March 2025 arXiv papers — page 131

K. Lakshmanan

We study the problem of finding a subgroup of a given order in a finite group, where the group is represented by its Cayley table. We analyze the complexity of the problem in the special case of abelian groups and present an optimal algorithm for finding a subgroup of a given order when the input is given in the form of a Cayley table. To the best of our kno

Rabimba Karanjai, Sam Blackshear, Lei Xu, Weidong Shi

This paper introduces UniTranslator, a visionary framework that re-imagines code translation as a collaborative endeavor among multiple, compact LLMs. By orchestrating the interaction of specialized agents, each focused on different aspects of the translation process and grounded in a deep understanding of programming concepts, UniTranslator achieves a level

Jesper Amilon, Dilian Gurov

Model checking temporal properties of software is algorithmically hard. To be practically feasible, it usually requires the creation of simpler, abstract models of the software, over which the properties are checked. However, creating suitable abstractions is another difficult problem. We argue that such abstract models can be obtained with little effort, wh

Kunle Adegoke, Robert Frontczak, Karol Gryszka

In this paper, we present a general framework for the derivation of interesting finite combinatorial sums starting with certain classes of polynomial identities. The sums that can be derived involve products of binomial coefficients and also harmonic numbers and squared harmonic numbers. We apply the framework to discuss combinatorial sums associated with so

Luka Lanča, Karlo Jakac, Sylvain Calinon, Stefan Ivić

This research addresses the challenge of performing search missions in dynamic environments, particularly for drifting targets whose movement is dictated by a flow field. This is accomplished through a dynamical system that integrates two partial differential equations: one governing the dynamics and uncertainty of the probability distribution, and the other

Faysal Rahman, Abdul Ahad Mamun, Auronno Ovid Hussain, Muhammad Anisuzzaman Talukder

Developing cost-effective and high-performance electrodes is critical for advancing hydrogen (H$_2$) production through electrochemical water splitting. In this study, we present a novel electrode design by depositing a $\beta$-MnO$_2$ monolayer on a conventional Ni(100) substrate (MnO$_2$(110)/Ni(100)) and systematically investigate its electrocatalytic pro

Pengfei Ma, Li Cai, Xuan Wang, Hao Gao

The immersed boundary (IB) method has become a leading approach in cardiac fluid-structure interaction (FSI) modeling due to its ability to handle large deformations and complex geometries without requiring mesh regeneration. However, the use of nonlinear, fiber-reinforced hyperelastic materials for modeling soft cardiac tissues introduces challenges in comp

Yi Xu, Zhiyuan Lu, Xiaochen Li, Jinxin Hu

The Transformer has proven to be a significant approach in feature interaction for CTR prediction, achieving considerable success in previous works. However, it also presents potential challenges in handling feature interactions. Firstly, Transformers may encounter information loss when capturing feature interactions. By relying on inner products to represen

Ahmed Frikha, Muhammad Reza Ar Razi, Krishna Kanth Nakka, Ricardo Mendes

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing but also pose significant privacy risks by memorizing and leaking Personally Identifiable Information (PII). Existing mitigation strategies, such as differential privacy and neuron-level interventions, often degrade model utility or fail to effectively preve

Tiantian Li, Qunbing Xia, Yue Li, Ruixiao Guo

Learning-based lossless image compression employs pixel-based or subimage-based auto-regression for probability estimation, which achieves desirable performances. However, the existing works only consider context dependencies in one direction, namely, those symbols that appear before the current symbol in raster order. We believe that the dependencies betwee

Chaithra P, Deniz Kus, R. Venkatesh

Infinite-dimensional Lie superalgebras, particularly Borcherds-Kac-Moody (BKM) superalgebras, play a fundamental role in mathematical physics, number theory, and representation theory. In this paper, we study the root multiplicities of BKM superalgebras via their denominator identities, deriving explicit combinatorial formulas in terms of graph invariants as

Ke Wang, Lei He, Kun Liu, Yan Deng

Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with a particular focus on evaluating the capabilities of the Generative Pre-trained Transformer (GPT) model, specifically GPT-4o. Our study investigates its ability to process speech

Zilong Li, Xin Ma, Siqi Wu, H. -Q. Yuan

Employing first-principles based calculations, we reexamined the high-pressure phases of the vacancy-ordered iron-selenides, i.e. A2Fe4Se5 phase. A magnetic transition from the block-spin antiferromagnetic phase to Neel-AM phase is observed under high pressure when the iron-vacancy order is preserved. The transition is first-order, driven by the collapse of

