May 2025 arXiv papers — page 85

Anna Heuser, Thomas Kesselheim

Motivated by stochastic optimization, we introduce the problem of learning from samples of contextual value distributions. A contextual value distribution can be understood as a family of real-valued distributions, where each sample consists of a context $x$ and a random variable drawn from the corresponding real-valued distribution $D_x$. By minimizing a co

Beixiong Zheng, Tiantian Ma, Changsheng You, Jie Tang

Non-fixed flexible antenna architectures, such as fluid antenna system (FAS), movable antenna (MA), and pinching antenna, have garnered significant interest in recent years. Among them, rotatable antenna (RA) is an emerging technology that offers significant potential to enhance wireless communication and sensing performance by flexibly adjusting the boresig

Bin Xie, Rui Shao, Gongwei Chen, Kaiwen Zhou

GUI automation faces critical challenges in dynamic environments. MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge. Traditional fine-tuning methods are costly for app-specific knowledge updates. We propose GUI-explorer, a training-free GUI agent that incorporates two fundamental mechanisms: (1) Autonomous Exploration of

Wei Sun, Wen Yang, Pu Jian, Qianlong Du

Recent advances have demonstrated that integrating reinforcement learning with rule-based rewards can significantly enhance the reasoning capabilities of large language models, even without supervised fine-tuning. However, prevalent reinforcement learning algorithms such as GRPO and its variants like DAPO, suffer from a coarse granularity issue when computin

Fatemeh Bagheri, Alex Glocer, Ramon E. Lopez

We investigate the effect of planetary corotation on energy dissipation within the magnetosphere-ionosphere system of exoplanets. Using MHD simulations, we find that tidally locked exoplanets have a higher cross-polar cap potential (CPCP) compared to fast-rotating planets with the same magnetic field strength, confirming previous studies. Our simulations sho

Yongick Cho, William M. Jacobs

Coupling between chemical fuel consumption and phase separation can lead to condensation at a nonequilibrium steady state, resulting in phase behaviors that are not described by equilibrium thermodynamics. Theoretical models of such "chemically driven fluids" typically invoke near-equilibrium approximations at small length scales. However, because dissipatio

Fan Wu, Jorik De Bruycker, Daan Delabie, Nobby Stevens

Received signal strength (RSS)-based optical wireless positioning (OWP) systems are becoming popular for indoor localization because they are low-cost and accurate. However, few open-source datasets are available to test and analyze RSS-based OWP systems. In this paper, we collected RSS values at a sampling frequency of 27 Hz, inertial measurement unit (IMU)

Xuancheng Shao, Mengdi Wang

We obtain polylogarithmic bounds in the polynomial Szemer\'{e}di theorem when the polynomials have distinct degrees and zero constant terms. Specifically, let $P_1, \dots, P_m \in \mathbb Z[y]$ be polynomials with distinct degrees, each having zero constant term. Then there exists a constant $c = c(P_1,\dots,P_m) > 0$ such that any subset $A \subset \{1,2,\d

Ziming Liu, Bryan Liu, Alvaro Valcarce, Xiaoli Chu

Integrating Large AI Models (LAMs) into 6G mobile networks is a key enabler of the AI-Native Air Interface (AI-AI), where protocol intelligence must scale beyond handcrafted logic. This paper presents, to our knowledge, the first standards-compliant emulation of the Radio Resource Control (RRC) layer using a decoder-only LAM (LLAMA-class) fine-tuned with Low

Jun-Peng Li, Sai Wang, Zhi-Chao Zhao, Kazunori Kohri

Produced nonlinearly by the enhanced linear cosmological curvature perturbations, the scalar-induced gravitational waves (SIGWs) can serve as a potentially powerful probe of primordial non-Gaussianity (PNG) in the early Universe. In this work, we comprehensively investigate the imprints of local-type PNG on the SIGW background beyond the widely used quadrati

Michael Anastos, Sahar Diskin, Dawid Ignasiak, Lyuben Lichev

We determine the sharp threshold for the containment of all $n$-vertex trees of bounded degree in random geometric graphs with $n$ vertices. This provides a geometric counterpart of Montgomery's threshold result for binomial random graphs, and confirms a conjecture of Espuny D\'iaz, Lichev, Mitsche, and Wesolek. Our proof is algorithmic and adapts to other f

Saiful Islam, Sanaz Mostaghim, Michael Hartmann

In this research a real time power hardware in loop configuration has been implemented for an microgrid with the combination of distribution energy resources such as photovoltaic, grid tied inverter, battery, utility grid, and a diesel generator. This paper introduces an unique adaptive multi-objective optimization approach that employs weighted optimization

