October 2025 arXiv papers — page 173

Alessandro Ottaviano, Andrino Meli, Paul Scheffler, Giovanni Bambini

Managing energy and thermal profiles is critical for many-core HPC processors with hundreds of application-class processing elements (PEs). Advanced model predictive control (MPC) delivers state-of-the-art performance but requires solving an online optimization problem over a thousand times per second (1 kHz control bandwidth), with computational and memory

Emma Kondrup, Anne Imouza

With the rapid progress of Large Language Models (LLMs), the general public now has easy and affordable access to applications capable of answering most health-related questions in a personalized manner. These LLMs are increasingly proving to be competitive, and now even surpass professionals in some medical capabilities. They hold particular promise in low-

Vincent Martin, Lorenzo Picinali

The objective of Audio Augmented Reality (AAR) applications are to seamlessly integrate virtual sound sources within a real environment. It is critical for these applications that virtual sources are localised precisely at the intended position, and that the acoustic environments are accurately matched. One effective method for spatialising sound on headphon

Le-Trung Nguyen, Enzo Tartaglione, Van-Tam Nguyen

As AI increasingly shapes daily life, energy consumption and data privacy have become pressing concerns. On-device learning trains models directly on edge devices, cutting energy consumption and safeguarding data privacy. However, the expanding scale of modern neural networks creates a major obstacle for on-device training. Although prior work has concentrat

Tianyi Chen, Mingcheng Zhu, Zhiyao Luo, Tingting Zhu

Electronic Health Records (EHRs) enable deep learning for clinical predictions, but the optimal method for representing patient data remains unclear due to inconsistent evaluation practices. We present the first systematic benchmark to compare EHR representation methods, including multivariate time-series, event streams, and textual event streams for LLMs. T

Seiya Ishikura, Hiroaki Yamada, Tatsuya Hiraoka, Hiroaki Yamada

This study proposes augmenting dialog data with think-aloud utterances (TAUs) for modeling individual personalities in text chat by LLM. TAU is a verbalization of a speaker's thought before articulating the utterance. We expect "persona LLMs" trained with TAU-augmented data can mimic the speaker's personality trait better. We tested whether the trained perso

Valentin Nesterenko, Petr Vishnevskiy, Anton Repko, Paul-Gerhard Reinhard

A short review on the toroidal dipole mode (TDM) in nuclei is done. The appearance of TDM in nuclei is justified. The experimental manifestation of TDM in (e,e') reaction in 58Ni is shortly reported. The relation of TDM and pygmy E1 resonance is discussed.

Jing Li, Zhijie Sun, Zhicheng Zhou, Suming Qiu

Current knowledge-enhanced large language models (LLMs) rely on static, pre-constructed knowledge bases that suffer from coverage gaps and temporal obsolescence, limiting their effectiveness in dynamic information environments. We present Agentic-KGR, a novel framework enabling co-evolution between LLMs and knowledge graphs (KGs) through multi-round reinforc

Mira Raheem, Michael Papazoglou, Bernd Krämer, Neamat El-Tazi

Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies an

Zhongze Cai, Xiaocheng Li

Pairwise preference data have played an important role in the alignment of large language models (LLMs). Each sample of such data consists of a prompt, two different responses to the prompt, and a binary label indicating which of the two responses is better. The labels are usually annotated by professional human annotators. In this paper, we consider an alte

Tanjim Rahman, Trupti Ranjan Lenka

High Electron Mobility Transistors (HEMTs) are most suitable for harsh environments as they operate reliably under extreme conditions such as high voltages, high temperatures, radiation exposure and corrosive atmospheres. In this article, gate field-plated engineering Al0.295GaN/GaN HEMT is proposed for achieving high breakdown voltage to reliably operate in

Euclid Collaboration, G. Cañas-Herrera, L. W. K. Goh, L. Blot

The Euclid mission aims to measure the positions, shapes, and redshifts of over a billion galaxies to provide unprecedented constraints on the nature of dark matter and dark energy. Achieving this goal requires a continuous reassessment of the mission's scientific performance, particularly in terms of its ability to constrain cosmological parameters, as our

Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang

Large language models (LLMs) often face a trade-off in post-training: improvements on specialized domains frequently come at the expense of general capabilities. Existing solutions attempt to mitigate this tension via regularization, selective parameter updates, or data-centric replay, but each imposes significant costs in computation, data access, or adapta

