March 2025 arXiv papers — page 63

Raquel Vidaurre, Elena Garces, Dan Casas

We present a data-driven method for learning to generate animations of 3D garments using a 2D image diffusion model. In contrast to existing methods, typically based on fully connected networks, graph neural networks, or generative adversarial networks, which have difficulties to cope with parametric garments with fine wrinkle detail, our approach is able to

Yifeng Chen, Peixu Guo, Chihyung Wen

Boundary-layer instability and transition control have drawn extensive attention from the hypersonic community. The acoustic metasurface has become a promising passive control method. Currently, the effects of the acoustic metasurface on the early and late transitional stages remain evidently less understood than the linear instability stage. In this study,

Xu Han, Yuan Tang, Jinfeng Xu, Xianzhi Li

We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing adapter-based and prompt-tuning 3D PEFT methods, MoST introduces no additional inference overhead and is compatible with many 3D representation learning backbones. At its core, we

Yuxi Wang, Jian Gao, Yi Ren, Bingqiu Chen

An extinction distribution of the Andromeda Galaxy (M31) is constructed with member stars as tracers by fitting multiband photometric data from UKIRT/WFCAM, PS1, and Gaia DR3. The resulting extinction distribution covers approximately 10 deg$^2$ of M31 with a resolution of approximately 50 arcsec, providing the largest coverage to date based on stellar obser

Lu Wangtao, Wei Yufei, Xu Jiadong, Jia Wenhao

Automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a mo

Animesh Hazra, Tanmoy Chakraborty, Anirban Mukherjee, Punyabrata Pradhan

We show that, on a $d-$dimensional hypercubic lattice with $d>1$, conserved-mass transport processes, with {\it multidirectional} hopping that respect all symmetries of the lattice, exhibit power-law correlations for generic parameter values $-$ even {\it far} from phase transition point, if any. The key idea for generating the algebraic decay is the notion

Chenxi Xie, Minghan Li, Hui Zeng, Jun Luo

High-resolution semantic segmentation is essential for applications such as image editing, bokeh imaging, AR/VR, etc. Unfortunately, existing datasets often have limited resolution and lack precise mask details and boundaries. In this work, we build a large-scale, matting-level semantic segmentation dataset, named MaSS13K, which consists of 13,348 real-world

Wenyuan Zhang, Yixiao Yang, Han Huang, Liang Han

Monocular depth priors have been widely adopted by neural rendering in multi-view based tasks such as 3D reconstruction and novel view synthesis. However, due to the inconsistent prediction on each view, how to more effectively leverage monocular cues in a multi-view context remains a challenge. Current methods treat the entire estimated depth map indiscrimi

Minakshi Subhadarshini, Amartya Pal, Pritam Chatterjee, Arijit Saha

We propose a theoretical model describing a Josephson junction featuring a magnetically textured barrier within two-dimensional (2D) $p$-wave superconductor, considering both $p_x + p_y$ and $p_x + ip_y$ type pairing symmetries. Our study reveals the influence of the magnetic barrier strength and its spatial periodicity on the system's topological properties

Wenyuan Zhang, Emily Yue-ting Jia, Junsheng Zhou, Baorui Ma

Recently, it has shown that priors are vital for neural implicit functions to reconstruct high-quality surfaces from multi-view RGB images. However, current priors require large-scale pre-training, and merely provide geometric clues without considering the importance of color. In this paper, we present NeRFPrior, which adopts a neural radiance field as a pri

Yiran Hu, Huanghai Liu, Qingjing Chen, Ning Zheng

As the scale and capabilities of Large Language Models (LLMs) increase, their applications in knowledge-intensive fields such as legal domain have garnered widespread attention. However, it remains doubtful whether these LLMs make judgments based on domain knowledge for reasoning. If LLMs base their judgments solely on specific words or patterns, rather than

Zhanzhong Pang, Fadime Sener, Angela Yao

Online Action Detection (OAD) detects actions in streaming videos using past observations. State-of-the-art OAD approaches model past observations and their interactions with an anticipated future. The past is encoded using short- and long-term memories to capture immediate and long-range dependencies, while anticipation compensates for missing future contex

Zhanzhong Pang, Fadime Sener, Shrinivas Ramasubramanian, Angela Yao

Temporal action segmentation in untrimmed procedural videos aims to densely label frames into action classes. These videos inherently exhibit long-tailed distributions, where actions vary widely in frequency and duration. In temporal action segmentation approaches, we identified a bi-level learning bias. This bias encompasses (1) a class-level bias, stemming