Tzu-Miao Chou

This paper investigates the connection between braid group representations, defect operators, and holography within the AdS/CFT framework. It focuses on the correspondence between bulk Wilson loops and boundary defect operators, emphasizing how braid group representations map to these operators. The study also explores fusion and braiding operations in modul

Gözde Üstün, Eleanor G. Rieffel, Simon J. Devitt, Jason Saied

Type-II fusion is a probabilistic entangling measurement that is essential to measurement-based linear optical quantum computing and can be used for quantum teleportation more broadly. However, it remains under-explored for high-dimensional qudits. Our main result gives a Type-II fusion protocol with proven success probability approximately $2/d^2$ for qudit

Chunyi Li, Jiaohao Xiao, Jianbo Zhang, Farong Wen

Embodied AI has developed rapidly in recent years, but it is still mainly deployed in laboratories, with various distortions in the Real-world limiting its application. Traditionally, Image Quality Assessment (IQA) methods are applied to predict human preferences for distorted images; however, there is no IQA method to assess the usability of an image in emb

Gaurav Kamath, Sowmya Vajjala

Named Entity Recognition(NER) for low-resource languages aims to produce robust systems for languages where there is limited labeled training data available, and has been an area of increasing interest within NLP. Data augmentation for increasing the amount of low-resource labeled data is a common practice. In this paper, we explore the role of synthetic dat

Yinhao Xu, Georg A. Gottwald, Zdenka Kuncic

Reservoir Computing (RC) with physical systems requires an understanding of the underlying structure and internal dynamics of the specific physical reservoir. In this study, physical nano-electronic networks with neuromorphic dynamics are investigated for their use as physical reservoirs in an RC framework. These neuromorphic networks operate as dynamic rese

Juan Pablo Lopez

In this work sufficient conditions on the order of the symbol are developed to ensure boundedness, compactness and r-nuclearity of pseudo-differential operators in $\hbar\mathbb{Z}^n$. In addition, these conditions allow us to obtain growth estimates for the eigenvalues of some elliptic operators, in particular perturbed discrete Schr\"odinger operator.

Shangquan Sun, Wenqi Ren, Juxiang Zhou, Shu Wang

Significant progress has been made in video restoration under rainy conditions over the past decade, largely propelled by advancements in deep learning. Nevertheless, existing methods that depend on paired data struggle to generalize effectively to real-world scenarios, primarily due to the disparity between synthetic and authentic rain effects. To address t

Bowen Zheng, Xiaolei Wang, Enze Liu, Xi Wang

Recently, large language models (LLMs) have been introduced into recommender systems (RSs), either to enhance traditional recommendation models (TRMs) or serve as recommendation backbones. However, existing LLM-based RSs often do not fully exploit the complementary advantages of LLMs (e.g., world knowledge and reasoning) and TRMs (e.g., recommendation-specif

Junze Wang, Lei Fan, Weipeng Jing, Donglin Di

Existing methods for multimodal MRI segmentation with missing modalities typically assume that all MRI modalities are available during training. However, in clinical practice, some modalities may be missing due to the sequential nature of MRI acquisition, leading to performance degradation. Furthermore, retraining models to accommodate newly available modali

Reza Naserasr, Lan Anh Pham, Cyril Pujol, Huan Zhou

A fractional coloring of a signed graph $(G, {\sigma})$ is an assignment of nonnegative weights to the balanced sets (sets which do not induce a negative cycle) such that each vertex has an accumulated weight of at least 1. The minimum total wight among all such colorings is defined to be the fractional balanced chromatic number, denoted by $\chi-{fb}(G, {\s

Zhuoran Li, Shufeng Tan, Zhen Gao, Yi Tao

Autonomous driving is reshaping the way humans travel, with millimeter wave (mmWave) radar playing a crucial role in this transformation to enabe vehicle-to-everything (V2X). Although chirp is widely used in mmWave radar systems for its strong sensing capabilities, the lack of integrated communication functions in existing systems may limit further advanceme

Kexin Zhang, Junlan Chen, Daifeng Li, Yuxuan Zhang

Large language models (LLMs) encounter difficulties in knowledge-intensive multi-step reasoning (KIMSR) tasks. One challenge is how to effectively extract and represent rationale evidence. The current methods often extract semantically relevant but logically irrelevant evidence, resulting in flawed reasoning and inaccurate responses. We propose a two-way evi

