October 2025 arXiv papers — page 172

Salah Mecheri, Ahmed Bachie, A. Segress

This work studies how certain problems in quantum theory have motivated some recent research in pure Mathematics in matrix and operator theory. The mathematical key is that of a commutator or a generalized commutator, that is, find an operator $X\in B(H)$ satisfying the operator equation $AX - XB = C$. By this we will show how and why to solve the operator e

Kaiwen Wei, Xiao Liu, Jie Zhang, Zijian Wang

Multimodal Retrieval-Augmented Generation (MRAG) enables Multimodal Large Language Models (MLLMs) to generate responses with external multimodal evidence, and numerous video-based MRAG benchmarks have been proposed to evaluate model capabilities across retrieval and generation stages. However, existing benchmarks remain limited in modality coverage and forma

Marie-Charlotte Brandenburg, Chiara Meroni

We present a complete computational classification of the combinatorial types of hyperplane sections, or slices, of the regular cube up to dimension six. For each dimension, we determine the exact number of distinct combinatorial types. When restricted to slices through the origin, our computations extend to dimension seven. The classification combines combi

Xi Luo, Tao Shi, Yue Yu, Long Liang

We aim to clarify the confusion and inconsistency in our recent works [1,2], and to address the incompleteness therein. In order to avoid the ill-defined nature of the free propagator of the gauge field in the ordered states of the $t$-$J$ model, we adopted a gauge fixing that was not of the Becchi-Rouet-Stora-Tyutin (BRST) exact form in our previous work [2

Sven Gowal, Rudy Bunel, Florian Stimberg, David Stutz

We introduce SynthID-Image, a deep learning-based system for invisibly watermarking AI-generated imagery. This paper documents the technical desiderata, threat models, and practical challenges of deploying such a system at internet scale, addressing key requirements of effectiveness, fidelity, robustness, and security. SynthID-Image has been used to watermar

Thi Bich Phuong Man, Luu Tu Nguyen, Vu Tram Anh Khuong, Thanh Ha Le

Micro-expressions (MEs) are subtle, transient facial changes with very low intensity, almost imperceptible to the naked eye, yet they reveal a person genuine emotion. They are of great value in lie detection, behavioral analysis, and psychological assessment. This paper proposes a novel MER method with two main contributions. First, we propose two complement

Simon Sekavčnik, Paul Kohl, Janis Nötzel

The generation of entangled photon pairs is highly useful for many types of quantum technologies. In this work an entangled photon pair generator that utilises the biexciton-exciton cascade in semiconductor quantum dots is described on a physical, mathematical, and software level. The system is implemented and simulated as a self-contained component in a fra

F. McNeill, S. A. Sim, C. E. Collins, L. J. Shingles

The extremely rapid evolution of kilonovae results in spectra that change on an hourly basis. These spectra are key to understanding the processes occurring within the event, but this rapid evolution is an unfamiliar domain compared to other explosive transient events, such as supernovae. In particular, the most obvious P Cygni feature in the spectra of AT20

Yongding Tao, Tian Wang, Yihong Dong, Huanyu Liu

Data contamination poses a significant threat to the reliable evaluation of Large Language Models (LLMs). This issue arises when benchmark samples may inadvertently appear in training sets, compromising the validity of reported performance. While detection methods have been developed for the pre-training and Supervised Fine-Tuning stages, a critical research

Haowen Xu, Jose Tupayachi, Xiao-Ying Yu

The development of web-based geospatial dashboards for risk analysis and decision support is often challenged by the difficulty in visualization of big, multi-dimensional environmental data, implementation complexity, and limited automation. We introduce a generative AI framework that harnesses Large Language Models (LLMs) to automate the creation of interac

Ahmad Z. Fino, Arlúcio Viana

In this paper, we investigate the nonexistence of global solutions to the Grushin-type heat equation with nonlinear reaction terms, including cases involving memory effects: $$ \left\{\begin{array}{ll} \displaystyle {u_{t}-\Delta_{\mathcal{G}} u = k_1 \int_0^t(t-s)^{-\gamma}\abs{u}^{p_1-1}u(s)\,\mathrm{d}s} + k_2|u|^{p_2-1}u, & (z,t)\in {\mathbb{R}}^{N+k}\ti

Eugenio Felipe Merlano

In the conventional regression-discontinuity (RD) design, the probability that units receive a treatment changes discontinuously as a function of one covariate exceeding a threshold or cutoff point. This paper studies an extended RD design where assignment rules simultaneously involve two or more continuous covariates. We show that assignment rules with more