Ryugo Mizuhiki, Junpei Maeda, Seiya Marumoto

Validating FPGA firmware logic used in particle physics is becoming increasingly difficult as the implementation logic scales and becomes more complex with the expansion of FPGA resources. In order to address this issue efficiently, we have developed a firmware validation system utilizing an FPGA embedded on the PCI-express board, referred to as FPGA acceler

Pradyumna Harlapur, Harshavardhan B, Mohit Kumar Jolly

The emergent dynamics of complex gene regulatory networks govern various cellular processes. However, understanding these dynamics is challenging due to the difficulty of parameterizing the computational models for these networks, especially as the network size increases. Here, we introduce a simulation library, Gene Regulatory Interaction Network Simulator

Yuto Sakai, Qiang Ma

Food is a key pleasure of traveling, but travelers face a trade-off between exploring curious new local food and choosing comfortable, familiar options. This creates demand for personalized recommendation systems that balance these competing factors. To the best of our knowledge, conventional recommendation methods cannot provide recommendations that offer b

Marcello Lanfranchi

Tangent category theory is a well-established categorical context for differential geometry. In a previous paper, a formal approach was adopted to provide a genuine Grothendieck construction in the context of tangent categories by introducing tangentads. A tangentad is to a tangent category as a formal monad is to a monad of a category. In this paper, we dis

Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao

Global Navigation Satellite Systems (GNSS) employ inter-satellite links (ISLs) to reduce dependency on ground stations, enabling precise ranging and communication across satellites. Beyond their traditional role, ISLs can support extended applications, including providing navigation and communication services to external entities. However, designing effectiv

Jinjin Zhang, Qiuyu Huang, Junjie Liu, Xiefan Guo

In this paper, we present Diffusion-4K, a novel framework for direct ultra-high-resolution image synthesis using text-to-image diffusion models. The core advancements include: (1) Aesthetic-4K Benchmark: addressing the absence of a publicly available 4K image synthesis dataset, we construct Aesthetic-4K, a comprehensive benchmark for ultra-high-resolution im

C. Lee, X. Zhang, A. Kavner, T. Parsons-Davis

The MAGNETO-$\nu$ experiment searches for keV-scale heavy neutral leptons (HNLs) through precise measurements of the $\beta^-$-decay spectrum of $^{241}$Pu. We present spectra comprising a total of 194 million $\beta^-$ decays recorded using decay energy spectrometry with metallic magnetic calorimeters, representing the most statistically precise measurement

Zekai Deng, Ye Shi, Kaiyang Ji, Lan Xu

Human-object interaction (HOI) synthesis is crucial for applications in animation, simulation, and robotics. However, existing approaches either rely on expensive motion capture data or require manual reward engineering, limiting their scalability and generalizability. In this work, we introduce the first unified physics-based HOI framework that leverages Vi

Ji-Hong Li

This paper presents an active model-based FTC (fault-tolerant control) method for the dynamic positioning of a class of underwater vehicles with thruster redundancy. Compared to the widely used state and parameter estimation methods, this proposed scheme directly utilizes the vehicle's motion control error (MCE) to construct a residual for detecting thruster

Wen Zheng Terence Ng, Jianda Chen, Yuan Xu, Tianwei Zhang

This work addresses the challenge of personalizing trajectories generated in automated decision-making systems by introducing a resource-efficient approach that enables rapid adaptation to individual users' preferences. Our method leverages a pretrained conditional diffusion model with Preference Latent Embeddings (PLE), trained on a large, reward-free offli

Mingwei Yang, Heng Wang, Jiayin Tang, Junping Luo

Rare-earth infinite-layer nickelates represent an emerging class of unconventional superconductors, with materials synthesis largely limited to early lanthanide compounds. Here, we report the synthesis and characterization of phase-pure superconducting samarium-based infinite-layer nickelate thin films, including the first demonstration of Sm$_{1-x}$Sr$_x$Ni

Zhongtang Luo, Adithya Bhat, Kartik Nayak, Aniket Kate

The Tor network enhances clients' privacy by routing traffic through an overlay network of volunteered intermediate relays. Tor employs a distributed protocol among nine hard-coded Directory Authority (DA) servers to securely disseminate information about these relays to produce a new consensus document every hour. With a straightforward voting mechanism to

