December 2024 arXiv papers — page 36

Aneesh Panchal, Kirti Beniwal, Vivek Kumar

Nonlinear mathematical models introduce the relation between various physical and biological interactions present in nature. One of the most famous models is the Lotka-Volterra model which defined the interaction between predator and prey species present in nature. However, predators, scavengers, and prey populations coexist in a natural system where scaveng

Shu Cai, Yazhou Zhou, Hualei Sun, Kai Zhang

The discovery of high critical temperature (Tc) superconductivity in pressurized La$_3$Ni$_2$O$_7$ has ignited renewed excitement in the search of novel high-Tc superconducting compounds with 3d transition metals. Compared to other ambient-pressure superconductors, such as copper-oxide and iron-oxypnictides, unraveling the mechanisms of the pressure-induced

Yunhao Shui, Fuhao Zhang, Can Gao, Hao Xue

To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that thi

Kunyu Peng, Di Wen, M. Saquib Sarfraz, Yufan Chen

Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. While the OSDG field has seen considerable advancements, the impact of label noise--a common issue in real-world datasets--has been largely over

Manar Attar, Shuai Wang, Ronald Siebes, Eirik Kultorp

The mission of resilience of Ukrainian cities calls for international collaboration with the scientific community to increase the quality of information by identifying and integrating information from various news and social media sources. Linked Data technology can be used to unify, enrich, and integrate data from multiple sources. In our work, we focus on

Pugeng Hou, Francesco Belli, Tiange Bi, Eva Zurek

Making use of first-principles calculations we analyze the effect of quantum ionic fluctuations and lattice anharmonicity on the crystal structure and superconductivity of I-43m CH4-H3S, one of the lowest enthalpy structures in the C-S-H system, in the 150-300 GPa pressure range within the stochastic self-consistent harmonic approximation. We predict a corre

Takashi Arima, Tommaso Ruggeri

We present a brief history of how the famous Maximum Entropy Principle was used as closure of moments of the Boltzmann equation. In particular, we want to remark on the important role of two fundamental papers by Wolfgang Dreyer, one in the classical framework and one in a relativistic context, to use this principle and to compare the result with the macrosc

Boxi Li, F. A. Cárdenas-López, Adrian Lupascu, Felix Motzoi

Qudits, generalizations of qubits to multi-level quantum systems, offer enhanced computational efficiency by encoding more information per lattice cell, avoiding costly swap operations and providing even exponential speedup in some cases. Utilizing the $d$-level manifold, however, requires high-speed gate operations because of the stronger decoherence at hig

Charles-Edouard Bréhier, Sonja Cox, Annie Millet

We establish weak convergence rates for spectral Galerkin approximations of the stochastic viscous Burgers equation driven by additive trace-class noise. Our results complement the known results regarding strong convergence; we obtain essential weak convergence rate 2. As expected, this is twice the known strong rate. The main ingredients of the proof are no

Xinyi Zhang, Chenshuo Sun, Renyu Zhang, Khim-Yong Goh

AI-generated content (AIGC), such as advertisement copy, product descriptions, and social media posts, is becoming ubiquitous in business practices. However, the value of AI-generated metadata, such as titles, remains unclear on user-generated content (UGC) platforms. To address this gap, we conducted a large-scale field experiment on a leading short-video p

Louis DeBiasio, Jie Han, Allan Lo, Theodore Molla

The P\'osa--Seymour conjecture determines the minimum degree threshold for forcing the $k$th power of a Hamilton cycle in a graph. After numerous partial results, Koml\'os, S\'ark\"ozy and Szemer\'edi proved the conjecture for sufficiently large graphs. In this paper we focus on the analogous problem for digraphs and for oriented graphs. We asymptotically de

Jiaxin Li, Weiqi Huang, Zan Wang, Wei Liang

Humans naturally rely on floor plans to navigate in unfamiliar environments, as they are readily available, reliable, and provide rich geometrical guidance. However, existing visual navigation settings overlook this valuable prior knowledge, leading to limited efficiency and accuracy. To eliminate this gap, we introduce a novel navigation task: Floor Plan Vi

Amir Weiss, Yuval Kochman, Gregory W. Wornell

The ubiquitous time-delay estimation (TDE) problem becomes nontrivial when sensors are non-co-located and communication between them is limited. Building on the recently proposed "extremum encoding" compression-estimation scheme, we address the critical extension to complex-valued signals, suitable for radio-frequency (RF) baseband processing. This extension

