May 2025 arXiv papers — page 121

Lars-Peter Meyer, Johannes Frey, Desiree Heim, Felix Brei

Current Large Language Models (LLMs) can assist developing program code beside many other things, but can they support working with Knowledge Graphs (KGs) as well? Which LLM is offering the best capabilities in the field of Semantic Web and Knowledge Graph Engineering (KGE)? Is this possible to determine without checking many answers manually? The LLM-KG-Ben

Francky Luddens, Corentin Lothodé, Ionut Danaila

Solving the Stefan problem, also referred as the heat conduction problem with phase change, is a necessary step to solve phase change problems with convection. In this article, we are interested in using the Lattice Boltzmann Method (LBM) to solve the Stefan problem using a regularized total enthalpy model. The liquid fraction is treated as a nonlinear sourc

Yousouf Taghzouti, Franck Michel, Tao Jiang, Louis-Félix Nothias

The SPARQL query language is the standard method to access knowledge graphs (KGs). However, formulating SPARQL queries is a significant challenge for non-expert users, and remains time-consuming for the experienced ones. Best practices recommend to document KGs with competency questions and example queries to contextualise the knowledge they contain and illu

Honglin Ren, Lin Chen

In this paper, we investigate the convex roof measure of quantum coherence, with a focus on their superadditive properties. We propose sufficient conditions and establish a framework for coherence superadditivity in tripartite and multipartite systems. Through theoretical derivation, the relevant theorems are given. These results not only expand our understa

Guo Chen, Kai Li, Runxuan Yang, Xiaolin Hu

Existing causal speech separation models often underperform compared to non-causal models due to difficulties in retaining historical information. To address this, we propose the Time-Frequency Attention Cache Memory (TFACM) model, which effectively captures spatio-temporal relationships through an attention mechanism and cache memory (CM) for historical inf

50 Collaboration, P. Agnes, I. F. Albuquerque, T. Alexander

We present a search for boosted dark matter from Primordial Black Holes (PBH) evaporation using the DarkSide-50 ionization-signal-only dataset corresponding to the experiment's ($12202\pm180$) ${\rm kg\: d}$ exposure. We focus on evaporation of PBHs with masses in the range [$10^{14},\,10^{16}$] g producing Dirac fermionic dark matter particles with sub-GeV

Dennis Frauen, Valentyn Melnychuk, Jonas Schweisthal, Mihaela van der Schaar

Decision-making across various fields, such as medicine, heavily relies on conditional average treatment effects (CATEs). Practitioners commonly make decisions by checking whether the estimated CATE is positive, even though the decision-making performance of modern CATE estimators is poorly understood from a theoretical perspective. In this paper, we study o

Yuanbo Wang, Zhaoxuan Zhang, Jiajin Qiu, Dilong Sun

Diffusion models have made breakthroughs in 3D generation tasks. Current 3D diffusion models focus on reconstructing target shape from images or a set of partial observations. While excelling in global context understanding, they struggle to capture the local details of complex shapes and limited to the occlusion and lighting conditions. To overcome these li

David Stap, Christof Monz

Prior research diverges on language diversity in LLM fine-tuning: Some studies report benefits while others find no advantages. Through controlled fine-tuning experiments across 132 translation directions, we systematically resolve these disparities. We find that expanding language diversity during fine-tuning improves translation quality for both unsupervis

Sondre Wold, Lucas Georges Gabriel Charpentier, Étienne Simon

Systematic generalization remains challenging for current language models, which are known to be both sensitive to semantically similar permutations of the input and to struggle with known concepts presented in novel contexts. Although benchmarks exist for assessing compositional behavior, it is unclear how to measure the difficulty of a systematic generaliz

Zhaoyi Wang, Shengyu Huang, Jemil Avers Butt, Yuanzhou Cai

Point cloud registration has seen significant advancements with the application of deep learning techniques. However, existing approaches often overlook the potential of integrating radiometric information from RGB images. This limitation reduces their effectiveness in aligning point clouds pairs, especially in regions where geometric data alone is insuffici

M. I. Ayzatsky

The results of modification of the CASCIE code aimed at implementing open boundary conditions are presented. The accelerator section developed at CERN was chosen as a prototype for the structured waveguide under testing. Results of testing the CASCIE-M code confirms that the implementation of matrix open boundary conditions gives possibility to consider the

Biel Tura Vecino, Subhadeep Maji, Aravind Varier, Antonio Bonafonte

The use of audio recordings of human speech to train LLMs poses privacy concerns due to these models' potential to generate outputs that closely resemble artifacts in the training data. In this study, we propose a speaker privacy-preserving representation learning method through the Universal Speech Codec (USC), a computationally efficient encoder-decoder mo

