean that that even if an adversary corrupts a small fraction of vertices i

Peng Chen, Zili Xie, Xiangqian Xiu, Dunjun Chen

III-V nitride semiconductors, represented by GaN, have attracted significant research attention. Driven by the growing interest in smart micro-displays, there is a strong desire to achieve enhanced light output from even smaller light-emitting diode (LED) chips. However, the most perplexing phenomenon and the most significant challenge in the study of emissi

Shaoshi Chen, Yang Li, Zhicong Lin, Sherry H. F. Yan

Arnol'd proved in 1992 that Springer numbers enumerate the Snakes, which are type $B$ analogs of alternating permutations. Chen, Fan and Jia in 2011 introduced the labeled ballot paths and established a ``hard'' bijection with snakes. Callan conjectured in 2012 and Han--Kitaev--Zhang proved recently that rc-invariant alternating permutations are counted by S

Ding Zhang, Yangning Li, Lichen Bai, Hao Zhang

Chinese grammatical error correction (CGEC) aims to detect and correct errors in the input Chinese sentences. Recently, Pre-trained Language Models (PLMS) have been employed to improve the performance. However, current approaches ignore that correction difficulty varies across different instances and treat these samples equally, enhancing the challenge of mo

Oleksandr Dobrovolskiy, Gleb Kakazei

Patterned magnetic nanostructures are advanced materials characterized by their unique magnetic properties at the nanoscale, which are the result of tailored geometric configurations and compositional engineering. As interest in nanotechnology continues to grow exponentially, the exploration of patterned magnetic nanostructures turns into a vibrant and criti

Elhoucine Elfatimi, Lahcen El Fatimi, Hanifa Bouchaneb

Model checking, a formal verification technique, ensures systems meet predefined requirements, playing a crucial role in minimizing errors and enhancing quality during development. This paper introduces a novel hybrid framework integrating model checking with deep learning for brain tumor detection and validation in medical imaging. By combining model-checki

Chia-Yuan Chang, Zhimeng Jiang, Vineeth Rakesh, Menghai Pan

Large Language Models (LLMs) are becoming essential tools for various natural language processing tasks but often suffer from generating outdated or incorrect information. Retrieval-Augmented Generation (RAG) addresses this issue by incorporating external, real-time information retrieval to ground LLM responses. However, the existing RAG systems frequently s

Yasunari Suzuki, Takanori Sugiyama, Tomochika Arai, Wang Liao

Demonstrating small error rates by integrating quantum error correction (QEC) into an architecture of quantum computing is the next milestone towards scalable fault-tolerant quantum computing (FTQC). Encoding logical qubits with superconducting qubits and surface codes is considered a promising candidate for FTQC architectures. In this paper, we propose an F

Hebin Wang, Yangning Li, Yinghui Li, Hai-Tao Zheng

The rapid development of multimodal large language models (MLLMs) has brought significant improvements to a wide range of tasks in real-world applications. However, LLMs still exhibit certain limitations in extracting implicit semantic information. In this paper, we apply MLLMs to the Multi-modal Entity Set Expansion (MESE) task, which aims to expand a handf

Aswathi Malanthara, Ishaan R Kale

This paper addresses the challenges faced by algorithms, such as the Firefly Algorithm (FA) and the Genetic Algorithm (GA), in constrained optimization problems. While both algorithms perform well for unconstrained problems, their effectiveness diminishes when constraints are introduced due to limitations in exploration, exploitation, and constraint handling

Adrián González Casanova, Noemi Kurt, Imanol Nuñez Morales, José Luis Pérez

We provide new connections between multitype $\Lambda$-coalescents and multitype continuous state branching processes via duality and a homeomorphism on their parameter space. The approach is based on a sequential sampling procedure for the frequency process of independent CSBPs, and provides forward and backward processes for multitype population models und

Hoang Long Vu, Phuong Tuan Dat, Pham Thao Nhi, Nguyen Song Hao

Recent research in speaker recognition aims to address vulnerabilities due to variations between enrolment and test utterances, particularly in the multi-genre phenomenon where the utterances are in different speech genres. Previous resources for Vietnamese speaker recognition are either limited in size or do not focus on genre diversity, leaving studies in

Jiahui Wu, Zheng An, Chao Zhang, Xuanran Zhu

Quantum state tomography (QST) is crucial for understanding and characterizing quantum systems through measurement data. Traditional QST methods face scalability challenges, requiring $\mathcal{O}(d^2)$ measurements for a general $d$-dimensional state. This complexity can be substantially reduced to $\mathcal{O}(d)$ in pure state tomography, indicating that