A. M. Dmytrenko, P. N. Fedorov, V. S. Akhmetov, A. B. Velichko

We present the results of determining the parameters characterizing the shape and orientation of residual velocity ellipsoids from the Gaia DR3 red giants and subgiants. We show the distribution of velocity dispersions in the Galactic plane obtained from three components of the spatial velocity, as well as the coordinate distribution of the intersection poin

Asari G. Prado, Morgaine I. Mandigo-Stoba, Kuan-Yu Wey, Setayesh Nekarae

We present PyAtoms, an interactive open-source software that rapidly simulates atomic-scale scanning tunneling microscopy (STM) and other scanning probe microscopy (SPM) images of two-dimensional (2D) layered materials, moir\'{e} systems, and superlattices. Rooted in a Fourier-space description of ideal atomic lattice images, PyAtoms is a Python-based graphi

Lisa T. Weinbrenner, Klára Baksová, Sophia Denker, Simon Morelli

Quantum correlations in the form of entanglement, quantum steering or Bell nonlocality are resources for various information-processing tasks, but their detailed quantification and characterization remain complicated. One counter-intuitive effect is the phenomenon of superactivation, meaning that two copies of a quantum state may exhibit forms of correlation

Jun Guo, Lei Wu, Bin Zhu

Phonon, the collective excitation of lattice vibration in the crystal, has been put forward as a means to search for light dark matter. However, the accurate modeling of the multi-phonon production process is challenging in theory. The anharmonicity of the crystal must be taken into account, as it has a significant impact on dark matter-nucleus scattering cr

Webster Adepoju, Mary Sanyaolu

Global demand for clean and eco-friendly energy sources has inspired decades of far-reaching research in power generation from renewable energy sources. Solar cells, wind, and tidal sources are limited in output power generation compared to the fast-rising power requirements of most industrial applications. Besides, the efficiency of conventional DC-DC boost

Abdul Hadi, Uha Isnaini, Indah Emilia Wijayanti, Martianus Frederic Ezerman

We propose constructions of codes over quotient rings of Eisenstein integers equipped with the Euclidean, square Euclidean, and hexagonal distances as a generalization of codes over Eisenstein integer fields. By set partitioning, we effectively divide the ring of Eisenstein integers into equal-sized subsets for distinct encoding. Unlike in Eisenstein integer

Yuchen Yang, Haoran Yan, Yanhao Chen, Qingqiang Wu

Vision Question Answering (VQA) tasks use images to convey critical information to answer text-based questions, which is one of the most common forms of question answering in real-world scenarios. Numerous vision-text models exist today and have performed well on certain VQA tasks. However, these models exhibit significant limitations in understanding human

Zhen Wang

Let $p\in (1,\infty)$ and let $G$ be a discrete group. G. An, J.-J. Lee and Z.-J. Ruan introduced $p$-nuclearity for $L^p$-operator algebras. They proved that the reduced group $L^p$-operator algebra $F^p_\lambda(G)$ is $p$-nuclear if $G$ is amenable. In this paper, we show that the converse is true. This answers an open problem concerning the $p$-nuclearity

Muhammad Farhan Taseer

We presented the rapidity dependence of directed flow ($v_1$) and its slope ($dv_1/dy$) for $\pi^\pm$, $K^\pm$ and $p(\bar{p}$) in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 -- 19.6 GeV from the Beam Energy Scan Phase-II, as well as in U+U collisions at $\sqrt{s_{NN}}$ = 193 GeV measured by the STAR experiment. The $v_1$ values are reported as a function of r

Wen Hao

We describe the construction of Frobenius manifold out of a cyclic (commutative) $BV_\infty$ algebra $(A,\Delta)$ under the assumption of a Hodge-to-de Rham degeneration property and the existence of a compatible homotopy retract of $A$ onto its cohomology. We then apply it to Jacobi manifolds and Hermitian manifolds, generalizing known results in literature

Chunhua Zeng, Hongxin Dong, Tianbo Liue, Peng Sun

We present a global analysis of Sivers functions, transversity distribution functions, and Collins fragmentation functions within the transverse momentum dependent factorization. This analysis encompasses the latest data from semi-inclusive deep inelastic scattering, Drell-Yan, and W/Z-boson production processes as recently reported by the COMPASS and STAR C