Franz Eckelt, Steffen Sykora, Xiaochen Hong, Vilmos Koscis

This work investigates nematic fluctuations and electronic correlations in the hole-doped iron pnictide superconductor Ba$_{1-x}$K$_x$Fe$_2$As$_2$ by means of longitudinal and transverse elastoresistance measurements over a wide doping range ($0.63 < x < 0.98$). For this purpose, the orbital character of the electronic response was revealed by decomposition

Hikaru Goto, Ryo Okugawa, Takami Tohyama

We investigate topological band structures of a kagome system coupled to a circularly polarized cavity mode, using a model based on a muffin-tin potential and quantum light-matter interaction. We show that Chern insulating phases emerge in the cavity-embedded kagome system due to the light-matter interaction that breaks time-reversal symmetry. We also find t

Isaac Layton, Jonathan Oppenheim, Zachary Weller-Davies

The measurement problem is the issue of explaining how the objective classical world emerges from a quantum one. Here we take a different approach. We assume that there is an objective classical system, and then ask that the standard rules of probability theory apply to it when it interacts with a quantum system. Under mild assumptions, we recover the unitar

Jianxiang Liu, Kai Liao, Yan Gong

Flux ratios of multiple images in strong gravitational lensing systems provide a powerful probe of dark matter substructure. Optical flux ratios of lensed quasars are typically affected by stellar microlensing, and thus studies of dark matter substructure often rely on emission regions that are sufficiently extended to avoid microlensing effects. To expand t

Euclid Collaboration, L. W. K. Goh, A. Nouri-Zonoz, S. Pamuk

Euclid is expected to establish new state-of-the-art constraints on extensions beyond the standard LCDM cosmological model by measuring the positions and shapes of billions of galaxies. Specifically, its goal is to shed light on the nature of dark matter and dark energy. Achieving this requires developing and validating advanced statistical tools and theoret

Petrus Mikkola, Luigi Acerbi, Arto Klami

We study density estimation from pairwise comparisons, motivated by expert knowledge elicitation and learning from human feedback. We relate the unobserved target density to a tempered winner density (marginal density of preferred choices), learning the winner's score via score-matching. This allows estimating the target by `de-tempering' the estimated winne

Mattia Mazzoli, Irma Varela-Lasheras, Sonia Namorado, Constantino Pereira Caetano

The pandemic served as an important test case of complementing traditional public health data with non-traditional data (NTD) such as mobility traces, social media activity, and wearables data to inform decision-making. Drawing on an expert workshop and a targeted survey of European modelers, we assess the promise and persistent limitations of such data in p

Clara Tomasini, Luis Riazuelo, Ana C. Murillo

Video bronchoscopy is a fundamental procedure in respiratory medicine, where medical experts navigate through the bronchial tree of a patient to diagnose or operate the patient. Surgeons need to determine the position of the scope as they go through the airway until they reach the area of interest. This task is very challenging for practitioners due to the c

Louis Esperet, Jean-Florent Raymond

In this paper we consider the multiparty equality problem in graphs, where every vertex of a graph $G$ is given an input, and the goal of the vertices is to decide whether all inputs are equal. We study this problem in the local broadcast model, where a message sent by a vertex is received by all its neighbors and the total cost of a protocol is the sum of t

Andrea Torricelli

We consider integral functionals with fast growth and the lagrangian explicitly depending on $u$. We prove that the local minimizers are locally Lipschitz continuous.

Euclid Collaboration, M. Martinelli, A. Pezzotta, D. Sciotti

The Euclid satellite will provide data on the clustering of galaxies and on the distortion of their measured shapes, which can be used to constrain and test the cosmological model. However, the increase in precision places strong requirements on the accuracy of the theoretical modelling for the observables and of the full analysis pipeline. In this paper, we

Gregory Potel

When a nuclear species (e.g., a nucleon or a deuteron nucleus) propagating freely is made to collide with a target nucleus, its trajectory is modified by exchanging variable amounts of energy, mass, linear and angular momentum with the target, according to its interaction with the nuclear medium. By addressing this perturbation away from the free path, one h

Stefania Sardellitti, Breno C. Bispo, Fernando A. N. Santos, Juliano B. Lima

One of the key challenges in many research fields is uncovering how different interconnected systems interact within complex networks, typically represented as multi-layer networks. Capturing the intra- and cross-layer interactions among different domains for analysis and processing calls for topological algebraic descriptors capable of localizing the homolo