Xuesong Chen, Linjiang Huang, Tao Ma, Rongyao Fang

The integration of Vision-Language Models (VLMs) into autonomous driving systems has shown promise in addressing key challenges such as learning complexity, interpretability, and common-sense reasoning. However, existing approaches often struggle with efficient integration and realtime decision-making due to computational demands. In this paper, we introduce

Sébastien Ott, Florian Schweiger

We study a family of integer-valued random interface models on the two-dimensional square lattice that include the solid-on-solid model and more generally $p$-SOS models for $0<p\le2$, and prove that at sufficiently high temperature the interface is delocalized logarithmically uniformly in the boundary data. Fr\"ohlich and Spencer had studied the analogous p

Nikolai Iorgov, Kohei Iwaki, Oleg Lisovyy, Yurii Zhuravlov

In recent years, the Fourier series (Zak transform) structure of the Painlev\'e I tau function has emerged in multiple contexts. Its main building block admits several conjectural interpretations, such as the partition function of an Argyres-Douglas gauge theory, the topological recursion partition function for the Weierstrass elliptic curve, and a 1-point c

Dominique Laurent, Nicolas Spyratos

We present an approach to computing consistent answers to queries possibly involving an aggregation operator in databases operating under a star schema and possibly containing missing values and inconsistent data. Our approach is based on earlier work concerning consistent query answering for standard queries (with no aggregate operator) in multi-table datab

Eleftherios Kalafatis, Konstantinos Mitsis, Konstantia Zarkogianni, Maria Athanasiou

Serious Games (SGs) are nowadays shifting focus to include procedural content generation (PCG) in the development process as a means of offering personalized and enhanced player experience. However, the development of a framework to assess the impact of PCG techniques when integrated into SGs remains particularly challenging. This study proposes a methodolog

Changbing Yang, Garrett Nicolai

We introduce a transformer-based morpheme segmentation system that augments a low-resource training signal through multitask learning and LLM-generated synthetic data. Our framework jointly predicts morphological segments and glosses from orthographic input, leveraging shared linguistic representations obtained through a common documentary process to enhance

KiHyun Nam, Jungwoo Heo, Jee-weon Jung, Gangin Park

A primary challenge when deploying speaker recognition systems in real-world applications is performance degradation caused by environmental mismatch. We propose a diffusion-based method that takes speaker embeddings extracted from a pre-trained speaker recognition model and generates refined embeddings. For training, our approach progressively adds Gaussian

Hanyue Lou, Jinxiu Liang, Minggui Teng, Yi Wang

Event-based cameras offer unique advantages such as high temporal resolution, high dynamic range, and low power consumption. However, the massive storage requirements and I/O burdens of existing synthetic data generation pipelines and the scarcity of real data prevent event-based training datasets from scaling up, limiting the development and generalization

Xijun Li, Jiexiang Yang, Jinghao Wang, Bo Peng

Combinatorial optimization (CO) problems, central to operation research and theoretical computer science, present significant computational challenges due to their NP-hard nature. While large language models (LLMs) have emerged as promising tools for CO--either by directly generating solutions or synthesizing solver-specific codes--existing approaches often

Thomas M. Bickley, Angus Mingare, Tim Weaving, Michael Williams de la Bastida

The advent of hybrid computing platforms consisting of quantum processing units integrated with conventional high-performance computing brings new opportunities for algorithm design. By strategically offloading select portions of the workload to classical hardware where tractable, we may broaden the applicability of quantum computation in the near term. In t

Mahdi Haghshenas

We study the linear wave equation on a class of spatially homogeneous and isotropic Friedmann-Lema\^itre-Robertson-Walker (FLRW) spacetimes in the decelerated regime with spatial topology $\mathbb{R}^3$. Employing twisted $t$-weighted multiplier vector fields, we establish uniform energy bounds and derive integrated local energy decay estimates across the en

Xiang Li, Yong Tao, Siyuan Zhang, Siwei Liu

Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth observation foundation models across six tasks and twelve t

Ziqiao Wang, Wangbo Zhao, Yuhao Zhou, Zekai Li

Diffusion Transformers (DiTs) deliver state-of-the-art image quality, yet their training remains notoriously slow. A recent remedy -- representation alignment (REPA) that matches DiT hidden features to those of a non-generative teacher (e.g. DINO) -- dramatically accelerates the early epochs but plateaus or even degrades performance later. We trace this fail