Andy S. Anker, Jonas H. Jensen, Miguel Gonzalez-Duque, Rodrigo Moreno

Controlled synthesis of materials with specified atomic structures underpins technological advances yet remains reliant on iterative, trial-and-error approaches. Nanoparticles (NPs), whose atomic arrangement dictates their emergent properties, are particularly challenging to synthesise due to numerous tunable parameters. Here, we introduce an autonomous appr

Bhera Ram, Bibhas Ranjan Majhi

Local thermal equilibrium generally implies the absence of heat flux within a fluid. We find the relations between a set of thermodynamic variables of a fluid on a general spacetime and those defined on a conformally connected spacetime, assuming both descriptions are at thermal equilibrium. The scaling relations appear to be consistent with Dicke's heuristi

Tailong Wang, Carl Blair, Ammar Al-Jodah, John Winterflood

Seismic isolation is crucial for gravitational wave detectors as it minimizes ground vibrations, enabling the detection of faint gravitational wave signals. An active seismic isolation platform for precision measurement experiments is described. The table features piezo actuation along five degrees of freedom: three translational actuations and two tip-tilt

Kyeongman Park, Seongho Joo, Kyomin Jung

We introduce MultiActor-Audiobook, a zero-shot approach for generating audiobooks that automatically produces consistent, expressive, and speaker-appropriate prosody, including intonation and emotion. Previous audiobook systems have several limitations: they require users to manually configure the speaker's prosody, read each sentence with a monotonic tone c

Xiaoyu Yang, Jie Lu, En Yu

This paper uncovers a critical yet overlooked phenomenon in multi-modal large language models (MLLMs): detrimental concept drift within chain-of-thought (CoT) reasoning during non-stationary reinforcement fine-tuning (RFT), where reasoning token distributions evolve unpredictably, thereby introducing significant biases in final predictions. To address this,

Annie G. Bryant, Oliver M. Cliff, James M. Shine, Ben D. Fulcher

Information theory is a powerful framework for quantifying complexity, uncertainty, and dynamical structure in time-series data, with widespread applicability across disciplines such as physics, finance, and neuroscience. However, the literature on these measures remains fragmented, with domain-specific terminologies, inconsistent mathematical notation, and

Xugang Lu, Peng Shen, Yu Tsao, Hisashi Kawai

Transferring linguistic knowledge from a pretrained language model (PLM) to acoustic feature learning has proven effective in enhancing end-to-end automatic speech recognition (E2E-ASR). However, aligning representations between linguistic and acoustic modalities remains a challenge due to inherent modality gaps. Optimal transport (OT) has shown promise in m

Ali Joundi, Yann Traonmilin, Alasdair Newson

Many crucial tasks of image processing and computer vision are formulated as inverse problems. Thus, it is of great importance to design fast and robust algorithms to solve these problems. In this paper, we focus on generalized projected gradient descent (GPGD) algorithms where generalized projections are realized with learned neural networks and provide sta

Xiang Fei, Jinghui Lu, Qi Sun, Hao Feng

Autoregressive models have become the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. This paper introduces Numerical Token Integrity Loss (NTIL) to address this gap. NTIL operates at two levels: (1) token-level,

Mykyta Mudryi, Markiyan Chaklosh, Grzegorz Wójcik

Autonomous browsing agents powered by large language models (LLMs) are increasingly used to automate web-based tasks. However, their reliance on dynamic content, tool execution, and user-provided data exposes them to a broad attack surface. This paper presents a comprehensive security evaluation of such agents, focusing on systemic vulnerabilities across mul

Sukyoon Oh, Monalisa Mallick, Thomas Siefke, Christian Spielmann

High-resolution extreme ultraviolet (XUV) imaging remains limited by conventional approaches that require complex optics such as multilayer mirrors and zone plates. These methods are expensive, suffer from chromatic aberrations and narrow fields of view, and demand highly stable, coherent beam sources typically found only at large-scale facilities. Criticall

Katsuya Hashino, Daiki Ueda

Gravitational wave (GW) observations offer a promising probe of new physics associated with a first-order electroweak phase transition. Precision studies of the Higgs potential, including Fisher matrix analyses, have been extensively conducted in this context. However, significant theoretical uncertainties in the GW spectrum, particularly those due to renorm

