November 2025 arXiv papers — page 159

Carine Menezes Rebello, Erbet Almeida Costa, Idelfonso B. R. Nogueira

Robust model predictive control (MPC) aims to preserve performance under model-plant mismatch, yet robust formulations for nonlinear MPC (NMPC) with data-driven surrogates remain limited. This work proposes an offset-free robust NMPC scheme based on symbolic regression (SR). Using a compact NARX structure, we identify interpretable surrogate models that expl

Maksym Luz, Mykhailo Moklyachuk

We consider stochastic sequences with periodically stationary generalized multiple increments of fractional order which combines cyclostationary, multi-seasonal, integrated and fractionally integrated patterns. We solve the interpolation problem for linear functionals constructed from unobserved values of a stochastic sequence of this type based on observati

Umberto Cappellazzo, Xubo Liu, Pingchuan Ma, Stavros Petridis

Large language models (LLMs) have recently achieved impressive results in speech recognition across multiple modalities, including Auditory Speech Recognition (ASR), Visual Speech Recognition (VSR), and Audio-Visual Speech Recognition (AVSR). Despite this progress, current LLM-based approaches typically address each task independently, training separate mode

Mateo Cárdenes Wuttig

We demonstrate that a machine learning framework based on kernel ridge regression can encode and predict the self-energy of one-dimensional Hubbard models using only mean-field features such as static and dynamic Hartree-Fock quantities and first-order GW calculations. This approach is applicable across a wide range of on-site Coulomb interaction strengths $

Valentina Bais, Alessio Di Prisa, Daniel Hartman, Chun-Sheng Hsueh

For every $n \geq 4$, we demonstrate the existence of non-isotopic smooth $(n-2)$-knots in $S^n$ with diffeomorphic traces by generalising the RBG link construction to all dimensions. Conversely, we prove that for every $n \geq 4$, the unknot in $S^n$ is detected by the diffeomorphism type of its surgery and hence by its trace.

Tianhao Peng, Haochen Wang, Yuanxing Zhang, Zekun Wang

The advent of Multimodal Large Language Models (MLLMs) has expanded AI capabilities to visual modalities, yet existing evaluation benchmarks remain limited to single-video understanding, overlooking the critical need for multi-video understanding in real-world scenarios (e.g., sports analytics and autonomous driving). To address this significant gap, we intr

Tsz Wai Ko, Runze Liu, Adesh Rohan Mishra, Zihan Yu

Electrostatics govern charge transfer and reactivity in materials. However, most foundation potentials (FPs) either neglect explicit electrostatic interactions or come at prohibitive computational cost. Here, we introduce charge-equilibrated TensorNet (QET), an equivariant, charge-aware architecture that achieves linear scaling with system size via an analyt

Stephen T. Hedetniemi, Douglas F. Rall

Given a set $U \subset V$ of vertices in a graph $G = (V, E)$, a {\it private neighbor with respect to the set $U$} is any vertex $w \in V$ having precisely one neighbor, say $v$, in $U$. If $w \in V - U$, then $w$ is called an {\it external private neighbor} of $v$ with respect to $U$. If $w \in U$ then $w$ is called an {\it internal private neighbor} of $v

Ruifeng Zheng, Pengjie Zhou, Pit Hofmann, Fatima Rani

This paper presents a Markov-based system model for microfluidic molecular communication (MC) channels. By discretizing the advection-diffusion dynamics, the proposed model establishes a physically consistent state-space formulation. The transition matrix explicitly captures diffusion, advective flow, reversible binding, and flow-out effects. The resulting d

Gautam Chandrasekaran, Adam R. Klivans, Konstantinos Stavropoulos, Arsen Vasilyan

We give the first fully polynomial-time algorithm for learning halfspaces with respect to the uniform distribution on the hypercube in the presence of contamination, where an adversary may corrupt some fraction of examples and labels arbitrarily. We achieve an error guarantee of $\eta^{O(1)}+\epsilon$ where $\eta$ is the noise rate. Such a result was not kno

Chandrima B. Pushpan, Amit Kumar Pal

We show that harnessing daemonic advantage is possible while charging a quantum battery by first time-evolving the battery collectively with an auxiliary charger, followed by an energy extraction via tracing out the charger. We define the difference between the minimum daemonic ergotropy and the maximum ergotropy of the battery as the daemonic gap at the tim

