May 2025 arXiv papers — page 49

Lujian Yao, Siming Zheng, Xinbin Yuan, Zhuoxuan Cai

Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatia

Yi Zhu, Xin Zou, Xindong Wu

Existing cyberbullying detection benchmarks were organized by the polarity of speech, such as "offensive" and "non-offensive", which were essentially hate speech detection. However, in the real world, cyberbullying often attracted widespread social attention through incidents. To address this problem, we propose a novel annotation method to construct a cyber

Lingyu Qiu, Ke Jiang, Xiaoyang Tan

Recent advancements in domain generalization for deepfake detection have attracted significant attention, with previous methods often incorporating additional modules to prevent overfitting to domain-specific patterns. However, such regularization can hinder the optimization of the empirical risk minimization (ERM) objective, ultimately degrading model perfo

Mengya He, Yaping Mao

The \textit{set-coloring Ramsey number} $\mathrm{R}_{r, s}(G_1,G_2,...,G_r)$ is the least $n \in \mathbb{N}$ such that every coloring $\chi: E\left(K_n\right) \rightarrow\binom{[r]}{s}$ contains a monochromatic copy of $G_i$, that is, a color $i \in[r]$ such that $i \in \chi(e)$ for every $e \in E(G_i)$. If $G_1=G_2=\cdots=G_r=G$, then we write $\mathrm{R}_{

L. M. C. Alencar, T. F. A. Alves, G. A. Alves, F. W. S. Lima

As far as we know, there is no flat-histogram algorithm to sample the stationary distribution of non-equilibrium stochastic processes. The present work addresses this gap by introducing a generalization of the Wang-Landau algorithm, applied to non-equilibrium stochastic processes with local transitions. The main idea is to sample macroscopic states using a k

HsiaoYuan Hsu, Yuxin Peng

In AI-empowered poster design, content-aware layout generation is crucial for the on-image arrangement of visual-textual elements, e.g., logo, text, and underlay. To perceive the background images, existing work demanded a high parameter count that far exceeds the size of available training data, which has impeded the model's real-time performance and genera

Mengmeng Chen, Xiaohu Wu, Qiqi Liu, Tiantian He

Multi-objective optimization (MOO) exists extensively in machine learning, and aims to find a set of Pareto-optimal solutions, called the Pareto front, e.g., it is fundamental for multiple avenues of research in federated learning (FL). Pareto-Front Learning (PFL) is a powerful method implemented using Hypernetworks (PHNs) to approximate the Pareto front. Th

Ian Langmore

The energy distance is used to test distributional equality, and as a loss function in machine learning. While $D^2(X, Y)=0$ only when $X\sim Y$, the sensitivity to different moments is of practical importance. This work considers $D^2(X, Y)$ in the case where the distributions are close. In this regime, $D^2(X, Y)$ is more sensitive to differences in the me

Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões, Esther Colombini

Understanding and controlling the informational complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and model capacity. While most approaches rely on entropy-based loss functions and statistical metrics, these measures often fail to capture deeper, causally relevant algorithmic regular

Kai Chen, Zihao He, Taiwei Shi, Kristina Lerman

Steerability, or the ability of large language models (LLMs) to adapt outputs to align with diverse community-specific norms, perspectives, and communication styles, is critical for real-world applications but remains under-evaluated. We introduce Steer-Bench, a benchmark for assessing population-specific steering using contrasting Reddit communities. Coveri

Chuheng Zhang, Tim Pearce, Pushi Zhang, Kaixin Wang

Latent action models (LAMs) aim to learn action-relevant changes from unlabeled videos by compressing changes between frames as latents. However, differences between video frames can be caused by controllable changes as well as exogenous noise, leading to an important concern -- do latents capture the changes caused by actions or irrelevant noise? This paper

Haoyu Zhang, Yisen Feng, Qiaohui Chu, Meng Liu

In this report, we present the method that achieves third place for Ego4D EgoSchema Challenge in CVPR 2025. To improve the reliability of answer prediction in egocentric video question answering, we propose an effective extension to the previously proposed HCQA framework. Our approach introduces a multi-source aggregation strategy to generate diverse predict

Bo Pan, Liang Zhao

Allocating more compute to large language models (LLMs) reasoning has generally been demonstrated to improve their effectiveness, but also results in increased inference time. In contrast, humans can perform tasks faster and better with increased experience and exposure. Hence, this paper aims to investigate the question: Can LLMs also become faster at reaso