Runnan Chen, Xiangyu Sun, Zhaoqing Wang, Youquan Liu

Open-vocabulary scene understanding using 3D Gaussian (3DGS) representations has garnered considerable attention. However, existing methods mostly lift knowledge from large 2D vision models into 3DGS on a scene-by-scene basis, restricting the capabilities of open-vocabulary querying within their training scenes so that lacking the generalizability to novel s

Michal Šindler, František Herman, Filip Kadlec, Christelle Kadlec

We investigate the terahertz conductivity of conventional superconductors in Voigt and Faraday magneto-optical configurations. First, we review theoretical approaches describing the fundamental processes of suppression of superconductivity in magnetic field and how the in-gap states are filled. In the Voigt geometry, thin superconducting films are fully pene

T. Şahin, F. Güney, S. A. Şentürk, N. Çınar

This study introduces a line list for the abundance analysis of F and G type stars across the 4080-9675 A wavelength range. A systematic search employing lower excitation potentials, accurate log gf values, and an updated multiplet table led to the identification of 592 lines across 33 species (25 elements), including C, O, Mg (ionized), Al, P, S, Cu, Zr (ne

Lijie Wu, Qun Yan Zhou, Jun Yan Dai, Siran Wang

Programmable metasurfaces have garnered significant attention due to their exceptional ability to manipulate electromagnetic (EM) waves in real time, leading to the emergence of a prominent area in wireless communication, namely reconfigurable intelligent surfaces (RISs), to control the signal propagation and coverage. However, the existing RISs usually suff

Honami Sakamoto, Ryoto Tange, Jun Ueki

Let $p$ be a prime number and let $K$ be a genus one two-bridge knot. In the spirit of arithmetic topology, we observe that if $p$ divides the size of the 1st homology group of some odd-th cyclic branched cover of the knot $K$, then its group $\pi_1(S^3-K)$ admits a liminal ${\rm SL}_2\mathbb{Z}_p$-character, where $\mathbb{Z}_p$ denotes the ring of $p$-adic

Ángel Javier Alonso, Enhao Liu

Persistence modules that decompose into interval modules are important in topological data analysis because we can interpret such intervals as the lifetime of topological features in the data. We can classify the settings in which persistence modules always decompose into intervals, by a recent result of Aoki, Escolar and Tada: these are standard single-para

George Yuanji Wang, Srisharan Murugesan, Aditya Prince Rohatgi

Identifying druggable genes is essential for developing effective pharmaceuticals. With the availability of extensive, high-quality data, computational methods have become a significant asset. Protein Interaction Network (PIN) is valuable but challenging to implement due to its high dimensionality and sparsity. Previous methods relied on indirect integration

Ling Fu, Zhebin Kuang, Jiajun Song, Mingxin Huang

Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest. Existing benchmarks have highlighted the impressive performance of LMMs in text recognition; however, their abilities in certain challenging tasks, such as text localization, handwritten content extraction, and logical reasoning, rem

Haibo Tong, Enmeng Lu, Yinqian Sun, Zhengqiang Han

With the widespread application of Artificial Intelligence (AI) in human society, enabling AI to autonomously align with human values has become a pressing issue to ensure its sustainable development and benefit to humanity. One of the most important aspects of aligning with human values is the necessity for agents to autonomously make altruistic, safe, and

Zhenjian Wang

We give an explicit formula for the self-intersection number of negative curves on Fermat surfaces. The formula offers us hints to either prove or disprove the Bounded Negativity Conjecture for the Fermat surfaces.

Jicheol Park, Boseung Jeong, Dongwon Kim, Suha Kwak

Text-based person search is the task of finding person images that are the most relevant to the natural language text description given as query. The main challenge of this task is a large gap between the target images and text queries, which makes it difficult to establish correspondence and distinguish subtle differences across people. To address this chal

Jiexin Wang, Yiju Guo, Bing Su

Human motion prediction (HMP) involves forecasting future human motion based on historical data. Graph Convolutional Networks (GCNs) have garnered widespread attention in this field for their proficiency in capturing relationships among joints in human motion. However, existing GCN-based methods tend to focus on either temporal-domain or spatial-domain featu

