October 2025 arXiv papers — page 97

Mohd Saif Ali Khan, Karthik RM, Samar Agnihotri

Efficient uplink processing in distributed massive multiple-input multiple-output (D-mMIMO) systems requires both effective local combining and scalable decoding to significantly mitigate inter-user interference. Recent zero-forcing (ZF)-based combining schemes, such as partial full-pilot ZF (PFZF) and protected weak PFZF (PWPFZF), rely on heuristic threshol

Vaishnavi Gajendragad, Suropriya Saha

We explore the concept of memory in scalar active matter, focusing on the collective dynamics of particles whose interactions depend on their evolutionary history rather than solely on their current configuration. We introduce the idea of an active particle whose velocity includes an active contribution that depends on its past trajectory suitably weighted b

Long Ma, Hongwen Zhang, Bowen Ai, Jiakun Zhuang

Ionic current rectification (ICR) induced by electroosmotic flow (EOF) under concentration gradients can find many applications in micro/nanofluidic sensing and ionic circuits. Here, we focused on the cases with micropores of moderate length-diameter ratios, through experimental research and systematical simulations, the EOF-induced ICR was found to exhibit

Gourav Banerjee

In this paper, we investigate and find a necessary and sufficient condition for a function to be absolutely continuous over $\mathbb{R}$ (denoted by $AC(\mathbb{R})$) or any unbounded interval in $\mathbb{R}$ . Note that the Lebesgue's Fundamental theorem of Calculus gives us a necessary and sufficient condition\cite{book:B} for a function defined over a clo

Zhe Liu, Hongwen Zhang, Di Liu, Tianyi Sui

Nanofluidic memristors have demonstrated great potential for neuromorphic system applications with the advantages of low energy consumption and excellent biocompatibility. Here, an effective way is developed to regulate the memristive behavior of conical nanopores by leveraging the reversible formation and dissolution of nanoprecipitates induced by ion enric

T. B. Charikova, A. Yu. Pavlova, M. R. Popov, A. V. Pozdin

We present the results of measurements of bulk current-voltage (I-V) characteristics and local surface I-V characteristics by atomic force microscopy (AFM) of iodine-doped PbS films. It is established that bulk I-V curves of both undoped and iodine-doped PbS films demonstrate a linear (ohmic) U(I) dependence. The tipe of local surface I-V characteristics is

Qiyuan Guan, Xiang Chen, Guiyue Jin, Jiyu Jin

Compared to daytime image deraining, nighttime image deraining poses significant challenges due to inherent complexities of nighttime scenarios and the lack of high-quality datasets that accurately represent the coupling effect between rain and illumination. In this paper, we rethink the task of nighttime image deraining and contribute a new high-quality ben

Zhengshen Zhang, Hao Li, Yalun Dai, Zhengbang Zhu

Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptability. Recent 3D integration techniques for VLAs either require specialized sensors and transfer poorly across modalities, or inject weak cues that lack geometry and degrade vision-lan

Christian Hercher

The famous problem of Busy Beavers can be stated as the question on how long a $n$-state Turing machine (using a 2-symbol alphabet or -- in a generalization -- a $m$-symbol alphabet) can run if it is started on the blank tape before it holds. Thus, not halting Turing machines are excluded. For up to four states the answer to this question is well-known. Rece

Manuela Daniela Danu, George Marica, Constantin Suciu, Lucian Mihai Itu

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While co

Vladyslav Zalevskyi, Dondu-Busra Bulut, Thomas Sanchez, Meritxell Bach Cuadra

Early identification of neurodevelopmental disorders relies on accurate segmentation of brain structures in infancy, a task complicated by rapid brain growth, poor tissue contrast, and motion artifacts in pediatric MRI. These challenges are further exacerbated in ultra-low-field (ULF, 0.064~T) MRI, which, despite its lower image quality, offers an affordable

Ido Farjoun, Reshef Meir

We consider the strategyproof facility location problem on a circle. We focus on the case of 5 agents, and find a tight bound for the PCD strategyproof mechanism, which selects the reported location of an agent in proportion to the length of the arc in front of it. We methodically "reduce" the size of the instance space and then use standard optimization tec

Rafael S. de Souza, Emille E. O. Ishida, Alberto Krone-Martins

In this short review, we trace the evolution of inference in astronomy, highlighting key milestones rather than providing an exhaustive survey. We focus on the shift from classical optimization to Bayesian inference, the rise of gradient-based methods fueled by advances in deep learning, and the emergence of adaptive models that shape the very design of scie

