March 2025 arXiv papers — page 152

Ahmed Alaa, Thomas Hartvigsen, Niloufar Golchini, Shiladitya Dutta

Medical large language models (LLMs) research often makes bold claims, from encoding clinical knowledge to reasoning like a physician. These claims are usually backed by evaluation on competitive benchmarks; a tradition inherited from mainstream machine learning. But how do we separate real progress from a leaderboard flex? Medical LLM benchmarks, much like

Chengyue Gong, Xiaoyu Li, Yingyu Liang, Jiangxuan Long

Flow matching has emerged as a powerful framework for generative modeling, offering computational advantages over diffusion models by leveraging deterministic Ordinary Differential Equations (ODEs) instead of stochastic dynamics. While prior work established the worst case optimality of standard flow matching under Wasserstein distances, the theoretical guar

Youngju Joung, Sehyun Lee, Jaesik Choi

To improve trust and transparency, it is crucial to be able to interpret the decisions of Deep Neural classifiers (DNNs). Instance-level examinations, such as attribution techniques, are commonly employed to interpret the model decisions. However, when interpreting misclassified decisions, human intervention may be required. Analyzing the attribu tions acros

Zhenrong Nong, Yinghui Wang, Huancheng Yao, Shihao Zhang

This paper establishes the global well-posedness of solutions to the Oldroyd-B model with purely horizontal viscosity and arbitrarily large initial data in two-dimensional settings, including the full space $\mathbb{R}^2$, the partially periodic domain $\mathcal{T}\times\mathbb{R}$ and the fully periodic torus $\mathcal{T}^2$, where $\mathcal{T}$ represents

Zangwei Zheng, Xiangyu Peng, Yuxuan Lou, Chenhui Shen

Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demons

Xin Wei Chia, Swee Liang Wong, Jonathan Pan

Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks, yet they remain vulnerable to adversarial manipulations such as jailbreaking via prompt injection attacks. These attacks bypass safety mechanisms to generate restricted or harmful content. In this study, we investigated the underlying latent subspaces of safe and jai

Robert L. Cook, Liwen Ko, K. Birgitta Whaley

We study the connection between exceptional points (EPs) and optimal parameter estimation, in a simple system consisting of two counter-propagating traveling wave modes in a microring resonator. The unknown parameter to be estimated is the strength of a perturbing cross-coupling between the two modes. Partially reflecting the output of one mode into the othe

Hojin Kim, Samantha M. Livermore, Stuart J. Rowan, Heinrich M. Jaeger

Memory-forming properties introduce a new paradigm to the design of adaptive materials. In dense suspensions, an adaptive response is enabled by non-Newtonian rheology; however, typical suspensions have little memory, which implies rapid cessation of any adapted behavior. Here we show how multiple adaptive responses can be achieved by designing suspensions w

Qianyu Liu, Xinran Li, Xiaocong Du, Quan Li

Knowledge dissemination in educational settings is profoundly influenced by the curse of knowledge, a cognitive bias that causes experts to underestimate the challenges faced by learners due to their own in-depth understanding of the subject. This bias can hinder effective knowledge transfer and pedagogical effectiveness, and may be exacerbated by inadequate

Longfei Chen, Shengxin Li, Ziang Li, Quan Li

Motion comics, a digital animation format that enhances comic book narratives, have wide applications in storytelling, education, and advertising. However, their creation poses significant challenges for amateur creators, primarily due to the need for specialized skills and complex workflows. To address these issues, we conducted an exploratory survey (N=58)

Ruofei Ma, Yu Zhao, Yuheng Shao, Yunjie Yao

MOBA (Multiplayer Online Battle Arena) games require a delicate interplay of strategic planning and real-time decision-making, particularly in professional esports, where players exhibit varying levels of skill and strategic insight. While team strategies have been widely studied, analyzing inconsistencies in professional matches remains a significant challe

Junsong Chen, Shuchen Xue, Yuyang Zhao, Jincheng Yu

This paper presents SANA-Sprint, an efficient diffusion model for ultra-fast text-to-image (T2I) generation. SANA-Sprint is built on a pre-trained foundation model and augmented with hybrid distillation, dramatically reducing inference steps from 20 to 1-4. We introduce three key innovations: (1) We propose a training-free approach that transforms a pre-trai