Tillmann Rheude, Roland Eils, Benjamin Wild

Real-world multimodal machine learning often faces missing, costly-to-acquire modalities, raising the problem of which samples to prioritize for additional acquisition under a budget. Prior work mainly studies per-sample or training-time acquisition while test-time, cohort-level acquisition is less explored. We propose Cohort-based Active Modality Acquisitio

Hyunjin Seo, Taewon Kim, Sihyun Yu, SungSoo Ahn

Masked diffusion models (MDMs) have achieved notable progress in modeling discrete data, while their potential in molecular generation remains underexplored. In this work, we explore their potential and introduce the surprising result that naively applying standards MDMs severely degrades the performance. We identify the critical cause of this issue as a sta

Punya Syon Pandey, Samuel Simko, Kellin Pelrine, Zhijing Jin

As large language models (LLMs) gain popularity, their vulnerability to adversarial attacks emerges as a primary concern. While fine-tuning models on domain-specific datasets is often employed to improve model performance, it can inadvertently introduce vulnerabilities within the underlying model. In this work, we investigate Accidental Vulnerability, unexpe

Ashish Sundar, Chunbo Luo, Xiaoyang Wang

Model-based reinforcement learning (MBRL) offers an intuitive way to increase the sample efficiency of model-free RL methods by simultaneously training a world model that learns to predict the future. These models constitute the large majority of training compute and time and they are subsequently used to train actors entirely in simulation, but once this is

Zhenzhen Ren, GuoBiao Li, Sheng Li, Zhenxing Qian

Despite providing superior performance, open-source large language models (LLMs) are vulnerable to abusive usage. To address this issue, recent works propose LLM fingerprinting methods to identify the specific source LLMs behind suspect applications. However, these methods fail to provide stealthy and robust fingerprint verification. In this paper, we propos

Jun Xie, Xiongjun Guan, Yingjian Zhu, Zhaoran Zhao

In this paper, we present the runner-up solution for the Ego4D EgoSchema Challenge at CVPR 2025 (Confirmed on May 20, 2025). Inspired by the success of large models, we evaluate and leverage leading accessible multimodal large models and adapt them to video understanding tasks via few-shot learning and model ensemble strategies. Specifically, diversified pro

Misbah Shahzadi, Georgios Lukes-Gerakopoulos, Martin Kološ

The worldline of an extended body in curved spacetime can be described by the Mathisson-Papapetrou-Dixon equations when its centroid, i.e., its center of mass, is fixed by a spin supplementary condition (SSC). Different SSC choices result in distinct worldlines. To examine the properties of these choices, we investigate the frequency of circular equatorial o

Xinghao Chen, Anhao Zhao, Heming Xia, Xuan Lu

Large Language Models (LLMs) have shown impressive performance on complex tasks through Chain-of-Thought (CoT) reasoning. However, conventional CoT relies on explicitly verbalized intermediate steps, which constrains its broader applicability, particularly in abstract reasoning tasks beyond language. To address this, there has been growing research interest

Qianlei Jia, Xinliang Zhou, Ondrej Krejcar, Enrique Herrera-Viedma

In group decision-making (GDM) scenarios, uncertainty, dynamic social structures, and vague information present major challenges for traditional opinion dynamics models. To address these issues, this study proposes a novel social network group decision-making (SNGDM) framework that integrates three-way decision (3WD) theory, dynamic network reconstruction, a

Thiago T. Bergamaschi

We provide a pedagogical introduction to the concepts underlying black hole information loss, intended for readers familiar with special relativity and quantum mechanics. We emphasize that there is no paradox of information loss, and that proposals suggesting deviations from well-established theories at arbitrary regimes are inherently contradictory.

Zhi-Xuan Meng, Shuai-Xia Xu, Yu-Qiu Zhao

In the present paper, we study the time-dependent correlation function of the one-dimensional impenetrable Bose gas, which can be expressed in terms of the Fredholm determinant of a time-dependent sine kernel and the solutions of the separated NLS equations. We derive the large time and distance asymptotic expansions of this determinant and the solutions of

J. W. González, E. Flórez, R. A. Gallardo, J. D. Correa

This study explores the potential of MoS$_2$ polymorphs, specifically the semiconducting 2H phase and the metallic 1T$^\prime$ phase, as anchoring materials to enhance the electrochemical performance of lithium-sulfur (Li--S) batteries. Using density functional theory calculations, we show that 1T$^\prime$-MoS$_2$ exhibits stronger Li--S interactions, greate