Tom Lyche, Carla Manni, Hendrik Speleers

We present a general method to obtain interesting subspaces of the $C^2$ cubic spline space defined on the cubic Wang-Shi refinement of a given arbitrary triangulation $\mathcal{T}$. These subspaces are characterized by specific Hermite degrees of freedom associated with only the vertices and edges of $\mathcal{T}$, or even only the vertices of $\mathcal{T}$

Safa Ben Atitallah, Chaima Ben Rabah, Maha Driss, Wadii Boulila

Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive study devoted to Graph Mamba, to address the critical gaps in understanding its applications, challenges, and future potential. We start by of

Fenghua Shao, Tong Zhang, Shang Gao, Qi Sun

This study mainly explores the application of natural gesture recognition based on computer vision in human-computer interaction, aiming to improve the fluency and naturalness of human-computer interaction through gesture recognition technology. In the fields of virtual reality, augmented reality and smart home, traditional input methods have gradually faile

Peng Yang, Shanquan Lan, Yu Tian, Yu-Kun Yan

The dynamics of superfluid systems exhibit significant similarities to their classical counterparts, particularly in the phenomenon of vortex shedding triggered by a moving obstacle. In such systems, the universal behavior of shedding patterns can be classified using the classical concept of the Reynolds number $Re=\frac{v \sigma}{\nu}$ (characteristic lengt

Huanjin Yao, Jiaxing Huang, Wenhao Wu, Jingyi Zhang

In this work, we aim to develop an MLLM that understands and solves questions by learning to create each intermediate step of the reasoning involved till the final answer. To this end, we propose Collective Monte Carlo Tree Search (CoMCTS), a new learning-to-reason method for MLLMs, which introduces the concept of collective learning into ``tree search'' for

Tore Boybeyi, Sebastien Clesse, Sachiko Kuroyanagi, Mairi Sakellariadou

Using the cross-correlation data from the first three observing runs of the LIGO-Virgo-KAGRA Collaboration, we search for a gravitational-wave background (GWB) from primordial black holes, arising from the superposition of compact binary coalescence events. We consider both early and late binary formation mechanisms and perform Bayesian parameter inference.

Tiago Duarte Guerreiro, Luca Giovenzana, Nivedita Viswanathan

We prove K-stability for infinitely many smooth members of the family 2.19 of the Mukai-Mori classification.

Ahmed E. Samy, Zekarias T. Kefatoa, Sarunas Girdzijauskasa

Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on heuristics commonly identified through trial and error and are effective only within some application domains. Also, it is not clear why on

Majid Nasiri Khormuji

By integrating feedback with Radio Frequency (RF) mirrors, we develop a closed-loop media-based modulation system for efficient utilization of the signal space. Specifically, this closed-loop construction optimizes the inherited signal constellation from the media, achieving a significantly larger minimum pairwise Euclidean distance than the original configu

Alexandra-Gabriela Şerban, Juan Alejandro de la Torre González, Marta Anguiano, Antonio M. Lallena

A new model for the nuclear elastic scattering of protons below 250 MeV has been recently included in FLUKA v4-4.0, motivated by the evaluation of radiation effects in electronics. Nonetheless, proton nuclear elastic scattering plays a significant role also in proton dosimetry applications, for which the new model necessitated an explicit validation. Therefo

Amir Weiss

The need to digitize signals with intricate spectral characteristics often challenges traditional analog-to-digital converters (ADCs). The recently proposed modulo-ADC architecture offers a promising alternative by leveraging inherent features of the input signals. This approach can dramatically reduce the number of bits required for the conversion while mai

Andronikos Paliathanasis

We present the complete solution to the classification problem regarding the variational symmetries of the generalized Brans-Dicke cosmological model in the presence of a second scalar field minimally coupled to gravity and the generalized Brans-Dicke scalar field theory. Through the symmetry analysis, we were able to specify the functional form of the field

Yuanbo Hou, Qiaoqiao Ren, Wenwu Wang, Dick Botteldooren

Emotion recognition and touch gesture decoding are crucial for advancing human-robot interaction (HRI), especially in social environments where emotional cues and tactile perception play important roles. However, many humanoid robots, such as Pepper, Nao, and Furhat, lack full-body tactile skin, limiting their ability to engage in touch-based emotional and g

