May 2025 arXiv papers — page 9

Claes Cramer

We develop a non-linear distributional renormalisation algebra for Gaussian Quantum Foam, built from sequences of scaled Gaussians on spacelike hypersurfaces of homotopic, globally hyperbolic spacetimes and their distributional limits. The algebra is closed under multiplication and second-order differentiation, with all non-linear operations defined on smoot

Sangmin Kim, Hae-Won Park

We propose a constrained Affine Geometric Heat Flow (AGHF) method that evolves so as to suppress the dynamics gaps associated with inadmissible control directions. AGHF provides a unified framework applicable to a wide range of motion planning problems, including both holonomic and non-holonomic systems. However, to generate admissible trajectories, it requi

Senya Shlosman

We show that the Dobrushin-Shlosman conditions CV for the uniqueness of the Gibbs state provide the exact value for the critical temperature of the d-dimensional Ising model.

Yehonathan Refael, Guy Smorodinsky, Tom Tirer, Ofir Lindenbaum

Low-rank gradient-based optimization methods have significantly improved memory efficiency during the training of large language models (LLMs), enabling operations within constrained hardware without sacrificing performance. However, these methods primarily emphasize memory savings, often overlooking potential acceleration in convergence due to their relianc

Matthew Bertucci, Sean Howe

We formulate an abstract notion of equidistribution for families of $\lambda$-probability spaces parameterized by admissible $\mathbb{Z}$-sets. Under the assumption of equidistribution, we show that the $\sigma$-moment generating functions of certain infinite sums of random variables can be computed as motivic Euler products. Combining this result with earli

Louis Alaerts, Yihuang Xiong, Sinéad M. Griffin, Geoffroy Hautier

The T center in silicon has recently drawn a lot of attention for its potential in quantum information science. The sensitivity of the zero-phonon line (ZPL) to electrical field was recently investigated by a combination of different experimental methods but there is still no first principles study on the Stark shift of the T center. Dealing with the defect-

Jiazhong Cen, Xudong Zhou, Jiemin Fang, Changsong Wen

Recent advancements in 3D Gaussian Splatting (3D-GS) enable high-quality 3D scene reconstruction from RGB images. Many studies extend this paradigm for language-driven open-vocabulary scene understanding. However, most of them simply project 2D semantic features onto 3D Gaussians and overlook a fundamental gap between 2D and 3D understanding: a 3D object may

Marta García-Olmos, Yuriko Baba, Alexander López, Mario Amado

We investigate the bulk-boundary correspondence in two-dimensional type-I semi-Dirac materials with band inversion and Rashba spin-orbit coupling. Employing a dimensional reduction framework, we identify the Zak phase along the quadratically dispersing direction as a topological invariant that captures the presence of edge states. In the non-trivial topologi

Pol Mestres, Jorge Cortés, Eduardo D. Sontag

We study the problem of designing a controller that satisfies an arbitrary number of affine inequalities at every point in the state space. This is motivated by the fact that a variety of key control objectives, such as stability, safety, and input saturation, are guaranteed by closed-loop systems whose controllers satisfy such inequalities. Many works in th

Manosh T. Manoharan

Few phenomenological models tend to favour higher values of the Hubble parameter, often at the expense of invoking phantom transitions. These models achieve this without introducing additional parameters, akin to the simplicity of the concordance $\Lambda$CDM model. In this work, we investigate two such models -- Phenomenologically Emergent Dark Energy (PEDE

Irene Plaza-Ortiz, Andres Munoz-Arcentales, Joaquín Salvachúa, Carlos Aparicio

Data has become a crucial resource in the digital economy, fostering initiatives for secure and sovereign data sharing frameworks such as Data Spaces. However, these distributed environments require fine-grained access control mechanisms that balance openness with sovereignty and security. This paper proposes an extension of the Open Digital Rights Language

Alfonso M. Ganan-Calvo, Miguel A. Herrada, Jens Eggers

Steady tip streaming in the vanishing flow rate limit has been evidenced both experimentally and numerically in the literature. However, local conical Stokes flow solutions supporting these results at vanishing small scales around the emitting tip have remained elusive. This work presents approximate local conical solutions in liquid-liquid flow focusing and

Kalina Dimitrova, Venelin Kozhuharov, Ruslan Nastaev, Peicho Petkov

Machine-learning-based methods can be developed for the reconstruction of clusters in segmented detectors for high energy physics experiments. Convolutional neural networks with autoencoder architecture trained on labeled data from a simulated dataset reconstruct events by providing information about the hit point and energy of each particle that has entered