Jian Zhang, Bifan Wei, Shihao Qi, haiping Zhu

The construction of Generalized Knowledge Graph (GKG), including knowledge graph, event knowledge graph and commonsense knowledge graph, is fundamental for various natural language processing tasks. Current studies typically construct these types of graph separately, overlooking holistic insights and potential unification that could be beneficial in computin

Abbaas Alif Mohamed Nishar, Alireza Marefat, Ashwin Ashok

Neuromorphic or event cameras, inspired by biological vision systems, capture changes in illumination with high temporal resolution and efficiency, producing streams of events rather than traditional images. In this paper, we explore the use of neuromorphic cameras for passive optical wireless communication (OWC), leveraging their asynchronous detection of i

Emanuele Bernardi, Tommaso Lorenzi, Mattia Sensi, Andrea Tosin

We develop a general modelling framework for compartmental epidemiological systems structured by continuous variables which are linked to the levels of expression of compartment-specific traits. We start by formulating an individual-based model that describes the dynamics of single individuals in terms of stochastic processes. Then we formally derive: (i) th

Xingtai Lv, Youbang Sun, Kaiyan Zhang, Shang Qu

State Space Models (SSMs) have emerged as a promising alternative to the popular transformer-based models and have been increasingly gaining attention. Compared to transformers, SSMs excel at tasks with sequential data or longer contexts, demonstrating comparable performances with significant efficiency gains. In this survey, we provide a coherent and system

Tianyang Li, Jiamin Liu, Shiqi Zheng, Baoyi Chen

We investigate the production of the $\Omega_{ccc}$ baryon in relativistic heavy-ion collisions. Unlike proton-proton collisions, nuclear collisions produce both deconfined matter and abundant charm quark pairs, which can coalesce to form the $\Omega_{ccc}$ baryon, thereby significantly enhancing its production. We employ the Langevin model and the Instantan

Moritz Hehl, Florentin Münch

In this paper, we establish Betti number estimates for graphs with non-negative Ollivier curvature, and for graphs with non-negative Bakry-\'Emery curvature, providing a discrete analogue of a classical result by Bochner for manifolds. Specifically, we show that for graphs with non-negative Ollivier curvature, the first Betti number is bounded above by half

Du Chen, Tianhe Wu, Kede Ma, Lei Zhang

Full-reference image quality assessment (FR-IQA) generally assumes that reference images are of perfect quality. However, this assumption is flawed due to the sensor and optical limitations of modern imaging systems. Moreover, recent generative enhancement methods are capable of producing images of higher quality than their original. All of these challenge t

S. V. Mousavi

This study investigates the effects of decoherence and squeezing on the dynamics of various kinds of quantum features--local quantum coherence, local entropy, EPR correlations, and entanglement--in the high-temperature limit of the double Caldeira-Leggett model, focusing on initially squeezed states. We compare two scenarios: (1) particles interacting with d

Yansheng Li, Yuning Wu, Gong Cheng, Chao Tao

Accurate fine-grained geospatial scene classification using remote sensing imagery is essential for a wide range of applications. However, existing approaches often rely on manually zooming remote sensing images at different scales to create typical scene samples. This approach fails to adequately support the fixed-resolution image interpretation requirement

Andong Lu, Mai Wen, Jinhu Wang, Yuanzhi Guo

Existing multimodal tracking studies focus on bi-modal scenarios such as RGB-Thermal, RGB-Event, and RGB-Language. Although promising tracking performance is achieved through leveraging complementary cues from different sources, it remains challenging in complex scenes due to the limitations of bi-modal scenarios. In this work, we introduce a general multimo

Romain Mussard, Fannia Pacheco, Maxime Berar, Gilles Gasso

Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, even when their classes are not fully shared. Few dedicated UniDA methods exist for Time Series (TS), which remains a challenging case. In general, UniDA approaches align common class samples and detect unknown target samples from emergi

Faneela, Jawad Ahmad, Baraq Ghaleb, Sana Ullah Jan

The rapid growth of cloud computing and data-driven applications has amplified privacy concerns, driven by the increasing demand to process sensitive data securely. Homomorphic encryption (HE) has become a vital solution for addressing these concerns by enabling computations on encrypted data without revealing its contents. This paper provides a comprehensiv

Suchanun Piriyasatit, Ercan Engin Kuruoglu, Mehmet Sinan Ozeren

Earthquake detection is essential for earthquake early warning (EEW) systems. Traditional methods struggle with low signal-to-noise ratios and single-station reliance, limiting their effectiveness. We propose a Spatio-Temporal Graph Convolutional Network (GCN) using Spectral Structure Learning Convolution (Spectral SLC) to model static and dynamic relationsh