Patrick Wienholt, Sophie Caselitz, Robert Siepmann, Philipp Bruners

To determine whether using discrete semantic entropy (DSE) to reject questions likely to generate hallucinations can improve the accuracy of black-box vision-language models (VLMs) in radiologic image based visual question answering (VQA). This retrospective study evaluated DSE using two publicly available, de-identified datasets: the VQA-Med 2019 benchmark

Chenyang Gu, Yewen Pu, Bruce Yang, Xiaofan Li

Enhancing LLMs with the ability to actively search external knowledge is crucial for complex and real-world tasks. Current approaches either rely on prompting to elicit the model's innate agent capabilities, or suffer from performance ceilings and collapse when applying RL to complex interactive tasks, leaving their true agentic potential untapped. To addres

Dominik Urbaniak, Alejandro Agostini, Pol Ramon, Jan Rosell

Learning-based motion planning can quickly generate near-optimal trajectories. However, it often requires either large training datasets or costly collection of human demonstrations. This work proposes an alternative approach that quickly generates smooth, near-optimal collision-free 3D Cartesian trajectories from a single artificial demonstration. The demon

Alina Elena Baia, Alessio Xompero, Andrea Cavallaro

Automatic systems for document understanding require multimodal models that accurately identify sensitive visual content, even in the presence of image degradations. Instruction-following large Vision-Language Models (VLMs) are expected to generalise across domains and tasks without requiring any specific adaptation. In this work, we systematically analyse w

MingSheng Li, Guangze Zhao, Sichen Liu

Large Vision-Language Models (LVLMs) have achieved remarkable progress in multimodal perception and generation, yet their safety alignment remains a critical challenge.Existing defenses and vulnerable to multimodal jailbreaks, as visual inputs introduce new attack surfaces, reasoning chains lack safety supervision, and alignment often degrades under modality

David A. Vogl, Noah L. Braitsch, Başak Ç. Özcan, Niklas S. Vart

We present a comprehensive experimental study of the neutral donor to donor-bound exciton transition (D$^0$$\rightarrow\,$D$^0$X) in isotopically enriched $^{28}$Si, focusing on the group-V donors P, As, and Sb under finely tuned uniaxial stress along the [100] and [110] crystal axes and magnetic fields from 3.5 mT to 1.7 T. From these measurements, donor-sp

Marcus Albrechtsen, Severin Krüger, Juan Loredo, Lucio Stefan

Quantum dots stand out as the most advanced and versatile light-matter interface available today. Their ability to deliver high-quality, high-rate, and pure photons has set benchmarks that far surpass other emitters. Yet, a critical frontier has remained elusive: achieving these exceptional capabilities at telecom wavelengths, bridging the gap to fiber-optic

Emilio Porcu, Tobia Filosi, Horst Simon

Shaw and Stevens call for a new paradigm in climate science criticizes Large Scale Determinism in favor of (i) embracing discrepancies, (ii) embracing hierarchies, and (iii) create disruption while keeping interpretability. The last 20 years have seen a plethora of contributions relating complex networks with climate data and climate models. We provide a vie

Javara A. Bukhsh, Maya Daneva, Marten van Sinderen

Phishing attacks pose a significant cybersecurity threat globally. This study investigates phishing susceptibility within the Pakistani population, examining the influence of demographic factors, technological aptitude and usage, previous phishing victimization, and email characteristics. Data was collected through convenient sampling; a total of 164 people

Zofia Bracha, Paweł Sakowski, Jakub Michańków

This paper explores the application of deep Q-learning to hedging at-the-money options on the S\&P~500 index. We develop an agent based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, trained to simulate hedging decisions without making explicit model assumptions on price dynamics. The agent was trained on historical intraday prices o

Peteris Daugulis, Vija Vagale, Emiliano Mancini, Filippo Castiglione

The problem of choosing appropriate values for missing data is often encountered in the data science. We describe a novel method containing both traditional mathematics and machine learning elements for prediction (imputation) of missing data. This method is based on the notion of distance between shifted linear subspaces representing the existing data and c

Zhao Guo, Ziqian Ning, Guobin Ma, Lei Xie

Voice Conversion (VC) aims to modify a speaker's timbre while preserving linguistic content. While recent VC models achieve strong performance, most struggle in real-time streaming scenarios due to high latency, dependence on ASR modules, or complex speaker disentanglement, which often results in timbre leakage or degraded naturalness. We present SynthVC, a