Xinliu Zhong, Ruiying Liu, Emily S. Nichols, Xuzhe Zhang

Accurate placental segmentation is essential for quantitative analysis of the placenta. However, this task is particularly challenging in T2*-weighted placental imaging due to: (1) weak and inconsistent boundary contrast across individual echoes; (2) the absence of manual ground truth annotations for all echo times; and (3) motion artifacts across echoes cau

Jerzy J. Langer, Ewelina Frackowiak, Katarzyna Ratajczak

We present here a new mechanism of light emission induced by the electric current in polyaniline micro- and nanostructures. This process involves the formation of excitons, exciton-polaritons and finally an exciton-polariton condensate, leading to laser-like emission.

Erchi Wang, Yuqing Zhu, Yu-Xiang Wang

This paper studies the problem of differentially private empirical risk minimization (DP-ERM) for binary linear classification. We obtain an efficient $(\varepsilon,\delta)$-DP algorithm with an empirical zero-one risk bound of $\tilde{O}\left(\frac{1}{\gamma^2\varepsilon n} + \frac{|S_{\mathrm{out}}|}{\gamma n}\right)$ where $n$ is the number of data points

Julio Cesar Cavalcanti, Gabriel Skantze

Turn-taking in dialogue follows universal constraints but also varies significantly. This study examines how demographic (sex, age, education) and individual factors shape turn-taking using a large dataset of US English conversations (Fisher). We analyze Transition Floor Offset (TFO) and find notable interspeaker variation. Sex and age have small but signifi

Yu Hin Au, Levent Tunçel

We study the lift-and-project relaxations of the stable set polytope of graphs generated by $\text{LS}_+$, the SDP lift-and-project operator devised by Lov\'{a}sz and Schrijver. Our focus is on $\ell$-minimal graphs: graphs on $3\ell$ vertices with $\text{LS}_+$-rank $\ell$, i.e., the smallest graphs realizing rank $\ell$. This manuscript makes two complemen

Gabriel Angelini-Knoll, Hana Jia Kong, J. D. Quigley

We introduce a theory of syntomic cohomology for ring spectra with involution, which we call Real syntomic cohomology. We show that our construction extends the theory of syntomic cohomology for rings with involution due to Park. Our construction also refines syntomic cohomology as developed by Bhatt--Morrow--Scholze, Morin, Bhatt--Lurie, and Hahn--Raksit--W

Jiaxu Zhang, Xianfang Zeng, Xin Chen, Wei Zuo

This paper presents DreamDance, a novel character art animation framework capable of producing stable, consistent character and scene motion conditioned on precise camera trajectories. To achieve this, we re-formulate the animation task as two inpainting-based steps: Camera-aware Scene Inpainting and Pose-aware Video Inpainting. The first step leverages a pr

Alan Sun

Extensively evaluating the capabilities of (large) language models is difficult. Rapid development of state-of-the-art models induce benchmark saturation, while creating more challenging datasets is labor-intensive. Inspired by the recent developments in mechanistic interpretability, we introduce circuit stability as a new way to assess model performance. Ci

Euan D. Mackay, Giulia Janzen, D. A. Matoz Fernandez, Rastko Sknepnek

Curvature plays a central role in the proper function of many biological processes. With active matter being a standard framework for understanding many aspects of the physics of life, it is natural to ask what effect curvature has on the collective behaviour of active matter. In this paper, we use the classical theory of surfaces to explore the active motio

Magamed Taimeskhanov, Damien Garreau

Feature attribution methods are a popular approach to explain the behavior of machine learning models. They assign importance scores to each input feature, quantifying their influence on the model's prediction. However, evaluating these methods empirically remains a significant challenge. To bypass this shortcoming, several prior works have proposed axiomati

Dongzi Jin, Yong Xiao, Yingyu Li

Federated Learning (FL) is a promising paradigm for realizing edge intelligence, allowing collaborative learning among distributed edge devices by sharing models instead of raw data. However, the shared models are often assumed to be ideal, which would be inevitably violated in practice due to various perturbations, leading to significant performance degrada

Xiaochen Zhang, Haoyi Xiong

This paper introduces a novel Knockoff-guided compressive sensing framework, referred to as \TheName{}, which enhances signal recovery by leveraging precise false discovery rate (FDR) control during the support identification phase. Unlike LASSO, which jointly performs support selection and signal estimation without explicit error control, our method guarant