Ikkei Sato

A horoboundary is one of the attempts to compactify metric spaces, and is constructed using continuous functions on metric spaces. It is a concept that includes global information of metric spaces, and its correspondence with an ideal boundary constructed using geodesics has been studied in nonpositive curvature spaces such as CAT(0) spaces and geodesic Grom

Kensuke Hori, Fumio Hashimoto, Kazuya Koyama, Takeyuki Hashimoto

In SPECT image reconstruction, limited-angle (LA) conditions lead to a loss of frequency components, which distort the reconstructed tomographic image along directions corresponding to the non-collected projection angle range. Although conventional iterative image reconstruction methods have been used to improve the reconstructed images in LA conditions, the

Kazuma Kitazawa, Takahito Aoto, Satoshi Ikehata, Tsuyoshi Takatani

Recently, the energy-efficient photometric stereo method using an event camera has been proposed to recover surface normals from events triggered by changes in logarithmic Lambertian reflections under a moving directional light source. However, EventPS treats each event interval independently, making it sensitive to noise, shadows, and non-Lambertian reflect

Huan Yan, Juan A. Fraire, Ziqi Yang, Kanglian Zhao

Cislunar space is emerging as a critical domain for human exploration, requiring robust infrastructure to support spatial users-spacecraft with navigation and communication demands. Deploying satellites at Earth-Moon three-body orbits offers an effective solution to construct cislunar space infrastructure (CLSI). However, scheduling satellite links to serve

Inpyo Hong, Youngwan Jo, Hyojeong Lee, Sunghyun Ahn

Zero-shot quantization (ZSQ) enables neural network compression without original training data, making it a promising solution for restricted data access scenarios. To compensate for the lack of data, recent ZSQ methods typically rely on synthetic inputs generated from the full-precision model. However, these synthetic inputs often lead to activation distort

Wenrui Cai, Qingjie Liu, Yunhong Wang

Most state-of-the-art trackers adopt one-stream paradigm, using a single Vision Transformer for joint feature extraction and relation modeling of template and search region images. However, relation modeling between different image patches exhibits significant variations. For instance, background regions dominated by target-irrelevant information require red

Zichen Miao, Wei Chen, Qiang Qiu

Transformer-based large pre-trained models have shown remarkable generalization ability, and various parameter-efficient fine-tuning (PEFT) methods have been proposed to customize these models on downstream tasks with minimal computational and memory budgets. Previous PEFT methods are primarily designed from a tensor-decomposition perspective that tries to e

Changlun Li, Yao Shi, Yuyu Luo, Nan Tang

Academic publishing is facing a crisis driven by exponential growth in submissions and an overwhelmed peer review system, leading to inconsistent decisions and a severe reviewer shortage. This paper introduces Panvas, a platform that reimagines academic publishing as a continuous, community-driven process. Panvas addresses these systemic failures with a nove

Yanna Ding, Malik Magdon-Ismail, Jianxi Gao

In networked dynamical systems, inferring governing parameters is crucial for predicting nodal dynamics, such as gene expression levels, species abundance, or population density. While many parameter estimation techniques rely on time-series data, particularly systems that converge over extreme time ranges, only noisy steady-state data is available, requirin

Haotian Zhai, Xinyu Chen, Can Zhang, Tianming Sha

Test-time adaptation (TTA) of visual language models has recently attracted significant attention as a solution to the performance degradation caused by distribution shifts in downstream tasks. However, existing cache-based TTA methods have certain limitations. They mainly rely on the accuracy of cached feature labels, and the presence of noisy pseudo-labels

Guo-Peng Li, Xi-Long Fan

We study the impact of environmental effects on the measurement of the Hubble constant ($H_0$) from gravitational wave (GW) observations of binary black hole mergers residing in active galactic nuclei (AGNs) near the central supermassive black hole. Using the potential hierarchical triple merger candidate GW190514-GW190521 in AGN J124942.3+344929 with its el

Willem Sijp

This report applies Principal Component Analysis (PCA) to regional house price indexes to uncover dominant trends in Australia's housing market. Regions are assigned PCA-derived scores that reveal which underlying market forces are most influential in each area, enabling broad classification of local housing markets. The approach highlights where price movem

Yen-Shao Chen, Tauhid Zaman

Influence campaigns in online social networks are often run by organizations, political parties, and nation states to influence large audiences. These campaigns are employed through the use of agents in the network that share persuasive content. Yet, their impact might be minimal if the audiences remain unswayed, often due to the bounded confidence phenomeno