Yi Zhan, Qi Liu, Weibo Gao, Zheng Zhang

Personalized programming tutoring, such as exercise recommendation, can enhance learners' efficiency, motivation, and outcomes, which is increasingly important in modern digital education. However, the lack of sufficient and high-quality programming data, combined with the mismatch between offline evaluation and real-world learning, hinders the practical dep

Yuan Wu, Zhiqiang Yan, Yigong Zhang, Xiang Li

Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose LIAR, a novel framewor

Yifan Li, Yuhang Chen, Anh Dao, Lichi Li

Existing Embodied Question Answering (EQA) benchmarks primarily focus on household environments, often overlooking safety-critical aspects and reasoning processes pertinent to industrial settings. This drawback limits the evaluation of agent readiness for real-world industrial applications. To bridge this, we introduce IndustryEQA, the first benchmark dedica

Guiping Cao, Tao Wang, Wenjian Huang, Xiangyuan Lan

Open-Ended object Detection (OED) is a novel and challenging task that detects objects and generates their category names in a free-form manner, without requiring additional vocabularies during inference. However, the existing OED models, such as GenerateU, require large-scale datasets for training, suffer from slow convergence, and exhibit limited performan

Wenhao You, Xingjian Diao, Wenjun Huang, Chunhui Zhang

While recent Multimodal Large Language Models exhibit impressive capabilities for general multimodal tasks, specialized domains like music necessitate tailored approaches. Music Audio-Visual Question Answering (Music AVQA) particularly underscores this, presenting unique challenges with its continuous, densely layered audio-visual content, intricate temporal

Ana M. Cabanas, Alma Pedro, Domingo Mery

Understanding how facial affect analysis (FAA) systems perform across different demographic groups requires reliable measurement of sensitive attributes such as ancestry, often approximated by skin tone, which itself is highly influenced by lighting conditions. This study compares two objective skin tone classification methods: the widely used Individual Typ

Jian Xiao, Ji Wang, Ming Zeng, Yuanwei Liu

This letter investigates the integration of pinching-antenna systems (PASS) with orthogonal frequency division multiplexing (OFDM) to ensure their compatibility and to explore the frequency-selective behavior inherent to PASS. First, an end-to-end channel model for OFDM PASS is proposed based on electromagnetic-compliant modeling of waveguides and coupled-mo

Akram Sheriff, Ken Huang, Zsolt Nemeth, Madjid Nakhjiri

This paper introduces the Adaptive Defense Agent (ADA), an innovative Automated Moving Target Defense (AMTD) system designed to fundamentally enhance the security posture of AI workloads. ADA operates by continuously and automatically rotating these workloads at the infrastructure level, leveraging the inherent ephemerality of Kubernetes pods. This constant

Jennifer Turliuk, Alejandro Sevilla, Daniela Gorza, Tod Hynes

This case study examines the ClimaTech Great Global Innovation Challenge's approach to selecting climate tech startups by integrating human and AI evaluations. The competition aimed to identify top startups and enhance the accuracy and efficiency of the selection process through a hybrid model. Research shows data-driven approaches help VC firms reduce bias

Zexu Pan, Shengkui Zhao, Tingting Wang, Kun Zhou

Audio-visual speaker extraction isolates a target speaker's speech from a mixture speech signal conditioned on a visual cue, typically using the target speaker's face recording. However, in real-world scenarios, other co-occurring faces are often present on-screen, providing valuable speaker activity cues in the scene. In this work, we introduce a plug-and-p

Jinwu Hu, Zhitian Zhang, Guohao Chen, Xutao Wen

While Large Language Models (LLMs) have exhibited remarkable emergent capabilities through extensive pre-training, they still face critical limitations in generalizing to specialized domains and handling diverse linguistic variations, known as distribution shifts. In this paper, we propose a Test-Time Learning (TTL) paradigm for LLMs, namely TLM, which dynam

Sergio G. Gómez-Galicia, Octavio B. Zapata-Fonseca

This paper uses the theory of covering graphs to characterize some of the edge-transitive graphs which can arise as token graphs.