Tianle Song, Chenhao Lin, Yang Cao, Zhengyu Zhao

Mobile motion sensors such as accelerometers and gyroscopes are now ubiquitously accessible by third-party apps via standard APIs. While enabling rich functionalities like activity recognition and step counting, this openness has also enabled unregulated inference of sensitive user traits, such as gender, age, and even identity, without user consent. Existin

Mengmeng Liu, Jiuming Liu, Yunpeng Zhang, Jiangtao Li

Remarkable advances in recent 2D image and 3D shape generation have induced a significant focus on dynamic 4D content generation. However, previous 4D generation methods commonly struggle to maintain spatial-temporal consistency and adapt poorly to rapid temporal variations, due to the lack of effective spatial-temporal modeling. To address these problems, w

ANTARES Collaboration, A. Albert, S. Alves, M. André

ANTARES, a neutrino detector located in the depths of the Mediterranean Sea, operated successfully for over 15 years before being decommissioned in 2022. The telescope offered an ideal vantage view of the Southern Sky and benefited from optimal water properties for enhanced angular resolution. This study makes use of data collected over the entire operationa

Seungheon Song, Jaekoo Lee

In autonomous driving and robotics, ensuring road safety and reliable decision-making critically depends on out-of-distribution (OOD) segmentation. While numerous methods have been proposed to detect anomalous objects on the road, leveraging the vision-language space-which provides rich linguistic knowledge-remains an underexplored field. We hypothesize that

Xin Qiu, Junlong Tong, Yirong Sun, Yunpu Ma

Large-scale models are at the forefront of time series (TS) forecasting, dominated by two paradigms: fine-tuning text-based Large Language Models (LLM4TS) and training Time Series Foundation Models (TSFMs) from scratch. Both approaches share a foundational assumption that scaling up model capacity and data volume leads to improved performance. However, we ob

Omar Swelam, Lennart Purucker, Jake Robertson, Hanne Raum

Causal discovery is fundamental for multiple scientific domains, yet extracting causal information from real world data remains a significant challenge. Given the recent success on real data, we investigate whether TabPFN, a transformer-based tabular foundation model pre-trained on synthetic datasets generated from structural causal models, encodes causal in

Kwanyoung Kim

Diffusion models have demonstrated strong generative performance when using guidance methods such as classifier-free guidance (CFG), which enhance output quality by modifying the sampling trajectory. These methods typically improve a target output by intentionally degrading another, often the unconditional output, using heuristic perturbation functions such

Erhan Bayraktar, Qi Feng, Zecheng Zhang, Zhaoyu Zhang

We propose a deep neural-operator framework for a general class of probability models. Under global Lipschitz conditions on the operator over the entire Euclidean space-and for a broad class of probabilistic models-we establish a universal approximation theorem with explicit network-size bounds for the proposed architecture. The underlying stochastic process

Sathish Krishna Anumula, SVSV Prasad Sanaboina, Ravi Kumar Nagula, R. Nagaraju

The growing need to automate processes in industrial settings has led to tremendous growth in the robotic systems and especially the robotic arms. The paper assumes the design, modeling and control of a robotic arm to suit industrial purpose like assembly, welding and material handling. A six-degree-of-freedom (DOF) robotic manipulator was designed based on

Roland Schurig, Pieter van Goor, Karl Worthmann, Rolf Findeisen

Extended dynamic mode decomposition (EDMD) is a powerful tool to construct linear predictors of nonlinear dynamical systems by approximating the action of the Koopman operator on a subspace spanned by finitely many observable functions. However, its accuracy heavily depends on the choice of the observables, which remains a challenge. We propose a systematic

Alexander Bauer, Klaus-Robert Müller

Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns. As collecting representative examples of all possible anomalies is infeasible, we tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs. To this end

D. Fossion, V. Champain, S. Mohapatra, A. Cavanna

The Kondo effect emerges when a localized spin is screened by conduction electrons, giving rise to a strongly-correlated many-body ground state. In this work, we investigate this phenomenon in a GaAs/AlGaAs quantum dot, focusing on the spatial extension of the Kondo screening cloud in the electron reservoirs. To probe its properties, the dot is coupled to an

Kyeongjin Ahn, YongHun Suh, Sungwon Han, Jeasurk Yang

Lack of access to Water, Sanitation, and Hygiene (WASH) services is a major public health concern in refugee camps, where extreme crowding accelerates the spread of communicable diseases. The Rohingya settlements in Cox's Bazar, Bangladesh, exemplify these conditions, with large populations living under severe spatial constraints. We develop a semi-supervise