Adel Omrani, Sajjad Sadeghi

Numerous types of antennas have been employed for microwave imaging of stratified media for ground penetrating radar (GPR), through-the-wall-radar imaging (TWRI), etc. This letter aims to investigate the impact of the different antennas with their characteristics on the image reconstruction of those media. Hence, three types of antennas, including horn anten

Hao Jiang, Chongjun Ouyang, Zhaolin Wang, Yuanwei Liu

The fundamental sensing limit of pinching-antenna systems (PASS) is studied from a Bayesian Cram\'er-Rao bound (BCRB) perspective. Compared to conventional CRB, BCRB is independent of the exact values of sensing parameters and is not restricted by the unbiasedness of the estimator, thus offering a practical and comprehensive lower bound for evaluating sensin

Aziz Bacha, Thomas George

Deep neural networks are often considered opaque systems, prompting the need for explainability methods to improve trust and accountability. Existing approaches typically attribute test-time predictions either to input features (e.g., pixels in an image) or to influential training examples. We argue that both perspectives should be studied jointly. This work

Amal Elgammal, Bernd J. Krämer, Michael P. Papazoglou, Mira Raheem

Personalized chronic care requires the integration of multimodal health data to enable precise, adaptive, and preventive decision-making. Yet most current digital twin (DT) applications remain organ-specific or tied to isolated data types, lacking a unified and privacy-preserving foundation. This paper introduces the Patient Medical Digital Twin (PMDT), an o

Hao Zeng, Jianguo Huang, Bingyi Jing, Hongxin Wei

Large reasoning models (LRMs) have achieved remarkable progress in complex problem-solving tasks. Despite this success, LRMs typically suffer from high computational costs during deployment, highlighting a need for efficient inference. A practical direction of efficiency improvement is to switch the LRM between thinking and non-thinking modes dynamically. Ho

Marco Ghirlanda

We present a general classification algorithm for reflexive simplices, which allows us to determine all reflexive simplices in dimensions five and six. In terms of algebraic geometry this means that we classify the Gorenstein fake weighted projective spaces in dimensions five and six. As a byproduct of our methods, we obtain explicit formulae for the Picard

T. V. Zaqarashvili, M. Dikpati, P. A. Gilman

The activity of Sun-like stars is governed by the magnetic field, which is believed to be generated in a thin layer between convective and radiative envelopes. The dynamo layer, also called the tachocline, permits the existence of Rossby waves (r-modes) described by magnetohydrodynamic shallow water models, which may lead to short-term cycles in stellar acti

Yansong Wang, Qihui Lin, Junjie Huang, Tao Jia

Recommendation systems have become indispensable in various online platforms, from e-commerce to streaming services. A fundamental challenge in this domain is learning effective embeddings from sparse user-item interactions. While contrastive learning has recently emerged as a promising solution to this issue, generating augmented views for contrastive learn

Manuel Bodirsky, Santiago Guzmán-Pro

The \emph{Sandwich Problem} (SP) for a graph class $\calC$ is the following computational problem. The input is a pair of graphs $(V,E_1)$ and $(V,E_2)$ where $E_1\subseteq E_2$, and the task is to decide whether there is an edge set $E$ where $E_1\subseteq E \subseteq E_2$ such that the graph $(V,E)$ belongs to $\calC$. In this paper we show that many SPs c

Orin Levy, Liad Erez, Alon Cohen, Yishay Mansour

We present regret minimization algorithms for the contextual multi-armed bandit (CMAB) problem over $K$ actions in the presence of delayed feedback, a scenario where loss observations arrive with delays chosen by an adversary. As a preliminary result, assuming direct access to a finite policy class $\Pi$ we establish an optimal expected regret bound of $ O (

Zachary Davis, Luca Comisso, Colby Haggerty, Joonas Nättilä

Coherent structures created through turbulent cascades play a key role in energy dissipation and particle acceleration. In this work, we investigate both current and vorticity sheets in 3D particle-in-cell simulations of decaying relativistic turbulence in pair plasma by training a self-organizing map to recognize these structures. We subsequently carry out

Satya P. Singh, Rashmi Chaudhry, Anand Srivastava, Jagath C. Rajapakse

Orthogonal moment-based image representations are fundamental in computer vision, but classical methods suffer from high computational complexity and numerical instability at large orders. Zernike and pseudo-Zernike moments, for instance, require coupled radial-angular processing that precludes efficient factorization, resulting in $\mathcal{O}(n^3N^2)$ to $