Victor de Lamo Castrillo, Habtom Kahsay Gidey, Alexander Lenz, Alois Knoll

This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs). Motivated by the limitations of traditional LLMs in real-world tasks, the research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities. Key components include a

Giacomo Gonella, Gian Maria Campedelli, Stefano Menini, Marco Guerini

Effectively identifying threats and mitigating their potential damage during crisis situations, such as natural disasters or violent attacks, is paramount for safeguarding endangered individuals. To tackle these challenges, AI has been used in assisting humans in emergency situations. Still, the use of NLP techniques remains limited and mostly focuses on cla

Sahaj Raj Malla

This study evaluates Artificial Intelligence (AI) agents for Dhumbal, a culturally significant multiplayer card game with imperfect information, through a systematic comparison of rule-based, search-based, and learning-based strategies. We formalize Dhumbal's mechanics and implement diverse agents, including heuristic approaches (Aggressive, Conservative, Ba

Marco Siino, Giuseppe Bonomo, Rosario Sorbello, Ilenia Tinnirello

The present study investigates the impact of the Rational Discrete Wavelet Transform (RDWT), used as a plug-in preprocessing step for motor imagery electroencephalographic (EEG) decoding prior to applying deep learning classifiers. A systematic paired evaluation (with/without RDWT) is conducted on four state-of-the-art deep learning architectures: EEGNet, Sh

Gustavo R. Ferreira, Anna Jové

We analyze the boundaries of multiply connected Fatou components of transcendental maps by means of universal covering maps and associated inner functions. A unified approach is presented, which includes invariant Fatou components (of any type) as well as wandering domains. We prove that any Fatou component admits a harmonic measure on its boundary whose sup

Jiangwei Chen, Kieu Thao Nguyen Pham, Rachael Hwee Ling Sim, Arun Verma

In collaborative data sharing and machine learning, multiple parties aggregate their data resources to train a machine learning model with better model performance. However, as the parties incur data collection costs, they are only willing to do so when guaranteed incentives, such as fairness and individual rationality. Existing frameworks assume that all pa

Javier Albert-Smet, Zoraida Frias, Luis Mendo, Sergio Melones

Characterizing application-layer user throughput in next-generation networks is increasingly challenging as the higher capacity of the 5G Radio Access Network (RAN) shifts connectivity bottlenecks towards deeper parts of the network. Traditional methods, such as drive tests and operator equipment counters, are costly, limited, or fail to capture end-to-end (

Francois-Igor Pris

A realist interpretation of quantum mechanics is proposed - Contextual Quantum Realism (CQR) - according to which there exists a categorical distinction between the ideal (theory, observation instrument) and the real (quantum physical systems, properties), and, consequently, quantum ontology is context-sensitive. CQR is compared with QBism and Relational Qua

Michele Buccoli, Yu Du, Jacob Soendergaard, Simone Shawn Cazzaniga

Upon choosing microphones for automotive hands-free communication or Automatic Speech Recognition (ASR) applications, OEMs typically specify wideband, super wideband or even fullband requirements following established standard recommendations (e.g., ITU-P.1110, ITU-P.1120). In practice, it is often challenging to achieve the preferred bandwidth for an automo

Haozhou Cai, Zhiming Xu, Jian Wu, Weiyi Pan

The coexistence of topological magnons and electrons in magnetic materials presents a compelling route toward developing low-dissipation, multifunctional spintronic devices. However, material systems enabling their simultaneous realization and control remain largely unexplored. Here, we propose the coexistence and concurrent tunability of magnonic and electr

Yin Shi

Spontaneous time-reversal symmetry breaking in superconductors with competing non-degenerate pairing channels is an exotic quantum phase transition that could give rise to robust topological superconductivity and unusual magnetism. It is proposed mostly in two-dimensional systems and is signaled by a nonzero relative phase between the two superconducting ord

Konstantin Lion, Piero Mazzolini, Kingsley Egbo, Toni Markurt

We present a comprehensive investigation of reconstructions on $\beta$-Ga$_2$O$_3$(001) combining first-principles calculations with experimental observations. Using ab initio atomistic thermodynamics and replica-exchange grand-canonical molecular dynamics simulations, we explore the configurational space of possible reconstructions under varying chemical po

Siddarth Marwaha, Pawel Kryszkiewicz, Eduard A. Jorswieck

Although multiple works have proposed energy-efficient resource allocation schemes for Massive Multiple-Input Multiple-Output (M-MIMO) system, most approaches overlook the potential of optimizing Power Amplifier (PA) transmission power while accounting for non-linear distortion effects. Furthermore, most M-MIMO studies assume narrow-band transmission, neglec