Hande N. Açıkgöz, Dong Hoon Shin, Inge C. van der Knijff, Allard J. Katan

High frequency acoustic devices based on two-dimensional (2D) materials are unique platforms to design and manipulate the spatiotemporal response of acoustic waves for next-generation sensing and contactless actuation applications. Conventional methods for actuating suspended membranes, however, cannot be applied to all 2D materials, or are limited in freque

Nhat A. Nghiem

The gradient descent method aims at finding local minima of a given multivariate function by moving along the direction of its gradient, and hence, the algorithm typically involves computing all partial derivatives of a given function, before updating the solution iteratively. In the work of Rebentrost et al. [New Journal of Physics, 21(7):073023, 2019], the

V. A. Dzuba, V. V. Flambaum

The 229Th nucleus possesses a unique low-frequency transition at 8.4 eV, which is being considered for the development of an extremely accurate nuclear clock. We investigate an electronic bridge process in the Th III ion, where nuclear excitation occurs via electronic transitions, and demonstrate that a proper choice of laser frequencies can lead to 10,000 e

Yuya Kusuki

The primary aim of these lecture notes is to introduce the modern approach to two-dimensional conformal field theory (2D CFT). The study of analytical methods in two-dimensional conformal field theory has developed over several decades, starting with BPZ. The development of analytical methods, particularly in rational conformal field theory (RCFT), has been

Osama Abdellaif, Abdelrahman Nader, Ali Hamdi

This paper presents ERPA, an innovative Robotic Process Automation (RPA) model designed to enhance ID data extraction and optimize Optical Character Recognition (OCR) tasks within immigration workflows. Traditional RPA solutions often face performance limitations when processing large volumes of documents, leading to inefficiencies. ERPA addresses these chal

Zehong Liang, Xiongbo Yan, Zhe Ning, Jun Hu

We have developed a Time-to-Digital Converter (TDC) application in a Xilinx Kintex-7 Field Programmable Gate Array (FPGA). This TDC, based on the Tapped-Delay Line (TDL) and Wave Union A (WU-A) techniques, achieves an independent time measurement on 32-channel rising edges and 32-channel falling edges. The average time resolution or the Least Significant Bit

Shijin Zhong, Wei Li, Guangzhen Dai, Daohua Wu

Grover's search algorithm has attracted great attention due to its quadratic speedup over classical algorithms in unsorted database search problems. However, Grover's algorithm is inefficient in multi-target search problems, except in the case of 1/4 of the data in the database satisfying the search conditions. Long presented a modified version of Grover's s

Osama A. Marzouk, E. David Huckaby

Several numerical simulations of a co-axial particle-laden swirling air flow in a vertical circular pipe were performed. The air flow was modeled using the unsteady Favre-averaged Navier-Stokes equations. A Lagrangian model was used for the particle motion. The gas and particles are coupled through two-way momentum exchange. The results of the simulations us

Hector Ochoa

It is argued that the specific heat of amorphous solids at low temperatures can be understood to arise from a single branch of collective modes. The idea is illustrated in a model of a correlated spin glass for which magnetic anisotropies are present but they are completely frustrated by disorder. The low-energy spectrum is dominated by soft modes correspond

Zhongjie Li

The Tur{\'a}n inequalities and the Laguerre inequalities are closely related to the Laguerre-P\'{o}lya class and the Riemann hypothesis. These inequalities have been extensively studied in the literature. In this paper, we propose a method to determine a positive integer $N$ such that the sequences $\{\sqrt[n]{a_n}/n!\}_{n \ge N}$ and $\{\sqrt[n+1]{a_{n+1}}/

Dongran Zhang, Jun Li

The extraction of spatial-temporal features is a crucial research in transportation studies, and current studies typically use a unified temporal modeling mechanism and fixed spatial graph for this purpose. However, the fixed spatial graph restricts the extraction of spatial features for similar but not directly connected nodes, while the unified temporal mo

Yushu Li, Yongyi Su, Adam Goodge, Kui Jia

Vision-language models (VLMs) have revolutionized machine learning by leveraging large pre-trained models to tackle various downstream tasks. Although label, training, and data efficiency have improved, many state-of-the-art VLMs still require task-specific hyperparameter tuning and fail to fully exploit test samples. To overcome these challenges, we propose