Naoki Otsuki, Yositake Takane

We propose a simple scenario to describe a dislocation-induced Majorana zero mode on the surface of an iron-based superconductor, using an illustrative model with a cylindrical hole of radius $R$ perpendicular to its top surface. Topological surface states on the inner surface of the hole form an effective chiral $p$-wave superconductor. When the top surface

Yansong Li, Paula Branco, Alexander M. Hoole, Manish Marwah

As Large Language Models (LLMs) evolve in understanding and generating code, accurately evaluating their reliability in analyzing source code vulnerabilities becomes increasingly vital. While studies have examined LLM capabilities in tasks like vulnerability detection and repair, they often overlook the importance of both structure and semantic reasoning cru

Bolin Lai, Sangmin Lee, Xu Cao, Xiang Li

Text-image-to-video (TI2V) generation is a critical problem for controllable video generation using both semantic and visual conditions. Most existing methods typically add visual conditions to text-to-video (T2V) foundation models by finetuning, which is costly in resources and only limited to a few pre-defined conditioning settings. To tackle these constra

Juan Ramirez, Meraj Hashemizadeh, Simon Lacoste-Julien

Recent efforts to develop trustworthy AI systems have increased interest in learning problems with explicit requirements, or constraints. In deep learning, however, such problems are often handled through fixed weighted-sum penalization: the constraints are added to the task loss with fixed coefficients, and the resulting scalarized objective is minimized. T

Zhekun Shi, Kaizhao Liu, Qi Long, Weijie J. Su

Nash Learning from Human Feedback is a game-theoretic framework for aligning large language models (LLMs) with human preferences by modeling learning as a two-player zero-sum game. However, using raw preference as the payoff in the game highly limits the potential of the game-theoretic LLM alignment framework. In this paper, we systematically study using wha

Yohai Mazuz, Janna Bruner, Lior Wolf

In text-to-image models, consistent character generation is the task of achieving text alignment while maintaining the subject's appearance across different prompts. However, since style and appearance are often entangled, the existing methods struggle to preserve consistent subject characteristics while adhering to varying style prompts. Current approaches

Sibo Xiao, Zixin Lin, Wenyang Gao, Hui Chen

Processing long contexts has become a critical capability for modern large language models (LLMs). Existing works leverage agent-based divide-and-conquer methods for processing long contexts. But these methods face crucial limitations, including prohibitive accumulated latency and amplified information loss from excessive agent invocations, and the disruptio

Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail

Online polarization poses a growing challenge for democratic discourse, yet most computational social science research remains monolingual, culturally narrow, or event-specific. We introduce POLAR, a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. Polariz

Qing Xiao, Rongyi Chen, Jingjia Xiao, Tianyang Fu

This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month e

Omid Halimi Milani, Amanda Nikho, Marouane Tliba, Lauren Mills

We introduce a novel deep learning framework for the automated staging of spheno-occipital synchondrosis (SOS) fusion, a critical diagnostic marker in both orthodontics and forensic anthropology. Our approach leverages a dual-model architecture wherein a teacher model, trained on manually cropped images, transfers its precise spatial understanding to a stude

Ting Xu, Zhichao Huang, Jiankai Sun, Shanbo Cheng

We present Sequential Policy Optimization for Simultaneous Machine Translation (SeqPO-SiMT), a new policy optimization framework that defines the simultaneous machine translation (SiMT) task as a sequential decision making problem, incorporating a tailored reward to enhance translation quality while reducing latency. In contrast to popular Reinforcement Lear

Shijie Liu, Andrew C. Cullen, Paul Montague, Sarah Erfani

Similar to other machine learning frameworks, Offline Reinforcement Learning (RL) is shown to be vulnerable to poisoning attacks, due to its reliance on externally sourced datasets, a vulnerability that is exacerbated by its sequential nature. To mitigate the risks posed by RL poisoning, we extend certified defenses to provide larger guarantees against adver

Corbin Reid

We consider volumes of two families of links that have been the focus of recent results on geometry, namely weakly generalised alternating (WGA) links and fully augmented links (FAL). Both have known lower bounds on hyperbolic volume in terms of their diagram combinatorics, but less is known about upper bounds. In fact, Kalfagianni and Purcell recently found

Z. Y. Zhao, F. Y. Li, C. Dong, R. Chen

Dimerized quantum magnets provide a useful arena for novel quantum states and phases transitions with the singlet-triplet type of triplon excitations. Here we study the triplon physics and the Bose-Einstein condensation in two isostructural dimerized antiferromagnets $A$Cu(SeO$_3$)$_2$ ($A$ = Hg, Cd). With the systematic measurements, we demonstrate a dimer