Fanch Coudreuse

We establish a version of the Li--Yau--Hamilton inequality for the Granular-Medium equation on the torus, both at the PDE level and for its time-discrete approximation given by the JKO scheme. We then apply this estimate to derive further quantitative results for the continuous and discrete JKO flows, including Lipschitz and $L^\infty$ bounds, as well as a q

Jindong Hong, Wencheng Zhang, Shiqin Qiao, Jianhai Chen

Shoulder disorders, such as frozen shoulder (a.k.a., adhesive capsulitis), are common conditions affecting the health of people worldwide, and have a high incidence rate among the elderly and workers engaged in repetitive shoulder tasks. In regions with scarce medical resources, achieving early and accurate diagnosis poses significant challenges, and there i

Yuyang Gao, Haofei Ma, Pai Zheng

We present Glovity, a novel, low-cost wearable teleoperation system that integrates a spatial wrench (force-torque) feedback device with a haptic glove featuring fingertip Hall sensor calibration, enabling feedback-rich dexterous manipulation. Glovity addresses key challenges in contact-rich tasks by providing intuitive wrench and tactile feedback, while ove

Vijay M. Galshetwar, Praful Hambarde, Prashant W. Patil, Akshay Dudhane

Adverse weather conditions such as haze, rain, and snow significantly degrade the quality of images and videos, posing serious challenges to intelligent transportation systems (ITS) that rely on visual input. These degradations affect critical applications including autonomous driving, traffic monitoring, and surveillance. This survey presents a comprehensiv

Hyundong Jin, Joonghyuk Hahn, Yo-Sub Han

Large language models (LLMs) show strong performance across natural language processing (NLP), mathematical reasoning, and programming, and recent large reasoning models (LRMs) further emphasize explicit reasoning. Yet their computational limits, particularly spatial complexity constrained by finite context windows, remain poorly understood. While recent wor

Klaus Weinbauer, Tieu-Long Phan, Peter F. Stadler, Thomas Gärtner

Machine learning models that predict the feasibility of chemical reactions have become central to automated synthesis planning. Despite their predictive success, these models often lack transparency and interpretability. We introduce a novel formulation of prime implicant explanations--also known as minimally sufficient reasons--tailored to this domain, and

Danel Slabbert, Simon Malan, Herman Kamper

Zero-resource word segmentation and clustering systems aim to tokenise speech into word-like units without access to text labels. Despite progress, the induced lexicons are still far from perfect. In an idealised setting with gold word boundaries, we ask whether performance is limited by the representation of word segments, or by the clustering methods that

Wangyu Wu, Xuhang Chen, Zhenhong Chen, Jing-En Jiang

Cross-Domain Sequential Recommendation (CDSR) plays a crucial role in modern consumer electronics and e-commerce platforms, where users interact with diverse services such as books, movies, and online retail products. These systems must accurately capture both domain-specific and cross-domain behavioral patterns to provide personalized and seamless consumer

Mubaris Nadeem, Madjid Fathi

In the field of medicine and healthcare, the utilization of medical expertise, based on medical knowledge combined with patients' health information is a life-critical challenge for patients and health professionals. The within-laying complexity and variety form the need for a united approach to gather, analyze, and utilize existing knowledge of medical trea

Jingyuan Sun, Chaoran Wang, Mingyu Zhang, Cui Miao

Seamless loco-manipulation in unstructured environments requires robots to leverage autonomous exploration alongside whole-body control for physical interaction. In this work, we introduce HANDO (Hierarchical Autonomous Navigation and Dexterous Omni-loco-manipulation), a two-layer framework designed for legged robots equipped with manipulators to perform hum

Marvin Rübenacke, Sebastian Cammerer, Michael Sullivan, Alexander Keller

Physical unclonable functions (PUFs) involve challenging practical applications of error-correcting codes (ECCs), requiring extremely low failure rates on the order of $10^{-6}$ and below despite raw input bit error rates as high as 22%. These requirements call for an efficient ultra-low rate code design. In this work, we propose a novel coding scheme tailor

Flavien Mabilat

$\lambda$-quiddities of size $n$ are $n$-tuples of elements from a fixed set that are solutions to a matrix equation which is fundamental in the study of the combinatorics of the modular group and Coxeter's friezes. To gain further insight into these objects, we use a notion of irreducibility, which allows restricting the study to a limited number of element

Dominic J. Williamson

The storage of large-scale quantum information at finite temperature requires an autonomous and reliable quantum hard drive, also known as a self-correcting quantum memory. It is a long-standing open problem to find a self-correcting quantum memory in three dimensions. The recently introduced Layer Codes achieve the best possible scaling of code parameters a