Zhichang Fu, Yunhai Li, Weiqing Zhou, Shengjun Yuan

The $O(N)$ stochastic propagation method, which relies on the numerical solution of the time-dependent Schr\"odinger equation using random initial states, is widely used in large-scale first-principles calculations. In this work, we eliminate the conventional sequential computation of intermediate states by introducing a concurrent strategy that minimizes in

Kuniaki Kudo, Sherine Devi

We have developed a Scalable CI/CD Pipeline to address internal challenges related to Japan 2025 cliff problem, a critical issue where the mass end of service life of legacy core IT systems threatens to significantly increase the maintenance cost and black box nature of these system also leads to difficult update moreover replace, which leads to lack of prog

Jin Sano, Naoki Yamamoto, Kazunori Ueda

Designing programming languages that enable intuitive and safe manipulation of data structures is a critical research challenge. Conventional destructive memory operations using pointers are complex and prone to errors. Existing type systems, such as affine types and shape types, address this problem towards safe manipulation of heaps and pointers, but desig

Aditi Dutta

Addressing climate change effectively requires more than cataloguing the number of policies in place; it calls for tools that can reveal their thematic priorities and their tangible impacts on development outcomes. Existing assessments often rely on qualitative descriptions or composite indices, which can mask crucial differences between key domains such as

Sanghoon Lim

ALICE 3 is the proposed next-generation heavy-ion experiment at the CERN Large Hadron Collider (LHC), envisioned for operation during Run 5. The tracking system of ALICE 3 will consist of a high-precision vertex detector integrated into a retractable structure inside the beam pipe, complemented by a large-area outer tracker covering a broad pseudorapidity ra

M. Folcarelli, D. Delicato, A. Acevedo-Rentería, L. E. Ardila-Perez

BULLKID is a cryogenic, solid-state detector designed for direct searches of particle Dark Matter candidates, with mass $\lesssim 1$ GeV/c$^2$, and coherent neutrino-nucleus scattering. It is based on an array of dice carved in 5 mm thick silicon crystal, sensed by phonon-mediated Kinetic Inductance Detectors. In previous works, the array was calibrated with

Yinbo Sun, Yuchen Fang, Zhibo Zhu, Jia Li

The rapid advancement of time series foundation models (TSFMs) has been propelled by migrating architectures from language models. While existing TSFMs demonstrate impressive performance, their direct adoption of cross-domain architectures constrains effective capture of multiscale temporal dependencies inherent to time series data. This limitation becomes p

Shaharyar Ahmed Khan Tareen, Filza Khan Tareen, Xiaojing Yuan

Keypoint detection is the foundation of many computer vision tasks, including image registration, structure-from-motion, 3D reconstruction, visual odometry, and SLAM. Traditional detectors (SIFT, ORB, BRISK, FAST, etc.) and learning-based methods (SuperPoint, R2D2, QuadNet, LIFT, etc.) have shown strong performance gains yet suffer from key limitations: sens

Duo Su, Huyu Wu, Huanran Chen, Yiming Shi

Dataset distillation aims to synthesize compact yet informative datasets from large ones. A significant challenge in this field is achieving a trifecta of diversity, generalization, and representativeness in a single distilled dataset. Although recent generative dataset distillation methods adopt powerful diffusion models as their foundation models, the inhe

Jennifer O Bartlett, Alfie J Myers Wilson, Christopher J Chunnilall, Rupesh Kumar

In Local-local Oscillator (LLO) based Continuous-Variable Quantum Key Distribution (CV-QKD), the phase reference of the transmitter and receiver, Alice and Bob, are naturally de-correlated due to their use of individual lasers. A phase reference signal is used, whose measurement is critical for estimating the phase difference and correcting the raw QKD data.

Mustafa F. Abdelwahed, Alice Toniolo, Joan Espasa, Ian P. Gent

Autonomous agents rely on automated planning algorithms to achieve their objectives. Simulation-based planning offers a significant advantage over declarative models in modelling complex environments. However, relying solely on a planner that produces a single plan may not be practical, as the generated plans may not always satisfy the agent's preferences. T

Chris Heunen, Nesta van der Schaaf

We develop relativistic causality theory in the setting of point-free topology by introducing a notion of causal coverage in ordered locales, generalising their canonical coverage relation to incorporate causal structure. This improves Christensen and Crane's construction of `causal sites'. We connect to sheaf theory by showing that causal coverages can be i