Xianing Chen, Si Huo, Borui Jiang, Hailin Hu

Few-shot counting estimates the number of target objects in an image using only a few annotated exemplars. However, domain shift severely hinders existing methods to generalize to unseen scenarios. This falls into the realm of single domain generalization that remains unexplored in few-shot counting. To solve this problem, we begin by analyzing the main limi

IceCube Collaboration

IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the dete

Vladislav Popkov, Xin Zhang, Carlo Presilla, Tomaž Prosen

The nonequilibrium steady state (NESS) of integrable spin chains experiencing strong boundary dissipation is accounted by introducing quasiparticles with a renormalized -- dissipatively dressed -- dispersion relation. This allows us to evaluate the spectrum of the NESS in terms of the Bethe ansatz equations for a related coherent system which has the same se

Michael Alexánder Rincón Villamizar, Timur Oikhberg

For a Banach lattice $X$, its lattice Sch\"affer constant is defined by: \begin{gather*} \lambda^+(X)=\inf\{\max\{\|x+y\|,\|x-y\|\}\,\colon\,\|x\|=\|y\|=1,x,y\geq{\bf0}\}. \end{gather*} In this paper, we investigate this constant, as well as the companion parameter \begin{gather*} \beta(X)=\inf\{\|x\vee y\|\,\colon\,\mbox{$\|x\|=\|y\|=1$, $x,y\geq{\bf0}$ and

Yiming Gao, Bin Wang, Chengwei Wei, Shuo Sun

Large language models (LLMs) have demonstrated strong instruction-following capabilities in text-based tasks. However, this ability often deteriorates in multimodal models after alignment with non-text modalities such as images or audio. While several recent efforts have investigated instruction-following performance in text and vision-language models, instr

Iván Matas, Carmen Serrano, Miguel Nogales, David Moreno

Deep learning has transformed computer vision but relies heavily on large labeled datasets and computational resources. Transfer learning, particularly fine-tuning pretrained models, offers a practical alternative; however, models pretrained on natural image datasets such as ImageNet may fail to capture domain-specific characteristics in medical imaging. Thi

Long Pei, Fengyang Xiao, Pan Zhang

We consider the traveling structure of symmetric solutions to the Rosenau-Kawahara-RLW equation and the perturbed R-KdV-RLW equation. Both equations are higher order perturbations of the classical KdV equation. For the Rosenau-Kawahara-RLW equation, we prove that classical and weak solutions with a priori symmetry must be traveling solutions. For the more co

Jiehan Cheng, Zhicheng Dou

We propose DailyQA, an automatically updated dynamic dataset that updates questions weekly and contains answers to questions on any given date. DailyQA utilizes daily updates from Wikipedia revision logs to implement a fully automated pipeline of data filtering, query generation synthesis, quality checking, answer extraction, and query classification. The be

Ramón Bécar, P. A. González, Eleftherios Papantonopoulos, Yerko Vásquez

We investigate the propagation of massive scalar fields in the background of four-dimensional Einstein-Gauss-Bonnet black holes with de Sitter (dS) asymptotics. Our study focuses on the various branches of quasinormal modes present in this background, employing the pseudospectral Chebyshev method and the third-order Wentzel-Kramers-Brillouin approximation. W

Beyazit Bestami Yuksel, Ayse Yilmazer Metin

This paper presents a comprehensive synthesis of major breakthroughs in artificial intelligence (AI) over the past fifteen years, integrating historical, theoretical, and technological perspectives. It identifies key inflection points in AI' s evolution by tracing the convergence of computational resources, data access, and algorithmic innovation. The analys

Meng-Hao Guo, Xuanyu Chu, Qianrui Yang, Zhe-Han Mo

The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and generate content across modalities such as text and images, marks a significant milestone in the evolution of intelligence. Systematic evaluation of their multi-modal output capabilities in visual thinking processes

Chang Meng, Weikang Qian, Giovanni De Micheli

Approximate computing is an effective computing paradigm for improving the energy efficiency of error-tolerant applications. Approximate logic synthesis (ALS) is an automatic process to generate approximate circuits with reduced area, delay, and power, while satisfying user-specified error constraints. This paper focuses on ALS under the maximum error constr

Mingchang Wang, Li Zeng, Bingbing Zhang, Qinghao Zhu

The generation of intense, femtosecond-scale X-ray pulses is crucial for probing matter under extreme temporal and field conditions. Current chirped-pulse amplification (CPA) techniques in free-electron lasers (FELs), however, face efficiency limitations in the soft X-ray regime due to the inherent constraints of conventional optical compressors. To address