Mirta Dumancic, Guy Heger, Ishai Luz, Maayan Vatarescu

Diffusing alpha-emitters radiation therapy (Alpha-DaRT) is a new modality that uses alpha particles to treat solid tumors. Alpha-DaRT employs interstitial sources loaded with low activities of Radium-224, which release a chain of short-lived alpha-emitters diffusing over a few millimeters around each source. Alpha-DaRT dosimetry is described, to first order,

Byeongchan Lee, Sehyun Lee

In self-supervised representation learning, Siamese networks are a natural architecture for learning transformation-invariance by bringing representations of positive pairs closer together. But it is prone to collapse into a degenerate solution. To address the issue, in contrastive learning, a contrastive loss is used to prevent collapse by moving representa

Jehoon Moon, Gisoo Lee, Jaehee Lee, Hansohl Cho

Long range order and symmetry in heterogeneous materials architected on crystal lattices lead to elastic and inelastic anisotropies and thus limit mechanical functionalities in particular crystallographic directions. Here, we present a facile approach for designing heterogeneous disordered materials that exhibit nearly isotropic mechanical resilience and ene

Daniel McKemie

This paper outlines how to leverage the Web MIDI API and web technologies to convert numerical data in JavaScript to Most Significant Byte and Least Significant Byte combos, stage the data as dual concurrent CC messages, use WebSockets to send it to multiple endpoints, and wire the browser to other music software. This method allows users to control their ow

Yufeng Wu, Chaofan Wang, Danqing Wang, Mingrui Xu

Parametric frequency converters (PFCs) play a critical role in bridging the frequency gap between quantum information carriers. PFCs in the microwave band are particularly important for superconducting quantum processors, but their operating bandwidth is often strongly limited. Here, we present a multimode kinetic metastructure for parametric frequency conve

Daniel McKemie

Analog-digital hybrid electronic music systems once existed out of necessity in order to facilitate a flexible work environment for the creation of live computer music. As computational power increased with the development of faster microprocessors, the need for digital functionality with analog sound production decreased, with the computer becoming more cap

Stefania Barsanti, Scott M. Croom, Matthew Colless, Joss Bland-Hawthorn

We explore the impact of the large-scale 3D density field, as defined by deep, wide-field galaxy surveys, on stellar spin ($\lambda_{\rm R_e}$) and the distributions of fast and slow rotators. We use the GAMA spectroscopic redshift survey to reconstruct the cosmic web and obtain spatially-resolved stellar kinematics from the SAMI Galaxy Survey. Among various

Shengyao Lu, Jiuding Yang, Aedan J. DeFrates, Keith G. Mills

We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reconstruction. Our method recasts the graph-level readout of a pretrained GNN as a weighted rule-level readout: grounded subgraph concepts are composed into logical rules, rule embeddings are computed directly from

Thuan Than, Nhat-Anh Nguyen-Dang, Dung Nguyen, Salwa K. Al Khatib

Semi-Supervised Semantic Segmentation reduces reliance on extensive annotations by using unlabeled data and state-of-the-art models to improve overall performance. Despite the success of deep co-training methods, their underlying mechanisms remain underexplored. This work revisits Cross Pseudo Supervision with dual heterogeneous backbones and introduces Know

Alvin Kimbowa, Arjun Parmar, Maziar Badii, David Liu

Automated knee cartilage segmentation using point-of-care ultrasound devices and deep-learning networks has the potential to enhance the management of knee osteoarthritis. However, segmentation algorithms often struggle with domain shifts caused by variations in ultrasound devices and acquisition parameters, limiting their generalizability. In this paper, we

Xuewen Dong, Jiachen Li, Shujun Li, Zhichao You

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Atta

Weiquan Wang, Jun Xiao, Yi Yang, Yueting Zhuang

Rendering realistic human-object interactions (HOIs) from sparse-view inputs is a challenging yet crucial task for various real-world applications. Existing methods often struggle to simultaneously achieve high rendering quality, physical plausibility, and computational efficiency. To address these limitations, we propose HOGS (Human-Object Rendering via 3D

Han Peng, Qiang Wang, Meng Xiao, Xiayi Wang

In recent years, twisting has emerged as a new degree of freedom that plays an increasingly important role in Bloch bands of various physical systems. However, there is currently a lack of reports on the non-trivial physics of topological degeneracy in twisted systems. In this work, we investigated the intrinsic physical correlation between twisting and topo