Tianhu Peng, Lingfan Bao, Chengxu Zhou

We present a unified gait-conditioned reinforcement learning framework that enables humanoid robots to perform standing, walking, running, and smooth transitions within a single recurrent policy. A compact reward routing mechanism dynamically activates gait-specific objectives based on a one-hot gait ID, mitigating reward interference and supporting stable m

Po-Yi Wu

This work introduces and rigorously analyzes a novel operator-splitting finite element scheme for approximating viscosity solutions of a broad class of constrained second-order partial differential equations. By decoupling the primary PDE evolution from the enforcement of constraints, the proposed method combines a stabilized finite element method for spatia

Shuai Liu, Quanmin Liang, Zefeng Li, Boyang Li

Multi-sensor fusion is crucial for improving the performance and robustness of end-to-end autonomous driving systems. Existing methods predominantly adopt either attention-based flatten fusion or bird's eye view fusion through geometric transformations. However, these approaches often suffer from limited interpretability or dense computational overhead. In t

Naiyu Fang, Zheyuan Zhou, Fayao Liu, Xulei Yang

3D semantic occupancy prediction offers an intuitive and efficient scene understanding and has attracted significant interest in autonomous driving perception. Existing approaches either rely on full supervision, which demands costly voxel-level annotations, or on self-supervision, which provides limited guidance and yields suboptimal performance. To address

Erick Efrain Cote-Valencia, Juan Ruben Gomez-Solano

We investigate a model of a stochastic engine operating cyclically at constant bath temperature, which consists of an overdamped Brownian harmonic oscillator that plays the role of working substance and is elastically coupled to an active particle. Stirling-like cycles are implemented by time-periodic changes of the active particle speed and the potential co

Omid Halimi Milani, Ahmet Enis Cetin, Bharati Prasad

Obstructive sleep apnea (OSA) is a significant risk factor for hypertension, primarily due to intermittent hypoxia and sleep fragmentation. Predicting whether individuals with OSA will develop hypertension within five years remains a complex challenge. This study introduces a novel deep learning approach that integrates Discrete Cosine Transform (DCT)-based

Jiaxiong He

This paper introduces EarthOL, a novel consensus protocol that attempts to replace computational waste in blockchain systems with verifiable human contributions within bounded domains. While recognizing the fundamental impossibility of universal value assessment, we propose a domain-restricted approach that acknowledges cultural diversity and subjective pref

Prasenjit Ghosh, Jayanta Ghosh, T. K. Samanta

Atomic system in fuzzy Hilbert space is introduced and the existence of the fuzzy atomic systems for a strongly fuzzy bounded linear operator is studied. The notion of a K-frame in fuzzy Hilbert space is presented and some of their characterizations are given. We will see that fuzzy frame operator of a fuzzy K-frame in fuzzy Hilbert space is invertible under

Ziju Shen, Naohao Huang, Fanyi Yang, Yutong Wang

Nowadays, formal theorem provers have made monumental progress on high-school and competition-level mathematics, but few of them generalize to more advanced mathematics. In this paper, we present REAL-Prover, a new open-source stepwise theorem prover for Lean 4 to push this boundary. This prover, based on our fine-tuned large language model (REAL-Prover-v1)

Peter Robicheaux, Matvei Popov, Anish Madan, Isaac Robinson

Vision-language models (VLMs) trained on internet-scale data achieve remarkable zero-shot detection performance on common objects like car, truck, and pedestrian. However, state-of-the-art models still struggle to generalize to out-of-distribution classes, tasks and imaging modalities not typically found in their pre-training. Rather than simply re-training

Zenghao Zheng, Lianping Yang, Jinshan Pan, Hegui Zhu

Transformer-based methods for 3D human pose estimation face significant computational challenges due to the quadratic growth of self-attention mechanism complexity with sequence length. Recently, the Mamba model has substantially reduced computational overhead and demonstrated outstanding performance in modeling long sequences by leveraging state space model

Xiaobao Wei, Xiaoan Zhang, Hao Wang, Qingpo Wuwu

We propose a novel framework for comprehensive indoor 3D reconstruction using Gaussian representations, called OmniIndoor3D. This framework enables accurate appearance, geometry, and panoptic reconstruction of diverse indoor scenes captured by a consumer-level RGB-D camera. Since 3DGS is primarily optimized for photorealistic rendering, it lacks the precise