Bushra Ashraf, Nils Brinkmann, Dave Austin, Duy Le

This study aims to elucidate the adsorption and surface chemistry of N-methylaniline (NMA) on Pt(111), using it as a model molecule to probe the activation mechanisms of aromatic amines on catalytic surfaces. Through a combination of density functional theory (DFT) calculations and experimental techniques such as temperature programmed X-ray photoelectron sp

Ji Liu, Allen Zang, Martin Suchara, Tian Zhong

Distributed quantum computing (DQC) offers a pathway for scaling up quantum computing architectures beyond the confines of a single chip. Entanglement is a crucial resource for implementing non-local operations in DQC, and it is required to allow teleportation of quantum states and gates. Remote entanglement generation in practical systems is probabilistic,

Chun Gu, Xiaofei Wei, Li Zhang, Xiatian Zhu

Inverse rendering aims to recover scene geometry, material properties, and lighting from multi-view images. Given the complexity of light-surface interactions, importance sampling is essential for the evaluation of the rendering equation, as it reduces variance and enhances the efficiency of Monte Carlo sampling. Existing inverse rendering methods typically

Ryutaro Enami, Kazuhiko Kuroki, Masayuki Ochi

We theoretically investigate the electronic structure of monolayer BC$_3$ and find that it hosts anisotropic multiple valleys originating from the splitting of the van Hove singularity in graphene. To make use of its favorable electronic structure, we investigate the electronic structure of alkali-metal-intercalated BC$_3$, where intercalated atoms not only

Donaji Esparza-Arredondo, Omaira Gonzalez-Martín, Deborah Dultzin, Cristina Ramos Almeida

Over ten mid-infrared (mid-IR) and X-ray models are currently attempting to describe the nuclear obscuring material of active galactic nuclei (AGNs), but many questions remain unresolved. This study aims to determine the physical parameters of the obscuring material in nearby AGNs and explore their relationship with nuclear activity. We selected 24 nearby Se

Jinjin Zhang, Guodong Wang, Yizhou Jin, Di Huang

Anomaly detection is valuable for real-world applications, such as industrial quality inspection. However, most approaches focus on detecting local structural anomalies while neglecting compositional anomalies incorporating logical constraints. In this paper, we introduce LogSAD, a novel multi-modal framework that requires no training for both Logical and St

Basim Azam, Naveed Akhtar

Ethical issues around text-to-image (T2I) models demand a comprehensive control over the generative content. Existing techniques addressing these issues for responsible T2I models aim for the generated content to be fair and safe (non-violent/explicit). However, these methods remain bounded to handling the facets of responsibility concepts individually, whil

Ivan Iorsh

We consider an altermagnet subject to the electron attractive potential mediated by the dispersive phonons. While altermagnetism suppresses superconductivity, scattering of electrons on the Fermi surface by thermal phonons suppresses altermagnetism. We show that this leads to the re-entrant superconductivity over temperature and to the stabilization of the F

Thomas W. Kephart, Qaisar Shafi

In the Pati-Salam gauge symmetry $SU(4)_c \times SU(2)_L \times SU(2)_R$ (4-2-2, for short), the observed quarks and leptons of each family reside in the bi-fundamental representations $(4,2,1)$ and $({\bar 4},1,2)$. There exist, however, the fundamental representations $(4,1,1)$, $(1,2,1)$ and $(1,1,2)$ and their hermitian conjugates, which show the presenc

Dong Jing, Nanyi Fei, Zhiwu Lu

In the realm of Large Multi-modal Models (LMMs), the instruction quality during the visual instruction tuning stage significantly influences the performance of modality alignment. In this paper, we assess the instruction quality from a unique perspective termed \textbf{Writing Manner}, which encompasses the selection of vocabulary, grammar and sentence struc

Zehao Li, Yijie Peng

This paper tackles the challenge of parameter calibration in stochastic models, particularly in scenarios where the likelihood function is unavailable in an analytical form. We introduce a gradient-based simulated parameter estimation framework, which employs a multi-time scale stochastic approximation algorithm. This approach effectively addresses the ratio