D. Pecchio, S. Sahoo, V. Scagnoli, L. J. Heyderman

Artificial spin ices (ASIs) provide a versatile platform to explore magnetic frustration and emergent phenomena. However, in kagome ASI, experimental access to the ground state remains elusive due to dynamical freezing. Here, we demonstrate a deterministic and rewritable approach to attain the ground state using ultrafast, site-selective laser annealing. By

Jiacheng Xie, Yang Yu, Yibo Chen, Hanyao Zhang

Traditional Chinese Medicine (TCM), with a history spanning over two millennia, plays a role in global healthcare. However, applying large language models (LLMs) to TCM remains challenging due to its reliance on holistic reasoning, implicit logic, and multimodal diagnostic cues. Existing TCM-domain LLMs have made progress in text-based understanding but lack

Chunlin Jiang, Hequn Li, Zhongwei Deng, Jie Shao

Accurate prediction of lithium-ion battery capacity and its associated uncertainty is essential for reliable battery management but remains challenging due to the stochastic nature of aging. This paper presents a new method, termed the Conditional Diffusion U-Net with Attention (CDUA), which integrates feature engineering and deep learning to address this ch

Goutam Das, Sven-Olaf Moch

We present precise results for the inclusive Higgs boson cross-section in gluon-gluon fusion at the LHC considering state-of-the-art fourth-order results in perturbative QCD arising from the dominant soft and virtual gluon emissions. Utilizing four-loop QCD results for the gluon-form factor, the splitting function and related anomalous dimensions, we study t

Seyed Mahdi Mastoor, Amirhossein Ahmadkhan Kordbacheh

Theoretical research has been conducted to study how geometry affects charge and spin transport in $\beta\mathrm{12}$ borophene quantum dots, which are confined systems. The study examined two distinct central regions, which included a circular disc and a regular hexagonal area that connected to semi-infinite zigzag and armchair borophene nanoribbon leads. T

Dripto Biswas

We extend the systematic construction of bosonic DDF operators to the light-like linear dilaton background to investigate how higher-spin string states behave beyond flat spacetime. Using previous results, we show that the spectrum-generating algebra is isomorphic to the flat spacetime case up to a few subtleties. This extension provides a controlled setting

Dmitry Bankov, Artem Krasilov, Artem Otmakhov, Pavel Savlukovich

5G Vehicle-to-Everything (V2X) is a new technology developed by 3GPP to support inter-vehicle communication. In contrast to 4G V2X which allows only broadcast communication, 5G V2X enables groupcast and unicast communication. Such types of communication are needed for new V2X scenarios: platooning, extended sensors, remote driving, etc. To improve the data t

Dmitrii Galimzianov, Viacheslav Vyshegorodtsev, Ivan Nezhivykh

Monitoring the behavior of stalled horses is essential for early detection of health and welfare issues but remains labor-intensive and time-consuming. In this study, we present a prototype vision-based monitoring system that automates the detection and tracking of horses and people inside stables using object detection and multi-object tracking techniques.

Halima I. Kure, Jishna Retnakumari, Augustine O. Nwajana, Umar M. Ismail

This paper presents a novel methodology that integrates trustworthy artificial intelligence (AI) with an energy-efficient robotic arm for intelligent waste classification and sorting. By utilizing a convolutional neural network (CNN) enhanced through transfer learning with MobileNetV2, the system accurately classifies waste into six categories: plastic, glas

William Ford

This work investigates several aspects related to quantitative stability in optimal transport, as well as uniqueness of the dual transport problem. Our main contributions are as follows. Chapter 1: Observations regarding the quantitative stability of optimal transport plans with respect to Wasserstein distance on the product space. Chapter 2: Extention of st

Tiezhi Wang, Wilhelm Haverkamp, Nils Strodthoff

Objective. Arrhythmia classification from electrocardiograms (ECGs) suffers from high false positive rates and limited cross-dataset generalization, particularly for atrial fibrillation (AF) detection where specificity ranges from 0.72 to 0.98 using conventional 30-s analysis windows. While most deep learning approaches analyze isolated 30-s ECG windows, man

Mardiyyah Oduwole, Prince Mireku, Fatimo Adebanjo, Oluwatosin Olajide

Multimodal AI research has overwhelmingly focused on high-resource languages, hindering the democratization of advancements in the field. To address this, we present AfriCaption, a comprehensive framework for multilingual image captioning in 20 African languages and our contributions are threefold: (i) a curated dataset built on Flickr8k, featuring semantica