Dennis Frauen, Maresa Schröder, Konstantin Hess, Stefan Feuerriegel

Estimating heterogeneous treatment effects (HTEs) is crucial for personalized decision-making. However, this task is challenging in survival analysis, which includes time-to-event data with censored outcomes (e.g., due to study dropout). In this paper, we propose a toolbox of novel orthogonal survival learners to estimate HTEs from time-to-event data under c

Jie Yan, Jing Liu, Zhong-Yuan Zhang

Federated clustering (FC) aims to discover global cluster structures across decentralized clients without sharing raw data, making privacy preservation a fundamental requirement. There are two critical challenges: (1) privacy leakage during collaboration, and (2) robustness degradation due to aggregation of proxy information from non-independent and identica

Shenghua Hu, Guangyang Zeng, Wenchao Xue, Haitao Fang

We study the problem of signal source localization using received signal strength measurements. We begin by presenting verifiable geometric conditions for sensor deployment that ensure the model's asymptotic localizability. Then we establish the consistency and asymptotic efficiency of the maximum likelihood (ML) estimator. However, computing the ML estimato

Ambre Marie, Ilias Maoudj, Guillaume Dardenne, Gwenolé Quellec

The 1st SpeechWellness Challenge conveys the need for speech-based suicide risk assessment in adolescents. This study investigates a multimodal approach for this challenge, integrating automatic transcription with WhisperX, linguistic embeddings from Chinese RoBERTa, and audio embeddings from WavLM. Additionally, handcrafted acoustic features -- including MF

Matthias Maier, Peter Munch, Murtazo Nazarov

In this paper, we investigate the effect of boundary surface roughness on numerical simulations of incompressible fluid flow past a cylinder in two and three spatial dimensions furnished with slip boundary conditions. The governing equations are approximated using a continuous finite element method, stabilized with a Galerkin least-squares approach. Through

Charlotte Bäcker, Valentin Link, Walter T. Strunz

We investigate the nature of memory effects in the non-Markovian dynamics of spin boson models. Local quantum memory criteria can be used to indicate that the reduced dynamics of an open system necessarily requires a quantum memory in its environment. We apply two such criteria, derived from different definitions put forward in the literature, to spin boson

Donlapark Ponnoprat, Masaaki Imaizumi

We investigate the estimation of an optimal transport map between probability measures on an infinite-dimensional space and reveal its minimax optimal rate. Optimal transport theory defines distances within a space of probability measures, utilizing an optimal transport map as its key component. Estimating the optimal transport map from samples finds several

Yueyun Ouyang, Feng Ding, Jun Yang

Terrestrial planets within the Venus zone surrounding M dwarf stars can retain surface ice caps on the perpetual dark side if atmospheric heat transport is inefficient, {as suggested by previous global climate simulations \citep[e.g.,][]{leconte2013}.} This condition is {proposed} to play a role in the potential regional habitability of these planets. Howeve

Yuma Hirobe, Taisei Kitamura, Youichi Yanase

The symmetry of Cooper pairs encodes key information about superconductivity and has been widely studied through the temperature dependence of the superfluid weight. However, in systems dominated by quantum geometry, conventional theories miss its essential properties. We study the temperature dependence of the quantum-geometric superfluid weight and classif

Yannik P. Wotte, Arne Sachtler, Alin Albu-Schäffer, Stefano Stramigioli

Multi-body mechanical systems have rich internal dynamics, whose solutions can be exploited as energy-efficient control targets. Yet, solutions non-trivially depend on system parameters, obscuring feasible properties for use as target trajectories. For periodic regulation tasks in robotics applications, we investigate properties of nonlinear oscillations col

ATLAS Collaboration

A calibration of the ATLAS flavour-tagging algorithms using a new calibration procedure based on optimal transportation maps is presented. Simultaneous, continuous corrections to the $b$-jet, $c$-jet, and light-flavour jet classification probabilities from jet-tagging algorithms in simulation are derived for $b$-jets using $t\bar t \to e\mu\nu\nu bb$ data. A

Yong Ren, Chenxing Li, Le Xu, Hao Gu

Humans can intuitively infer sounds from silent videos, but whether multimodal large language models can perform modal-mismatch reasoning without accessing target modalities remains relatively unexplored. Current text-assisted-video-to-audio (VT2A) methods excel in video foley tasks but struggle to acquire audio descriptions during inference. We introduce th

Chengtang Yao, Zhidan Liu, Jiaxi Zeng, Lidong Yu

3D visual illusion is a perceptual phenomenon where a two-dimensional plane is manipulated to simulate three-dimensional spatial relationships, making a flat artwork or object look three-dimensional in the human visual system. In this paper, we reveal that the machine visual system is also seriously fooled by 3D visual illusions, including monocular and bino