Xiang-Song Fang, Jian-Rong Shi, Ming-Yi Ding, Zi-Huang Cao

This study utilized LAMOST low-resolution spectra to identify M-type YSOs and characterize their accretion signatures. We measured characteristic features, including hydrogen Balmer, Li {\sc i}, He~{\sc i}, Na {\sc i}, and Ca {\sc ii} lines, as well as molecular absorption bands such as CaH. These features were evaluated for their potential to distinguish be

Ruijia Zhang, Mingxi Lei, Meng Ding, Zihang Xiang

In this paper, we study the problem of (finite sum) minimax optimization in the Differential Privacy (DP) model. Unlike most of the previous studies on the (strongly) convex-concave settings or loss functions satisfying the Polyak-Lojasiewicz condition, here we mainly focus on the nonconvex-strongly-concave one, which encapsulates many models in deep learnin

Fei Zuo, Junghwan Rhee, Yung Ryn Choe

Advanced Persistent Threats (APTs) have caused significant losses across a wide range of sectors, including the theft of sensitive data and harm to system integrity. As attack techniques grow increasingly sophisticated and stealthy, the arms race between cyber defenders and attackers continues to intensify. The revolutionary impact of Large Language Models (

Runqi Kang, Qingqin Hu, Xiao Cai, Wenlong Yu

The dark photon is a well-motivated candidate of dark matter due to its potential to open the window of new physics beyond the Standard Model. A fundamental mass-range-sensitivity dilemma is always haunting the dark photon searching experiments: The resonant haloscopes have excellent sensitivity but are narrowband, and vice versa for the non-resonant ones. A

Changlun Li, Yao Shi, Yuyu Luo, Nan Tang

Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but their effectiveness in financial decision-making remains inadequately evaluated. Current benchmarks primarily assess LLMs' understanding on financial documents rather than the ability to manage assets or dig out trading opportunities in dynamic market condition

Jianlong Jin, Chenglong Zhao, Ruixin Zhang, Sheng Shang

Palmprint recognition is significantly limited by the lack of large-scale publicly available datasets. Previous methods have adopted B\'ezier curves to simulate the palm creases, which then serve as input for conditional GANs to generate realistic palmprints. However, without employing real data fine-tuning, the performance of the recognition model trained o

Mohammed K. Alqedra, Pierre Brosseau, Ali W. Elshaari, Jun Gao

Quantum state tomography is a central technique for the characterization and verification of quantum systems. Standard tomography is widely used for low-dimensional systems, but for larger systems, it becomes impractical due to the exponential scaling of experimental complexity with the number of qubits. Here, we present an experimental realization of Fourie

Gernot Akemann, Sung-Soo Byun, Yong-Woo Lee

We investigate real eigenvalues of real elliptic Ginibre matrices of size $n$, indexed by the parameter of asymmetry $\tau \in [0,1]$. In both the strongly and weakly non-Hermitian regimes, where $\tau \in [0,1)$ is fixed or $1-\tau=O(1/n)$, respectively, we derive the asymptotic expansion of the probability $p_{n,n-2l}$ that all but a finite number $2l$ of

Zhidi Lin, Ying Li, Feng Yin, Juan Maroñas

Gaussian process state-space models (GPSSMs) offer a principled framework for learning and inference in nonlinear dynamical systems with uncertainty quantification. However, existing GPSSMs are limited by the use of multiple independent stationary Gaussian processes (GPs), leading to prohibitive computational and parametric complexity in high-dimensional set

Adarsh Salagame, Sasank Potluri, Keshav Bharadwaj Vaidyanathan, Kruthika Gangaraju

This paper presents the development and integration of a vision-guided loco-manipulation pipeline for Northeastern University's snake robot, COBRA. The system leverages a YOLOv8-based object detection model and depth data from an onboard stereo camera to estimate the 6-DOF pose of target objects in real time. We introduce a framework for autonomous detection

Haiqi Liu, C. L. Philip Chen, Tong Zhang

Cross-subject EEG emotion recognition is challenged by significant inter-subject variability and intricately entangled intra-subject variability. Existing works have primarily addressed these challenges through domain adaptation or generalization strategies. However, they typically require extensive target subject data or demonstrate limited generalization p

Adarsh Salagame, Shashwat Pandya, Ioannis Mandralis, Eric Sihite

Multi-rotors face significant risks, as actuator failures at high altitudes can easily result in a crash and the robot's destruction. Therefore, rapid fault recovery in the event of an actuator failure is necessary for the fault-tolerant and safe operation of unmanned aerial robots. In this work, we present a fault recovery approach based on the unification