Ling-Zheng Xia, Lixin Xu, Wei-Jia Li

Fractonic phases of matter, a class of states in which collective excitations with constrained mobility exist, were originally discovered in the study of quantum error-correcting codes in solvable lattice spin models such as Haah's code and the X-cube model. Recently, they have also drawn the attention of the high-energy physics community due to the UV/IR mi

Stella N. Arinze, Patrick U. Okafor, Onyekachi M. Egwuagu, Augustine O. Nwajana

This paper presents the development of a process automation architecture leveraging Radio Frequency Identification (RFID) technology for secure, transparent and efficient voting systems. The proposed architecture automates the voting workflow through RFID-enabled voter identification, encrypted vote casting, and secure data transmission. Each eligible voter

Jiacheng Xie, Shuai Zeng, Yang Yu, Xiaoting Tang

Traditional Chinese Medicine (TCM) presents a rich and structurally unique knowledge system that challenges conventional applications of large language models (LLMs). Although previous TCM-specific LLMs have shown progress through supervised fine-tuning, they often face limitations in alignment, data quality, and evaluation consistency. In this study, we int

David Aguilera-Luzon, Dave de Jonge, Javier Larrosa

Recently, a very simple new bilateral negotiation strategy called MiCRO was introduced that does not make use of any kind of opponent modeling or machine learning techniques and that does not require fine-tuning of any parameters. Despite its simplicity, it was shown that MiCRO performs similar to -- or even better than -- most state-of-the-art negotiation s

Artan Sheshmani, Shing-Tung Yau, Benjamin Zhou

We show that super Gromov-Witten invariants can be defined and computed by methods of tropical geometry. When the target is a point, the super invariants are descendant invariants on the moduli space of curves, which can be computed tropically. When the target is a convex, toric variety $X$, we describe a procedure to compute the tropical Euler class of the

Jialiang He, Jintao Luo, Shuguo Zhang

We show that if the Ramsey property holds (in a class of sets), then there is no MAD family (in this class, provided it satisfies some modest closure properties), proving a conjecture made by A.R.D.\ Mathias in 1977. As the technique we introduce for this proof is useful in a variety of related problems, we take the opportunity to announce 4 theorems, which

Shubham Kejriwal, Enrico Barausse, Alvin J. K. Chua

The upcoming Laser Interferometer Space Antenna (LISA) will detect up to thousands of extreme-mass-ratio inspirals (EMRIs). These sources will spend $\sim 10^5$ cycles in band, and are therefore sensitive to tiny changes in the general-relativistic dynamics, potentially induced by astrophysical environments or modifications of general relativity (GR). Previo

Camille Moisset, Stéphane Mathis, Louis Amard

Stellar tachoclines are thin regions located between the radiative core and the convective envelope of solar-type stars. They are defined as layers where the rotation of the radiative interior transitions to the differential rotation of the convective envelope, generating strong shear and turbulence. As such, understanding the dynamics of the transport and m

Dmitry Bankov, Artem Krasilov, Artem Otmakhov, Aleksei Shashin

The emerging road safety and autonomous vehicle applications require timely and reliable data delivery between vehicles and between vehicles and infrastructure. To satisfy this demand, 3GPP develops a 5G Vehicle-to-Everything (V2X) technology. Depending on the served traffic type, 5G V2X specifications propose two channel access methods: (i) Mode 1, accordin

Alejandro Guerra-Manzanares, Farah E. Shamout

The aim of multimodal neural networks is to combine diverse data sources, referred to as modalities, to achieve enhanced performance compared to relying on a single modality. However, training of multimodal networks is typically hindered by modality overfitting, where the network relies excessively on one of the available modalities. This often yields sub-op

Kefan Chen, Hussain Ahmad, Diksha Goel, Claudia Szabo

Large Language Models (LLMs) have recently gained popularity in stock trading for their ability to process multimodal financial data. However, most existing methods focus on single-stock trading and lack the capacity to reason over multiple candidates for portfolio construction. Moreover, they typically lack the flexibility to revise their strategies in resp

Sonu Kumar, Arjun S. Nair, Bhawna Chaudhary, Mukul Lokhande

We present a Cortical Neural Pool (CNP) architecture featuring a high-speed, resource-efficient CORDIC based Hodgkin-Huxley (RCHH) neuron model. Unlike shared CORDIC-based DNN approaches, the proposed neuron leverages modular and performance-optimised CORDIC stages with a latency-area trade-off. We introduce a novel Constraint-Aware Modular Parallelism (CAMP