Shelly Bensal, Umar Jamil, Christopher Bryant, Melisa Russak

We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-reflections when it answers incorrectly, we demonstrate that a model's ability to solve complex, verifiable tasks can be enhanced even when generating synthetic data is infeasible an

Jun Gao, Jin Chang, Bruno Lopez Rodriguez, Iman Esmaeil Zadeh

Superconducting nanowire single-photon detectors (SNSPDs) have emerged as essential devices that push the boundaries of photon detection with unprecedented sensitivity, ultrahigh timing precision, and broad spectral response. Recent advancements in materials engineering, superconducting electronics integration, and cryogenic system design are enabling the ev

Xihan Xiong, Zhipeng Wang, Qin Wang, Endong Liu

Can you imagine, blockchain transactions can talk! In this paper, we study how they talk and what they talk about. We focus on the input data field of Ethereum transactions, which is designed to allow external callers to interact with smart contracts. In practice, this field also enables users to embed natural language messages into transactions. Users can l

Lukas M. Fuchs, Ben Steinfurth, Jakob G. R. von Saldern, Julien Weiss

This study investigates the low-frequency dynamics of a turbulent separation bubble (TSB) over a backward-facing ramp, with a focus on large-scale coherent structures associated with the so-called 'breathing motion'. Using time-resolved particle image velocimetry (PIV) in both streamwise and spanwise planes, we examine the role of sidewall confinement. Spect

Neil He, Rishabh Anand, Hiren Madhu, Ali Maatouk

Large language models (LLMs) have shown great success in text modeling tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric structure, which current LLMs do not capture completely owing to their reliance on Euclidean operations. Recent studies have also shown that not respecting the geometry of token em

Nick Rossenbach, Benedikt Hilmes, Leon Brackmann, Moritz Gunz

Memristor-based hardware offers new possibilities for energy-efficient machine learning (ML) by providing analog in-memory matrix multiplication. Current hardware prototypes cannot fit large neural networks, and related literature covers only small ML models for tasks like MNIST or single word recognition. Simulation can be used to explore how hardware prope

János Kollár

We prove Amitsur's conjecture for Severi-Brauer varieties whose index is not a prime power.

Amanda Dias Falqueto, Farid Tari

We investigate the geometry of holomorphic curves and complex surfaces from the perspective of singularity theory. We show that, with a suitable choice of a complex bilinear symmetric form, the families of functions and mappings that measure the contact between curves or surfaces and model objects become holomorphic. This allows the application of singularit

Jisheng Dang, Jingze Wu, Teng Wang, Xuanhui Lin

Recent advancements in reinforcement learning, particularly through Group Relative Policy Optimization (GRPO), have significantly improved multimodal large language models for complex reasoning tasks. However, two critical limitations persist: 1) they often produce unfocused, verbose reasoning chains that obscure salient spatiotemporal cues and 2) binary rew

Benjamin Holzschuh, Qiang Liu, Georg Kohl, Nils Thuerey

We introduce PDE-Transformer, an improved transformer-based architecture for surrogate modeling of physics simulations on regular grids. We combine recent architectural improvements of diffusion transformers with adjustments specific for large-scale simulations to yield a more scalable and versatile general-purpose transformer architecture, which can be used

Christopher Buss, Mahdis Safari, Arash Termehchy, Stefan Lee

The growing need to integrate information from a large number of diverse sources poses significant scalability challenges for data integration systems. These systems often rely on manually written schema mappings, which are complex, source-specific, and costly to maintain as sources evolve. While recent advances suggest that large language models (LLMs) can

Jiazheng Kang, Mingming Ji, Zhe Zhao, Ting Bai

Large Language Models (LLMs) face a crucial challenge from fixed context windows and inadequate memory management, leading to a severe shortage of long-term memory capabilities and limited personalization in the interactive experience with AI agents. To overcome this challenge, we innovatively propose a Memory Operating System, i.e., MemoryOS, to achieve com

Fabio Fehr, Prabhu Teja Sivaprasad, Luca Franceschi, Giovanni Zappella

In this paper, we introduce CoRet, a dense retrieval model designed for code-editing tasks that integrates code semantics, repository structure, and call graph dependencies. The model focuses on retrieving relevant portions of a code repository based on natural language queries such as requests to implement new features or fix bugs. These retrieved code chun

Junyu Luo, Zhizhuo Kou, Liming Yang, Xiao Luo

Multimodal Large Language Models (MLLMs) have experienced rapid development in recent years. However, in the financial domain, there is a notable lack of effective and specialized multimodal evaluation datasets. To advance the development of MLLMs in the finance domain, we introduce FinMME, encompassing more than 11,000 high-quality financial research sample