Rasmus Kyng, Maximilian Probst Gutenberg, Tim Rieder

We present a new and surprisingly simple analysis of random-shift decompositions -- originally proposed by Miller, Peng, and Xu [SPAA'13]: We show that decompositions for exponentially growing scales $D = 2^0, 2^1, \ldots, 2^{\log_2(\operatorname{diam}(G))}$, have a tight constant trade-off between distance-to-center and separation probability on average acr

Gennaro Auricchio, Giuseppe Toscani

We study the rate of convergence to equilibrium of the solutions to Fokker-Planck type equations with linear drift by means of Cram\'er and Energy distances, which have been recently widely used in problems related to AI, in particular for tasks related to machine learning. In all cases in which the Fokker-Planck type equations can be treated through these d

Louis Denis, Benoît Vanderheyden, Christophe Geuzaine

The simulation of large-scale high-temperature superconducting (HTS) magnets is a computational challenge due to the multiple spatial scales involved, from the magnet to the detailed turn-to-turn geometry. To reduce the computational cost associated with finite-element (FE) simulations of insulated HTS coils, the simultaneous multi-scale homogeneous (SMSH) m

Dominik Winter, Mai Bui, Monica Azqueta Gavaldon, Nicolas Triltsch

Scarcity of annotated data, particularly for rare or atypical morphologies, present significant challenges for cell and nuclei segmentation in computational pathology. While manual annotation is labor-intensive and costly, synthetic data offers a cost-effective alternative. We introduce a Multimodal Semantic Diffusion Model (MSDM) for generating realistic pi

L. Jarjat, B. Hue, T. Philippe-Kagan, B. Neukelmance

We demonstrate the parametric modulation of a double quantum dot charge dipole coupled to a cavity, at the cavity frequency, achieving an amplified readout signal compared to conventional dispersive protocols. Our findings show that the observed cavity field displacement originates from dipole radiation within the cavity, rather than from a longitudinal coup

Nina Kessler, Timea Csengeri, David Cornu, Sylvain Bontemps

Complex organic molecules (COMs) are observed to be abundant in various astrophysical environments, in particular toward star forming regions they are observed both toward protostellar envelopes as well as shocked regions. Emission spectrum especially of heavier COMs may consists of up to hundreds of lines, where line blending hinders the analysis. However,

M. M. Fedin, A. A. Morozov

We prove the decomposition of arbitrary diagonal operators into tensor and matrix products of smaller matrices, focusing on the analytic structure of the resulting formulas and their inherent symmetries. Diagrammatic representations are introduced, providing clear visualizations of the structure of these decompositions. We also discuss symmetries of the sugg

Euclid Collaboration, V. F. Cardone, S. Joudaki, L. Blot

As the statistical precision of cosmological measurements increases, the accuracy of the theoretical description of these measurements needs to increase correspondingly in order to infer the underlying cosmology that governs the Universe. To this end, we have created the Cosmology Likelihood for Observables in Euclid (CLOE), which is a novel cosmological par

Izzatjon Allayarov, Vittorio Aita, Diane J. Roth, Boaz van Casteren

Dielectric metasurfaces can achieve strong light-matter interaction based on several types of collective (nonlocal) resonances, such as surface lattice resonances (SLRs) and quasi bound states in the continuum (quasi-BICs). Spectral selectivity, field enhancement, and high and controllable Q-factors make these resonances appealing for technological applicati

Enze Zhang, Jiaying Wang, Mengxi Xiao, Jifei Liu

Large language models (LLMs) have substantially advanced machine translation (MT), yet their effectiveness in translating web novels remains unclear. Existing benchmarks rely on surface-level metrics that fail to capture the distinctive traits of this genre. To address these gaps, we introduce DITING, the first comprehensive evaluation framework for web nove

Mesut Arslandok

Ultrarelativistic heavy-ion collisions are considered ideal environments for exploring the QCD phase diagram and probing the properties of the QGP as functions of temperature and baryon chemical potential. At the highest energies, such as those reached at the Large Hadron Collider (LHC), and near vanishing baryon chemical potential, the transition from hadro

Zhi Yang, Changwu Huang, Ke Tang, Xin Yao

While significant progress has been made in conventional fairness-aware machine learning (ML) and differentially private ML (DPML), the fairness of privacy protection across groups remains underexplored. Existing studies have proposed methods to assess group privacy risks, but these are based on the average-case privacy risks of data records. Such approaches