Shmulik Markovich-Golan, Daniel Ohayon, Itay Niv, Yair Hanani

Quantization is essential for Neural Network (NN) compression, reducing model size and computational demands by using lower bit-width data types, though aggressive reduction often hampers accuracy. Mixed Precision (MP) mitigates this tradeoff by varying the numerical precision across network layers. This study focuses on automatically selecting an optimal MP

Letizia Branca, Giovanni Catino, Davide Dameno

Exploiting the deformation method introduced by Aubin in his seminal work to construct constant negative scalar curvature metrics, we show the existence, on every closed manifold of dimension four, of a metric whose Bach tensor is pinched by the scalar curvature.

Gabriel Béna, Maxence Faldor, Dan F. M. Goodman, Antoine Cully

Cellular automata have long been celebrated for their ability to generate complex behaviors from simple, local rules, with well-known discrete models like Conway's Game of Life proven capable of universal computation. Recent advancements have extended cellular automata into continuous domains, raising the question of whether these systems retain the capacity

Xiaoyu Cheng, Tiantao Qu, Yaqing Yang, Jun Chen

Exceptional points (EPs) are spectral degeneracies unique to non-Hermitian systems which underpin phenomena from enhanced sensing to unconventional topology. While disorder is usually viewed as detrimental, it can also drive topological phase transitions (TPTs). Here, we show that random disorder alone can generate EPs and concurrent TPTs in a multiorbital n

James W. Trayford, Joop Schaye, Camila Correa, Sylvia Ploeckinger

While marginal in mass terms, dust grains play an outsized role in both the physics and observation of the interstellar medium (ISM). However, explicit modelling of this ISM constituent remains uncommon in large cosmological simulations. In this work, we present a model for the life-cycle of dust in the ISM that couples to the forthcoming COLIBRE galaxy form

Jonathan Ott, Maximilian Stahlke, Tobias Feigl, Bjoern M. Eskofier

Unsupervised representation learning for wireless channel state information (CSI)reduces reliance on labeled data, thereby lowering annotation costs, and often improves performance on downstream tasks. However, state-of-the-art approaches take little or no account of domain-specific knowledge, forcing the model to learn well-known concepts solely from data.

Max Grobbel, Daniel Flögel, Philipp Rigoll, Sören Hohmann

Task performance in terms of task completion time in teleoperation is still far behind compared to humans conducting tasks directly. One large identified impact on this is the human capability to perform transformations and alignments, which is directly influenced by the point of view and the motion retargeting strategy. In modern teleoperation systems, moti

Amelie S. Robrecht, Christoph R. Kowalski, Stefan Kopp

Adapting to the addressee is crucial for successful explanations, yet poses significant challenges for dialogsystems. We adopt the approach of treating explanation generation as a non-stationary decision process, where the optimal strategy varies according to changing beliefs about the explainee and the interaction context. In this paper we address the quest

Tuan Thai, TrungTin Nguyen, Dat Do, Nhat Ho

Mixture of Experts (MoE) models constitute a widely utilized class of ensemble learning approaches in statistics and machine learning, known for their flexibility and computational efficiency. They have become integral components in numerous state-of-the-art deep neural network architectures, particularly for analyzing heterogeneous data across diverse domai

Adam Onus, Primoz Skraba

The topology of periodic spaces has attracted a lot of interest in recent years in order to study and classify crystalline structures and other large homogeneous data sets, such as the distribution of galaxies in cosmology. In practice, these objects are studied by taking a finite sample and introducing periodic boundary conditions, however this introduces a

Beibei Lin, Zifeng Yuan, Tingting Chen

Polarization images provide rich physical information that is fundamentally absent from standard RGB images, benefiting a wide range of computer vision applications such as reflection separation and material classification. However, the acquisition of polarization images typically requires additional optical components, which increases both the cost and the

Geng Chen, Guowu Yang, Wenjie Sun, Lianhui Yu

Neutral atom quantum computers are one of the most promising quantum architectures, offering advantages in scalability, dynamic reconfigurability, and potential for large-scale implementations. These characteristics create unique compilation challenges, especially regarding compilation efficiency while adapting to hardware flexibility. However, existing meth

Hongrui Kou, Jingkai Li, Ziyu Wang, Zhouhang Lv

Accurate prediction of traffic flow parameters and real time identification of congestion states are essential for the efficient operation of intelligent transportation systems. This paper proposes a Periodic Pattern Transformer Network (PPTNet) for traffic flow prediction, integrating periodic pattern extraction with the Transformer architecture, coupled wi