Yumi Arai, Kouichi Takemura

We reformulate the $q$-convolution and the $q$-middle convolution introduced by Sakai and Yamaguchi, and we introduce $q$-analogues of the addition which is related to the gauge-transformation. A merit of the reformulation is the additivity on composition of two $q$-middle convolutions. We obtain sufficient conditions that the Jackson integrals associated wi

Fengchen He, Dayang Zhao, Hao Xu, Tingwei Quan

Many studies utilize dual-pixel (DP) sensor phase characteristics for various applications, such as depth estimation and deblurring. However, since the DP image features are entirely determined by the camera hardware, DP-depth paired datasets are very scarce, especially when performing depth estimation on customized cameras. To overcome this, studies simulat

Trishan Mondal

In this paper, we introduce fundamental notions of homotopy theory, including homotopy excision and the Freudenthal suspension theorem. We then explore framed cobordism and its connection to stable homotopy groups of spheres through the Pontryagin-Thom construction. Using this framework, we compute the stable stems in dimensions $0$, $1$, and $2$. This work

Hideki Miyachi, Ken'Ichi Ohshika, Athanase Papadopoulos

We examine connections between the mathematics behind methods of drawing geographical maps due, on the one hand to Marinos and Ptolemy (1st-2nd c. CE) and on the other hand to Delisle and Euler (18th century). A recent work by the first two authors of this article shows that methods of Delisle and Euler for drawing geographical maps, which are improvements o

Ilias Willems, Jad Beyhum, Ingrid Van Keilegom

We propose a semiparametric model to study the effect of covariates on the distribution of a censored event time while making minimal assumptions about the censoring mechanism. The result is a partially identified model, in the sense that we obtain bounds on the covariate effects, which are allowed to be time-dependent. Moreover, these bounds can be interpre

Zekai Zhang, Dan Li, Shunyu Wu, Junya Cai

Prognostic and Health Management (PHM) are crucial ways to avoid unnecessary maintenance for Cyber-Physical Systems (CPS) and improve system reliability. Predicting the Remaining Useful Life (RUL) is one of the most challenging tasks for PHM. Existing methods require prior knowledge about the system, contrived assumptions, or temporal mining to model the lif

Maximilian Graf, Victor Thuot, Nicolas Verzelen

We study the problem of clustering a set of items based on bandit feedback. Each of the $n$ items is characterized by a feature vector, with a possibly large dimension $d$. The items are partitioned into two unknown groups such that items within the same group share the same feature vector. We consider a sequential and adaptive setting in which, at each roun

D. Novel, A. Lega, T. Facchinelli, R. Iuppa

This work presents a novel solution for the packaging of ALPIDE chips that facilitates non-planar assembly with a minimal material budget. This solution represents a technological advancement based on methodologies developed for the ALICE ITS1 and the STAR tracker two decades ago. The core of this approach involves the use of flexible cables composed of alum

Giacomo Camposampiero, Michael Hersche, Roger Wattenhofer, Abu Sebastian

This work presents a first evaluation of two state-of-the-art Large Reasoning Models (LRMs), OpenAI's o3-mini and DeepSeek R1, on analogical reasoning, focusing on well-established nonverbal human IQ tests based on Raven's progressive matrices. We benchmark with the I-RAVEN dataset and its extension, I-RAVEN-X, which tests the ability to generalize to longer

Andres Larroza, Javier Naranjo-Alcazar, Vicent Ortiz, Maximo Cobos

Spiking Neural Networks (SNNs) offer energy efficient processing suitable for edge applications, but conventional sensor data must first be converted into spike trains for neuromorphic processing. Environmental sound, including urban soundscapes, poses challenges due to variable frequencies, background noise, and overlapping acoustic events, while most spike

Leqi Shen, Tao He, Guoqiang Gong, Fan Yang

Training-free video large language models (LLMs) leverage pretrained Image LLMs to process video content without the need for further training. A key challenge in such approaches is the difficulty of retaining essential visual and temporal information, constrained by the token limits in Image LLMs. To address this, we propose a two-stage method for selecting

Colin Scarato, Kilian Hanke, Ants Remm, Stefania Lazăr

Continuous gate sets are a key ingredient for near-term quantum algorithms. Here, we demonstrate a hardware-efficient, continuous set of controlled arbitrary-phase ($\mathrm{C}Z_{\theta}$) gates acting on flux-tunable transmon qubits. This implementation is robust to control pulse distortions on time scales longer than the duration of the gate, making it sui