Roni Koitermaa, Marzhan Toktaganova, Andreas Kyritsakis, Tauno Tiirats

High magnetic fields can increase the occurrence of vacuum arcing, suggesting that both electric and magnetic fields can play a role in the vacuum arcing process. The mechanism of vacuum arcing in high magnetic fields is believed to involve both the cathode and the anode, with the cathode serving as the originator of field-emitting nanoprotrusions or tips, w

Yuqiong Cheng, Yuan-Song Zeng, Wanyue Xiao, Tong Fu

Geometric phase is a far-reaching concept in quantum and classical physics. The first discovered geometric phase, the Pancharatnam-Berry (PB) phase, has profoundly shaped nanophotonics through metasurfaces. However, the PB phase arises from SU(2) polarization evolution and is constrained to a 2D polarization space, failing to capture the full polarization de

Zhidong Zhang

This article gives a brief overview on recent advances in experiments of critical exponents in three groups of magnetic materials. Revisiting experimental data verifies that a universality class with the critical exponents beta = 3/8, gamma = 5/4 and delta = 13/3 occurs in the three-dimensional (3D) Ising magnets, such as transition-metal intermetallics, rar

Weikai Huang, Jieyu Zhang, Taoyang Jia, Chenhao Zheng

Visual grouping -- operationalized through tasks such as instance segmentation, visual grounding, and object detection -- enables applications ranging from robotic perception to photo editing. These fundamental problems in computer vision are powered by large-scale, painstakingly annotated datasets. Despite their impact, these datasets are costly to build, b

Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia

Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal ef

Lukas Krodinger, Altin Hajdari, Stephan Lukasczyk, Gordon Fraser

Machine learning (ML) libraries such as PyTorch and TensorFlow are essential for a wide range of modern applications. Ensuring the correctness of ML libraries through testing is crucial. However, ML APIs often impose strict input constraints involving complex data structures such as tensors. Automated test generation tools such as Pynguin are not aware of th

Irash Perera, Uthayasanker Thayasivam

Intelligent analysis of medical imaging plays a crucial role in assisting clinical diagnosis, especially for identifying subtle pathological features. This paper introduces a novel multi-branch ConvNeXt architecture designed specifically for the nuanced challenges of medical image analysis. While applied here to the specific problem of COVID-19 diagnosis, th

Yongjie Wang, Yue Yu, Kaisong Song, Jun Lin

Large Language Models (LLMs) have enabled a wide range of applications through their powerful capabilities in language understanding and generation. However, as LLMs are trained on static corpora, they face difficulties in addressing rapidly evolving information or domain-specific queries. Retrieval-Augmented Generation (RAG) was developed to overcome this l

Soroush Mahdi, Maryam Amirmazlaghani, Saeed Saravani, Zahra Dehghanian

In this paper, we propose a new approach called MemLoss to improve the adversarial training of machine learning models. MemLoss leverages previously generated adversarial examples, referred to as 'Memory Adversarial Examples,' to enhance model robustness and accuracy without compromising performance on clean data. By using these examples across training epoc

Yimu Zhang, Yuanshi Liu, Cong Fang

In the training of large language models, momentum is widely used and often demonstrated to achieve significant acceleration. However, storing momentum typically presents memory challenges. In this paper, we propose AdaPM, an adaptive training strategy that leverages partial momentum to implement a memory-efficient optimizer. To this end, AdaPM utilizes a no

Firuz Rakhmonov

For sufficiently large integers $K$, $x$, $y$, and $q$ satisfying $K \le y < x$, where $f(u) = \alpha u^n + \alpha_{n-1}u^{n-1} + \ldots + \alpha_1 u$ is a polynomial of degree $n$ with real coefficients, $n$ is a fixed positive integer, $\alpha$ is a real number such that $\left|\alpha - \frac{a}{q}\right| \le \frac{1}{q^2}$, $(a, q) = 1$, $q \ge 1$ and $\m

Anne-Catherine de la Hamette, Viktoria Kabel, Časlav Brukner

A fundamental question in the field of quantum reference frames concerns what global properties of a system can be determined by observers operating entirely from within that system. We investigate this question by extending both the perspectival and perspective-neutral approaches beyond the commonly studied zero total momentum case to arbitrary fixed charge