Hyungjun Park, Chang-Yun Woo, Seungjo Lim, Seunghwan Lim

Objective To develop an LLM based realtime compound diagnostic medical AI interface and performed a clinical trial comparing this interface and physicians for common internal medicine cases based on the United States Medical License Exam (USMLE) Step 2 Clinical Skill (CS) style exams. Methods A nonrandomized clinical trial was conducted on August 20, 2024. W

Penggan Xu

This study replicates the findings of Wang et al. (2017) on reference-dependent preferences and their impact on the risk-return trade-off in the Chinese stock market, a unique context characterized by high retail investor participation, speculative trading behavior, and regulatory complexities. Capital Gains Overhang (CGO), a proxy for unrealized gains or lo

Terrance Liu, Shuyi Wang, Daniel Preotiuc-Pietro, Yash Chandarana

While large language models (LLMs) achieve strong performance on text-to-SQL parsing, they sometimes exhibit unexpected failures in which they are confidently incorrect. Building trustworthy text-to-SQL systems thus requires eliciting reliable uncertainty measures from the LLM. In this paper, we study the problem of providing a calibrated confidence score th

Rushil Mallarapu, Mark Sellke

In the number partitioning problem (NPP) one aims to partition a given set of $N$ real numbers into two subsets with approximately equal sum. The NPP is a well-studied optimization problem and is famous for possessing a statistical-to-computational gap: when the $N$ numbers to be partitioned are i.i.d. standard gaussian, the optimal discrepancy is $2^{-\Thet

Dancheng Liu, Amir Nassereldine, Chenhui Xu, Jinjun Xiong

Whisper's robust performance in automatic speech recognition (ASR) is often attributed to its massive 680k-hour training set, an impractical scale for most researchers. In this work, we examine how linguistic and acoustic diversity in training data affect the robustness of the ASR model and reveal that transcription generalization is primarily driven by acou

Xin Liu, Hsiang-nan Li, Zhen-Jun Xiao

Motivated by the recent LHCb measurements of the ratios between the branching ratios (BRs), $R_{\chi_{c2}/J/\psi} \equiv {\rm BR}(B_c^+ \to \chi_{c2} \pi^+)/{\rm BR}(B_c^+ \to J/\psi \pi^+)$ and $R_{\chi_{c1}/\chi_{c2}} \equiv {\rm BR}(B_c^+ \to \chi_{c1} \pi^+)/{\rm BR}(B_c^+ \to \chi_{c2} \pi^+)$, we analyze the decays $B_c^+ \to \chi_{cJ} (P, V)^+$ in the

Alberto Padoan

Model reduction simplifies complex dynamical systems while preserving essential properties. This paper revisits a recently proposed system-theoretic framework for least squares moment matching. It interprets least squares model reduction in terms of two steps process: constructing a surrogate model to satisfy interpolation constraints, then projecting it ont

Yufei Zhao, Zhiqiang Mao, Binghai Yan

Phase transitions in solids are often accompanied by structural changes, but subtle lattice distortions can remain hidden from conventional crystallographic probes, hindering the identification of the correct order parameters. A case in point is Ca$_3$Ru$_2$O$_7$, a correlated polar ruthenate with well-characterized phase transitions, whose ground state stru

Yonghan Sun, Deren Han, Jiaxin Xie

Randomized iterative methods, such as the randomized Kaczmarz method, have gained significant attention for solving large-scale linear systems due to their simplicity and efficiency. Meanwhile, Krylov subspace methods have emerged as a powerful class of algorithms, known for their robust theoretical foundations and rapid convergence properties. Despite the i

Alexander M. McKillop, Marissa L. Weichman

Polariton chemistry has been hailed as a potential new route to direct molecular processes with electromagnetic fields. To make further strides, it is essential for the community to clarify which unusual polaritonic phenomena are true hallmarks of cavity quantum electrodynamics and which can be rationalized with classical optical physics. Here, we provide a

Xiaoxiao Jiang, Suyi Li, Lingyun Yang, Tianyu Feng

Generative image editing using diffusion models has become a prevalent application in today's AI cloud services. In production environments, image editing typically involves a mask that specifies the regions of an image template to be edited. The use of masks provides direct control over the editing process and introduces sparsity in the model inference. In

Huihong Jiang

We construct a family of examples of complete $(2+n)-$dimensional ($n\ge 2$) open manifolds with positive Ricci curvature, sectional curvature bounded from below and infinite Betti numbers $b_2,b_n$, moreover its volume growth can be arbitrarily close to quadratic volume growth. Compared with some known result of finite topology for manifolds with nonnegativ