Jongmin Lee, Ernest K. Ryu

Although there is an extensive body of work characterizing the sample complexity of discounted-return offline RL with function approximations, prior work on the average-reward setting has received significantly less attention, and existing approaches rely on restrictive assumptions, such as ergodicity or linearity of the MDP. In this work, we establish the f

Seouh-won Yi, Min-hwan Oh

We propose feature perturbation, a simple yet effective exploration strategy for contextual bandits that injects randomness directly into feature inputs, instead of randomizing unknown parameters or adding noise to rewards. Remarkably, this algorithm achieves $\tilde{\mathcal{O}}(d\sqrt{T})$ worst-case regret bound for generalized linear contextual bandits,

Numaan Naeem, Abdellah El Mekki, Muhammad Abdul-Mageed

Large language models (LLMs) are transforming education by answering questions, explaining complex concepts, and generating content across a wide range of subjects. Despite strong performance on academic benchmarks, they often fail to tailor responses to students' grade levels. This is a critical need in K-12 education, where age-appropriate vocabulary and e

Henry Lim, Kwan Hui Lim

Instruction-tuned large language models (IT-LLMs) exhibit strong zero-shot reasoning, yet their ability to execute simple, self-contained instructions remains underexplored, despite this being foundational to complex instruction-following. We evaluate 20 IT-LLMs on modified MMLU and MMLU-Pro benchmarks, by systematically varying the format of option labels (

Hamza Abouabid, Abdesslam Arhrib, Jaouad El Falaki, Bin Gong

We present a complete one-loop analysis of charged scalar boson pair production in photon-photon collisions, $\gamma\gamma \to H^\pm H^\mp$, within the framework of the Inert Doublet Model (IDM). The calculation is carried out in the on-shell renormalization scheme and incorporates both weak corrections and QED effects, including soft and hard photon radiati

Elaheh Hosseinkhani, Martin Leucker

Traditional approaches to inference of deterministic finite-state automata (DFA) stem from symbolic AI, including both active learning methods (e.g., Angluin's L* algorithm and its variants) and passive techniques (e.g., Biermann and Feldman's method, RPNI). Meanwhile, sub-symbolic AI, particularly machine learning, offers alternative paradigms for learning

Jiajin Tang, Zhengxuan Wei, Ge Zheng, Sibei Yang

Humans can perform previously unexperienced interactions with novel objects simply by observing others engage with them. Weakly-supervised affordance grounding mimics this process by learning to locate object regions that enable actions on egocentric images, using exocentric interaction images with image-level annotations. However, extracting affordance know

Ludovica Schaerf

This paper examines the evolving nature of internal representations in generative visual models, focusing on the conceptual and technical shift from GANs and VAEs to diffusion-based architectures. Drawing on Beatrice Fazi's account of synthesis as the amalgamation of distributed representations, we propose a distinction between "synthesis in a strict sense",

Rishabh Jain, Keisuke Okumura, Michael Amir, Amanda Prorok

Finding near-optimal solutions for dense multi-agent pathfinding (MAPF) problems in real-time remains challenging even for state-of-the-art planners. To this end, we develop a hybrid framework that integrates a learned heuristic derived from MAGAT, a neural MAPF policy with a graph attention scheme, into a leading search-based algorithm, LaCAM. While prior w

Achref Jaziri, Martin Rogmann, Martin Mundt, Visvanathan Ramesh

Detecting out-of-distribution (OOD) data is critical for machine learning, be it for safety reasons or to enable open-ended learning. However, beyond mere detection, choosing an appropriate course of action typically hinges on the type of OOD data encountered. Unfortunately, the latter is generally not distinguished in practice, as modern OOD detection metho

Julen Cestero, Carmine Delle Femine, Kenji S. Muro, Marco Quartulli

Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions among various components. Reinforcement Learning (RL) is gaining prominence as a solution for addressing the challenges of Optimal Power Flow in smart grids. However, RL needs to

Bogdan R. Bułka, Tadeusz Domański, Karol I. Wysokiński

We investigate a hybrid device consisting of two quantum dots placed between a BCS superconductor and a semiconductor with a strong spin-orbit interaction. Assuming charge tunneling between quantum dots through spin-flip processes, we study the molecular Andreev bound states appearing in the proximized quantum dots. We show that the spin-orbit coupling split

Wei Xu, Xiaoyi Jiang, Lixiang Xu, Dechao Tang

In recent years, deep neural networks have been extensively employed in perceptual systems to learn representations endowed with invariances, aiming to emulate the invariance mechanisms observed in the human brain. However, studies in the visual and auditory domains have confirmed that significant gaps remain between the invariance properties of artificial n