Badr M. Abdullah, Matthew Baas, Bernd Möbius, Dietrich Klakow

Arabic dialect identification (ADI) systems are essential for large-scale data collection pipelines that enable the development of inclusive speech technologies for Arabic language varieties. However, the reliability of current ADI systems is limited by poor generalization to out-of-domain speech. In this paper, we present an effective approach based on voic

Guido Ivetta, Marcos J. Gomez, Sofía Martinelli, Pietro Palombini

Most resources for evaluating social biases in Large Language Models are developed without co-design from the communities affected by these biases, and rarely involve participatory approaches. We introduce HESEIA, a dataset of 46,499 sentences created in a professional development course. The course involved 370 high-school teachers and 5,370 students from 1

O. Feher, S. E. Ragan, F. D. Priestley, P. C. Clark

Giant Molecular Filaments are opportune locations in our Galaxy to study the star-forming interstellar matter and its accumulation on spatial scales comparable to those now becoming available for external galaxies. We mapped the emission of HCN(1$-$0), HCO$^+$(1$-$0), and N$_2$H$^+$(1$-$0) towards two of these filaments, one associated with the Sagittarius a

Luis Costero, Jorge Villarrubia, Francisco D. Igual

Command line learning and Bash usage are fundamental skills in systems administration, software development, and data science environments. However, their teaching has been neglected in many curricula, despite its relevance in the professional field. To address this gap, we developed an interactive competition that encourages students to improve their Bash s

Wei Chen, Jiahao Zhang, Haipeng Zhu, Boyan Xu

Large language models (LLMs) have shown great potential in decision-making due to the vast amount of knowledge stored within the models. However, these pre-trained models are prone to lack reasoning abilities and are difficult to adapt to new environments, further hindering their application to complex real-world tasks. To address these challenges, inspired

Soichiro Nishimori, Yu-Jie Zhang, Thanawat Lodkaew, Masashi Sugiyama

Optimizing policies based on human preferences is key to aligning language models with human intent. This work focuses on reward modeling, a core component in reinforcement learning from human feedback (RLHF), and offline preference optimization, such as direct preference optimization. Conventional approaches typically assume accurate annotations. However, r

Jonas Nitzler, Bugrahan Z. Temür, Phaedon-Stelios Koutsourelakis, Wolfgang A. Wall

High-dimensional Bayesian inverse analysis (dim >> 100) is mostly unfeasible for computationally demanding, nonlinear physics-based high-fidelity (HF) models. Usually, the use of more efficient gradient-based inference schemes is impeded if the multi-physics models are provided by complex legacy codes. Adjoint-based derivatives are either exceedingly cumbers

Sanju Vaidya, Cheng Chang

This paper establishes sharp bounds for the vulnerability measures of closeness and generalized closeness in graphs and identifies graphs that attain these bounds. It further develops bounds incorporating Zagreb indices for triangle- and quadrangle-free graphs, yielding formulas for closeness and generalized closeness in such graphs with diameter at most 3.

Esteban Cárdenas

In this article we consider a large system of fermions in a combined mean-field and semiclassical limit, in three dimensions. We investigate the convergence of the Wigner function of the ground state, towards the classical Thomas-Fermi theory. The main novelty of the present article is quantifying the convergence rate with respect to the semi-classical param

Raman Jha, Adithya Lenka, Mani Ramanagopal, Aswin Sankaranarayanan

In nighttime conditions, high noise levels and bright illumination sources degrade image quality, making low-light image enhancement challenging. Thermal images provide complementary information, offering richer textures and structural details. We propose RT-X Net, a cross-attention network that fuses RGB and thermal images for nighttime image enhancement. W

Hideaki Kim, Tomoharu Iwata, Akinori Fujino

Kernel method-based intensity estimators, formulated within reproducing kernel Hilbert spaces (RKHSs), and classical kernel intensity estimators (KIEs) have been among the most easy-to-implement and feasible methods for estimating the intensity functions of inhomogeneous Poisson processes. While both approaches share the term "kernel", they are founded on di

Dennis Jacob, Chong Xiang, Prateek Mittal

Deep learning techniques have enabled vast improvements in computer vision technologies. Nevertheless, these models are vulnerable to adversarial patch attacks which catastrophically impair performance. The physically realizable nature of these attacks calls for certifiable defenses, which feature provable guarantees on robustness. While certifiable defenses

Zhenghao Li, Shuang Su

Let $(X,ω)$ be a compact Hermitian manifold. Assume that the Hermitian form $ω$ satisfies $\partial \overline{\partial} ω=0$, $\partial ω\wedge \overline{\partial}ω=0$. We prove that the relative non-pluripolar product is well-defined on $X$ and satisfies the monotonicity property, generalizing Vu's results in the Kähler setting.