Neil Sinclair, Samantha I. Davis, Nikolai Lauk, Chang Li

We develop analytical models for realistic photonic quantum teleportation experiments with time-bin qubits, utilizing phase space methods from quantum optics. These models yield analytical expressions for Hong-Ou-Mandel interference visibilities and qubit fidelities, accounting for imperfections such as loss, photon distinguishability, and unwanted multi-pho

Guangsheng Ou, Mingwei Liu, Yuxuan Chen, Xueying Du

Large language models (LLMs) have behaved well in function-level code translation without repository-level context. However, the performance of LLMs in repository-level context code translation remains suboptimal due to complex dependencies and context, hindering their adoption in industrial settings. In this work, we propose a novel LLM-based code translati

Hongsheng Xu, Meng Zhan

In the transient stability analysis of renewable energy grid-tied systems, although a large amount of works have devoted to the detailed electromagnetic transient simulation and the stability analyses of during-fault stage, the whole low-voltage ride through (LVRT) process and relevant transient stability mechanism remain to be uncovered. Taking the doubly f

Jin Kim

As large language models (LLMs) like ChatGPT become increasingly integrated into our everyday lives--from customer service and education to creative work and personal productivity--understanding how people interact with these AI systems has become a pressing issue. Despite the widespread use of LLMs, researchers lack standardized tools for systematically stu

Qingyue Long, Can Rong, Huandong Wang, Shaw Rajib

In the real world, trajectory data is often sparse and incomplete due to low collection frequencies or limited device coverage. Trajectory recovery aims to recover these missing trajectory points, making the trajectories denser and more complete. However, this task faces two key challenges: 1) The excessive sparsity of individual trajectories makes it diffic

Haifeng Li, Jiajun Guo, Xuanxin Fan, Dezhen Song

Localization of robots using subsurface features observed by ground-penetrating radar (GPR) enhances and adds robustness to common sensor modalities, as subsurface features are less affected by weather, seasons, and surface changes. We introduce an innovative multimodal odometry approach using inputs from GPR, an inertial measurement unit (IMU), and a wheel

Xi Wu, Dan Zhang, Chao Zhou, Liangwei Yang

Recommender systems (RecSys) have become essential in modern society, driving user engagement and satisfaction across diverse online platforms. Most RecSys focuses on designing a powerful encoder to embed users and items into high-dimensional vector representation space, with loss functions optimizing their representation distributions. Recent studies reveal

Oliver Knill

The geodesic flow on a finite discrete q-manifold with or without boundary is defined as as a permutation of its ordered q-simplices. This allows to define geodesic sheets and a notion of sectional curvature.

Mucuy-kak Guevara, Teresa I. Hoekstra-Mendoza, Miguel Licona-Velazquez

In this paper, we introduce the concept of up-color kernel, which is a generalization of a kernel for vertex-colored digraphs. We give sufficient and necessary conditions for several families of digraphs to have an up-color kernel, as well as for certain products of digraphs.

Tonmoy Ghosh, Edward Sazonov

Food image recognition is a challenging task in computer vision due to the high variability and complexity of food images. In this study, we investigate the potential of Noisy Vision Transformers (NoisyViT) for improving food classification performance. By introducing noise into the learning process, NoisyViT reduces task complexity and adjusts the entropy o

Yishen Liu

Medical report generation requires specialized expertise that general large models often fail to accurately capture. Moreover, the inherent repetition and similarity in medical data make it difficult for models to extract meaningful features, resulting in a tendency to overfit. So in this paper, we propose a multimodal model, Co-Attention Triple-LSTM Network

Mengya Xu, Zhongzhen Huang, Jie Zhang, Xiaofan Zhang

In robot-assisted minimally invasive surgery, we introduce the Surgical Action Planning (SAP) task, which generates future action plans from visual inputs to address the absence of intraoperative predictive planning in current intelligent applications. SAP shows great potential for enhancing intraoperative guidance and automating procedures. However, it face

Jindrich Zapletal

It is consistent relative to an inaccessible cardinal that ZF+DC holds, the hypergraph of equilateral triangles on a given Euclidean space has countable chromatic number, while the hypergraph of isosceles triangles in the plane does not.