Yannik Kunz, Julian Schüler, Finlay Ryburn, Kevin Künstle

We demonstrate the efficient excitation of spin waves in the ultra-low magnetic damping material yttrium-iron-garnet (YIG) by surface acoustic waves (SAWs). To this end, we employ interdigital transducers fabricated on a piezoelectric zinc oxide (ZnO) thin film covering the YIG. This enables the excitation of coherent, propagating Rayleigh-type and Sezawa-ty

Srinivas Ravishankar, Nora Zajzon, Virginia de Sa

Patients with extreme forms of paralysis face challenges in communication, adversely impacting their quality of life. Recent studies have reported higher-than-chance performance in decoding handwritten letters from EEG signals, potentially allowing these subjects to communicate. However, all prior works have attempted to decode handwriting from EEG during ac

Valentin Moos, Ignazio Scimemi, Alexey Vladimirov, Pia Zurita

We present a fit of the transverse momentum spectrum for Drell-Yan and semi-inclusive deep inelastic scattering data, based on transverse momentum dependent (TMD) factorization at N$^4$LL accuracy. Our analysis shows good agreement with the data and confirms the findings of previous studies. Based on this, we extract the unpolarized TMD parton distribution f

Boris Andreianov, Simone Fagioli, Massimiliano D. Rosini, Graziano Stivaletta

We investigate stability issues for the one-dimensional variant of the celebrated Hughes model for pedestrian evacuation. The cost function is assumed to be affine, which is a setting where existence of solutions with BV loc in space regularity, away from the so-called turning curve, was recently established. We provide a uniqueness result for solutions havi

Li Cui, Yang Ding, Richard Hartley, Zirui Xie

We propose a novel, vision-only object-level SLAM framework for automotive applications representing 3D shapes by implicit signed distance functions. Our key innovation consists of augmenting the standard neural representation by a normalizing flow network. As a result, achieving strong representation power on the specific class of road vehicles is made poss

Yang A. Li, Luis C. Ho, Jinyi Shangguan, Zhao-Yu Li

Quiescent galaxies (QGs) typically have little cold gas to form stars. The discovery of gas-rich QGs challenges our conventional understanding of the evolutionary paths of galaxies. We take advantage of a new catalog of nearby, massive galaxies with robust, uniformly derived physical properties to better understand the origin of gas-rich QGs. We perform a co

Gang Li, Jizhong Liu, Heinrich Dinkel, Yadong Niu

Recently, reinforcement learning (RL) has been shown to greatly enhance the reasoning capabilities of large language models (LLMs), and RL-based approaches have been progressively applied to visual multimodal tasks. However, the audio modality has largely been overlooked in these developments. Thus, we conduct a series of RL explorations in audio understandi

Anas Jnini, Harshinee Goordoyal, Sujal Dave, Flavio Vella

The Physics-Constrained DeepONet (PC-DeepONet), an architecture that incorporates fundamental physics knowledge into the data-driven DeepONet model, is presented in this study. This methodology is exemplified through surrogate modeling of fluid dynamics over a curved backward-facing step, a benchmark problem in computational fluid dynamics. The model was tra

Shree Singhi, Aayan Yadav, Aayush Gupta, Shariar Ebrahimi

As AI-generated sensitive images become more prevalent, identifying their source is crucial for distinguishing them from real images. Conventional image watermarking methods are vulnerable to common transformations like filters, lossy compression, and screenshots, often applied during social media sharing. Watermarks can also be faked or removed if models ar

Qiuxia Lin, Kerui Gu, Linlin Yang, Angela Yao

Online test-time adaptation for 3D human pose estimation is used for video streams that differ from training data. Ground truth 2D poses are used for adaptation, but only estimated 2D poses are available in practice. This paper addresses adapting models to streaming videos with estimated 2D poses. Comparing adaptations reveals the challenge of limiting estim

Lan-Anh T. Nguyen, Mallesh Baithi, Tuan Dung Nguyen, Krishna P. Dhakal

Efficient magnetic switching is a cornerstone for advancing spintronics, particularly for energy-efficient data storage and memory devices. Here, we report the electrical switching of spin-flips in V-doped WSe2 multilayers, a van der Waals (vdW)-layered diluted magnetic semiconductor (DMS), demonstrating ultralow-power switching operation at room temperature