Yuyin Li, Jing Zhou, Ziwen Yan, Xianfei Zhang

We fabricated polygonal nanoholes in the top p-GaN layer of the InGaN/GaN light-emitting diode, followed by the deposition of Au/Al metal thin film within the nanoholes to create metal microcavities, thereby constructing the surface plasmon structure. The findings indicate that with increased current injection, the light output of the LEDs rose by 46%, accom

Siqi Zhao, Wangyang Li, Xiru Wang, Stevie Foglia

Machine learning (ML) and deep learning (DL) techniques have been widely applied to analyze electroencephalography (EEG) signals for disease diagnosis and brain-computer interfaces (BCI). The integration of multimodal data has been shown to enhance the accuracy of ML and DL models. Combining EEG with other modalities can improve clinical decision-making by a

Mahir Labib Dihan, Md Tanvir Hassan, Md Tanvir Parvez, Md Hasebul Hasan

Recent advancements in foundation models have improved autonomous tool usage and reasoning, but their capabilities in map-based reasoning remain underexplored. To address this, we introduce MapEval, a benchmark designed to assess foundation models across three distinct tasks - textual, API-based, and visual reasoning - through 700 multiple-choice questions s

Jiexin Wang, Yiju Guo, Bing Su

Exploring the bridge between historical and future motion behaviors remains a central challenge in human motion prediction. While most existing methods incorporate a reconstruction task as an auxiliary task into the decoder, thereby improving the modeling of spatio-temporal dependencies, they overlook the potential conflicts between reconstruction and predic

Hanvit Kim, Hyunwoo Park, Sunwoo Kim

This paper proposes a Quantum-MUSIC, the first multiple signal classification (MUSIC) algorithm for quantum wireless sensing of multi-user. Since an atomic receiver for quantum wireless sensing can only measure the magnitude of a received signal, sensing performance degradation of traditional antenna-based signal processing algorithms is inevitable. To overc

Hao Shi, Chu Li, Ding Pan, Xi Dai

Phonon polaritons (PhPs) are hybrid light-matter modes. We investigate them in two-dimensional (2D) materials with twisted moir\'{e} structures, revealing that the moir\'{e} potential creates a new class of `moir\'{e} PhPs'. These exhibit a fundamental spectral reconstruction into multiple branches and, crucially, electromagnetic wavefunctions that are nano-

Chuxiong Sun, Peng He, Qirui Ji, Zehua Zang

Communication is essential in coordinating the behaviors of multiple agents. However, existing methods primarily emphasize content, timing, and partners for information sharing, often neglecting the critical aspect of integrating shared information. This gap can significantly impact agents' ability to understand and respond to complex, uncertain interactions

Qiguo Tian, Lei Hao, Yipeng Zhou, Xiheng Shi

We present an analysis of the absorption-line system in the Very Large Telescope/Ultraviolet and Visual Echelle Spectrograph spectrum at a redshift of $z_{\rm a}={3.1448}$ associated with the quasar SDSS J122040.23+092326.96, whose systematic redshift is $z_{\rm e}=3.1380\pm0.0007$, measured from the ${\rm H}\beta$+[O III] emission lines in our newly acquire

Feng Wu, Yuelin Zhao, Li Zhu

How to accurately quantify the uncertainty of stochastic dynamical responses affected by uncertain loads and structural parameters is an important issue in structural safety and reliability analysis. In this paper, the conditional uncertainty quantification analysis for the dynamical response of stochastic structures considering the measurement data with ran

Haoyu Han, Yu Wang, Harry Shomer, Kai Guo

Retrieval-augmented generation (RAG) is a powerful technique that enhances downstream task execution by retrieving additional information, such as knowledge, skills, and tools from external sources. Graph, by its intrinsic "nodes connected by edges" nature, encodes massive heterogeneous and relational information, making it a golden resource for RAG in treme

Bibhabasu De

The traditional methods of estimating the Dark Matter (DM) mass scale crucially depend on the assumptions about the interaction mechanism between the DM and the Standard Model (SM) sectors, making it challenging to achieve precise mass measurements. However, in case of a successful scattering event of a high-energy matter beam on a DM particle, the DM mass c

Yixuan Li, Can Chen, Jiajun Li, Jiahui Duan

By exploiting the correlation between the structure and the solution of Mixed-Integer Linear Programming (MILP), Machine Learning (ML) has become a promising method for solving large-scale MILP problems. Existing ML-based MILP solvers mainly focus on end-to-end solution learning, which suffers from the scalability issue due to the high dimensionality of the