Jaechul Roh, Andrew Yuan, Jinsong Mao

Text-to-Image (T2I) diffusion models have rapidly advanced, enabling the generation of high-quality images that align closely with textual descriptions. However, this progress has also raised concerns about their misuse for propaganda and other malicious activities. Recent studies reveal that attackers can embed biases into these models through simple fine-t

Longkun Yu, Chenxing Zhang, Dongya Guo, Yaqing Liu

The High Energy cosmic-Radiation Detection (HERD) facility is a dedicated high energy astronomy and particle physics experiment planned to be installed on the Chinese space station, aiming to detect high-energy cosmic rays (GeV $\sim$ PeV) and high-energy gamma rays ($>$ 500 MeV). The Plastic Scintillator Detector (PSD) is one of the sub-detectors of HERD, w

Wen Ku, Yihan Liu, Wei Zhang, Pengcheng An

AI-generated media products are increasingly prevalent in the news industry, yet their impacts on audience perception remain underexplored. Traditional media often employs negative framing to capture attention and capitalize on news consumption, and without oversight, AI-generated news could reinforce this trend. This study examines how different framing sty

Jiaxin Guo, Daimeng Wei, Yuanchang Luo, Shimin Tao

With the widespread application of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), enhancing their performance has become a research hotspot. This paper presents a novel multi-prompt ensemble decoding approach designed to bolster the generation quality of LLMs by leveraging the aggregation of outcomes from multiple prompts. Gi

Xi Ding, Lei Wang

Video anomaly detection (VAD) has witnessed significant advancements through the integration of large language models (LLMs) and vision-language models (VLMs), addressing critical challenges such as interpretability, temporal reasoning, and generalization in dynamic, open-world scenarios. This paper presents an in-depth review of cutting-edge LLM-/VLM-based

Eshwar Ram Arunachaleswaran, Natalie Collina, Jon Schneider

We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoff by optimally responding against the learner's algorithm. The learning agent knows their own payoff function, but is uncertain about the payoff of their opponent (knowing only that it is drawn from some d

Qi Liu, Wanjing Ma

Data corruption, including missing and noisy data, poses significant challenges in real-world machine learning. This study investigates the effects of data corruption on model performance and explores strategies to mitigate these effects through two experimental setups: supervised learning with NLP tasks (NLP-SL) and deep reinforcement learning for traffic s

Christian Di Maio, Cristian Cosci, Marco Maggini, Valentina Poggioni

The growing ubiquity of Retrieval-Augmented Generation (RAG) systems in several real-world services triggers severe concerns about their security. A RAG system improves the generative capabilities of a Large Language Models (LLM) by a retrieval mechanism which operates on a private knowledge base, whose unintended exposure could lead to severe consequences,

Yusuke Shirato, Natsumi Oka, Akira Terui, Masahiko Mikawa

This paper presents an advanced method for addressing the inverse kinematics and optimal path planning challenges in robot manipulators. The inverse kinematics problem involves determining the joint angles for a given position and orientation of the end-effector. Furthermore, the path planning problem seeks a trajectory between two points. Traditional approa

Shaofei Cai, Zhancun Mu, Kaichen He, Bowei Zhang

Minecraft's complexity and diversity as an open world make it a perfect environment to test if agents can learn, adapt, and tackle a variety of unscripted tasks. However, the development and validation of novel agents in this setting continue to face significant engineering challenges. This paper presents MineStudio, an open-source software package designed

Zhixuan Shen, Haonan Luo, Kexun Chen, Fengmao Lv

Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentrali

Junyi Lu, Xiaojia Li, Zihan Hua, Lei Yu

Code review is a vital but demanding aspect of software development, generating significant interest in automating review comments. Traditional evaluation methods for these comments, primarily based on text similarity, face two major challenges: inconsistent reliability of human-authored comments in open-source projects and the weak correlation of text simil

Krai Cheamsawat, Thiparat Chotibut

Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet identifying the mechanisms that underlie enhanced performance remains challenging, particularly in many-body open systems where nonlinear interactions and dissipation intertwine in complex ways. Here, w

Umberto Marini Bettolo Marconi, Lorenzo Caprini

We study a two-dimensional chiral active crystal composed of underdamped chiral active particles. These particles, characterized by intrinsic handedness and persistence, interact via linear forces derived from harmonic potentials. Chirality plays a pivotal role in shaping the system's behavior: it reduces displacement and velocity fluctuations while inducing