Ziyang Zheng, Hanqiong Jia, Bilal Tüdes, Anton Chudaykin

We investigate the nonlinear evolution of cosmological perturbations in theories with scale-dependent perturbation growth, first in general and then focusing on Horndeski gravity. Within the framework of standard perturbation theory, we derive the second- and third-order kernels and show that they are fully determined by two effective functions, \( h_1 \) an

Dongzhe Zheng

This paper introduces a geometric mechanics framework for constrained systems on principal bundles through \emph{compatible pairs} $(\mathcal{D}, \lambda)$, addressing fundamental challenges in gauge-constrained physical systems. We characterize the strong transversality condition by pairing constraint distributions $\mathcal{D}$ with Lie algebra dual functi

Jianing Geng, Biao Yi, Zekun Fei, Ruiqi He

Jailbreak attacks pose a serious threat to Large Language Models (LLMs) by bypassing their safety mechanisms. A truly advanced jailbreak is defined not only by its effectiveness but, more critically, by its stealthiness. However, existing methods face a fundamental trade-off between semantic stealth (hiding malicious intent) and linguistic stealth (appearing

Philip Heltweg, Georg-Daniel Schwarz, Dirk Riehle

In many application domains, domain-specific languages can allow domain experts to contribute to collaborative projects more correctly and efficiently. To do so, they must be able to understand program structure from reading existing source code. With high-quality data becoming an increasingly important resource, the creation of data pipelines is an importan

Hongji Yang, Yucheng Zhou, Wencheng Han, Jianbing Shen

Text-to-image models are powerful for producing high-quality images based on given text prompts, but crafting these prompts often requires specialized vocabulary. To address this, existing methods train rewriting models with supervision from large amounts of manually annotated data and trained aesthetic assessment models. To alleviate the dependence on data

Fabio Durastante, Miryam Gnazzo, Beatrice Meini

We address the algorithmic problem of determining the reversible Markov chain $\tilde X$ that is closest to a given Markov chain $X$, with an identical stationary distribution. More specifically, $\tilde X$ is the reversible Markov chain with the closest transition matrix, in the Frobenius norm, to the transition matrix of $X$. To compute the transition matr

Jian Liu, Jing Xu, Song Guo, Jing Li

Existing pretrained models for 3D mesh generation often suffer from data biases and produce low-quality results, while global reinforcement learning (RL) methods rely on object-level rewards that struggle to capture local structure details. To address these challenges, we present Mesh-RFT, a novel fine-grained reinforcement fine-tuning framework that employs

Tzu-Miao Chou

This paper investigates the holographic realization of anyons in \(SU(N)_k\) Chern-Simons theory within the AdS/CFT framework. The study extends traditional models, such as \(SU(2)\), to higher-rank groups like \(SU(3)\) and \(SU(4)\), focusing on the fusion, braiding, and quantum dimensions of anyons. A correspondence between Wilson loops in the bulk and bo

Jacopo Bassi, Roberto Conti

The diadic $C^*$-algebra $\cQ_2$ contains canonically a copy of the Cuntz algebra $\cO_2$. It is shown that the inclusion $\cO_2 \subset \cQ_2$ is $C^*$-irreducible and rigid. It follows that the injective envelopes of these two $C^*$-algebras are $*$-isomorphic.

Sean Memery, Kevin Denamganai, Anna Kapron-King, Kartic Subr

Inverse problems are central to a wide range of fields, including healthcare, climate science, and agriculture. They involve the estimation of inputs, typically via iterative optimization, to some known forward model so that it produces a desired outcome. Despite considerable development in the explainability and interpretability of forward models, the itera

Qian-Xi Zhao, Jian-Jun Dong, Zi-Xiang Hu

We develop a Monte Carlo framework to analyze the statistics of quantum work in correlated electron systems. Using the Ising-Kondo model in heavy fermions as a paradigmatic platform, we thoroughly illustrate the process of determining the moment generating function of quantum work under nonequilibrium conditions in detail. Based on this function, we systemat

Farhan Abedin, William M Feldman

We study the regularity of minimizers of a two-phase energy functional in periodic media. Our main result is a large scale Lipschitz estimate. We also establish improvement-of-flatness for non-degenerate minimizers, which is a key ingredient in the proof of the Lipschitz estimate. As a consequence, we obtain a Liouville property for entire non-degenerate min