Dimitrios G. Konstantinides, Charalampos D. Passalidis

In this paper we consider a multivariate risk model with common renewal process, while the logarithmic returns of the insurers investment portfolio, are described by a Levy process. In the two main results are established an asymptotic expression for the entrance probability of the discounted aggregate claims in some rare sets x A. This asymptotic expression

Yongmin Li, Jia Li, Ge Li, Zhi Jin

Language model-based code generation and completion tools have been widely adopted, but they may sometimes produce code that does not meet necessary constraints, such as syntactic correctness or API existence. Constrained decoding techniques are developed to help the model generate code adhering to the constraints by greedily eliminating generation options t

Zheng-Chuan Wang

On the basis of the non-equilibrium Green function formalism, we derived a spinor Boltzmann equation for the Bose cold atom gases with high spin, which is achieved by a quantum Wigner transformation on the equation satisfied by the lesser Green function. After a Taylor series expansion on the scattering terms, a temperature-dependent spinor damping force can

Annamaria Canino, Simone Mauro

We prove a multiplicity result for non-constant weak solutions $u \in H^1(\Omega)$ for the quasilinear elliptic equation \[ \begin{cases} \displaystyle-\text{div}(A(x,u)\nabla u) + \frac{1}{2} D_sA(x,u)\nabla u \cdot \nabla u = g(x,u) - \lambda u & \text{in } \Omega \\ A(x,u)\nabla u \cdot \eta = 0 & \text{on } \partial \Omega \end{cases} \] where $\lambda \

Yintao Zhou, Wei Huang, Zhengyu Li, Jing Huang

Parkinson's disease (PD) severity diagnosis is crucial for early detecting potential patients and adopting tailored interventions. Diagnosing PD based on facial expression is grounded in PD patients' "masked face" symptom and gains growing interest recently for its convenience and affordability. However, current facial expression-based approaches often rely

Paweł Borsukiewicz, Fadi Boutros, Iyiola E. Olatunji, Charles Beumier

The deployment of facial recognition systems has created an ethical dilemma: achieving high accuracy requires massive datasets of real faces collected without consent, leading to dataset retractions and potential legal liabilities under regulations like GDPR. While synthetic facial data presents a promising privacy-preserving alternative, the field lacks com

Mohammad Boveiri, Mohammad Khosravi, Peyman Mohajerin Esfahani

In this paper, we propose a new class of parameter estimation laws for adaptive systems, called \emph{normalized parameter estimation laws}. A key feature of these estimation laws is that they accelerate the convergence of the system state, $\mathit{x(t)}$, to the origin. We quantify this improvement by showing that our estimation laws guarantee finite integ

Jie Sheng, Tsutomu T. Yanagida

Although the axion is the most compelling solution to the strong CP problem, the ad hoc introduced global Peccei-Quinn symmetry suffers from a severe fine-tuning problem known as the quality problem. In this Letter, we show that the discrete gauge symmetry $\mathbb Z_4 \times \mathbb Z_3$ motivated from the internal structure of the Standard Model can natura

Haochen Su, Cristian Meo, Francesco Stella, Andrea Peirone

Robotic systems are increasingly expected to operate in human-centered, unstructured environments where safety, adaptability, and generalization are essential. Vision-Language-Action (VLA) models have been proposed as a language guided generalized control framework for real robots. However, their deployment has been limited to conventional serial link manipu

Yuequn Li, Alessandro Palmieri

In this paper, we study a semilinear weakly coupled system of wave equations with power nonlinearities. More precisely, we couple (through the nonlinear terms) a wave equation and a damped wave equation with a time-dependent coefficient for the damping term. For the coefficient of the damping term we consider two cases: the scale-invariant case and the scatt

Atila Poro, Raul Michel, Jean-François Coliac, Maryam Nastaran

In this work, we present a detailed investigation of five contact binary systems of the W Ursae Majoris (W UMa) type. Multiband photometric observations were conducted using ground-based telescopes in both the northern and southern hemispheres, yielding new times of minima. O-C diagram analysis reveals that two systems exhibit parabolic trends, indicating a

Liu Hong, Hong Qian

The general theory of a complex system of nonlinear chemical reactions is a primary language of chemistry that includes chemical engineering and cellular biochemistry. Its significance as an analytical framework, however, has not been fully appreciated outside the community of physical chemists. In this review, we discuss the latest advances in the kinetics