Faneela, Jawad Ahmad, Baraq Ghaleb, Imdad Ullah Khan

Threshold Signature Scheme (TSS) protocols have gained significant attention over the past ten years due to their widespread adoption in cryptocurrencies. The adoption is mainly boosted by Gennaro and Goldfedder's TSS protocol. Since then, various TSS protocols have been introduced with different features, such as security and performance, etc. Large organiz

Yifan Wang, Yifei Liu, Yingdong Shi, Changming Li

Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. While prior research has attempted to demystify these models through input attribution and neuron role analysis, there's been a notable gap in considering layer-level information and the holistic path of informatio

Lipei Du

Electromagnetic (EM) probes, including photons and dileptons, do not interact strongly after their production in heavy-ion collisions, allowing them to carry undistorted information from their points of origin. This makes them powerful tools for studying early-stage equilibration and the thermodynamic properties of the quark-gluon plasma (QGP). In these proc

Wahab Khawaja, Rune H. Jacobsen, Sajid Hussain, Ismail Guvenc

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literat

Yusuke Nakae

Crossed product algebras are fundamental in the study of C*-algebras, traditionally under the assumption of continuity of group actions. Recent work by Grundling and Neeb introduced the crossed product host, an analog of the crossed product for a singular action. In this paper, we investigate the structure of the crossed product host and its relation to the

Steven Heilman, Omer Tamuz

We consider Lionel Levine's notorious hat puzzle with two players. Each player has a stack of hats on their head, and each hat is chosen independently to be either black or white. After observing only the other player's hats, players simultaneously choose one of their own hats. The players win if both chosen hats are black. In this note, we observe an upper

Hafsa Wazir, Jawad Ahmad, Muazzam A. Khan, Sana Ullah Jan

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper presents a new hybrid neural network named ConSGruNet for precise and efficient hand gesture recognition. The proposed model comprises convolutional neural networks with smart skip connections in conjunction with a G

Xinyu Zhang, Haonan Chang, Yuhan Liu, Abdeslam Boularias

Gaussian splatting has emerged as a powerful tool for high-fidelity reconstruction of dynamic scenes. However, existing methods primarily rely on implicit motion representations, such as encoding motions into neural networks or per-Gaussian parameters, which makes it difficult to further manipulate the reconstructed motions. This lack of explicit controllabi

Lihui Yi, Xiaochun Niu, Ermin Wei

Federated learning offers a decentralized approach to machine learning, where multiple agents collaboratively train a model while preserving data privacy. In this paper, we investigate the decision-making and equilibrium behavior in federated learning systems, where agents choose between participating in global training or conducting independent local traini

Waleed Ahmed Farooqui, Jawad Ahmad, Nadeem Kureshi, Fawad Ahmed

Securing image data in IoT networks and other insecure information channels is a matter of critical concern. This paper presents a new image encryption scheme using DNA encoding, snake permutation and chaotic substitution techniques that ensures robust security of the image data with reduced computational overhead. The DNA encoding and snake permutation modu

Polina Pantyukhina, Andrey Bogdanov, Kirill Koshelev

We investigate the formation of excitonic bound states in the continuum in van der Waals (vdW) heterostructures composed of two-dimensional excitonic vdW layers and an optically resonant patterned vdW thin film. We show that the radiative losses of the exciton can be completely suppressed - not through conventional methods such as total internal reflection,

Christos Panagiotou, Iossif Papadakis, Erin Kara, Marios Papoutsis

Over the last years, a number of broadband reverberation mapping campaigns have been conducted to explore the short-term UV and optical variability of nearby AGN. Despite the extensive data collected, the origin of the observed variability is still debated in the literature. Frequency-resolved time lags offer a promising approach to distinguish between diffe

Shawn Azdam, Pranav Doma, Aliasghar Moj Arab

The next generation of active safety features in autonomous vehicles should be capable of safely executing evasive hazard-avoidance maneuvers akin to those performed by professional stunt drivers to achieve high-agility motion at the limits of vehicle handling. This paper presents a novel framework, ManeuverGPT, for generating and executing high-dynamic stun

Ryota Shii

Let $E$ be an elliptic curve defined over $\mathbb{Q}$ with supersingular reduction at $p \geq 5$, and $K$ be an imaginary quadratic field such that $p$ is inert in $K/\mathbb{Q}$. In this paper, we prove the analogous of the ``weak'' Mazur--Tate refined conjecture for an anticyclotomic tower over $K$ using the result by A. Burungale--K. B\"{u}y\"{u}kboduk--