Yiming Lu, Xu Zhu, Long Zhang, Hua Zhou

Gas sampling methods have been crucial for the advancement of combustion science, enabling analysis of reaction kinetics and pollutant formation. However, the measured composition can deviate from the true one because of the potential residual reactions in the sampling probes. This study formulates the initial composition estimation in stiff chemically react

Suchanuch Piriyasatit, Chaohao Yuan, Ercan Engin Kuruoglu

Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classification and community detection. Several embedding methods have been proposed to capture structural proximity among nodes in a network, where densely connected communities are pre

Lorenzo Gervani, Daniele Bertacca, Nicola Bartolo

In this work we provide a detailed derivation of the observed galaxy number over-density obtained by computing cosmological perturbations up to third order in redshift space and on very large scales. We compute all the relativistic and projection effects, arising from the observation of galaxies on the past light cone, including all redshift effects, i.e. pe

Faouzi Hakimi, Tarek Khaled, Mohammed Al-Kharaz, Arthur Cartel Foahom Gouabou

This paper introduces a novel strategy aimed at enhancing productivity and minimizing non-productive movements within container terminals, specifically focusing on container yards. It advocates for the implementation of a digital twin-based methodology to streamline the operations of stacking cranes (SCs) responsible for container handling. The proposed appr

Zhuoyuan Mao, Mengjie Zhao, Qiyu Wu, Zhi Zhong

Music-to-music-video generation is a challenging task due to the intrinsic differences between the music and video modalities. The advent of powerful text-to-video diffusion models has opened a promising pathway for music-video (MV) generation by first addressing the music-to-MV description task and subsequently leveraging these models for video generation.

Ju Hyeon Lee, Bongjae Kim

Density functional theory (DFT) has been widely applied to a variety of realistic materials but often struggles to explain the properties of correlated systems. The DFT + U method, which introduces a Hubbard U correction to the DFT, has been instrumental in providing the treatment of systems such as transition metal oxide. The methodological details of DFT +

Leqi Shen, Guoqiang Gong, Tao He, Yifeng Zhang

Video Large Language Models have demonstrated strong video understanding capabilities, yet their practical deployment is hindered by substantial inference costs caused by redundant video tokens. Existing pruning techniques fail to effectively exploit the spatiotemporal redundancy present in video data. To bridge this gap, we perform a systematic analysis of

Maxence Grand, Damien Pellier, Francis Jambon

The primary objective of the dataset is to provide a better understanding of the coupling between human actions and gaze in a shared working environment with a cobot, with the aim of signifcantly enhancing the effciency and safety of humancobot interactions. More broadly, by linking gaze patterns with physical actions, the dataset offers valuable insights in

Yingjie Zhang, Tong Liu, Zhe Zhao, Guozhu Meng

LLMs remain vulnerable to jailbreak attacks that exploit adversarial prompts to circumvent safety measures. Current safety fine-tuning approaches face two critical limitations. First, they often fail to strike a balance between security and utility, where stronger safety measures tend to over-reject harmless user requests. Second, they frequently miss malici

Yuki Uchida

We introduce $n$-fold torsion(-free) classes of an abelian category. These are a generalization of ordinary torsion(-free) classes in the sense that $1$-fold torsion(-free) classes coincide with torsion(-free) classes. In the category of finitely generated modules over a finite dimensional algebra, we can naturally construct $n$-fold torsion classes from $\t

Leideng Shi, Juan Zhang

Referring remote sensing image segmentation (RRSIS) is a novel visual task in remote sensing images segmentation, which aims to segment objects based on a given text description, with great significance in practical application. Previous studies fuse visual and linguistic modalities by explicit feature interaction, which fail to effectively excavate useful m

Zhe Yang, Yi Huang, Yaqin Chen, Xiaoting Wu

Recent advancements in large language models have revolutionized text generation with their remarkable capabilities. These models can produce controlled texts that closely adhere to specific requirements when prompted appropriately. However, designing an optimal prompt to control multiple attributes simultaneously can be challenging. A common approach is to

Arthur Cartel Foahom Gouabou, Mohammed Al-Kharaz, Faouzi Hakimi, Tarek Khaled

Container terminals, pivotal nodes in the network of empty container movement, hold significant potential for enhancing operational efficiency within terminal depots through effective collaboration between transporters and terminal operators. This collaboration is crucial for achieving optimization, leading to streamlined operations and reduced congestion, t

Luca Martini, Daniele Zolezzi, Saverio Iacono, Gianni Viardo Vercelli

The reconstruction of low-resolution football broadcast images presents a significant challenge in sports broadcasting, where detailed visuals are essential for analysis and audience engagement. This study introduces a multi-stage generative upscaling framework leveraging Diffusion Models to enhance degraded images, transforming inputs as small as $64 \times