Vaggelis Dorovatas, Soroush Seifi, Gunshi Gupta, Rahaf Aljundi

Video Large Language Models (Video-LLMs) excel at understanding videos in-context, provided they have full access to the video when answering queries. However, these models face challenges in streaming scenarios where hour-long videos must be processed online, and questions need timely responses. In this work, we propose a training-free approach compatible w

Ayan Banerjee, Bobur Turimov, Sulton Usanov, Murodbek Vapaev

In this work, we investigate static configurations of dark energy stars within the framework of Rastall-Rainbow (R-R) gravity, which combines an energy-dependent deformation of spacetime with a nonminimal coupling between matter and geometry. We begin by deriving the modified field equations corresponding to R-R gravity and subsequently reformulate the stell

Shiming Liu, Jianhua Xie, Yan Wang

The stringent spatial constraints and the demand for high antenna efficiency in modern wireless earphones present significant design challenges. To address these issues, this paper presents and thoroughly investigates a novel earphone antenna design specifically tailored for open earbud wireless earphones. In contrast to traditional earphone antennas that re

Luca Cassia, Victor Mishnyakov

$q,t$-deformed matrix models give rise to representations of the deformed Virasoro algebra and more generally of the quantum toroidal $\mathfrak{gl}_1$ algebra. These representations are described in terms of finite difference equations that induce recursion relations for correlation functions. Under suitable assumptions, these recursions admit unique soluti

Christian Bean, Paul C. Bell, Abigail Ollson

We adapt the vertical and horizontal insertion encodings of Cayley permutations to enumerate restricted growth functions, which are in bijection with unordered set partitions. For both insertion encodings, we fully classify the classes for which these languages are regular. For the horizontal insertion encoding, we also prove that the conditions to be regula

Joachim Diederich

We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovations are (1) a locality dial, a tunable parameter that dynamically controls the degree of localization d

Leo P. Mulholland, Catherine A. Ramsbottom, Connor P. Ballance, Albert Sneppen

Spectral modelling of kilonovae (KNe) require large amounts of collisional excitation and photoionization atomic data for lowly ionised (neutral, singly and doubly ionised) species of heavy elements. Much of the data currently used is calculated using approximate hydrogenic results or adopts semi-empirical formulae. We present atomic data for ions of telluri

Gijin Kim, Purun-hanul Kim, Suk Gyu Hahm, Myongjong Kwon

Area-selective atomic layer deposition (AS-ALD) is an emerging technology in semiconductor manufacturing. However, accurately understanding inhibitor reactivity on surfaces remains challenging, particularly when the substrate is amorphous. In this study, we employ density functional theory (DFT) to investigate reaction pathways and quantify the reactivity of

Kejia Chen, Jiawen Zhang, Jiacong Hu, Kewei Gao

Vision-Language Models (VLMs) have become essential backbones of modern multimodal intelligence, yet their outputs remain prone to hallucination-plausible text misaligned with visual inputs. Existing alignment approaches often rely on expensive fine-tuning with annotated preference data or sequence-level inference strategies that provide only coarse, delayed

Ashmi Banerjee, Melih Mert Aksoy, Wolfgang Wörndl

Tourism is a major contributor to global carbon emissions and over-tourism, creating an urgent need for recommender systems that not only inform but also gently steer users toward more sustainable travel decisions. Such choices, however, often require balancing complex trade-offs between environmental impact, cost, convenience, and personal interests. To add

Chenghao Zhang, Guanting Dong, Xinyu Yang, Zhicheng Dou

Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing large language models (LLMs) by retrieving relevant documents from an external corpus. However, existing RAG systems primarily focus on unimodal text documents, and often fall short in real-world scenarios where both queries and documents may contain mixed modalities (such

Nima Haghighat, Hem B. Motra, Amir S. Sattari, Frank Wuttke

Data on the coupled influence of pressure and temperature on rock thermal conductivity remain limited. This study adapts a multi-anvil cubic press to enable steady-state thermal conductivity measurements under controlled in-situ conditions. The setup concurrently records P- and S-wave velocities along three orthogonal directions, providing unique real-time c

Xenia de la Ossa, Mohamed Elmi

We study the Hulek--Verrill families of Calabi--Yau threefolds. They are birationally equivalent to fibred products of elliptic surfaces, so we expect to be able to compute periods on these threefolds by integrating products of elliptic periods over a contour on $\mathbb{P}^1$. We numerically verify this in several examples. This article was submitted to MAT