Tianyu Ruan, Shihua Zhang

Attention mechanism has been extensively integrated within mainstream neural network architectures, such as Transformers and graph attention networks. Yet, its underlying working principles remain somewhat elusive. What is its essence? Are there any connections between it and traditional machine learning algorithms? In this study, we inspect the process of c

Sheng Xiang, Mingzhi Zhu, Dawei Cheng, Enxia Li

Credit card fraud incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based classifiers to detect fraudulent behavior from labeled transaction records. But labeled data are usually a small proportion of billions of real transactions due to expensive labeling costs, which implies that they do not well

Debojyoti Ballav, Shailesh Kulkarni

We study the asymptotic symmetries of near-horizon extremal BTZ black holes in higher derivative theories of gravity, such as New Massive Gravity and Topological Massive Gravity. By employing a particular boundary condition and the regularization prescription proposed earlier for the Einstein gravity, we demonstrate the existence of two centrally extended Vi

Ayan Kumar Nai, Vimlesh Kumar, M. Ebrahim-Zadeh, G. K. Samanta

Device-independent quantum random number generators (DI-QRNGs) are crucial for information processing, ensuring certified quantumness and genuine randomness. However, existing implementations often face low bit rates due to quantumness testing challenges. Here, we present a high-bit-rate DI-QRNG with live quantumness certification through the Bell test. Usin

Molla Basir Ahamed, Rajesh Hossain

The primary objective of this paper is to derive sharp bounds for the norms of the Schwarzian and pre-Schwarzian derivatives in the Ozaki close-to-convex functions $f$, expressed in terms of their value $f^{\prime\prime}(0)$, in particular, when the quantity is equal to zero. Additionally, we obtain sharp bounds for distortion and growth theorems. We will al

Niket Patel, Guido Montufar

We define the local complexity of a neural network with continuous piecewise linear activations as a measure of the density of linear regions over an input data distribution. We show theoretically that ReLU networks that learn low-dimensional feature representations have a lower local complexity. This allows us to connect recent empirical observations on fea

Zihan Ye, Xinyuan Ru, Shiming Chen, Yaochu Jin

Feature Generative Adversarial Networks have emerged as powerful generative models in producing high-quality representations of unseen classes within the scope of Zero-shot Learning (ZSL). This paper delves into the pivotal influence of unseen class priors within the framework of transductive ZSL (TZSL) and illuminates the finding that even a marginal prior

Zhenzhou Jin, Li You, Huibin Zhou, Yuanshuo Wang

Massive multiple-input multiple-output (MIMO) offers significant advantages in spectral and energy efficiencies, positioning it as a cornerstone technology of fifth-generation (5G) wireless communication systems and a promising solution for the burgeoning data demands anticipated in sixth-generation (6G) networks. In recent years, with the continuous advance

Nantana Monkata, Prin Sawasdipol, Nongnapat Ponkhuha, Ratirat Suntharawirat

In this work, we investigate the Heavy-Quark Spin Symmetry (HQSS) exhibited in the effective Lagrangians governing the three-point interactions of $D$ mesons, charmed baryons, and nucleons. We first construct the effective Lagrangians, and there are 12 distinct terms. As a result, we observe that the invariant Lagrangian under HQSS manifests exclusively in t

Jiacai Liu, Chaojie Wang, Chris Yuhao Liu, Liang Zeng

The role of reinforcement learning (RL) in enhancing the reasoning of large language models (LLMs) is becoming increasingly significant. Despite the success of RL in many scenarios, there are still many challenges in improving the reasoning of LLMs. One challenge is the sparse reward, which makes optimization difficult for RL and necessitates a large amount

Shinya Kanemura, Shao-Ping Li

The origin of neutrino masses can be simply attributed to a new scalar beyond the Standard Model. We demonstrate that leptogenesis can explain the baryon asymmetry of the universe already in such a minimal framework, where the electroweak scalar is favored to enhance the baryon asymmetry. Different from traditional leptogenesis, the realization here exploits

Xiaohao Liu, Xiaobo Xia, Zhuo Huang, See-Kiong Ng

Multi-modal learning has achieved remarkable success by integrating information from various modalities, achieving superior performance in tasks like recognition and retrieval compared to uni-modal approaches. However, real-world scenarios often present novel modalities that are unseen during training due to resource and privacy constraints, a challenge curr