Rui Shi, Xiaodong Yu, Shengming Wang, Yijia Zhang

In this paper, we propose RFUAV as a new benchmark dataset for radio-frequency based (RF-based) unmanned aerial vehicle (UAV) identification and address the following challenges: Firstly, many existing datasets feature a restricted variety of drone types and insufficient volumes of raw data, which fail to meet the demands of practical applications. Secondly,

Younwoo Choi, Muhammad Adil Asif, Ziwen Han, John Willes

Prompting Large Language Models (LLMs), or providing context on the expected model of operation, is an effective way to steer the outputs of such models to satisfy human desiderata after they have been trained. But in rapidly evolving domains, there is often need to fine-tune LLMs to improve either the kind of knowledge in their memory or their abilities to

Jisu Park, M. K. Cheoun, J. H. Choi, J. Y. Choi

The JSNS2-II (the second phase of JSNS2, J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment aimed at searching for sterile neutrinos. This experiment has entered its second phase, employing two liquid scintillator detectors located at near and far positions from the neutrino source. Recently, the far detector of the experime

Kazuhiro Matsuyama, Usman Anjum, Satoko Matsuyama, Tetsuo Shoda

Knowledge distillation is a technique to imitate a performance that a deep learning model has, but reduce the size on another model. It applies the outputs of a model to train another model having comparable accuracy. These two distinct models are similar to the way information is delivered in human society, with one acting as the "teacher" and the other as

Boyang Xue, Qi Zhu, Hongru Wang, Rui Wang

Present Large Language Models (LLM) self-training methods always under-sample on challenging queries, leading to inadequate learning on difficult problems which limits LLMs' ability. Therefore, this work proposes a difficulty-aware self-training (DAST) framework that focuses on improving both the quantity and quality of self-generated responses on challengin

Satish Vedula

Integration of Inverter-based Resources (IBRs) such as solar-powered plants which lack the intrinsic characteristics such as the inertial response of the traditional synchronous-generator (SG) based sources presents a new challenge in the form of analyzing the grid stability under their presence. For example, solar power is available for approximately from 9

Zongshang Pang, Mayu Otani, Yuta Nakashima

Temporally localizing user-queried events through natural language is a crucial capability for video models. Recent methods predominantly adapt video LLMs to generate event boundary timestamps for temporal localization tasks, which struggle to leverage LLMs' pre-trained semantic understanding capabilities due to the uninformative nature of timestamp outputs.

Rakheon Kim, Irina Gaynanova

Sparse covariance matrices play crucial roles by encoding the interdependencies between variables in numerous fields such as genetics and neuroscience. Despite substantial studies on sparse covariance matrices, existing methods face several challenges such as the correlation among the elements in the sample covariance matrix, positive definiteness and unbias

Yibin Ye, Xichao Teng, Shuo Chen, Leqi Liu

Absolute Visual Localization (AVL) enables an Unmanned Aerial Vehicle (UAV) to determine its position in GNSS-denied environments by establishing geometric relationships between UAV images and geo-tagged reference maps. While many previous works have achieved AVL with image retrieval and matching techniques, research in low-altitude multi-view scenarios stil

Qiji Zhou, Yifan Gong, Guangsheng Bao, Hongjie Qiu

Counterfactual reasoning is crucial for robust video understanding but remains underexplored in existing multimodal benchmarks. In this paper, we introduce \textbf{COVER} (\textbf{\underline{CO}}unterfactual \textbf{\underline{V}}id\textbf{\underline{E}}o \textbf{\underline{R}}easoning), a multidimensional multimodal benchmark that systematically evaluates M

Logan Barnhart, Reza Akbarian Bafghi, Stephen Becker, Maziar Raissi

Reinforcement Learning from Human Feedback (RLHF) is increasingly used to align large language models (LLMs) with human preferences. However, the effectiveness of RLHF in addressing underlying biases remains unclear. This study investigates the relationship between RLHF and both covert and overt biases in LLMs, particularly focusing on biases against African

Xu Han, Zhiwen Wu, Xin Xia, Jiaqi Ma

This paper introduces and tests a framework integrating traffic regulation compliance into automated driving systems (ADS). The framework enables ADS to follow traffic laws and make informed decisions based on the driving environment. Using RGB camera inputs and a vision-language model (VLM), the system generates descriptive text to support a regulation-awar