Hailong Ning, Siying Wang, Tao Lei, Xiaopeng Cao

Remote Sensing Image-Text Retrieval (RSITR) plays a critical role in geographic information interpretation, disaster monitoring, and urban planning by establishing semantic associations between image and textual descriptions. Existing Parameter-Efficient Fine-Tuning (PEFT) methods for Vision-and-Language Pre-training (VLP) models typically adopt symmetric ad

Ayano Nakai-Kasai, Tadashi Wadayama

Graph-structured data is a type of data to be obtained associated with a graph structure where vertices and edges describe some kind of data correlation. This paper proposes a regression method on graph-structured data, which is based on multi-output Gaussian processes (MOGP), to capture both the correlation between vertices and the correlation between assoc

Hannah Markgraf, Michael Eichelbeck, Daria Cappey, Selin Demirtürk

Offline reinforcement learning (RL) has gained traction as a powerful paradigm for learning control policies from pre-collected data, eliminating the need for costly or risky online interactions. While many open-source libraries offer robust implementations of offline RL algorithms, they all rely on datasets composed of experience tuples consisting of state,

Hao Chen, Hongyi Ruan, Hua Xing Zhu

Energy-energy correlator (EEC) is an event shape observable that characterizes the distribution of energy flux in collision events. We initiate the study of full-range EEC at hadron colliders, generalizing the extensively studied EEC in $e^+e^-$ collision as well as the transverse EEC in hadron collisions. We derive celestial blocks from Lorentz symmetry to

Hao Guo, Erpeng Xue, Lei Huang, Shichao Wang

Deep Learning Recommendation Models (DLRMs) often rely on extensive manual feature engineering to improve accuracy and user experience, which increases system complexity and limits scalability of model performance with respect to computational resources. Recently, Meta introduced a generative ranking paradigm based on HSTU block that enables end-to-end learn

V. Domínguez Tubío, M. C. Dijksman, J. Borregaard

Satellite-assisted entanglement distribution is a promising approach for realizing long-range quantum networking. However, the limited coherence time of existing quantum memories makes it challenging to obtain multiple event-ready entangled pairs between ground stations since one pair decoheres before the successful distribution of another. We demonstrate ho

Jonathan E. Ron, Ram M. Adar

Cell migration is closely linked to cell shape, yet cell size is often assumed to remain constant. This assumption is challenged by recent experiments showing that cells undergo volume loss during spreading and swelling upon activation, with migration velocity correlated to cell size. In this Letter, we present a minimal theoretical framework for cellular si

Julien Laasri, Marc Revol

As Kalyan T. Talluri and Garrett J. Van Ryzin describe in their work [3], Revenue Management aims to maximize an organization's revenue by considering three types of decision categories: structural, pricing, and quantity. In this document, our primary focus will be on decisions related to pricing and quantity for the sale of airline tickets on a direct fligh

Theo Elenius

We consider notions of weak solutions to a general class of parabolic problems of linear growth, formulated independently of time regularity. Equivalence with variational solutions is established using a stability result for weak solutions. A key tool in our arguments is approximation of parabolic BV functions using time mollification and Sobolev approximati

A. S. Miñarro, G. Herranz

The interplay between spin and orbital degrees of freedom gives rise to a variety of emergent phases in correlated 4d and 5d transition-metal systems. Strong spin-orbit coupling (SOC) significantly alters Jahn-Teller (JT) physics, often suppressing static distortions or promoting dynamic fluctuations, thereby reducing or even quenching orbital polarization.

Wojciech Przybyszewski, Szymon Toruńczyk

Monadic stability and the more general monadic dependence (or NIP) are tameness conditions for classes of logical structures, studied in the 80's in Shelah's classification program in model theory. They recently emerged in algorithmic and structural graph theory and finite model theory as central notions in relation with the model checking problem for first-

Vytautas Abramavicius, Melvin Mathé, Gergana V. Velikova, João P. Moutinho

Programming analog quantum processing units (QPUs), such as those produced by Pasqal, can be achieved using specialized low-level pulse libraries like Pulser. However, few currently offer the possibility to optimize pulse sequence parameters. In this paper, we introduce PulserDiff, a user-friendly and open-source Pulser extension designed to optimize pulse s

Florentin Beck, William Rudman, Carsten Eickhoff

Large Language Models (LLMs) present significant computational and memory challenges due to their extensive size, making pruning essential for their efficient deployment. Existing one-shot pruning methods often apply uniform sparsity constraints across layers or within each layer, resulting in suboptimal performance, especially at high sparsity ratios. This