Lingxiao Yin, Wei Tao, Dongyue Zhao, Tadayuki Ito

U-Net models with encoder, decoder, and skip-connections components have demonstrated effectiveness in a variety of vision tasks. The skip-connections transmit fine-grained information from the encoder to the decoder. It is necessary to maintain the feature maps used by the skip-connections in memory before the decoding stage. Therefore, they are not friendl

Petr Kouba, Joan Planas-Iglesias, Jiri Damborsky, Jiri Sedlar

Generative machine learning models are increasingly being used to design novel proteins for therapeutic and biotechnological applications. However, the current methods mostly focus on the design of proteins with a fixed backbone structure, which leads to their limited ability to account for protein flexibility, one of the crucial properties for protein funct

Shammur Absar Chowdhury, Hind Almerekhi, Mucahid Kutlu, Kaan Efe Keles

This paper presents a comprehensive overview of the first edition of the Academic Essay Authenticity Challenge, organized as part of the GenAI Content Detection shared tasks collocated with COLING 2025. This challenge focuses on detecting machine-generated vs. human-authored essays for academic purposes. The task is defined as follows: "Given an essay, ident

Hojun Choi, Junsuk Choe, Hyunjung Shim

Existing open-vocabulary object detection (OVD) develops methods for testing unseen categories by aligning object region embeddings with corresponding VLM features. A recent study leverages the idea that VLMs implicitly learn compositional structures of semantic concepts within the image. Instead of using an individual region embedding, it utilizes a bag of

Prakitr Srisuma, George Barbastathis, Richard D. Braatz

This article describes a new, efficient way of finding control and state trajectories in optimal control problems by reformulation as a system of differential-algebraic equations (DAEs). The optimal control and state vectors can be obtained via simulation of the resulting DAE system with the selected DAE solver, eliminating the need for an optimization solve

Zhi Wang, Wen Yan

We present OpenMM-Python-Force, a plugin designed to extend OpenMM's functionality by enabling integration of energy and force calculations from external Python programs via a callback mechanism. During molecular dynamics simulations, data exchange can be implemented through torch.Tensor or numpy.ndarray, depending on the specific use case. This enhancement

Thierry Poibeau

In this paper, we explore the relevance of large language models (LLMs) for annotating references to Roman and Greek mythological entities in modern and contemporary French literature. We present an annotation scheme and demonstrate that recent LLMs can be directly applied to follow this scheme effectively, although not without occasionally making significan

Shuichiro Ebata, Wataru Horiuchi

Background: Saturation of nuclear density is a fundamental property of atomic nuclei but in reality, the nuclear internal density distribution is not uniform, e.g., some nuclei are known to have the so-called bubble structure, in which the central density is depressed. Purpose: We aim to unveil the emergent mechanism of the non-uniformity of the nucleon dens

Akhil S Anand, Arash Bahari Kordabad, Mario Zanon, Sebastien Gros

Optimality is a critical aspect of Model Predictive Control (MPC), especially in economic MPC. However, achieving optimality in MPC presents significant challenges, and may even be impossible, due to inherent inaccuracies in the predictive models. Predictive models often fail to accurately capture the true system dynamics, such as in the presence of stochast

Xiong Zhang, Cheng Xie, Haoran Duan, Beibei Yu

Real-world graph data environments intrinsically exist noise (e.g., link and structure errors) that inevitably disturb the effectiveness of graph representation and downstream learning tasks. For homogeneous graphs, the latest works use original node features to synthesize a similarity graph that can correct the structure of the noised graph. This idea is ba

Clément Cren

We show an isomorphism between an algebra which is naturally constructed from the Toeplitz algebra generated by d-shifts, and an ideal of the C * -algebra of the (2d + 1)-dimensional Heisenberg group. This is a particular case of a more general result for graded nilpotent Lie groups involving symbols in the filtered calculus. The proof presented here however

Antonio DeSimone, Luciano Teresi

Origami morphing, obtained with patches of piecewise smooth isometries separated by straight fold lines, is an exquisite art that has already received considerable attention in the mathematics and mechanics literature. Curved fold lines, leading to curved creases and curved pleated structures, introduce the additional complexity of mechanical coupling betwee