Yunxuan Dong, Siyuan Chen, Jisen Zhang

Genotype-to-Phenotype (G2P) prediction plays a pivotal role in crop breeding, enabling the identification of superior genotypes based on genomic data. Rice (Oryza sativa), one of the most important staple crops, faces challenges in improving yield and resilience due to the complex genetic architecture of agronomic traits and the limited sample size in breedi

Lina Jaurigue, Kathy Lüdge

Physical systems exhibiting hysteresis are increasingly being used in neuromorphic and in-memory computing research. Generally, the resistance switching of devices with rate-independent hysteresis are being investigated for their use as trainable weights in neural networks, whereas the dynamics of devices showing rate-dependent hysteresis are being investiga

Hiroshi Yamashita, Takayuki Yamaguchi, Nobuhiko J. Suematsu, Shunsuke Izumi

Microorganisms are known to alter their motility in response to external stimuli. A typical example is the responseknown as taxis, which includes behaviors such as moving toward a light source (positive phototaxis) or away from it(negative phototaxis). In this study, we focused on bioconvection induced by the negative phototaxis of a Euglenasuspension expose

Thieu Long Phan, Akansel Cosgun

This study investigates the subjective experiences of users in two robotic object delivery methods: direct handover and table placement, when users are occupied with another task. A user study involving 15 participants engaged in a typing game revealed that table placement significantly enhances user experience compared to direct handovers, particularly in t

Binlong Li, Ziqing Sang, Shipeng Wang

Let $\mathcal{F}$ be a set of connected graphs, and let $G$ be a graph. We say that $G$ is \emph{$\mathcal{F}$-free} if it does not contain $F$ as an induced subgraph for all $F\in\mathcal{F}$, and we call $\mathcal{F}$ a forbidden pair if $|\mathcal{F}|=2$. A \emph{$\varTheta$-graph} is the graph consisting of three internally disjoint paths with the same p

Yini Li, Nantheera Anantrasirichai

Low-light and underwater videos suffer from poor visibility, low contrast, and high noise, necessitating enhancements in visual quality. However, existing approaches typically rely on paired ground truth, which limits their practicality and often fails to maintain temporal consistency. To overcome these obstacles, this paper introduces a novel zero-shot lear

Guillem Domènech, Alexander Ganz

We find a connection between relativistic Modified Newtonian Dynamics (MOND) theories and (scalar) mimetic gravity. We first demonstrate that any relativistic MOND model featuring a unit-timelike vector field, such as TeVeS or Aether-scalar-tensor theory, can be embedded within a conformal/disformal-invariant framework. Gauge fixing the conformal/disformal s

Se-Heon Oh, Jing Wang

We propose a new method for extracting bulk motion gases in the disk of a galaxy from HI data cubes, offering improvements over classical techniques like moment analysis and line profile fitting. Our approach decomposes the line-of-sight velocity profiles into multiple Gaussian components, which are then classified into (underlying and dominant) bulk and non

Zhen Tan, Xieyuanli Chen, Jinpu Zhang, Lei Feng

3D Gaussian Splatting (3DGS) has achieved impressive rendering performance in novel view synthesis. However, its efficacy diminishes considerably in sparse image sequences, where inherent data sparsity amplifies geometric uncertainty during optimization. This often leads to convergence at suboptimal local minima, resulting in noticeable structural artifacts

Pingyuan Wei, Qiao Huang, Jinqiao Duan

Jacobi structures are known to generalize Poisson structures, encompassing symplectic, cosymplectic, and Lie-Poisson manifolds. Notably, other intriguing geometric structures -- such as contact and locally conformal symplectic manifolds -- also admit Jacobi structures but do not belong to the Poisson category. In this paper, we employ global stochastic analy

Yibin Xu, Liang Yang, Hao Chen, Hua Wang

The limitation of graphical user interface (GUI) data has been a significant barrier to the development of GUI agents today, especially for the desktop / computer use scenarios. To address this, we propose an automated GUI data generation pipeline, AutoCaptioner, which generates data with rich descriptions while minimizing human effort. Using AutoCaptioner,

Vida Gholamiyan, Yaning Zhao, Wafa Labidi, Holger Boche

Molecular communication (MC) is an emerging paradigm that enables data transmission through biochemical signals rather than traditional electromagnetic waves. This approach is particularly promising for environments where conventional wireless communication is impractical, such as within the human body. However, security and privacy pose significant challeng