Wuzhen Li, Zhiyuan Zhou, Guangcan Guo, Baosen Shi

Given that the phase amplification method based on harmonic generation exhibits significant phase super-resolution capability in interferometric precision measurement, extending this technology to birefringence interferometers to achieve super-resolution characterization of birefringent crystal properties has important research significance and application v

Wenjie Qu, Yuguang Zhou, Yongji Wu, Tingsong Xiao

Large language models (LLMs) have been widely applied for their remarkable capability of content generation. However, the practical use of open-source LLMs is hindered by high resource requirements, making deployment expensive and limiting widespread development. The collaborative inference is a promising solution for this problem, in which users collaborate

Kai Li, Bo Zhang, Haiwen Zhang

This paper aims to solve numerically the two-dimensional inverse medium scattering problem with far-field data. This is a challenging task due to the severe ill-posedness and strong nonlinearity of the inverse problem. As already known, it is necessary but also difficult numerically to employ an appropriate regularization strategy which effectively incorpora

Liang Lu, Yuan Jiang, Christoph Treude, Shuzheng Gao

Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity. However, even functionally correct LLM-generated code may exhibit non-functional quality issues that violate coding standards and best practices, such as poor style and limited maintainability. To address th

Tsukasa Fukusato, Naoki Kita

This paper proposes a method to design protective foam for packaging 3D objects. Users first load a 3D object and define a block-based design space by setting the block resolution and the size of each block. The system then constructs a block map in the space using depth textures of the input object, separates the map into two regions, and outputs the region

Kei Takahashi, Hikaru Sasaki, Takamitsu Matsubara

Imitation learning through a demonstration interface is expected to learn policies for robot automation from intuitive human demonstrations. However, due to the differences in human and robot movement characteristics, a human expert might unintentionally demonstrate an action that the robot cannot execute. We propose feasibility-aware behavior cloning from o

Yuhang Liu, Jindou Jia, Zihan Yang, Kexin Guo

This letter proposes an anti-disturbance control scheme for rotor drones to counteract voltage drop (VD) disturbance caused by voltage drop of the battery, which is a common case for long-time flight or aggressive maneuvers. Firstly, the refined dynamics of rotor drones considering VD disturbance are presented. Based on the dynamics, a voltage drop observer

Zaiyi. Li, Xiyuan. Zhang, Congcong. Wang, Haolan. Qu

Silicon Carbide (SiC) demonstrates significant potential for high-energy particle detection in complex radiation environments due to its exceptional radiation resistance, excellent environmental adaptability, and fast response time. Compared to silicon (Si) detectors, SiC detectors exhibit distinct radiation resistance characteristics depending on the type o

Haodong Zhang, Liang Zhang, Zhenghan Chen, Lu Chen

Natural and lifelike locomotion remains a fundamental challenge for humanoid robots to interact with human society. However, previous methods either neglect motion naturalness or rely on unstable and ambiguous style rewards. In this paper, we propose a novel Generative Motion Prior (GMP) that provides fine-grained motion-level supervision for the task of nat

Jihua Yang, Qipeng Zhang

For a polynomial differential system $$\dot{x}=-y+\sum\limits_{i+j=3}\alpha_{i,j}x^iy^j,\quad \dot{y}=x+\sum\limits_{i+j=3}\beta_{i,j}x^iy^j,$$ Pleshkan (Differ. Equations, 1969) proved that the origin is an isochronous center of this system iff it can be brought to one of $S^*_1$, $S^*_2$, $S^*_3$ or $S^*_4$. The bifurcation of limit cycles for these four t

Rongxin Liao, Feng Li, Yanyan Wei, Zenglin Shi

Universal adverse weather removal (UAWR) seeks to address various weather degradations within a unified framework. Recent methods are inspired by prompt learning using pre-trained vision-language models (e.g., CLIP), leveraging degradation-aware prompts to facilitate weather-free image restoration, yielding significant improvements. In this work, we propose

Amirmohammad Azadi, Sina Zamani, Mohammadmostafa Rostamkhani, Sauleh Eetemadi

This paper describes our system for SemEval 2025 Task 7: Previously Fact-Checked Claim Retrieval. The task requires retrieving relevant fact-checks for a given input claim from the extensive, multilingual MultiClaim dataset, which comprises social media posts and fact-checks in several languages. To address this challenge, we first evaluated zero-shot perfor