Tejul Pandit, Meet Raval, Dhvani Upadhyay

Aspect-Based Sentiment Analysis (ABSA) offers granular insights into opinions but often suffers from the scarcity of diverse, labeled datasets that reflect real-world conversational nuances. This paper presents an approach for generating synthetic ABSA data using Large Language Models (LLMs) to address this gap. We detail the generation process aimed at prod

Edwin Langmann

In a project with Gordon Semenoff on 1+1 dimensional QCD many years ago (when he was my postdoc advisor), we stumbled over a method to solve Calogero-Moser-Sutherland models using gauge theories. Since then, these models have reappeared in different forms in many of my research projects. In this contribution, I describe a recent such project where a second q

Alexandr Grebennikov, Matthew Kwan

Consider nonzero vectors $a_{1},\dots,a_{n}\in\mathbb{C}^{k}$, independent Rademacher random variables $\xi_{1},\dots,\xi_{n}$, and a set $S\subseteq\mathbb{C}^{k}$. What upper bounds can we prove on the probability that the random sum $\xi_{1}a_{1}+\dots+\xi_{n}a_{n}$ lies in $S$? We develop a general framework that allows us to reduce problems of this type

Rodrigo Menéndez, Andres Munoz-Arcentales, Joaquín Salvachúa, Carlos Aparicio

Identity verification in Data Spaces is a fundamental aspect of ensuring security and privacy in digital environments. This paper presents an identity verification protocol tailored for shared data environments within Data Spaces. This protocol extends the Grant Negotiation and Authorization Protocol (GNAP) and integrates OpenID Connect for Verifiable Presen

Daniel Grady

We identify some of the $k$-invariants for the Postnikov tower of the stable and unstable 4-sphere. Assuming the stable Hypothesis H of Fiorenza--Sati--Schreiber, we use the resulting obstruction theory to prove that the Chern--Simons term in the effective action of M-theory is well defined. In particular, we do not assume the presence of an $E_8$-gauge fiel

Gabriele Franciolini, Mauro Pieroni, Angelo Ricciardone, Joseph D. Romano

We present a systematic study of likelihood functions used for Stochastic Gravitational Wave Background (SGWB) searches. By dividing the data into many short segments, one customarily takes advantage of the Central Limit Theorem to justify a Gaussian crosscorrelation likelihood. We show, with a hierarchy of ever more realistic examples, beginning with a sing

Pedro C. Vieira, Miguel E. P. Silva, Pedro Manuel Pinto Ribeiro

Graph Neural Networks (GNNs) are a predominant method for graph representation learning. However, beyond subgraph frequency estimation, their application to network motif significance-profile (SP) prediction remains under-explored, with no established benchmarks in the literature. We propose to address this problem, framing SP estimation as a task independen

Luca Aiello, Raffaele Argiento, Sirio Legramanti, Lucia Paci

Air pollution is a major global health hazard, with fine particulate matter (PM10) linked to severe respiratory and cardiovascular diseases. Hence, analyzing and clustering spatio-temporal air quality data is crucial for understanding pollution dynamics and guiding policy interventions. This work provides a review of Bayesian nonparametric clustering methods

Julio Silva-Rodríguez, Ismail Ben Ayed, Jose Dolz

Vision-language models pre-trained at large scale have shown unprecedented adaptability and generalization to downstream tasks. Although its discriminative potential has been widely explored, its reliability and uncertainty are still overlooked. In this work, we investigate the capabilities of CLIP models under the split conformal prediction paradigm, which

Derek Everett, Fred Lu, Edward Raff, Fernando Camacho

Canonical algorithms for multi-armed bandits typically assume a stationary reward environment where the size of the action space (number of arms) is small. More recently developed methods typically relax only one of these assumptions: existing non-stationary bandit policies are designed for a small number of arms, while Lipschitz, linear, and Gaussian proces

Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro, Tatiana Tommasi

Our comprehension of video streams depicting human activities is naturally multifaceted: in just a few moments, we can grasp what is happening, identify the relevance and interactions of objects in the scene, and forecast what will happen soon, everything all at once. To endow autonomous systems with such holistic perception, learning how to correlate concep