Wumwi Sun, Hongwei Liu, Xiaoyu Wang

The graph partitioning problem is a well-known NP-hard problem. In this paper, we formulate a 0-1 quadratic integer programming model for the graph partitioning problem with vertex weight constraints and fixed vertex constraints, and propose a recursive bipartition algorithm based on the subspace minimization conjugate gradient method. To alleviate the diffi

Haonan Wang, Qixiang Zhang, Lehan Wang, Xuanqi Huang

Decoding visual stimuli from neural activity is essential for understanding the human brain. While fMRI methods have successfully reconstructed static images, fMRI-to-video reconstruction faces challenges due to the need for capturing spatiotemporal dynamics like motion and scene transitions. Recent approaches have improved semantic and perceptual alignment

Matteo Ferrari, Ilaria Perugia

We present a novel space-time isogeometric discretization of the acoustic wave equation in second-order formulation that is intrinsically unconditionally stable. The method relies on a variational framework inspired by [Walkington 2014], with an exponential weight introduced in the time integrals. Conformity requires at least $C^1$ regularity in time and $C^

Xiaodan Chen, Junwei Zi

Brouwer conjectured that the sum of the first $k$ largest Laplacian eigenvalues of an $n$-vertex graph is less than or equal to the number of its edges plus $\binom{k+1}{2}$ for each $k\in \{1,2,\cdots,n\}$, which has come to be known as Brouwer's conjecture. Recently, Li and Guo further considered the case when the equalities hold in these conjectured inequ

Chi Xu, Gefei Zhang, Yantong Zhu, Luca Benini

N:M structured pruning is essential for large language models (LLMs) because it can remove less important network weights and reduce the memory and computation requirements. Existing pruning methods mainly focus on designing metrics to measure the importance of network components to guide pruning. Apart from the impact of these metrics, we observe that diffe

Bharath K Rameshbabu, Sumukh S Balakrishna, Brian Flynn, Vinarak Kapoor

We present a benchmarking study of vision-based robotic grasping algorithms with distinct approaches, and provide a comparative analysis. In particular, we compare two machine-learning-based and two analytical algorithms using an existing benchmarking protocol from the literature and determine the algorithm's strengths and weaknesses under different experime

Mohamed Shabeer Mohamed Rafi, Mehran Behjati, Ahmad Sahban Rafsanjani

The integration of the Internet of Things (IoT) in smart agriculture has transformed farming practices by enabling real time monitoring, data-driven decision making, and automation. However, ensuring reliable connectivity in diverse agricultural environments remains a critical challenge. This paper analyzes the performance trade offs between Low Power Wide A

Mikhail Anikushin

We develop a functional-analytical machinery for studying the quadratic regulator problem arising from spectra perturbations of infinite-dimensional dynamical systems. In particular, we are interested in applications to inertial manifolds theory. For certain nonautonomous Hamiltonian systems associated with such problems, we show the existence and uniform no

Guanhua Zheng, Jitao Sang, Changsheng Xu

Attributions aim to identify input pixels that are relevant to the decision-making process. A popular approach involves using modified backpropagation (BP) rules to reverse decisions, which improves interpretability compared to the original gradients. However, these methods lack a solid theoretical foundation and exhibit perplexing behaviors, such as reduced

Junbiao Pang, Tianyang Cai

Quantization-Aware Training (QAT) is one of the prevailing neural network compression solutions. However, its stability has been challenged for yielding deteriorating performances as the quantization error is inevitable. We find that the sharp landscape of loss, which leads to a dramatic performance drop, is an essential factor that causes instability. Theor

Ulrich D. Jentschura

We discuss relativistic and radiative corrections to the energies of quantum cyclotron states. In particular, it is shown analytically that the leading logarithmic radiative (self-energy) correction to the bound-state energy levels of quantum cyclotron states is state-independent, and must be interpreted as a magnetic-field-dependent correction to the electr

Robert J. Berman

We introduce new probabilistic and variational constructions of (twisted) K\"ahler-Einstein metrics on complex projective algebraic varieties, drawing inspiration from Onsager's statistical mechanical model of turbulence in two-dimensional incompressible fluids. The probabilistic construction involves microcanonical measures associated with the level sets of

Yassine El Gantouh, Yang Liu

In this paper, we introduce the notion of boundary delay equations, establishing a unified framework for analyzing linear time-invariant systems with pure time-delayed boundary conditions. We establish mild sufficient conditions for the existence, uniqueness, and positivity of solutions. Furthermore, we derive spectral criteria for exponential stability. The