Florian Lehmann, Daniel Buschek

Interactive systems are commonly prototyped as web applications. This approach enables studies with functional prototypes on a large scale. However, setting up these studies can be complex due to implementing experiment procedures, integrating questionnaires, and data logging. To enable such user studies, we developed the software system StudyAlign which off

Serge Lvovski

Suppose that $F$ is a smooth and connected complex surface (not necessarily compact) containing a smooth rational curve $C$ with positive self-intersection. We prove that there exists a neighborhood $U\supset C$ such that any meromorphic function defined on a connected neighborhood of $C$ in $U$ can be extended to a meromorphic function on the entire $U$.

Rebecca Westhäußer, Frederik Berenz, Wolfgang Minker, Sebastian Zepf

Large language models (LLMs) have advanced the field of artificial intelligence (AI) and are a powerful enabler for interactive systems. However, they still face challenges in long-term interactions that require adaptation towards the user as well as contextual knowledge and understanding of the ever-changing environment. To overcome these challenges, holist

Hao-Ran Yang, Xiaohui Chen, Chuan-Xian Ren

Aiming to generalize the well-trained gaze estimation model to new target domains, Cross-domain Gaze Estimation (CDGE) is developed for real-world application scenarios. Existing CDGE methods typically extract the domain-invariant features to mitigate domain shift in feature space, which is proved insufficient by Generalized Label Shift (GLS) theory. In this

G. Café de Miranda, Gubio G. de Lima, Tiago de S. Farias

Machine learning techniques have emerged as powerful tools to tackle various challenges. The integration of machine learning methods with Physics has led to innovative approaches in understanding, controlling, and simulating physical phenomena. This article aims to provide a practical introduction to neural network and their basic concepts. It presents some

Fynn Fromme, Hans Harder, Christine Allen-Blanchette, Sebastian Peitz

The use of machine learning for modeling, understanding, and controlling large-scale physics systems is quickly gaining in popularity, with examples ranging from electromagnetism over nuclear fusion reactors and magneto-hydrodynamics to fluid mechanics and climate modeling. These systems - governed by partial differential equations - present unique challenge

Manuel Siegl, Julian Zanon, Joseph Sink, Adonai Rodrigues da Cruz

We present the first scanning tunneling microscopy (STM) images of hydrogenic acceptor wave functions in silicon. These acceptor states appear as square ring-like features in STM images and originate from near-surface defects introduced by high-energy bismuth implantation into a silicon (001) wafer. Scanning tunneling spectroscopy confirms the formation of a

N. V. Antonov, P. I. Kakin, N. M. Lebedev, A. Yu. Luchin

We study a strongly anisotropic self-organized critical system coupled to an isotropic random fluid environment. The former is described by a continuous (coarse-grained) model due to Hwa and Kardar. The latter is modeled by the Navier--Stokes equation with a random stirring force of a rather general form that includes, in particular, the overall shaking of t

Xiao Wu, Xiaoqing Zhang, Zunjie Xiao, Lingxi Hu

Efficient convolutional neural network (CNN) architecture design has attracted growing research interests. However, they typically apply single receptive field (RF), small asymmetric RFs, or pyramid RFs to learn different feature representations, still encountering two significant challenges in medical image classification tasks: 1) They have limitations in

Li Chen, Jinwook Jung, Peter Pickl, Zhenfu Wang

The derivation of effective descriptions for interacting many-body systems is an important branch of applied mathematics. We prove a propagation of chaos result for a system of $N$ particles subject to Newtonian time evolution with or without additional white noise influencing the velocities of the particles. We assume that the particles interact according t

M. Rodriguez Zarate, T. Thiemann

In previous works in this series we focussed on Hamiltonian renormalisation of free field theories in all spacetime dimensions or interacting theories in spacetime dimensions lower than four. In this paper we address the Hamiltonian renormalisation of the U(1)**3 model for Euclidian general relativity in four spacetime dimensions which is self-interacting. T

Sai Koneru, Maike Züfle, Thai-Binh Nguyen, Seymanur Akti

The scope of the International Workshop on Spoken Language Translation (IWSLT) has recently broadened beyond traditional Speech Translation (ST) to encompass a wider array of tasks, including Speech Question Answering and Summarization. This shift is partly driven by the growing capabilities of modern systems, particularly with the success of Large Language