Chao Han, Yijuan Liang, Zihao Xuan, Daokuan Wu

The deployment of large language models (LLMs) in real-world applications is increasingly limited by their high inference cost. While recent advances in dynamic token-level computation allocation attempt to improve efficiency by selectively activating model components per token, existing methods rely on greedy routing--a myopic execute-or-skip mechanism that

Tao Feng, Lizhen Qu, Niket Tandon, Gholamreza Haffari

Causal discovery is fundamental to scientific research, yet traditional statistical algorithms face significant challenges, including expensive data collection, redundant computation for known relations, and unrealistic assumptions. While recent LLM-based methods excel at identifying commonly known causal relations, they fail to uncover novel relations. We i

Sai Pradeep Muppaneni, Vineetha Yogesh, A. Chockalingam

In this paper, we consider the problem of estimating the delay-Doppler (DD) domain input-output (I/O) relation in Zak-OTFS modulation, which is needed for signal detection. Two approaches, namely, model-dependent and model-free approaches, can be employed for this purpose. The model-dependent approach requires explicit estimation of the physical channel para

Juan Carlos García-Ardila, Francisco Marcellán, Misael E. Marriaga

Consider the following truncated Freud linear functional $\mathbf{u}_z$ depending on a parameter $z$, $$\langle\mathbf{u}_z,p\rangle=\int_0^\infty p(x)e^{-zx^4}dx,\quad z>0.$$ The aim of this work is to analyze the properties of the sequence of orthogonal polynomials $(P_n)_{n\geq 0}$ with respect to $\mathbf{u}_z$. Such a linear functional is semiclassical

L. Thulliez, N. Berton, R. Boudouin, N. Cavalière

High-Current Accelerator-driven Neutron Sources (HiCANS) are currently under development across Europe to address the shortage of medium-scale neutron sources, as many research nuclear reactors have been decommissioned over the past several years. At CEA-Saclay, a HiCANS has been developed utilizing the IPHI accelerator, which delivers a 3 MeV proton beam wi

Wuyang Li, Wentao Pan, Po-Chien Luan, Yang Gao

We propose Stable Video Infinity (SVI) that is able to generate infinite-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines. While existing long-video methods attempt to mitigate accumulated errors via handcrafted anti-drifting (e.g., modified noise scheduler, frame anchoring), they remain limited

Tilman Hinnerichs, Reuben Gardos Reid, Jaap de Jong, Bart Swinkels

Program synthesis -- the automatic generation of code given a specification -- is one of the most fundamental tasks in artificial intelligence (AI) and the dream of many programmers. Numerous synthesizers have been developed for program synthesis, offering different approaches to the exponentially growing program space. Although such state-of-the-art tools e

Yiqi Li, Yusheng Liao, Zhe Chen, Yanfeng Wang

When performing reasoning tasks with user-specific requirements, such as strict output formats, large language models (LLMs) often prioritize reasoning over adherence to detailed instructions. Fine-tuning LLMs on supervised datasets to address this is impractical due to high computational costs and limited parameter access. To tackle this, we propose DICE, a

Yifan Zhu, Lijia Yu, Xiao-Shan Gao

In recent years, data poisoning attacks have been increasingly designed to appear harmless and even beneficial, often with the intention of verifying dataset ownership or safeguarding private data from unauthorized use. However, these developments have the potential to cause misunderstandings and conflicts, as data poisoning has traditionally been regarded a

Yuki Kuroda, Tomoya Takahashi, Cristian C Beltran-Hernandez, Masashi Hamaya

Electric prosthetic hands should be lightweight to decrease the burden on the user, shaped like human hands for cosmetic purposes, and have motors inside to protect them from damage and dirt. In addition to the ability to perform daily activities, these features are essential for everyday use of the hand. In-hand manipulation is necessary to perform daily ac

Quan Zhao, Guilai Liu

In order to generalize the fact that an averaging commutative algebra gives rise to a perm algebra to the bialgebra level, the notion of a special apre-perm algebra was introduced as a new splitting of perm algebras, and it has been shown that an averaging commutative and cocommutative infinitesimal bialgebra gives rise to a special apre-perm bialgebra. In t

Ze Tao, Fujun Liu, Yuxi Jin, Ke Xu

Thermal field reconstruction in post-exposure bake (PEB) is critical for advanced lithography, yet current physics-informed neural networks (PINNs) suffer from inconsistent accuracy due to a misalignment between geometric coordinates, physical fields, and differential operators. To resolve this, we introduce a novel architecture that unifies these elements o