Jianping Xiong, Qiyuan Cheng, Xiaodian Chen, Jiao Li

Aims. Asteroseismic radius and Gaia distance (ARD) method has been proposed to establish the SBCRs for late-type stars. Methods. We select Kepler RGB stars with high-precision asteroseismic radii (uncertainties < 1%) and cross-match them with 2MASS, APASS, and Gaia to obtain Johnson-B, Johnson-V, G, J, H, and Ks-band photometric data. After applying selectio

Shaotian Yan, Chen Shen, Wenxiao Wang, Liang Xie

Few-shot Chain-of-Thought (CoT) significantly enhances the reasoning capabilities of large language models (LLMs), functioning as a whole to guide these models in generating reasoning steps toward final answers. However, we observe that isolated segments, words, or tokens within CoT demonstrations can unexpectedly disrupt the generation process of LLMs. The

Cédric Ho Thanh

Quantum simulation is a popular application of quantum computing, but its practical realization is hindered by the technical limitations of current devices. In this work, we focus on preprocessing Hamiltonians before Trotterization to generate shallower evolution circuits, which are less prone to noise and decoherence. Specifically, we apply graph coloring t

Taiki Yukitake, Daisuke Hara, Shuichi Murakami

Recently, some $Z_2$ monopole charges were defined for Dirac semimetals with $\mathcal{GT}$ symmetry ($\mathcal{G}$: glide, $\mathcal{T}$: time-reversal) in previous works, and the charges are believed to lead to double-helicoid surface states. However, no proof of the bulk-surface correspondence is given there. In this paper, we point out one of the $Z_2$ c

Jihyun Lim, Junhyuk Jo, Tuo Zhang, Sunwoo Lee

Online Knowledge Distillation (KD) is recently highlighted to train large models in Federated Learning (FL) environments. Many existing studies adopt the logit ensemble method to perform KD on the server side. However, they often assume that unlabeled data collected at the edge is centralized on the server. Moreover, the logit ensemble method personalizes lo

Mikhail Anikushin, Andrey Romanov

We present an analytical-numerical method providing robust upper estimates for the topological entropy or, more generally, uniform volume growth exponents of differentiable mappings. By introducing varying metrics, we simplify the analysis at the cost of generally rougher bounds, but keeping the prospect of choosing more relatable metrics to refine the estim

Michael Brannan, Daniel Gromada, Junichiro Matsuda, Adam Skalski

We establish a quantum version of Frucht's Theorem, proving that every finite quantum group is the quantum automorphism group of an undirected finite quantum graph. The construction is based on first considering several quantum Cayley graphs of the quantum group in question, and then providing a method to systematically combine them into a single quantum gra

Shuhui Yang, Zunwei Fu, Dachun Yang, Yan Lin

Combining the linear canonical transform and the Riesz transform, we introduce the linear canonical Riesz transform (for short, LCRT), which is further proved to be a linear canonical multiplier. Using this LCRT multiplier, we conduct numerical simulations on images. Notably, the LCRT multiplier significantly reduces the complexity of the algorithm. Based on

Junhyuk Jo, Jihyun Lim, Sunwoo Lee

Sharpness-Aware Minimization (SAM) is an optimization method that improves generalization performance of machine learning models. Despite its superior generalization, SAM has not been actively used in real-world applications due to its expensive computational cost. In this work, we propose a novel asynchronous-parallel SAM which achieves nearly the same grad

Jisoo Kim, Sungmin Kang, Sunwoo Lee

Expensive communication cost is a common performance bottleneck in Federated Learning (FL), which makes it less appealing in real-world applications. Many communication-efficient FL methods focus on discarding a part of model updates mostly based on gradient magnitude. In this study, we find that recycling previous updates, rather than simply dropping them,

Neng Wang, Huimin Lu, Zhiqiang Zheng, Hesheng Wang

Accurate and robust simultaneous localization and mapping (SLAM) is crucial for autonomous mobile systems, typically achieved by leveraging the geometric features of the environment. Incorporating semantics provides a richer scene representation that not only enhances localization accuracy in SLAM but also enables advanced cognitive functionalities for downs

Rachel S. Y. Teo, Tan M. Nguyen

Large-scale pre-training of deep models, followed by fine-tuning them, has become the cornerstone of natural language processing (NLP). The prevalence of data coupled with computational resources has led to large models with a considerable number of parameters. While the massive size of these models has led to remarkable success in many NLP tasks, a detrimen