Sander Land, Catherine Arnett

Byte Pair Encoding (BPE) tokenizers, widely used in Large Language Models, face challenges in multilingual settings, including penalization of non-Western scripts and the creation of tokens with partial UTF-8 sequences. Pretokenization, often reliant on complex regular expressions, can also introduce fragility and unexpected edge cases. We propose SCRIPT (Sc

Qinglin Zhu, Runcong Zhao, Hanqi Yan, Yulan He

Large Language Models (LLMs) struggle with complex reasoning due to limited diversity and inefficient search. We propose Soft Reasoning, an embedding-based search framework that optimises the embedding of the first token to guide generation. It combines (1) embedding perturbation for controlled exploration and (2) Bayesian optimisation to refine embeddings v

Shengyuan Liu, Wenting Chen, Boyun Zheng, Wentao Pan

Tumor data synthesis offers a promising solution to the shortage of annotated medical datasets. However, current approaches either limit tumor diversity by using predefined masks or employ computationally expensive two-stage processes with multiple denoising steps, causing computational inefficiency. Additionally, these methods typically rely on binary masks

Enrico Caprioglio, Pedro A. M. Mediano, Luc Berthouze

High-order interdependencies are central features of complex systems, yet a mechanistic explanation for their emergence remains elusive. Currently, it is unknown under what conditions high-order interdependencies, quantified by the information-theoretic construct of synergy, arise in systems governed by pairwise interactions. We solve this problem by providi

Vasilis Katos, Emily Rosenorn-Lanng, Jane Henriksen-Bulmer, Ala Yankouskaya

This paper explores the evolving dynamics of cybersecurity in the age of advanced AI, from the perspective of the introduced Human Layer Kill Chain framework. As traditional attack models like Lockheed Martin's Cyber Kill Chain become inadequate in addressing human vulnerabilities exploited by modern adversaries, the Humal Layer Kill Chain offers a nuanced a

Zihao Chen, Yu Xiang, Wenyong Wang

Despite the success in learning semantically meaningful, unsupervised disentangled representations, variational autoencoders (VAEs) and their variants face a fundamental theoretical challenge: substantial evidence indicates that unsupervised disentanglement is unattainable without implicit inductive bias, yet such bias remains elusive. In this work, we focus

Dayeon Ki, Kevin Duh, Marine Carpuat

As people increasingly use AI systems in work and daily life, feedback mechanisms that help them use AI responsibly are urgently needed, particularly in settings where users are not equipped to assess the quality of AI predictions. We study a realistic Machine Translation (MT) scenario where monolingual users decide whether to share an MT output, first witho

Robert Hammann, Kai Böse, Steffen Form, Luisa Hötzsch

Xenon, in both its gaseous and liquid phase, offers excellent scintillation and ionization properties, making it an ideal target medium for rare event searches. We report on measurements performed with a dual-phase xenon time projection chamber sensitive to wavelengths from 170 nm to 1700 nm. In addition to the well-established ultraviolet (UV) scintillation

Katalin Feher, Marton Demeter

Generative AI transforms knowledge production, validation, and dissemination, raising academic integrity and credibility concerns. This study examines 53 academic influencer videos that reached 5.3 million viewers to identify an emerging, structured, implementation-ready pipeline balancing originality, ethical compliance, and human-AI collaboration despite t

Xinrui Chen, Haoli Bai, Tao Yuan, Ruikang Liu

Layer pruning has emerged as a widely used technique for compressing large language models (LLMs). However, existing layer pruning approaches often incur substantial performance degradation. We identify the majority of this degradation to a single yet previously overlooked issue: \textit{the mismatch of activation magnitudes at the pruning interface}. The pr

Evangelos Sariyanidi, Lisa Yankowitz, Robert T. Schultz, John D. Herrington

The Facial Action Coding System (FACS) has been used by numerous studies to investigate the links between facial behavior and mental health. The laborious and costly process of FACS coding has motivated the development of machine learning frameworks for Action Unit (AU) detection. Despite intense efforts spanning three decades, the detection accuracy for man

Erkang Li, Jinhong Liu, Yanqing Ge, Mingjian Shi

Nonreciprocity is fundamental to photonic and optoelectronic devices such as all-optical diodes for ultrafast optical signal processing. However, previous nonreciprocity is mainly based on linear optical response instead of nonlinear optical response based on recently developed two-dimensional (2D) van der Waals heterostructures. Herein, an all-optical diode