Fiseha B. Tesema, Alejandro Guerra Manzanares, Tianxiang Cui, Qian Zhang

Colorectal cancer (CRC) is a major global cause of cancer-related deaths, with early polyp detection and removal during colonoscopy being crucial for prevention. While deep learning methods have shown promise in polyp segmentation, challenges such as high computational costs, difficulty in segmenting small or low-contrast polyps, and limited generalizability

Jiayi Yao, Haibo Sun, Nianwen Xue

In this paper, we evaluate the ability of Large Language Models (LLMs) to assess the veracity of claims in ''news reports'' generated by themselves or other LLMs. Our goal is to determine whether LLMs can effectively fact-check their own content, using methods similar to those used to verify claims made by humans. Our findings indicate that LLMs are more eff

Chen Zhang, Kuntai Du, Shu Liu, Woosuk Kwon

Large language models (LLMs) are widely used but expensive to run, especially as inference workloads grow. To lower costs, maximizing the request batch size by managing GPU memory efficiently is crucial. While PagedAttention has recently been proposed to improve the efficiency of memory management, we find that the growing heterogeneity in the embeddings dim

Dallas R. Trinkle

We compute the phase separation of the immiscible liquid alloy Fe-Cu-Ni. Our computational approach uses a virtual semigrand canonical Widom approach to determine differences in excess chemical potentials between different species. Using an embedded atom potential for Fe-Cu-Ni, we simulate liquid states over a range of compositions and temperatures. This raw

Paul K. Mandal

In this paper, I investigate the effectiveness of dataset cartography for extractive question answering on the SQuAD dataset. I begin by analyzing annotation artifacts in SQuAD and evaluate the impact of two adversarial datasets, AddSent and AddOneSent, on an ELECTRA-small model. Using training dynamics, I partition SQuAD into easy-to-learn, ambiguous, and h

Seyed Naseh Sajadi, Supakchai Ponglertsakul, Dhruba Jyoti Gogoi

In this paper, we study the different properties of static spherically symmetric black hole solutions of Einstein-Bel-Robinson gravity (EBR), a modified four-dimensional theory of gravity quartic in curvature. We look at the orbit of massless and massive test bodies near a black hole, specifically computing the innermost stable circular orbit and photon sphe

Mingming Wang, Guowu Yuan, Hao Zhou, Chengming Tan

Solar radio bursts (SRBs) detection is crucial for solar physics research and space weather forecasting. The main challenges faced are noise interference in the spectrum and the diversity of SRBs. However, most research focuses on classifying whether SRBs exist or detecting a single type of SRBs. Existing detection models exhibit deficiencies in the accuracy

Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang

With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant concerns regarding misuse. Current efforts to distinguish between real and AI-generated images may lack generalization, being effective for only certain types of generative models

Allen Herman, Surinder Kaur

Assume $F$ is a finite field of order $p^f$ and $q$ is an odd prime for which $p^f-1=sq^m$, where $m \ge 1$ and $(s,q)=1$. In this article, we obtain the order of symmetric and unitary subgroup of the semisimple group algebra $FC_q.$ Further, for the extension $G$ of $C_q = \langle b \rangle$ by an abelian group $A$ of order $p^n$ with $C_{A}(b) = \{e\}$, we

Jiacheng Yao, Wei Shi, Wei Xu, Zhaohui Yang

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL para

Bojun Liu, Yangzhi Ma, Ao Luo, Li Li

Voxel-based methods are among the most efficient for point cloud geometry compression, particularly with dense point clouds. However, they face limitations due to a restricted receptive field, especially when handling high-bit depth point clouds. To overcome this issue, we introduce a stage-wise Space-to-Channel (S2C) context model for both dense point cloud

Kazuhiro Yamada, Li Yin, Qingrui Hu, Ning Ding

Multi-object tracking, player identification, and pose estimation are fundamental components of sports analytics, essential for analyzing player movements, performance, and tactical strategies. However, existing datasets and methodologies primarily target mainstream team sports such as soccer and conventional 5-on-5 basketball, often overlooking scenarios in

Lister Staveley-Smith

The effects of diffraction, reflection and mutual coupling on the spectral smoothness of radio telescopes becomes increasingly important at low frequencies, where the observing wavelength may be significant compared with the antenna or array dimensions. These effects are important for both traditional parabolic antennas, which are prone to the 'standing wave

Mykhaylo V. Khoma

This paper presents a new approach for the computation of eigenvalues of the generalized spheroidal wave equations. The novelty of the present method is in the use of the analytical derivatives of the eigenvalues to minimize losses in accuracy. The derivatives are constructed in the form of three-term recurrent relations within the method of continued fracti