Anthony N. Papathanassiou

Negative activation volumes for dielectric relaxation are rarely reported in solid state physics and are limited to atomic scale processes. Broadband Dielectric Spectroscopy on heterogeneous systems, such as polycrystalline calcite and magnesite, hosting water in their porous spce, detectedintense dielectric relaxation mechanisms related to the interfacial p

Shashank Gupta, Pilhwa Lee

Minimum attention applies the least action principle to changes of control concerning state and time, first proposed by Brockett. The involved regularization is highly relevant in emulating biological control, such as motor learning. We apply minimum attention in reinforcement learning (RL) as part of the rewards and investigate its connection to meta-learni

Alya Zouzou, Léo andéol, Mélanie Ducoffe, Ryma Boumazouza

We explore the use of conformal prediction to provide statistical uncertainty guarantees for runway detection in vision-based landing systems (VLS). Using fine-tuned YOLOv5 and YOLOv6 models on aerial imagery, we apply conformal prediction to quantify localization reliability under user-defined risk levels. We also introduce Conformal mean Average Precision

Yu Chen, Shuai-Xia Xu, Yu-Qiu Zhao

We consider the asymptotics of the partition function of the extended Gross-Witten-Wadia unitary matrix model by introducing an extra logarithmic term in the potential. The partition function can be written as a Toeplitz determinant with entries expressed in terms of the modified Bessel functions of the first kind and furnishes a $\tau$-function sequence of

A. R. Lopes, A. V. Smith Castelli, A. C. Krabbe, J. A. Hernandez-Jimenez

The Fornax cluster, the second-largest galaxy cluster within 20 Mpc, presents an ideal environment for studying environmental effects on galaxy evolution. Utilizing data from the Southern Photometric Local Universe Survey (S-PLUS), this study explores the H$\alpha$+[NII] emission maps across an area of approximately 208 square degrees around NGC 1399. For su

Chengcan Wu, Zhixin Zhang, Zeming Wei, Yihao Zhang

Large language models (LLMs) have achieved remarkable success across many applications, but their ability to generate harmful content raises serious safety concerns. Although safety alignment techniques are often applied during pre-training or post-training, recent studies show that subsequent fine-tuning on adversarial or even benign data can still compromi

Bang Trinh Tran To, Thai Le

This work presents LURK (Latent UnleaRned Knowledge), a novel framework that probes for hidden retained knowledge in unlearned LLMs through adversarial suffix prompting. LURK automatically generates adversarial prompt suffixes designed to elicit residual knowledge about the Harry Potter domain, a commonly used benchmark for unlearning. Our experiments reveal

Nicolas Keriven

Oversmoothing has long been identified as a major limitation of Graph Neural Networks (GNNs): input node features are smoothed at each layer and converge to a non-informative representation, if the weights of the GNN are sufficiently bounded. This assumption is crucial: if, on the contrary, the weights are sufficiently large, then oversmoothing may not happe

Youngmoon Jung, Yong-Hyeok Lee, Myunghun Jung, Jaeyoung Roh

For text enrollment-based open-vocabulary keyword spotting (KWS), acoustic and text embeddings are typically compared at either the phoneme or utterance level. To facilitate this, we optimize acoustic and text encoders using deep metric learning (DML), enabling direct comparison of multi-modal embeddings in a shared embedding space. However, the inherent het

Bang You, Puze Liu, Huaping Liu, Jan Peters

Simplicity is a powerful inductive bias. In reinforcement learning, regularization is used for simpler policies, data augmentation for simpler representations, and sparse reward functions for simpler objectives, all that, with the underlying motivation to increase generalizability and robustness by focusing on the essentials. Supplementary to these technique

Ziwei Luo, Fredrik K. Gustafsson, Jens Sjölund, Thomas B. Schön

This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditional diffusion-based approaches that rely on a coupled forward-backward diffusion scheme, FoD directly learns image restoration through a single forward diffusion process, yielding a simple yet efficient framework.

Hany Abdulsamad, Sahel Iqbal, Simo Särkkä

Optimal decision-making under partial observability requires agents to balance reducing uncertainty (exploration) against pursuing immediate objectives (exploitation). In this paper, we introduce a novel policy optimization framework for continuous partially observable Markov decision processes (POMDPs) that explicitly addresses this challenge. Our method ca

Teepanis Chachiyo

The complete elliptic integral of the first and second kind, K(k) and E(k), appear in a multitude of physics and engineering applications. Because there is no known closed-form, the exact values have to be computed numerically. Here, approximations for the integrals are proposed based on their asymptotic behaviors. An inverse of K is also presented. As a res