Robin Riegel

We use the framework of Morse theory with differential graded coefficients to study certain operations on the total space of a fibration. More particularly, we focus in this paper on a chain-level description of the Chas-Sullivan product on the homology of the free loop space of an oriented, closed and connected manifold. The idea of ''intersecting on the ba

Shihao Shao, Yikang Li, Zhouchen Lin, Qinghua Cui

Irreducible Cartesian tensors (ICTs) play a crucial role in the design of equivariant graph neural networks, as well as in theoretical chemistry and chemical physics. Meanwhile, the design space of available linear operations on tensors that preserve symmetry presents a significant challenge. The ICT decomposition and a basis of this equivariant space are di

Yacine Izza, Joao Marques-Silva

Recent work revealed a tight connection between adversarial robustness and restricted forms of symbolic explanations, namely distance-based (formal) explanations. This connection is significant because it represents a first step towards making the computation of symbolic explanations as efficient as deciding the existence of adversarial examples, especially

Benoit Perthame, Chiara Villa

We consider a mathematical model of cancer cell invasion of the extracellular matrix (ECM), comprising a strongly degenerate parabolic partial differential equation for the cell volume fraction, coupled with an ordinary differential equation for the ECM volume fraction ($0 \leq {\phi} \leq 1$). The model captures the intricate link between the dynamics of in

Xuefeng Jiang, Lvhua Wu, Sheng Sun, Jia Li

Code vulnerability detection (CVD) is essential for addressing and preventing system security issues, playing a crucial role in ensuring software security. Previous learning-based vulnerability detection methods rely on either fine-tuning medium-size sequence models or training smaller neural networks from scratch. Recent advancements in large pre-trained la

Tianyu Zhao, Yue Zhou, Ruijun Shi, Peng Xu

As next-generation gravitational-wave (GW) observatories approach unprecedented sensitivities, the need for robust methods to analyze increasingly complex, overlapping signals becomes ever more pressing. Existing matched-filtering approaches and deep-learning techniques can typically handle only one or two concurrent signals, offering limited adaptability to

Caroline Ismeurt-Walmsley, Patrizia Giannoni, Florence Servant, Linda-Nora Mekki

The gut-brain axis has emerged as a key player in the regulation of brain function and cognitive health. Gut microbiota dysbiosis has been observed in preclinical models of Alzheimer's disease and patients. Manipulating the composition of the gut microbiota enhances or delays neuropathology and cognitive deficits in mouse models. Accordingly, the health stat

Juan Yao

In this report, we propose a novel quantum diagonalization algorithm based on the optimization of variational quantum circuits. Diagonalizing a quantum state is a fundamental yet computationally challenging task in quantum information science, especially as the system size increases. To address this challenge, we reformulate the problem as a variational opti

Lan-Zhe Guo, Lin-Han Jia, Jie-Jing Shao, Yu-Feng Li

Semi-supervised learning (SSL) aims to improve performance by exploiting unlabeled data when labels are scarce. Conventional SSL studies typically assume close environments where important factors (e.g., label, feature, distribution) between labeled and unlabeled data are consistent. However, more practical tasks involve open environments where important fac

Pufan Zou, Shijia Zhao, Weijie Huang, Qiming Xia

Recently, Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks. However, their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor conditions, and the abundance of unknown objects. In this work, we

Xinquan Yu, Ziqi Sheng, Wei Lu, Xiangyang Luo

Multimodal fake news detection aims to automatically identify real or fake news, thereby mitigating the adverse effects caused by such misinformation. Although prevailing approaches have demonstrated their effectiveness, challenges persist in cross-modal feature fusion and refinement for classification. To address this, we present a residual-aware compensati

Peng Wang, Qidong Fu, Vladimir V. Konotop, Yaroslav V. Kartashov

Quasicrystals are ubiquitous in nature. Beyond crystalline solids, they can be created as optically induced or technologically fabricated structures in photonic and phononic systems, as potentials for cold atoms and Bose-Einstein condensates (BECs). On a parwith the unusual structural properties of quasicrystals, nowadays the problem of wave propagation in s

Shunyi Liang, Juncheng Liang, Kezhu Song, Yijie Jiang

Ionization chambers are essential for activity determinations in radionuclide metrology. We have developed a high-precision integrating-differentiating (int-diff) system for measuring small currents. It is anticipated to enhance the ionization current measurement capability of the 4{\pi}{\gamma} ionization chamber radioactivity standard at the National Insti