Harshdeep Singh, Sonjoy Majumder, Sabyashachi Mishra

We present an efficient approach to simulate real-time quantum dynamics using Projected Variational Quantum Dynamics (PVQD), where the computational cost is reduced by strategically optimizing only a subset of the variational parameters at each time step. Typically, the variational ansatz consists of repeated blocks of parameterized quantum circuits, where a

Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang

Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Previous studies have noted that visual tokens tend to receive less attention than text tokens, suggesting their lower importance during inference and potential for pruning. However,

Jun-Jie Wei

One of the manifestations of Lorentz invariance violation (LIV) is vacuum birefringence, which leads to an energy-dependent rotation of the polarization plane of linearly polarized photons arising from an astrophysical source. Here we use the energy-resolved polarization measurements in the prompt $\gamma$-ray emission of five bright gamma-ray bursts (GRBs)

Yuming Huang, Wei Gao, Zhiyuan Zhang, Maani Ghaffari

OpenStreetMap (OSM) has gained popularity recently in autonomous navigation due to its public accessibility, lower maintenance costs, and broader geographical coverage. However, existing methods often struggle with noisy OSM data and incomplete sensor observations, leading to inaccuracies in trajectory planning. These challenges are particularly evident in c

Xulang Liu, Ning Tan

3D Gaussian Splatting (3DGS) has recently emerged as a powerful representation of geometry and appearance for dense Simultaneous Localization and Mapping (SLAM). Through rapid, differentiable rasterization of 3D Gaussians, many 3DGS SLAM methods achieve near real-time rendering and accelerated training. However, these methods largely overlook inertial data,

Sam Nariman, Mehdi Yazdi

This document compiles problems proposed and discussed during the problem session at the conference Foliations and Diffeomorphism Groups (CIRM, 2024), organized by H\'el\`ene Eynard-Bontemps, Ga\"el Meigniez, Sam Nariman, and Mehdi Yazdi. The problems were contributed by participants and have been lightly edited by the organizers for clarity and coherence.

Raza Ul Mustafa, Roi Dupart, Gabrielle Smith, Noman Ashraf

In recent years, Islamophobia has gained significant traction across Western societies, fueled by the rise of digital communication networks. This paper performs a large-scale analysis of specialized, semi-coded Islamophobic terms such as (muzrat, pislam, mudslime, mohammedan, muzzies) floated on extremist social platforms, i.e., 4Chan, Gab, Telegram, etc. M

Mert Yazan, Suzan Verberne, Frederik Situmeang

Personalization with retrieval-augmented generation (RAG) often fails to capture fine-grained features of authors, making it hard to identify their unique traits. To enrich the RAG context, we propose providing Large Language Models (LLMs) with author-specific features, such as average sentiment polarity and frequently used words, in addition to past samples

Massimo Ostilli

In a graph, we say that two nodes are topologically equivalent if their sets of first neighbors, excluding the two nodes, coincide. We prove that nonlinearly coupled heterogeneous oscillators located on a group of topologically equivalent nodes can get easily synchronized when the group forms a fully connected subgraph (or combinations thereof), regardless o

Aidyn Aitzhan, Peyman Givi, Hessam Babaee

A dynamical low-rank approximation is developed for reduced-order modeling (ROM) of the filtered density function (FDF) transport equation, which is utilized for large eddy simulation (LES) of turbulent reacting flows. In this methodology, the evolution of the composition matrix describing the FDF transport via a set of Langevin equations is constrained to a

Manjunath Krishnapur, Erik Lundberg, Koushik Ramachandran

Erd\"os posed in 1940 the extremal problem of studying the minimal area of the lemniscate $\{|p(z)|<1\}$ of a monic polynomial $p$ of degree $n$ all of whose zeros are in the closed unit disc. In this article, we prove that there exist positive constants $c,C$ independent of the degree $n$ such that \[ \dfrac{c}{\log n} \leq \min \text{Area}( \{ |p(z)|<1 \}

Yue Zhou

The Selberg integral, an $n$-dimensional generalization of the Euler beta integral, plays a central role in random matrix theory, Calogero--Sutherland quantum many body systems, Knizhnik--Zamolodchikov equations, and multivariable orthogonal polynomial theory. The Selberg integral is known to be equivalent to the Morris constant term identity. In 1998, Baker