Yue Li, Shida Sun, Yu Hong, Feihu Xu

Transient measurements, captured by the timeresolved systems, are widely employed in photon-efficient reconstruction tasks, including line-of-sight (LOS) and non-line-of-sight (NLOS) imaging. However, challenges persist in their 3D reconstruction due to the low quantum efficiency of sensors and the high noise levels, particularly for long-range or complex sc

Huimin Liu, Jing Gao, Daria Baran, AxelX Montout

Robust behaviour recognition in real-world farm environments remains challenging due to several data-related limitations, including the scarcity of well-annotated livestock video datasets and the substantial domain gap between large-scale pre-training corpora and agricultural surveillance footage. To address these challenges, we propose Cattle-CLIP, a domain

Yumin Choi, Dongki Kim, Jinheon Baek, Sung Ju Hwang

Large Language Models (LLMs) have shown remarkable success, and their multimodal expansions (MLLMs) further unlock capabilities spanning images, videos, and other modalities beyond text. However, despite this shift, prompt optimization approaches, designed to reduce the burden of manual prompt crafting while maximizing performance, remain confined to text, u

Mukilan Karuppasamy, Shankar Gangisetty, Shyam Nandan Rai, Carlo Masone

Autonomous driving (AD) systems are becoming increasingly capable of handling complex tasks, mainly due to recent advances in deep learning and AI. As interactions between autonomous systems and humans increase, the interpretability of decision-making processes in driving systems becomes increasingly crucial for ensuring safe driving operations. Successful h

Andrei Buciulea, Bishwadeep Das, Elvin Isufi, Antonio G. Marques

Graph learning aims to infer a network structure directly from observed data, enabling the analysis of complex dependencies in irregular domains. Traditional methods focus on scalar signals at each node, ignoring dependencies along additional dimensions such as time, configurations of the observation device, or populations. In this work, we propose a graph s

Linhao Zhang, Tao Liu, Ye Zou, Penghui Yang

The Super Tau-Charm Facility (STCF) is a new-generation $e^+e^-$ collider proposed in China, designed to operate in the center-of-mass (CoM) energy range of 2-7 GeV. To achieve the design luminosity exceeding 5*10^34 cm^-2s^-1 at the optimal CoM energy of 4 GeV, a large crossing angle combined with the crab-waist correction scheme is adopted. However, this s

Om Prakash, Vikram Sharma

The independence polynomial of a graph $G$ is the generating polynomial corresponding to its independent sets of different sizes. More formally, if $a_k(G)$ denotes the number of independent sets of $G$ of size $k$ then \[I(G,z) \as \sum_{k}^{} (-1)^k a_k(G) z^k.\] The study of evaluating $I(G,z)$ has several deep connections to problems in combinatorics, co

Prabhanka Deka, Fangzhou Luo, Baichuan Wu

In this paper, we study rare events in spherical and Gaussian random geometric graphs in high dimensions. In these models, the vertices correspond to points sampled uniformly at random on the $d$ dimensional unit sphere or correspond to $d$ dimensional standard Gaussian vectors, and edges are added between two vertices if the inner-product between their corr

Marian Aprodu, Călin Spiridon

This work revolves around the question of whether a given resonance variety is associated with a vector bundle. We show the existence of a family of natural morphisms on a stratification of the resonance variety to a suitable family of a Quot scheme and provide some applications in the curve case. The existence of this family of morphisms represents an obstr

Simone Grillo, Chiara Cignarella, Friedhelm Bechstedt, Paola Gori

We report a comprehensive first-principles study of the electronic and optical properties of recently identified exfoliable one-dimensional semiconducting materials, focusing on chalcogenide-based atomic chains derived from van der Waals-bonded bulk crystals. Specifically, we investigate covalently bonded S3 and Te3 chains, and polar-bonded As2S3 and Bi2Te3

Giacomo Dimarco, Federica Ferrarese, Lorenzo Pareschi

In this work, we propose a data augmentation strategy aimed at improving the training phase of neural networks and, consequently, the accuracy of their predictions. Our approach relies on generating synthetic data through a suitable compartmental model combined with the incorporation of uncertainty. The available data are then used to calibrate the model, wh

Adrian Faucher, David Benisty, David F. Mota

An independent determination of the Hubble constant is crucial given the persistent tension between early- and late-Universe measurements. In this study, we analyze the dynamics of the Centaurus~A (CenA) and M83 galaxies, along with their associated dwarf companions identified via Tip of the Red Giant Branch (TRGB) distance measurements, to constrain both th