Mengshou Wang, Liangrong Peng, Baoguo Jia, Liu Hong

During epidemic outbreaks, information dissemination enhances individual protection, while social institutions influence the transmission through measures like government interventions, media campaigns, and hospital resource allocation. Here we develop a tripartite physical-information-social epidemic model and derive the corresponding kinetic equations in d

Runjie Zhang, Kaiping Qu, Changhong Zhao, Wanjun Huang

The integration of intermittent renewable energy sources into distribution networks introduces significant uncertainties and fluctuations, challenging their operational security, stability, and efficiency. This paper considers robust distribution network reconfiguration (RDNR) with renewable generator resizing, modeled as a two-stage robust optimization (RO)

Mihir Bhaskar, Jun Tao Luo, Zihan Geng, Asmita Hajra

Despite well-documented consequences of the U.S. government's 1930s housing policies on racial wealth disparities, scholars have struggled to quantify its precise financial effects due to the inaccessibility of historical property appraisal records. Many counties still store these records in physical formats, making large-scale quantitative analysis difficul

Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj, Ian Foster

Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata plays a key role in this context, capturing the origin, transformation, and usage of research artifacts. However, existing solutions often fall short when applied to distributed, mul

Qu-Zhi Li, Zhiguang Xiao, Han-Qing Zheng

We summarize recent results on studies of $\pi\pi$ and $\pi N$ scatterings. They include the finding of a negative-parity nucleon pole with a mass lower than the nucleon mass, and the pole trajectory of $f_0(500)$ as the pion mass varies. The results are obtained from model-independent dispersion analyses. We also study the thermal properties of $f_0(500)$ b

Daniel A. Martin, Qian-Yuan Tang, Dante R. Chialvo

Recent work has introduced the concept of finite-time scaling to characterize bifurcation diagrams at finite times in deterministic discrete dynamical systems, drawing an analogy with finite-size scaling used to study critical behavior in finite systems. In this work, we extend the finite-time scaling approach in several key directions. First, we present num

Xiaorui Wu, Xiaofeng Mao, Fei Li, Xin Zhang

Large Language Models (LLMs) excel in various natural language processing tasks but remain vulnerable to generating harmful content or being exploited for malicious purposes. Although safety alignment datasets have been introduced to mitigate such risks through supervised fine-tuning (SFT), these datasets often lack comprehensive risk coverage. Most existing

Dayeon Ki, Rachel Rudinger, Tianyi Zhou, Marine Carpuat

Large Language Models (LLMs) need to adapt their predictions to diverse cultural contexts to benefit diverse communities across the world. While previous efforts have focused on single-LLM, single-turn approaches, we propose to exploit the complementary strengths of multiple LLMs to promote cultural adaptability. We introduce a Multi-Agent Debate framework,

Erik Christensen

We present a formula for the Schur multiplier norm of a complex self-adjoint matrix, and a formula for the norm, which is dual to the Schur multiplier norm, of a self-adjoint matrix. For a complex self-adjoint $n \times n $ matrix $X$ we show that its Schur multiplier norm is determined by $$ \|X\|_S = \min \{\, \|\mathrm{diag}(P)\|_\infty \, :\, - P \leq X

Chengzhi Wu, Hao Fu, Jan-Philipp Kaiser, Erik Tabuchi Barczak

The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of produ

Jonghyun Ham, Maximilian Fleissner, Debarghya Ghoshdastidar

Modern deep neural networks exhibit strong generalization even in highly overparameterized regimes. Significant progress has been made to understand this phenomenon in the context of supervised learning, but for unsupervised tasks such as denoising, several open questions remain. While some recent works have successfully characterized the test error of the l

Jiahe Chen, Jiahe Ying, Shen Wang, Jianwei Zheng

Confronting the critical challenge of insufficiently annotated samples in medical domain, semi-supervised medical image segmentation (SSMIS) emerges as a promising solution. Specifically, most methodologies following the Mean Teacher (MT) or Dual Students (DS) architecture have achieved commendable results. However, to date, these approaches face a performan

Lua F. T. Airoldi, Gustavo F. S. Alves, Yuber F. Perez-Gonzalez, Gabriel M. Salla

A black hole is expected to end its lifetime in a cataclysmic runaway burst of Hawking radiation, emitting all Standard Model particles with ultra-high energies. Thus, the explosion of a nearby primordial black hole (PBH) has been proposed as a possible explanation for the $\sim 220$~PeV neutrino-like event recently reported by the KM3NeT collaboration. If t