Itamar Procaccia, Tuhin Samanta

Dissipation, or the loss of energy conservation, is necessarily related to symmetry breaking. Here we study the symmetry breaking that is responsible for dissipation in the mechanical response of amorphous solids to quasi-static strains. To this aim we consider carefully just one cycle of strain, to reveal the source of dissipation. In general the response c

Márton Kardos, Kenneth C. Enevoldsen, Kristoffer Laigaard Nielbo

Topic models are statistical tools that allow their users to gain qualitative and quantitative insights into the contents of textual corpora without the need for close reading. They can be applied in a wide range of settings from discourse analysis, through pretraining data curation, to text filtering. Topic models are typically parameter-rich, complex model

Vijay Ekambaram, Subodh Kumar, Arindam Jati, Sumanta Mukherjee

Time-series tasks often benefit from signals expressed across multiple representation spaces (e.g., time vs. frequency) and at varying abstraction levels (e.g., local patterns vs. global semantics). However, existing pre-trained time-series models entangle these heterogeneous signals into a single large embedding, limiting transferability and direct zero-sho

Ziyang Ma, Yinghao Ma, Yanqiao Zhu, Chen Yang

We introduce MMAR, a new benchmark designed to evaluate the deep reasoning capabilities of Audio-Language Models (ALMs) across massive multi-disciplinary tasks. MMAR comprises 1,000 meticulously curated audio-question-answer triplets, collected from real-world internet videos and refined through iterative error corrections and quality checks to ensure high q

Yicheng Xiao, Lin Song, Yukang Chen, Yingmin Luo

Recent text-to-image systems face limitations in handling multimodal inputs and complex reasoning tasks. We introduce MindOmni, a unified multimodal large language model that addresses these challenges by incorporating reasoning generation through reinforcement learning. MindOmni leverages a three-phase training strategy: i) design of a unified vision langua

M. Rodriguez Zarate, T. Thiemann

In previous works in this series we focussed on Hamiltonian renormalisation of free field theories in all spacetime dimensions. In this paper we address the Hamiltonian renormalisation of the self-interacting scalar field in two spacetime dimensions with polynomial potential, called P(Phi,2). We consider only the finite volume case. The P(Phi,2) theory is on

Nan Xu, Zhaolong Huang, Xiaonan Zhi

With the development of deep learning, speech enhancement has been greatly optimized in terms of speech quality. Previous methods typically focus on the discriminative supervised learning or generative modeling, which tends to introduce speech distortions or high computational cost. In this paper, we propose MDDM, a Multi-view Discriminative enhanced Diffusi

Sayon Palit, Daniel Woods

Large Language Models (LLMs) are increasingly integrated into critical systems in industries like healthcare and finance. Users can often submit queries to LLM-enabled chatbots, some of which can enrich responses with information retrieved from internal databases storing sensitive data. This gives rise to a range of attacks in which a user submits a maliciou

Zihan Gu, Ruoyu Chen, Han Zhang, Hua Zhang

Positional encodings enable Transformers to incorporate sequential information, yet their theoretical understanding remains limited to two properties: distance attenuation and translation invariance. Because natural language lacks purely positional data, the interplay between positional and semantic information is still underexplored. We address this gap by

Jack Chen, Fazhong Liu, Naruto Liu, Yuhan Luo

Large language models (LLMs) excel at mathematical reasoning and logical problem-solving. The current popular training paradigms primarily use supervised fine-tuning (SFT) and reinforcement learning (RL) to enhance the models' reasoning abilities. However, when using SFT or RL alone, there are respective challenges: SFT may suffer from overfitting, while RL

Jiyuan Pei, Yi Mei, Jialin Liu, Mengjie Zhang

Meta-Black-Box Optimization (MetaBBO) garners attention due to its success in automating the configuration and generation of black-box optimizers, significantly reducing the human effort required for optimizer design and discovering optimizers with higher performance than classic human-designed optimizers. However, existing MetaBBO methods conduct one-off tr

Luiz Emilio Allem, Carlos Hoppen, João Lazzarin, Lucas Siviero Sibemberg

In this note we show that the minimum number of distinct eigenvalues of a threshold graph is at most $4$. Moreover, given any threshold graph $G$ and any nonzero real number $\lambda$, we explicitly construct a matrix $M$ associated with $G$ such that DSpec$(M)\subseteq\{-\lambda,0,\lambda,2\lambda\}$.