Changjiang Gao, Zixian Huang, Jingyang Gong, Shujian Huang

General Large Language Models (LLMs) excel in reasoning, but those enhanced for translation struggle with reasoning tasks. To address this, we propose a novel translationenhanced recipe that begins with instruct models and applies layer-selective tuning only on parallel data. Following this pipeline, we introduce the Qwen3-XPlus models, which demonstrate sig

Zihao Mao, Yunheng Wang, Yunting Ji, Yi Yang

Multi-robot navigation in unknown, structurally constrained, and GPS-denied environments presents a fundamental trade-off between global strategic foresight and local tactical agility, particularly under limited communication. Centralized methods achieve global optimality but suffer from high communication overhead, while distributed methods are efficient bu

Michele Loi, Marcello Di Bello, Nicolò Cangiotti

The growing philosophical literature on algorithmic fairness has examined statistical criteria such as equalized odds and calibration, causal and counterfactual approaches, and the role of structural and compounding injustices. Yet an important dimension has been overlooked: whether the evidential value of an algorithmic output itself depends on structural i

Sungwoo Kang

Cricket shot classification from video sequences remains a challenging problem in sports video analysis, requiring effective modeling of both spatial and temporal features. This paper presents the first comprehensive baseline study comparing seven different deep learning approaches across four distinct research paradigms for cricket shot classification. We i

T. H. A. van der Reep, W. Löffler

In experiments probing human vision at the few-photon level, precise alignment of the eye is necessary such that stimuli reach the most sensitive region of the retina. However, in literature there seems to be no consensus on the optimal eye alignment for such experiments. Typically, experiments are performed by presenting stimuli nasally or temporally, but t

Stephane Hess, Sander van Cranenburgh

Models allowing for random heterogeneity, such as mixed logit and latent class, are generally observed to obtain superior model fit and yield detailed insights into unobserved preference heterogeneity. Using theoretical arguments and two case studies on revealed and stated choice data, this paper highlights that these advantages do not translate into any ben

Lucas Georges Gabriel Charpentier, Pierre Lison

Text de-identification techniques are often used to mask personally identifiable information (PII) from documents. Their ability to conceal the identity of the individuals mentioned in a text is, however, hard to measure. Recent work has shown how the robustness of de-identification methods could be assessed by attempting the reverse process of _re-identific

Jianxiao Jiang, Yu Zhang

In the age of AI-powered educational (AIED) innovation, evaluating the developmental consequences of novel designs before they are exposed to students has become both essential and challenging. Since such interventions may carry irreversible effects, it is critical to anticipate not only potential benefits but also possible harms. This study proposes a stude

Nicolas Rouger, Luiz Villa, Matthieu Masson, Pauline Kergus

The switching losses of power transistors are generally measured using the so-called double pulse method. Measuring the opposition of two switching cells is a complementary method that is more accurate but indirect. However, implementing this method can be more complex and requires calibration steps and comprehensive control, with the added issue of thermal

Johann-Friedrich Feiden, Tim Küchler, Denis Zavadski, Bogdan Savchynskyy

Depth estimation from monocular video has become a key component of many real-world computer vision systems. Recently, Video Depth Anything (VDA) has demonstrated strong performance on long video sequences. However, it relies on batch-processing which prohibits its use in an online setting. In this work, we overcome this limitation and introduce online VDA (

Ze Peng, Jian Zhang, Jintao Guo, Lei Qi

Continual learning seeks the human-like ability to accumulate new skills in machine intelligence. Its central challenge is catastrophic forgetting, whose underlying cause has not been fully understood for deep networks. In this paper, we demystify catastrophic forgetting by revealing that the new-task training is implicitly an adversarial attack against the

Peichen Xie, Xian Zhang, Shuo Chen

Non-determinism and non-reproducibility present significant challenges in deep learning, leading to inconsistent results across runs and platforms. These issues stem from two origins: random number generation and floating-point computation. While randomness can be controlled through deterministic configurations, floating-point inconsistencies remain largely

Le Ngoc Kien, Nguyen Van Tuyen, Tran Van Nghi

This paper investigates the behavior of sets and functions at infinity by introducing new concepts, namely directional normal cones at infinity for unbounded sets, along with limiting and singular subdifferentials at infinity in the direction for extended real-valued functions. We develop several calculus rules for these concepts and then apply them to nonsm