Hanlin Yu, Søren Hauberg, Marcelo Hartmann, Arto Klami

Real world data often lie on low-dimensional Riemannian manifolds embedded in high-dimensional spaces. This motivates learning degenerate normalizing flows that map between the ambient space and a low-dimensional latent space. However, if the manifold has a non-trivial topology, it can never be correctly learned using a single flow. Instead multiple flows mu

Daniel Severo, Brian Karrer, Niklas Nolte

Learning distributions over permutations is a fundamental problem in machine learning, with applications in ranking, combinatorial optimization, structured prediction, and data association. Existing methods rely on mixtures of parametric families or neural networks with expensive variational inference procedures. In this work, we propose a novel approach tha

Claudio Alexandre Piedade, Philippe Tranchida

Coset incidence geometries, introduced by Jacques Tits, provide a versatile framework for studying the interplay between group theory and geometry. In this article, we build upon that idea by extending classical group-theoretic constructions (amalgamated products, HNN-extensions, semi-direct products, and twisting) to the setting of coset geometries. This gi

Zhen Wu, Qi Zhao, Zhihao Ma

The super-additivity of quantum channel capacity is an important feature of quantum information theory different from classical theory, which has been attracting attention. Recently a special channel called ``platypus channel'' exhibits super-additive quantum capacity when combined with qudit erasure channels. Here we consider the ``generalized platypus chan

Jaroslav Merc, Martín A. Guerrero, Jesús A. Toalá, Roberto Ortiz

A number of asymptotic giant branch (AGB) stars are known to exhibit UV excess and/or X-ray emission. These have been considered signposts of a hot white dwarf (WD) companion in a symbiotic system (SySt), but AGB stars are so bright that they easily outshine these companions hampering their detection at optical wavelengths. A recent multi-wavelength investig

Indujan Sivanesarajah, Leon Abelmann, Uwe Hartmann

Thin-film magnetic sensors using the giant magnetoimpedance (GMI) effect show great promise for sensitive low-field magnetic measurements. Optimising sensor performance requires a thorough understanding of the properties of various soft magnetic materials. This study examines the electric, magnetic, and GMI properties of sputtered single-layer amorphous CoNb

Yeseon Hong, Junhyuk Choi, Minju Kim, Bugeun Kim

Large language models (LLMs) are increasingly being used in conversational roles, yet little is known about how intimacy emerges in human-LLM interactions. Although previous work emphasized the importance of self-disclosure in human-chatbot interaction, it is questionable whether gradual and reciprocal self-disclosure is also helpful in human-LLM interaction

Hongbo Zeng

This paper is concerned with some stronger forms of transitivity in non-autonomous discrete systems$(f_{ 1,\infty})$ generated by a uniformly convergent sequence of continuous self maps. Firstly, we present two counterexamples to show that Theorem 3.1 obtained by Salman and Das in [Multi-transitivity in nonautonomous discrete systems Topol. Appl. 278(2020)10

Dimitrios Damianos, Georgios Paraskevopoulos, Alexandros Potamianos

In this work, we investigate the Meta PL unsupervised domain adaptation framework for Automatic Speech Recognition (ASR). We introduce a Multi-Stage Domain Adaptation pipeline (MSDA), a sample-efficient, two-stage adaptation approach that integrates self-supervised learning with semi-supervised techniques. MSDA is designed to enhance the robustness and gener

Frederike Lübeck, Jonas Wildberger, Frederik Träuble, Maximilian Mordig

Cardiovascular disease (CVD) risk prediction models are essential for identifying high-risk individuals and guiding preventive actions. However, existing models struggle with the challenges of real-world clinical practice as they oversimplify patient profiles, rely on rigid input schemas, and are sensitive to distribution shifts. We developed AdaCVD, an adap

Maria Rafaela Gkeka, Bowen Sun, Evgenia Smirni, Christos D. Antonopoulos

Continuous advancements in deep learning have led to significant progress in feature detection, resulting in enhanced accuracy in tasks like Simultaneous Localization and Mapping (SLAM). Nevertheless, the vulnerability of deep neural networks to adversarial attacks remains a challenge for their reliable deployment in applications, such as navigation of auton

Moritz Grauer, Johannes Hanika, Carsten Dachsbacher

Memory bandwidth constraints continue to be a significant limiting factor in ray tracing performance, particularly as scene complexity grows and computational capabilities outpace memory access speeds. This paper presents a memory-efficient ray tracing methodology that integrates compressed data structures with ray stream techniques to reduce memory traffic.