O. P. Santillán

The present letter considers the quantization method developed in [1]-[9], which postulates that, in several situations, negative norm or ghost states can be avoided in order to give positive probabilities. These authors also postulate a candidate for a path integral for those theories, following pioneer works initiated by Dirac [10]} and Pauli \[11]. Howeve

Daiju Nakayama, Kin-ya Oda, Koichiro Yasuda

The theoretical framework of electromagnetism played a foundational role in Einstein's development of special relativity. To support conceptual understanding, we present a fully special relativistic computer simulation that visualizes electromagnetic fields from the perspective of a moving observer. In this simulation, the user observes electromagnetic pheno

Ke-Hong Zhai, Lei-Hua Liu, Hai-Qing Zhang

The generalized Lanczos algorithm can provide a universal method for constructing the wave function under the group structure of Hamiltonian. Based on this fact, we obtain an open two-mode squeezed state as the quantum origin for the curvature perturbation. In light of this wave function in the open system, we successfully develop a new method to calculate i

Guangzhe Xu

In this paper, we develope an equivariant theory of Chern characters for coherent sheaves on compact complex manifolds with finite group actions, taking values in Bott-Chern cohomology classes. Furthermore, we establish the corresponding Riemann-Roch-Grothendieck theorem in this context.

Marvin Lenk, Sayak Biswas, Anna Posazhennikova, Johann Kroha

One of the fundamental problems of quantum statistical physics is how an ideally isolated quantum system can ever reach thermal equilibrium behavior despite the unitary time evolution of quantum-mechanical systems. Here, we study, via explicit time evolution for the generic model system of an interacting, trapped Bose gas with discrete single-particle levels

Peng-Fei Ye, Kai Li, Fei Liu, Xiang Gao

Using high-precision photometric data from TESS, medium resolution spectroscopic data from LAMOST, and long-term eclipse timings, we provide orbital parameters for two early-type detached eclipsing binary systems: TYC 3740-2072-1 and TYC 2888-780-1, and analyze the orbital period variations and evolutionary status of these two targets. TYC 3740-2072-1, with

Galor Geva, Arin Escobar, Paula Magrinya, Pablo Llombart

Transport phenomena in complex and dynamic microscopic environments are fundamentally shaped by hydrodynamic interactions. In particular, microparticle transport in porous media is governed by the delicate interplay between particle-substrate friction and pressure forces. Here, we systematically investigate the motion of externally driven rotating magnetic m

Nikola Vuk Maruszewski

With ever-increasing main memory stall times, we need novel techniques to reduce effective memory access latencies. Prefetching has been shown to be an effective solution, especially with contiguous data structures that follow the traditional principles of spatial and temporal locality. However, on linked data structures$-$made up of many nodes linked togeth

C. A. Lindstrøm, E. Adli, J. B. B. Chen, P. Drobniak

Particle physicists aim to construct a electron-positron Higgs factory as the next major particle collider. However, the high associated costs motivate the development of more affordable collider designs. Plasma-wakefield acceleration is a promising technology to this end. HALHF is a proposal for a Higgs factory that utilizes beam-driven plasma-wakefield acc

Giacomo Trupiano, Giorgio De Simoni, Francesco Giazotto

We introduce a superconducting transconductance amplifier based on the thermal modulation of a SINIS (Superconductor-Insulator-Normal metal-Insulator-Superconductor) configuration. The device is composed of a normal metal island interfaced with two superconducting leads through tunnel barriers, establishing a voltage-biased symmetric SINIS setup. An addition

P. Drobniak, E. Adli, H. B. Anderson, K. N. Sjobak

Plasma lensing provides compact focusing of electron beams, since they offer strong focusing fields (kT/m) in both planes simultaneously. This becomes particularly important for highly diverging beams with a large energy spread such as those typically originating from plasma accelerators. The lens presented here is a nonlinear active plasma lens, with a cont

Yi-Cheng Lin, Kang-Chieh Chen, Zhe-Yan Li, Tzu-Heng Wu

Large language model (LLM)-driven multi-agent systems (MAS) are transforming how humans and AIs collaboratively generate ideas and artifacts. While existing surveys provide comprehensive overviews of MAS infrastructures, they largely overlook the dimension of \emph{creativity}, including how novel outputs are generated and evaluated, how creativity informs a