Carlo Rigoni, Max Philipp Holl, Alberto Scacchi, Emil Stråka

Ferrofluids, colloidal dispersions of magnetic nanoparticles, are renowned for pattern formation like few other materials. The Rosensweig instability of a horizontal ferrofluid-air interface in perpendicular magnetic field is especially well known classically, this instability sets the air-ferrofluid interface into an array of spikes that correspond to a new

Chihiro Yano, Kosuke Yamada, Hayato Tsukagoshi, Ryohei Sasano

Semantic frame induction is the task of clustering frame-evoking words according to the semantic frames they evoke. In recent years, leveraging embeddings of frame-evoking words that are obtained using masked language models (MLMs) such as BERT has led to high-performance semantic frame induction. Although causal language models (CLMs) such as the GPT and Ll

Xinhu Li, Ayush Jain, Zhaojing Yang, Yigit Korkmaz

Learning from demonstrations enables experts to teach robots complex tasks using interfaces such as kinesthetic teaching, joystick control, and sim-to-real transfer. However, these interfaces often constrain the expert's ability to demonstrate optimal behavior due to indirect control, setup restrictions, and hardware safety. For example, a joystick can move

Kleanthis Avramidis, Tiantian Feng, Woojae Jeong, Jihwan Lee

Neurophysiological recordings such as electroencephalography (EEG) offer accessible and minimally invasive means of estimating physiological activity for applications in healthcare, diagnostic screening, and even immersive entertainment. However, these recordings yield high-dimensional, noisy time-series data that typically require extensive pre-processing a

Youwei Zheng, Yuxi Ren, Xin Xia, Xuefeng Xiao

Diffusion Transformer (DiT) has demonstrated remarkable performance in text-to-image generation; however, its large parameter size results in substantial inference overhead. Existing parameter compression methods primarily focus on pruning, but aggressive pruning often leads to severe performance degradation due to reduced model capacity. To address this lim

Dennis Rall, Bernhard Bauer, Mohit Mittal, Thomas Fraunholz

Large language models (LLMs) are now routinely used to autonomously execute complex tasks, from natural language processing to dynamic workflows like web searches. The usage of tool-calling and Retrieval Augmented Generation (RAG) allows LLMs to process and retrieve sensitive corporate data, amplifying both their functionality and vulnerability to abuse. As

Juanqin Liu, Leonardo Plotegher, Eloy Roura, Shaoming He

The extensive application of unmanned aerial vehicles (UAVs) in military reconnaissance, environmental monitoring, and related domains has created an urgent need for accurate and efficient multi-object tracking (MOT) technologies, which are also essential for UAV situational awareness. However, complex backgrounds, small-scale targets, and frequent occlusion

Jaeseong Lee, Junghee Lee

This paper addresses one of the most noteworthy issues in the recent virtual asset market, the privacy concerns related to token transactions of Real-World Assets tokens, known as RWA tokens. Following the advent of Bitcoin, the virtual asset market has experienced explosive growth, spawning movements to link real-world assets with virtual assets. However, d

Ting Yu, Zhongli Zhang, Hongyu Gong, Zhigang Wen

Low-frequency spectral studies of radio pulsars represent a key method for uncovering their emission mechanisms, magnetospheric structure, and signal interactions with the surrounding interstellar medium (ISM). In recent years, more next-generation low-frequency radio telescopes (e.g., LOFAR, LWA and MWA) have enriched the observational window below 350 MHz,

Laxmiraju Kandikatla, Branislav Radeljic

As Artificial Intelligence (AI) technologies continue to advance, protecting human autonomy and promoting ethical decision-making are essential to fostering trust and accountability. Human agency (the capacity of individuals to make informed decisions) should be actively preserved and reinforced by AI systems. This paper examines strategies for designing AI

Jikai Wang, Yunqi Cheng, Kezhi Wang, Zonghai Chen

Visual Teach-and-Repeat Navigation is a direct solution for mobile robot to be deployed in unknown environments. However, robust trajectory repeat navigation still remains challenged due to environmental changing and dynamic objects. In this paper, we propose a novel visual teach-and-repeat navigation system, which consists of a flexible map representation,

Gregory D. Baker

In this paper, we introduce Narrative Learning, a methodology where models are defined entirely in natural language and iteratively refine their classification criteria using explanatory prompts rather than traditional numerical optimisation. We report on experiments to evaluate the accuracy and potential of this approach using 3 synthetic and 3 natural data