Yimao Guo, Zuomin Qu, Wei Lu, Xiangyang Luo

With the increasing prevalence of diffusion-based malicious image manipulation, existing proactive defense methods struggle to safeguard images against tampering under unknown conditions. To address this, we propose Anti-Inpainting, a proactive defense approach that achieves protection comprising three novel modules. First, we introduce a multi-level deep fe

Philippe Jehiel, Giacomo Weber

Normal-form two-player games are categorized by players into K analogy classes so as to minimize the prediction error about the behavior of the opponent. This results in Clustered Analogy-Based Expectation Equilibria in which strategies are analogy-based expectation equilibria given the analogy partitions and analogy partitions minimize the prediction errors

Federico Del Pup, Andrea Zanola, Louis Fabrice Tshimanga, Alessandra Bertoldo

Deep learning is significantly advancing the analysis of electroencephalography (EEG) data by effectively discovering highly nonlinear patterns within the signals. Data partitioning and cross-validation are crucial for assessing model performance and ensuring study comparability, as they can produce varied results and data leakage due to specific signal prop

Donato Bini, Giorgio Di Russo

We study deviations from geodesic motions in a Topological Star spacetime for either massive, charged and spinning particles, elucidating different behaviours with the Schwarzschild spacetime. We also consider the deviations for the motion of electrically charged stringy probes in $D=5$, framing all cases within a unified picture.

Stijn De Backer, Luis E. C. Rocha, Jan Ryckebusch, Koen Schoors

The analysis of logarithmic return distributions defined over large time scales is crucial for understanding the long-term dynamics of asset price movements. For large time scales of the order of two trading years, the anticipated Gaussian behavior of the returns often does not emerge, and their distributions often exhibit a high level of asymmetry and bimod

Abhishek Baruah, Prabwal Phukon

This study, situated within the framework of the AdS/CFT correspondence, employs wave optics methods to investigate the Einstein ring structure of quantum corrected AdS Reissner Nordstr$\ddot{o}$m black holes governed by Euler Heisenberg nonlinear electrodynamics. A wave source placed on the AdS boundary yields a response function on the antipodal side, from

Dang Thoai Phan, Tuan Anh Huynh, Van Tuan Pham, Cao Minh Tran

The Continuous Wavelet Transform (CWT) is an effective tool for feature extraction in acoustic recognition using Convolutional Neural Networks (CNNs), particularly when applied to non-stationary audio. However, its high computational cost poses a significant challenge, often leading researchers to prefer alternative methods such as the Short-Time Fourier Tra

Zijian Zark Wang

When decision makers evaluate a sequence of rewards, they may pay more attention to larger rewards and, given attention is limited, less attention to smaller rewards. They may also become less attentive to each reward when attention is spread over a longer period of time. Such reductions in attention could lead to greater discounting of the rewards' values.

Jorge Campos Pazmino, Renée-Claude Bider, Véronique Fortier, Ives R. Levesque

Purpose: To develop a fat-water separation approach that corrects bipolar readout gradient induced effects, without additional scans, that is compatible with any fat-water separation method. Theory and Methods: The proposed approach combines joint fat-water separation of the odd and even echoes of a bipolar multi-echo gradient echo acquisition with an invers

Wei-Chuan Shen

We consider any compact CR manifold whose Levi form is non-degenerate of constant signature $(n_-,n_+)$, $n_-+n_+=n$. For $\lambda>0$ and $q\in\{0,\cdots,n\}$, we let $\Pi_\lambda^{(q)}$ be the spectral projection of the Kohn Laplacian of $(0,q)$-forms corresponding to the interval $[0,\lambda]$. For certain classical pseudodifferential operators $P$, we stu

Artan Sheshmani, Xiaopeng Xia, Beihui Yuan

The commuting scheme $\mathfrak{C}^{d}_{\mathfrak{g}}$ for reductive Lie algebra $\mathfrak{g}$ over an algebraically closed field $\mathbb{K}$ is the subscheme of $\mathfrak{g}^{d}$ defined by quadratic equations, whose $\mathbb{K}$-valued points are $d$-tuples of commuting elements in $\mathfrak{g}$ over $\mathbb{K}$. There is a long-standing conjecture th

Anthony Bardou, Patrick Thiran

Time-Varying Bayesian Optimization (TVBO) is the go-to framework for optimizing a time-varying black-box objective function that may be noisy and expensive to evaluate, but its excellent empirical performance remains to be understood theoretically. Is it possible for the instantaneous regret of a TVBO algorithm to vanish asymptotically, and if so, when? We a

Yubin Li, Xingyu Liu, Guozhang Chen

The brain's intricate connectome, a blueprint for its function, presents immense complexity, yet it arises from a compact genetic code, hinting at underlying low-dimensional organizational principles. This work bridges connectomics and representation learning to uncover these principles. We propose a framework that combines subgraph extraction from the Droso