Junya Tanaka

We investigate whether a black hole(BH) surrounded by a dark matter (DM) halo has scalar hair/superradiant instability in the scalar tensor theory of gravity. In the scalar tensor theory, the coupling of matter and the scalar field creates effective mass, this effective mass causes the hairless BH to have scalar hair (spontaneous Scalarization). In the case

Hossein Goli, Michael Gimelfarb, Nathan Samuel de Lara, Haruki Nishimura

Off-policy evaluation (OPE) estimates the performance of a target policy using offline data collected from a behavior policy, and is crucial in domains such as robotics or healthcare where direct interaction with the environment is costly or unsafe. Existing OPE methods are ineffective for high-dimensional, long-horizon problems, due to exponential blow-ups

Yin-Long Liu, Yuanchao Li, Rui Feng, Liu He

This paper presents our submission to the PROCESS Challenge 2025, focusing on spontaneous speech analysis for early dementia detection. For the three-class classification task (Healthy Control, Mild Cognitive Impairment, and Dementia), we propose a cascaded binary classification framework that fine-tunes pre-trained language models and incorporates pause enc

Elena Bogliolo

We study the bounded cohomology and the stable commutator length of verbal wreath products $Γ\wr^{_W}A$, where $A$ has trivial bounded cohomology for a sufficiently large class of coefficients.\\ We prove that the stable commutator length always vanishes, and that the bounded cohomology vanishes in positive degrees for some such verbal wreath products; inclu

Hannes Mildner

This paper presents a computer code for analyzing electroweak precision data (EWPD) in the framework of the Standard Model Effective Field Theory (SMEFT), highlights the importance of recent ATLAS and CMS precision measurements, and introduces a novel analysis of the forward-backward asymmetry at the LHC. The computer code provides the likelihood of SMEFT Wi

Giulia Salatino, Gianluca Passarelli, Angelo Russomanno, Giuseppe E. Santoro

We investigate the effect of dissipation on the dynamics of edge modes in the monitored Su-Schrieffer-Heeger (SSH) model. Our study considers both a linear observable and a nonlinear entanglement measure, namely the two-point correlation function and the Disconnected Entanglement Entropy (DEE), as diagnostic tools. While dissipation inevitably alters the ent

Peng Gao, Liangyi Zhao

We develop $L$-functions ratios conjecture with one shift in the numerator and denominator in certain ranges for the family of cubic Hecke $L$-functions of prime moduli over the Eisenstein field using multiple Dirichlet series under the generalized Riemann hypothesis. As applications, we evaluate asymptotically the first moment of central values as well as t

Sairam Gurajada, Eser Kandogan, Sajjadur Rahman

NL2SQL approaches have greatly benefited from the impressive capabilities of large language models (LLMs). In particular, bootstrapping an NL2SQL system for a specific domain can be as simple as instructing an LLM with sufficient contextual information, such as schema details and translation demonstrations. However, building an accurate system still requires

Xu-Jie Lin, Yitao Yang, Wei-Peng Nie, Xiao-Yong Yan

Analyzing and modeling the mobility process with tour behavior is fundamental to understanding a wide range of complex systems, including animal foraging, human mobility and freight transportation. However, despite their importance, the distribution of tour length has long been neglected in individual human mobility models. To fill this gap, we analyze Fours

Mahdi Pourmirzaei, Farzaneh Esmaili, Salhuldin Alqarghuli, Mohammadreza Pourmirzaei

The diverse nature of protein prediction tasks has traditionally necessitated specialized models, hindering the development of broadly applicable and computationally efficient Protein Language Models (PLMs). In this work, we introduce Prot2Token, a unified framework that overcomes these challenges by converting a wide spectrum of protein-related predictions-

Vigneshwaran Krishnamurthy, Yann Carteret, Caroline Piaulet-Ghorayeb, Jared Splinter

The detection of helium escaping the atmosphere of exoplanets has revolutionized our understanding of atmospheric escape and exoplanetary evolution. Using high-precision spectroscopic observations from the James Webb Space Telescope (JWST) NIRISS-SOSS mode, we report the detection of significant helium absorption during the pre-transit phase of WASP-107b (17

L. Xayavong, Y. Lim, N. A. Smirnova, Calvin W. Johnson

We investigate the contribution of radial excitations to Fermi $\beta$-decay matrix element. To this end, exact no-core shell model calculations are performed for the mirror $\beta$ decay of tritium, where full convergence can be achieved on an ordinary computer. The differences between the isospin-mixing correction values obtained in the full and in a restr