Sauro Succi, Claudio Sanavio, Peter Love

Quantum algorithms for classical physics problems expose new patterns of quantum information flow as compared to the many-body Schr\"{o}dinger equation. As a result, besides their potential practical applications, they also offer a valuable theoretical and computational framework to elucidate the foundations of quantum mechanics, particularly the validity of

Mihriban Ceylan, David J. Prömel

The universal approximation property uniformly with respect to weakly compact families of measures is established for several classes of neural networks. To that end, we prove that these neural networks are dense in Orlicz spaces, thereby extending classical universal approximation theorems even beyond the traditional $L^p$-setting. The covered classes of ne

Casper van Laar, Khubaib Ahmed

This study introduces a WaveNet-based deep learning model designed to automate the classification of intracranial electroencephalography (iEEG) signals into physiological activity, pathological (epileptic) activity, power-line noise, and other non-cerebral artifacts. Traditional methods for iEEG signal classification, which rely on expert visual review, are

Shinnosuke Kato, Gaku Mitsuka

As of July 2025, the SuperKEKB accelerator, which collides 7 GeV electrons with 4 GeV positrons to abundantly produce particles such as B mesons and tau leptons, holds the world record for the highest instantaneous luminosity. Continuous operation and upgrades are underway to achieve even higher luminosities. Maintaining a high instantaneous luminosity requi

Ling Zhan, Junjie Huang, Xiaoyao Yu, Wenyu Chen

Functional brain network (FBN) modeling often relies on local pairwise interactions, whose limitation in capturing high-order dependencies is theoretically analyzed in this paper. Meanwhile, the computational burden and heuristic nature of current hypergraph modeling approaches hinder end-to-end learning of FBN structures directly from data distributions. To

Yuchen Zhang, Yao Lu, Johannes Betz

Most object detectors operate under a closed-world assumption, recognizing only the classes annotated in the training dataset and failing when encountering novel objects. Open-World Object Detection (OWOD) relaxes this assumption by enabling unseen objects to be detected as "Unknown". However, collapsing all novel objects into a single undifferentiated label

Herminio García-González

Nowadays, software is one of the cornerstones when conducting research in several scientific fields which employ computer-based methodologies to answer new research questions. However, for these experiments to be completely reproducible, research software should comply with the FAIR principles, yet its metadata can be represented following different data mod

Paul Mayr, Alessandro Pisano, Stefan Koch, Markus Reichhartinger

A sliding-mode-based adaptive boundary control law is proposed for a class of uncertain thermal reaction-diffusion processes subject to matched disturbances. The disturbances are assumed to be bounded, but the corresponding bounds are unknown, thus motivating the use of adaptive control strategies. A boundary control law comprising a proportional and discont

Hiroki Onozato, Yoshifusa Ita, Yoshikazu Nakada

Understanding the age and metallicity dependence of the absolute magnitude and colour of red clump (RC) stars is crucial for validating the accuracy of stellar evolution models and enhancing their reliability as a standard candle. However, this dependence has previously been investigated in the near-infrared across multiple bands only for -1.05 $\leq$ [Fe/H]

Minmao Wang, Xingchen Liu, Shijie Yi, Likang Wu

Recommender Systems (RS) are fundamental to modern online services. While most existing approaches optimize for short-term engagement, recent work has begun to explore reinforcement learning (RL) to model long-term user value. However, these efforts face significant challenges due to the vast, dynamic action spaces inherent in RS, which hinder stable policy

Víctor Blanco, Diego Laborda, Miguel Martínez-Antón

We study the conditions under which the convex relaxation of a mixed-integer linear programming formulation for ordered optimization problems, where sorting is part of the decision process, yields integral optimal solutions. Thereby solving the problem exactly in polynomial time. Our analysis identifies structural properties of the input data that influence

Santiago Hernández-Díaz, Beate Stelzer, Axel Schwope, Daniela Muñoz-Giraldo

Context. Determining the orbital periods of cataclysmic variable stars (CVs) is essential for confirming candidates and for the understanding of their evolutionary state. The Transiting Exoplanet Survey Satellite (TESS) provides month-long photometric data across nearly the entire sky that can be used to search for periodic variability in such systems. Aims.

Prithvi Gundlapalli, Philipp J. Vetter, Genko Genov, Michael Olney-Fraser

Quantum networks offer a way to overcome the size and complexity limitations of single quantum devices by linking multiple nodes into a scalable architecture. Group-IV color centers in diamond, paired with long-lived nuclear spins, have emerged as promising building blocks demonstrating proof-of-concept experiments such as blind quantum computing and quantum