Qiang Zhang, Zhang Zhang, Wei Cui, Jingkai Sun

The perceptual system design for humanoid robots poses unique challenges due to inherent structural constraints that cause severe self-occlusion and limited field-of-view (FOV). We present HumanoidPano, a novel hybrid cross-modal perception framework that synergistically integrates panoramic vision and LiDAR sensing to overcome these limitations. Unlike conv

Abe Bohan Hou, Hongru Du, Yichen Wang, Jingyu Zhang

Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the avail

Robert Parsons, Kaylx Jang, Yuyang Wang, Asher Novick

Silicon photonic devices fundamental to high-density wavelength-division multiplexed (DWDM) optical links and photonic switching networks, such as resonant modulators and Mach-Zehnder interferometers (MZIs), are highly sensitive to fabrication variations and operational temperature swings. However, thermal tuning to compensate for fabrication and operational

Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan, Michael Bronstein

Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited insight into long-range interactions. Current evaluations primarily compare models employing global attention (e.g., graph transformers) with those using local neighborhood aggregatio

Jiancheng Zhang, Xiangting Li, Xiaolu Guo, Zhaoyi You

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions (``intrinsic noise'') and (ii) heterogeneity of cellular states across dif

Yuan Chang, Shiji Li, Tianyu He, Hongsheng Liu

Cu is most used substrate to grow monolayer graphene under a temperature near melting point. In this study, we elaborated a remarkable amount of Cu clusters were continuously evaporated during the graphene growth, resulting into the vapor pressure comparable with the CH4. Importantly, the decomposition barrier of CH4 on Cu clusters is similar or even lower t

Tsurugi Takata, Kanji Mori, Ko Nakamura, Kei Kotake

We perform spherically symmetric simulations of core-collapse supernovae with the aid of heavy axion-like particles (ALPs) which interact with photons and redistribute energy within supernova matter. We explore a wide ALP parameter space that includes MeV-scale ALP mass $m_{\,a}$ and the ALP-photon coupling constant $g_{\,a \gamma} \sim 10^{\,-10} \, \rm{GeV

Jihua Yang, Qipeng Zhang

This paper is devoted to study the limit cycle problem of a cubic reversible system with an isochronous center, when it is perturbed inside a class of polynomials. An upper bound of the number of limit cycles is obtained using the Abelian integral. The algebraic structure of the Abelian integral is acquired thanks to some iterative formulas, which differs in

Mayank Singh, Abhijeet Kumar, Sasidhar Donaparthi, Gayatri Karambelkar

Data catalogs serve as repositories for organizing and accessing diverse collection of data assets, but their effectiveness hinges on the ease with which business users can look-up relevant content. Unfortunately, many data catalogs within organizations suffer from limited searchability due to inadequate metadata like asset descriptions. Hence, there is a ne

Chenyuan Yang, Zijie Zhao, Zichen Xie, Haoyu Li

Static analysis is a powerful technique for bug detection in critical systems like operating system kernels. However, designing and implementing static analyzers is challenging, time-consuming, and typically limited to predefined bug patterns. While large language models (LLMs) have shown promise for static analysis, directly applying them to scan large syst

Nicoly da Silva Menezes, Thayssa Águila da Rocha, Lucas Samuel Santiago Camelo, Marcelle Pereira Mota

Context: As the demand for digital solutions adapted to different user profiles increases, creating more inclusive and diverse software development teams becomes an important initiative to improve software product accessibility. Problem: However, neurodivergent professionals are underrepresented in this area, encountering obstacles from difficulties in commu

Sameh T. Korashy

This paper investigates the interaction dynamics of a four-level tripod-type atomic system coupled to a q-deformed binomial field state within a Kerr-medium. The interaction model incorporates time-dependent coupling parameter and detuning parameter, providing a more adaptable framework for describing atom-field interactions. Special focus is placed on exami

Jin-Long Xu, Ming Zhu, Nai-Ping Yu, Chuan-Peng Zhang

Based on a high-sensitivity HI survey using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we identified an isolated HI cloud with a system velocity of ~127.0 km/s, which is associated with an optical galaxy KK153 in space. The HI gas of KK153 shows a typical disk-galaxy structure. Using the Baryonic Tully-Fisher relation, we obtained that