Shinji Tsujikawa

Four-dimensional gravitational theories derived from an infinite sum of Lovelock curvature invariants, combined with a conformal rescaling of the metric, are equivalent to a subclass of shift-symmetric Horndeski theories that possess a single scalar degree of freedom. Under the assumption of a homogeneous and isotropic cosmological background, the theory adm

Ben Rahman

Implementation of digital health systems in low-middle-income countries (LMICs) often fails due to a lack of evaluations that take into account infrastructure limitations, local policies, and community readiness. We introduce HOT-FIT-BR, a contextual evaluation framework that expands the HOT-FIT model with three new dimensions: (1) Infrastructure Index to me

Linfeng, Zhao, Rishul Bhuvanagiri, Blake Gonzales

Mpox (formerly monkeypox) is a zoonotic disease caused by an orthopoxvirus closely related to variola and remains a significant global public health concern. During outbreaks, social media platforms like X (formerly Twitter) can both inform and misinform the public, complicating efforts to convey accurate health information. To support local response efforts

Michael O. Harding, Kirthevasan Kandasamy

We consider the problem of identifying the best arm in a multi-armed bandit model. Despite a wealth of literature in the traditional fixed budget and fixed confidence regimes of the best arm identification problem, it still remains a mystery to most practitioners as to how to choose an approach and corresponding budget or confidence parameter. We propose a n

Yizhou Zhao, Chunjiang Liu, Haoyu Chen, Bhiksha Raj

Face reenactment and portrait relighting are essential tasks in portrait editing, yet they are typically addressed independently, without much synergy. Most face reenactment methods prioritize motion control and multiview consistency, while portrait relighting focuses on adjusting shading effects. To take advantage of both geometric consistency and illuminat

Michael Arnold Bruna

This paper introduces Resonance Complexity Theory (RCT), which proposes that consciousness emerges from stable interference patterns of oscillatory neural activity. These patterns, shaped by recursive feedback and constructive interference, must exceed critical thresholds in complexity, coherence, gain, and fractal dimensionality to give rise to conscious ex

Xingyu Chen, Shihao Ma, Runsheng Lin, Jiecong Lin

Designing regulatory DNA sequences that achieve precise cell-type-specific gene expression is crucial for advancements in synthetic biology, gene therapy and precision medicine. Although transformer-based language models (LMs) can effectively capture patterns in regulatory DNA, their generative approaches often struggle to produce novel sequences with reliab

Junhong Liu, Qinfei Long, Rong-Peng Liu, Wenjie Liu

The massive integration of uncertain distributed renewable energy resources into power systems raises power imbalance concerns. Peer-to-peer (P2P) energy trading provides a promising way to balance the prosumers' volatile energy power generation and demands locally. Particularly, to protect the privacy of prosumers, distributed P2P energy trading is broadly

Michael B. Law

We establish a symmetry principle for asymptotically cylindrical steady gradient Ricci solitons (GRSs) and asymptotically conical expanding GRSs with homogeneous links. Using this, we show that the Bryant steady soliton is the unique asymptotically cylindrical steady GRS that has a round spherical link and satisfies a particular quantitative rigidity conditi

Junhong Liu, Fei Teng, Yunhe Hou

In the era of digitization, data centers have emerged as integral contributors sustaining our interlinked world, bearing responsibility for an increasing proportion of the world's energy consumption. To facilitate the their fast rollout while progressing towards net-zero energy systems, the synergy of hierarchical data centers (cloud-fog-edge) and power netw

Can Polat, Mehmet Tuncel, Mustafa Kurban, Erchin Serpedin

Recent progress in multimodal graph neural networks has demonstrated that augmenting atomic XYZ geometries with textual chemical descriptors can enhance predictive accuracy across a range of electronic and thermodynamic properties. However, naively appending large sets of heterogeneous descriptors often degrades performance on tasks sensitive to molecular sh

Jiabao Ji, Yongchao Chen, Yang Zhang, Ramana Rao Kompella

Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even state-ofthe-art LLMs, such as GPT-o4mini, frequently produce invalid action plans that violate physical constraints, such as directing a robot to an unreachable location or causing co

B. A. Toledo

Contrary to the established view of the Lorenz system as an archetype of dissipative chaos lacking conserved quantities, this work rigorously demonstrates the existence of a novel class of history-dependent dynamical invariants. Through a constructive method that augments the phase space, we derive a non-local invariant whose value remains constant along any