Himel Ghosh, Ahmed Mosharafa, Georg Groh

Media bias detection is a critical task in ensuring fair and balanced information dissemination, yet it remains challenging due to the subjectivity of bias and the scarcity of high-quality annotated data. In this work, we perform sentence-level bias classification by fine-tuning a RoBERTa-based model on the expert-annotated BABE dataset. Using McNemar's test

Jorge Paz-Ruza, Amparo Alonso-Betanzos, Bertha Guijarro-Berdiñas, Carlos Eiras-Franco

In health-related topics, user toxicity in online discussions frequently becomes a source of social conflict or promotion of dangerous, unscientific behaviour; common approaches for battling it include different forms of detection, flagging and/or removal of existing toxic comments, which is often counterproductive for platforms and users alike. In this work

Paris Avgeriou, Ipek Ozkaya, Heiko Koziolek, Zadia Codabux

This is the Dagstuhl Perspectives Workshop 24452 manifesto on Reframing Technical Debt. The manifesto begins with a one-page summary of Values, Beliefs, and Principles. It then elaborates on each Value, Belief, and Principle to explain their rationale and clarify their meaning. Subsequently, the paper describes the current landscape of Technical Debt Managem

He Ye, Aidan Z. H. Yang, Chang Hu, Yanlin Wang

Automated program repair (APR) has shown promising results, particularly with the use of neural networks. Currently, most APR tools focus on code transformations specified by test suites, rather than reasoning about the program intent and the high-level bug specification. Without a proper understanding of program intent, these tools tend to generate patches

James E. Warner, Tristan A. Shah, Patrick E. Leser, Geoffrey F. Bomarito

The ability to accurately model random fields plays a critical role in science and engineering for problems involving uncertain, spatially-varying quantities such as heterogeneous material properties and turbulent flows. Deep generative models offer a powerful tool for sampling high- or infinite-dimensional uncertainties like random fields, but their relianc

Yuyang Li, Philip J. M. Kerbusch, Raimon H. R. Pruim, Tobias Käfer

Airports from the top 20 in terms of annual passengers are highly dynamic environments with thousands of flights daily, and they aim to increase the degree of automation. To contribute to this, we implemented a Conversational AI system that enables staff in an airport to communicate with flight information systems. This system not only answers standard airpo

Lyalya Guseva, Alexander Novikov

We prove that the Kuznetsov--Polishchuk exceptional collections on rational homogeneous spaces of the symplectic groups $\mathrm{Sp}(2n,\mathbb{C})$ are full and consist of vector bundles. To achieve this, we construct several classes of complexes, which we call generalized staircase complexes, symplectic staircase complexes and secondary staircase complexes

Yuhao Qing, Boyu Zhu, Mingzhe Du, Zhijiang Guo

Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To address this gap, we introduce EffiBench-X, the first multi-language benchmark designed to measure the efficiency of LLM-generated code. EffiBench-X supports Python, C++, Java, JavaScri

Masaya Matsumura, Taiki Haga

We investigate a phase transition from linear to nonlinear information processing in echo state networks, a widely used framework in reservoir computing. The network consists of randomly connected recurrent nodes perturbed by a noise and the output is obtained through linear regression on the network states. By varying the standard deviation of the input wei

Dongjae Lee, Bongjoon Hyun, Youngjin Kwon, Minsoo Rhu

The ability to dynamically allocate memory is fundamental in modern programming languages. However, this feature is not adequately supported in current general-purpose PIM devices. To identify key design principles that PIM must consider, we conduct a design space exploration of PIM memory allocators, examining various strategies for metadata placement and m

Oleg E. Parfenov, Dmitry V. Averyanov, Ivan S. Sokolov, Alexey N. Mihalyuk

Altermagnetism, a newly discovered magnetic order, combines zero net magnetization with non-relativistic spin splitting of electronic bands. Its ability to utilize the advantages of both antiferromagnets and ferromagnets is highly promising for spintronic applications. Currently, the merge of altermagnetism and weak ferromagnetism in a single material excite

Jiaqi Li, Xiaolong Lin, Zhekai Li, Shixi Huang

Neural audio codecs form the foundational building blocks for language model (LM)-based speech generation. Typically, there is a trade-off between frame rate and audio quality. This study introduces a low-frame-rate, semantically enhanced codec model. Existing approaches distill semantically rich self-supervised (SSL) representations into the first-layer cod