Zhang Zheng, Deheng Ye, Peilin Zhao, Hao Wang

Large language model (LLM) agents have shown remarkable progress in social deduction games (SDGs). However, existing approaches primarily focus on information processing and strategy selection, overlooking the significance of persuasive communication in influencing other players' beliefs and responses. In SDGs, success depends not only on making correct dedu

Raúl M. Falcón, Jaime Gutiérrez, Jorge Jiménez Urroz

Every Latin square of prime power order $q$ is uniquely described by a local permutation polynomial (LPP) in the polynomial ring $\mathbb{F}_q[x,y]$. Despite this equivalence, one may find in the literature only some preliminary results on the relationship among Latin squares and LPPs. This paper delves into this topic by showing how the coefficients of any

Atul Shree, Harshith Jupuru

CTC-based ASR systems face computational and memory bottlenecks in resource-limited environments. Traditional CTC decoders, requiring up to 90% of processing time in systems (e.g., wav2vec2-large on L4 GPUs), face inefficiencies due to exhaustive token-level operations. This paper introduces Frame Level Token Pruning for Connectionist Temporal Classification

Dildar Ali, Suman Benerjee, Yamuna Prasad

We study the problem of minimizing regret in multi-mode advertisement settings, where an influence provider allocates advertising resources such as social network seeds and billboard slots to multiple advertisers with specified influence demands and payments. Unlike prior work focusing on a single mode of advertising, we consider the interplay between online

Philip Heinzel, René Sondenheimer

Gaussian states are an essential building block for various applications in quantum optics and quantum information science, yet the precise relation between their second- and third-order correlation functions remains not fully explored. We discuss connections between these correlation functions by constructing an explicit decomposition formula for arbitrary

Bicheng Wang, Junping Wang, Yibo Xue

Learning complex network dynamics is fundamental to understanding, modelling and controlling real-world complex systems. There are two main problems in the task of predicting the dynamic evolution of complex networks: on the one hand, existing methods usually use simple graphs to describe the relationships in complex networks; however, this approach can only

Bram Kraaijeveld, Andrei C. Jalba, Anna Vilanova, Maxime Chamberland

Real-time rendering of dynamic line sets is relevant in many visualization tasks, including unsteady flow visualization and interactive white matter reconstruction from Magnetic Resonance Imaging. High-quality global illumination and transparency are important for conveying the spatial structure of dense line sets, yet remain difficult to achieve at interact

Shinwoo Park, Hyejin Park, Hyeseon An, Yo-Sub Han

As large language models (LLMs) continue to advance rapidly, reliable governance tools have become critical. Publicly verifiable watermarking is particularly essential for fostering a trustworthy AI ecosystem. A central challenge persists: balancing text quality against detection robustness. Recent studies have sought to navigate this trade-off by leveraging

Shannon Liu, Maria Teresa Parreira, Wendy Ju

As robots become more integrated into society, detecting robot errors is essential for effective human-robot interaction (HRI). When a robot fails repeatedly, how can it know when to change its behavior? Humans naturally respond to robot errors through verbal and nonverbal cues that intensify over successive failures-from confusion and subtle speech changes

Emilio Mastriani, Alessandro Costa, Federico Incardona, Kevin Munari

Concerning machine learning, segmentation models can identify state changes within time series, facilitating the detection of transitions between normal and anomalous conditions. Specific techniques such as Change Point Detection (CPD), particularly algorithms like ChangeFinder, have been successfully applied to segment time series and improve anomaly detect

Gurprit Singh, Wenzel Jakob

Generative artificial intelligence (AI) has made unprecedented advances in vision language models over the past two years. During the generative process, new samples (images) are generated from an unknown high-dimensional distribution. Markov Chain Monte Carlo (MCMC) methods are particularly effective in drawing samples from such complex, high-dimensional di

Bhupesh Kumar, Graham D. Bruce, Luca Dal Negro, Sebastian A. Schulz

Disorder-driven, integrated speckle spectrometers offer exceptional spectral resolution within a compact design. They benefit from enhanced optical path lengths due to multiple light scattering events, however, often at the cost of low optical throughput. Here, we investigate the relationship between these two figures of merit by systematically varying the s

Ori Livson, Mikhail Prokopenko

Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The theorem is often described as a generalisation of Condorcet's Paradox, wherein pairwise majority voting may fail to jointly s

Michael Winn

The current status of the experimental investigation of quarkonia in heavy-ion collisions as probes for deconfinement is reviewed. The emphasis is put on the qualitative understanding of charmonia (ccbar bound states) and bottomonia (bbbar) based on the results in the past few years. In particular, the observation of non-primordial J/psi (ccbar) production a