Wei Ruan, Tianze Yang, Yifan Zhou, Tianming Liu

Model merging has achieved significant success, with numerous innovative methods proposed to enhance capabilities by combining multiple models. However, challenges persist due to the lack of a unified framework for classification and systematic comparative analysis, leading to inconsistencies in terminologies and categorizations. Meanwhile, as an increasing

Rajeev Kumar, Harishankar Kumar, Kumari Shalini

Personalized messaging plays an essential role in improving communication in areas such as healthcare, education, and professional engagement. This paper introduces a framework that uses the Knowledge Graph (KG) to dynamically rephrase written communications by integrating individual and context-specific data. The knowledge graph represents individuals, loca

Dikai Liu, Tianwei Zhang, Jianxiong Yin, Simon See

Quadrupeds have gained rapid advancement in their capability of traversing across complex terrains. The adoption of deep Reinforcement Learning (RL), transformers and various knowledge transfer techniques can greatly reduce the sim-to-real gap. However, the classical teacher-student framework commonly used in existing locomotion policies requires a pre-train

Younghun Hong, Yukihide Tadano, Changhun Yang

The discrete Schr\"odinger equation on a two-dimensional honeycomb lattice is a fundamental tight-binding approximation model that describes the propagation of waves on graphene. For free evolution, we first show that the degenerate frequencies of the dispersion relation are completely characterized by three symmetric periodic curves (Theorem 2.1), and that

Tomohiro Fukaya, Eduardo Martínez-Pedroza, Takumi Matsuka

The first author and Oguni introduced a class of groups of non-positive curvature, called coarsely convex group. The recent success of the theory of groups which are hyperbolic relative to a collection of subgroups has motivated the study of other properties of groups from the relative perspective. In this article, we propose definitions for the notions of w

Kaixin Zhang, Hongzhi Wang, Ziqi Li, Yabin Lu

Research on learned cardinality estimation has made significant progress in recent years. However, existing methods still face distinct challenges that hinder their practical deployment in production environments. We define these challenges as the ``Trilemma of Cardinality Estimation'', where learned cardinality estimation methods struggle to balance general

Milad Rahmati

Autonomous vehicles (AVs) are transforming modern transportation, but their reliability and safety are significantly challenged by harsh weather conditions such as heavy rain, fog, and snow. These environmental factors impair the performance of cameras, LiDAR, and radar, leading to reduced situational awareness and increased accident risks. Conventional clou

Dong Ding, Ying-Qiu He, Ting Gao, Feng-Li Yan

We investigate the generalization of symmetric quantum joint measurements on multiple qubits. We first describe a method for constructing a symmetric joint measurement basis for three qubits by utilizing single-qubit states corresponding to the four vertices of a tetrahedron on the Bloch sphere. We demonstrate the expected tetrahedral symmetry of the current

Xuzhong Hu, Zaipeng Duan, Pei An, Jun zhang

Fusing LiDAR and image features in a homogeneous BEV domain has become popular for 3D object detection in autonomous driving. However, this paradigm is constrained by the excessive feature compression. While some works explore dense voxel fusion to enable better feature interaction, they face high computational costs and challenges in query generation. Addit

Shahnewaz Karim Sakib, Anindya Bijoy Das, Shibbir Ahmed

Adversarial factuality refers to the deliberate insertion of misinformation into input prompts by an adversary, characterized by varying levels of expressed confidence. In this study, we systematically evaluate the performance of several open-source large language models (LLMs) when exposed to such adversarial inputs. Three tiers of adversarial confidence ar

Vasudev Gohil

Despite recent advancements in Large Language Models (LLMs) and their alignment, they can still be jailbroken, i.e., harmful and toxic content can be elicited from them. While existing red-teaming methods have shown promise in uncovering such vulnerabilities, these methods struggle with limited success and high computational and monetary costs. To address th

Thomas J. Clark, Jiaxing Ma, Jack C. Sankey

We simultaneously cool $\gtrsim$100 mechanical modes of a membrane with a photothermally modified optical cavity driven by a single blue-detuned laser. In contrast to radiation pressure and bolometric forces applied directly to the mechanical system, this cooling effect does not depend on the sign of detuning, allowing for single-laser stabilization (i.e., s