Silvia Tolo, John Andrews

Although Fault Tree and Event Tree analysis are still today the standard approach to system safety analysis for many engineering sectors, these techniques lack the capabilities of fully capturing the realistic, dynamic behaviour of complex systems, which results in a dense network of dependencies at any level, i.e. between components, trains of components or

Gabriel Rivière, Maria Teresa Rotolo

We consider linear and time-dependent perturbations of periodic transport equations on the two-dimensional torus. For generic perturbations, we prove the existence of a large class of initial data whose Sobolev norms diverge exponentially fast. In higher dimensions, this remains true under a Morse-Smale assumption on the resonant part of the perturbation. In

Leigh N. Fletcher, Amy Simon, Michael H. Wong, Jonathan D. Nichols

Visible and ultraviolet imaging and spectroscopy of Solar System giant planets can set the paradigm for the atmospheric, ionospheric, and magnetospheric processes shaping the diversity of giant exoplanets, brown dwarfs, and their interactions with stellar hosts. Spectra of their molecular absorptions, aerosol scattering, airglow, and auroral emissions can re

Qisi Zhou, Tao Jiang, Qingqian Kang, Teng Zhao

The hybrid interferometer integrating an optical parametric amplifier and a beam splitter has the potential to outperform the SU(1,1) interferometer. However, photon loss remains a critical limitation for practical implementation. To address this challenge, we propose a quantum metrology scheme utilizing multi-photon subtraction at the output and replacing t

Marc Jourdan, Achraf Azize

Best Arm Identification (BAI) algorithms are deployed in data-sensitive applications, such as adaptive clinical trials or user studies. Driven by the privacy concerns of these applications, we study the problem of fixed-confidence BAI under global Differential Privacy (DP) for Bernoulli distributions. While numerous asymptotically optimal BAI algorithms exis

Jingqian Wu, Yunbo Jia, Shengpeng Xu, Edmund Y. Lam

Event cameras provide a promising sensing modality for high-speed and high-dynamic-range vision by asynchronously capturing brightness changes. A fundamental task in event-based vision is event-to-video (E2V) reconstruction, which aims to recover intensity videos from event streams. Most existing E2V approaches formulate reconstruction as a temporal--spatial

Peihong Zhang, Zhixin Li, Yuxuan Liu, Rui Sang

Deep learning approaches for heart-sound (PCG) segmentation built on time-frequency features can be accurate but often rely on large expert-labeled datasets, limiting robustness and deployment. We present TopSeg, a topological representation-centric framework that encodes PCG dynamics with multi-scale topological features and decodes them using a lightweight

Peihong Zhang, Yuxuan Liu, Rui Sang, Zhixin Li

Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that structure data presentation by ordering or reweighting training examples from easy-to-hard to facilitate learning; however, existing curricula are static, fixing the ordering or the w

Nicolas Bousquet, Amer E. Mouawad, Stephanie Maaz, Naomi Nishimura

Solution discovery asks whether a given (infeasible) starting configuration to a problem can be transformed into a feasible solution using a limited number of transformation steps. This paper investigates meta-theorems for solution discovery for graph problems definable in monadic second-order logic (MSO$_1$ and MSO$_2$) and first-order logic (FO) where the

Alexander Iksanov, Anatolii Nikitin, Roman Yakymiv

We further explore a connection initially unveiled in Iksanov (2025) between critical beta-splitting trees and infinite `balls-in-boxes' schemes. Using the connection, we derive a new joint central limit theorem for components of the height of a leaf chosen uniformly at random in the discrete version of a critical beta-splitting tree. Also, we obtain a joint

Alberto Ceresoli, Viola Bernazzoli, Roberto Pegurri, Ilario Filippini

Accurate positioning is a key enabler for emerging 5G applications. While the standardized Location Management Function (LMF) operates centrally within the core network, its scalability and latency limitations hinder low-latency and fine-grained localization. A practical alternative is to shift positioning intelligence toward the radio access network (RAN),

Fan Shao, Satoshi Endo, Sandra Hirche, Fanny Ficuciello

Human collaboration with robots requires flexible role adaptation, enabling the robot to switch between an active leader and a passive follower. Effective role switching depends on accurately estimating human intentions, which is typically achieved through external force analysis, nominal robot dynamics, or data-driven approaches. However, these methods are

Linus Götzfried

We prove the following monotonicity result for the holonomy group: Given a sequence of metric connections converging in $C^0$ such that all its members have holonomy contained in a closed group $H$, also their limit connection needs to have holonomy contained in $H$. As a corollary, for a sequence of Riemannian metrics converging in $C^1$ and having special