Manisg Yadav, Archana Dixit, M. S. Barak, Anirudh Pradhan

In this study, we explore the impact of various combinations of CMB-independent datasets, including the recent DESI BAO measurements, on the equation of state (EoS) of dark energy and other cosmological parameters within the framework of the dynamical dark energy model ($w$CDM). Assuming a constant EoS parameter for dark energy, we derive constraints on the

Matthew Sotoudeh

As computer systems grow ever larger and more complex, a crucial task in software development is for one person (the system expert) to communicate to another (the system novice) how a certain program works. This paper reports on the author's experiences with a paradigm for program documentation that we call literate tracing. A literate trace explains a softw

Upasana Tiwari, Rupayan Chakraborty, Sunil Kumar Kopparapu

Effectiveness of speech emotion recognition in real-world scenarios is often hindered by noisy environments and variability across datasets. This paper introduces a two-step approach to enhance the robustness and generalization of speech emotion recognition models through improved representation learning. First, our model employs EDRL (Emotion-Disentangled R

Yitong Chen, Xinyao Xu, Ping Zhu, Xinyong Han

Flexible microelectrode (FME) implantation into brain cortex is challenging due to the deformable fiber-like structure of FME probe and the interaction with critical bio-tissue. To ensure reliability and safety, the implantation process should be monitored carefully. This paper develops an image-based anomaly detection framework based on the microscopic came

Alexander Grohsjean

The CMS Collaboration recently observed a pseudoscalar excess of top quark-antiquark ($t\bar{t}$) events near the production threshold. One possible interpretation involves the formation of a short-lived quasi-bound state of $t\bar{t}$. This proceeding summarizes the first presentation of the results at the 59th Rencontres de Moriond and outlines its potenti

Yamaan Attwa, Sam Mattheus, Tibor Szabó, Jacques Verstraete

We provide two novel constructions of $r$ edge-disjoint $K_{k+1}$-free graphs on the same vertex set, each of which has the property that every small induced subgraph contains a complete graph on $k$ vertices. The main novelty of our argument is the combination of an algebraic and a probabilistic coloring scheme, which utilizes the beneficial algebraic and c

Van Nam Dinh, Van Vy Phan, Thai Son Dang, Van Du Phan

This paper proposes a novel methodology for trajectory planning in autonomous vehicles (AVs), addressing the complex challenge of negotiating speed bumps within a unified Mixed-Integer Quadratic Programming (MIQP) framework. By leveraging Model Predictive Control (MPC), we develop trajectories that optimize both the traversal of speed bumps and overall passe

Fangye Lin, Mingbo Li

We investigate the phenomenon of air entrainment in turbulent water jets exiting a pool near the free surface. Our experimental results reveal that bubble entrainment occurs only within a specific region close to the point where the jet exits the water and is dictated solely by the jet's exit velocity, rather than the Reynolds number. The morphology of the j

Jiahui Huang, Ken Ohsuga, Hua Feng, Hui Li

We perform 3 dimensional moving-mesh hydrodynamical simulations of bubble nebulae around ultraluminous X-ray sources, using state-of-the-art software AREPO. We use a Monte-Carlo method to inject outflows with uniform mass outflow rate and momentum, in a conical funnel with a specific half opening angle. Simulation results show that the morphology of the bubb

Akira Takahashi, Shusuke Takahashi, Yuki Mitsufuji

We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By leveraging knowledge about the relationship between video/text and audio learned through a pretrained audio generative model, we can train the model more efficiently, i.e., the model does not need to be trained from scr

Philipp Bach, Sven Klaassen, Jannis Kueck, Mara Mattes

Difference-in-differences (DiD) is one of the most popular approaches for empirical research in economics, political science, and beyond. Identification in these models is based on the conditional parallel trends assumption: In the absence of treatment, the average outcome of the treated and untreated group are assumed to evolve in parallel over time, condit

Marc Distel

Alon, Seymour, and Thomas [J. Amer. Math. Soc. 1990] famously showed that every $n$-vertex $K_h$-minor-free graph has treewidth $O_h(\sqrt{n})$. Recently, Distel, Dujmovi\'c, Eppstein, Hickingbotham, Joret, Micek, Morin, Seweryn, and Wood [SIAM J. Discrete Math. 2024] refined this by showing that these graphs are $O_h(\sqrt{n})$-blowups of treewidth $4$ grap