Shotaro Tada, Hajime Kawahara, Yui Kawashima, Takayuki Kotani

We propose a new method for investigating atmospheric inhomogeneities in exoplanets through transmission spectroscopy. Our approach links chromatic variations in conventional transit model parameters (central transit time, total and full durations, and transit depth) to atmospheric asymmetries. By separately analyzing atmospheric asymmetries during ingress a

Gabriel Durham, Anil Battalahalli, Amy Kilbourne, Andrew Quanbeck

In many health policy settings, adaptive interventions target a population of clusters (e.g., schools), with the ultimate intent of impacting outcomes at the level of individuals within the clusters. Health policy researchers can use clustered, sequential, multiple assignment, randomized trials (SMARTs) to answer important scientific questions concerning clu

Farshad Rostami Ghadi, Kai-Kit Wong, Masoud Kaveh, F. Javier Lopez-Martinez

This paper considers communication between a base station (BS) to two users, each from one side of a simultaneously transmitting-reflecting reconfigurable intelligent surface (STAR-RIS) in the absence of a direct link. Rate-splitting multiple access (RSMA) strategy is employed and the STAR-RIS is subjected to phase errors. The users are equipped with a plana

Bokai Xu, Jiayi Zhang, Zhongtao Chen, Bingyang Cheng

The rapid development of the quantum technology presents huge opportunities for 6G communications. Leveraging the quantum properties of highly excited Rydberg atoms, Rydberg atom-based antennas present distinct advantages, such as high sensitivity, broad frequency range, and compact size, over traditional antennas. To realize efficient precoding, accurate ch

Dongjun Lee, Juyong Lee, Kyuyoung Kim, Jihoon Tack

Recent advances in large language models (LLMs) have led to a growing interest in developing LLM-based agents for automating web tasks. However, these agents often struggle with even simple tasks on real-world websites due to their limited capability to understand and process complex web page structures. In this work, we introduce LCoW, a framework for Learn

Takeshi Matsumoto, Dipankar Roy, Konstantin Khanin, Rahul Pandit

We study decaying turbulence in the 1D Burgers equation (Burgulence) and 3D Navier-Stokes (NS) turbulence. We first investigate the decay in time $t$ of the energy $E(t)$ in Burgulence, for a fractional Brownian initial potential, with Hurst exponent $H$, and demonstrate rigorously a self-similar time-decay of $E(t)$, previously determined heuristically. Thi

Siqiong Zhou, Ashif S. Iquebal, Esma S. Gel

Partially Observable Markov Decision Processes (POMDPs) are fundamental to decision-making under uncertainty. We introduce a novel scalable approach to accelerate upper bound estimation in Point-Based Value Iteration (PBVI) algorithms, the leading method to solve large-scale POMDPs. PBVI approximates the value function using a set of belief points rather tha

Catriona A. Thomson, Michael E. Tobar, Maxim Goryachev

We report the observation of transient nonlinear optical effects in a macroscopic whispering gallery mode resonator made of rutile TiO$_2$, demonstrating strong optical-microwave transduction under laser irradiation. By comparing the effects of ultraviolet (UV, 385 nm) and near-infrared (NIR, 700 nm) radiation, we find that the UV-induced effects are signifi

Yuhang Liu, Dong Gong, Yichao Cai, Erdun Gao

Recent empirical evidence shows that LLM representations encode human-interpretable concepts. Nevertheless, the mechanisms by which these representations emerge remain largely unexplored. To shed further light on this, we introduce a novel generative model that generates tokens on the basis of such concepts formulated as latent discrete variables. Under mild

William G. Resh, Yi Ming, Xinyao Xia, Michael Overton

This study investigates the near-future impacts of generative artificial intelligence (AI) technologies on occupational competencies across the U.S. federal workforce. We develop a multi-stage Retrieval-Augmented Generation system to leverage large language models for predictive AI modeling that projects shifts in required competencies and to identify vulner

Tadesse Destaw Belay, Ahmed Haj Ahmed, Alvin Grissom, Iqra Ameer

NLP research has increasingly focused on subjective tasks such as emotion analysis. However, existing emotion benchmarks suffer from two major shortcomings: (1) they largely rely on keyword-based emotion recognition, overlooking crucial cultural dimensions required for deeper emotion understanding, and (2) many are created by translating English-annotated da

Mourad Gridach, Jay Nanavati, Khaldoun Zine El Abidine, Lenon Mendes

The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agenti