Viet-Thanh Pham, Minghan Wang, Hao-Han Liao, Thuy-Trang Vu

Adapting large language models to full document translation remains challenging due to the difficulty of capturing long-range dependencies and preserving discourse coherence throughout extended texts. While recent agentic machine translation systems mitigate context window constraints through multi-agent orchestration and persistent memory, they require subs

Jaehong Cho, Hyunmin Choi, Jongse Park

This paper introduces LLMServingSim2.0, a system simulator designed for exploring heterogeneous hardware in large-scale LLM serving systems. LLMServingSim2.0 addresses two key limitations of its predecessor: (1) integrating hardware models into system-level simulators is non-trivial due to the lack of a clear abstraction, and (2) existing simulators support

Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Fengpeng An

Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory (JUNO) experiment in China, which has recently completed construction, is expected to collect a sample comparable in si

Matteo Cederle, Saverio Bolognani, Gian Antonio Susto

Mobility-on-demand systems like ride-hailing have transformed urban transportation, but they have also exacerbated socio-economic inequalities in access to these services, also due to surge pricing strategies. Although several fairness-aware frameworks have been proposed in smart mobility, they often overlook the temporal and situational variability of user

Gabe Hoshino, Kristin Dona, Keisuke Harigaya, David W. Miller

We discuss a phenomenological model that extends the minimal supersymmetric standard model to contain axions and their supersymmetric partner, the axino. In the supersymmetric DFSZ axion model, the axino has tree level couplings to the higgs sector. In the case where $R$-parity is conserved, collider experiments may be sensitive to displaced decays of heavie

Mohammad Hasan Payandeh, Lin-Ping Yuan, Jian Zhao

Computational notebooks have become popular for Exploratory Data Analysis (EDA), augmented by LLM-based code generation and result interpretation. Effective LLM assistance hinges on selecting informative context -- the minimal set of cells whose code, data, or outputs suffice to answer a prompt. As notebooks grow long and messy, users can lose track of the m

JiaKui Hu, Shanshan Zhao, Qing-Guo Chen, Xuerui Qiu

This paper presents Omni-View, which extends the unified multimodal understanding and generation to 3D scenes based on multiview images, exploring the principle that "generation facilitates understanding". Consisting of understanding model, texture module, and geometry module, Omni-View jointly models scene understanding, novel view synthesis, and geometry e

Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen, Ms. Nilakshi Deka

A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way

Anne E. Sansom, Ignacio Ferreras, Benjamin F. McDonald

Satellite galaxies that are near to massive primary galaxies in close pairs can have stellar population ages that are more similar to their primaries than expected. This is one way in which close pairs of galaxies show galactic conformity, which is thought to be driven by assembly bias. Such conformity is seen in ages, morphologies and star formation rates i

Yakov Bloch

A spin precessing in a magnetic field is often used as a quantum clock, for example in tunneling-time measurements. We show that such a clock can exist in a coherent superposition of opposite temporal orientations, treating the arrow of time as a quantum two-level system. A Mach-Zehnder interferometer with equal and opposite magnetic fields provides a simple

Shuyue Jiang, Wenjing Ma, Shaojun Yu, Chang Su

Biological age, which may be older or younger than chronological age due to factors such as genetic predisposition, environmental exposures, serves as a meaningful biomarker of aging processes and can inform risk stratification, treatment planning, and survivorship care in cancer patients. We propose EpiCAge, a multimodal framework that integrates epigenetic

Masaya Nishihata

Organizations often concentrate scarce, high-value tasks on proven performers, but doing so may limit opportunities to develop and learn about alternative workers. We study this trade-off using temporary injuries to high-performing Major League Baseball players, which generate plausibly exogenous reallocations of playing opportunities. Tracking allocation an

Alessio Cesarano, Peter Gangl

In this work we deal with the shape optimization of an electric machine considering time-dependent effects such as eddy currents. The considered electric machine is an interior permanent magnet synchronous machine and we minimize the average dissipated power due to the eddy currents in the magnets over a period of time corresponding to a rotation, while at t

Said Lantigua, Gilson Giraldi, Renato Portugal

In this work, we introduce the Quantum-Classical Hybrid Physics-Informed Neural Network with Multiplicative and Additive Couplings (QPINN-MAC): a novel hybrid architecture that integrates the framework of Physics-Informed Neural Networks (PINNs) with that of Quantum Neural Networks (QNNs). Specifically, we prove that through strategic couplings between class

Khoi Anh Pham, Giuseppe Strangi

Random lasers (RL) emit light through multiple scattering in disordered gain media, typically resulting in isotropic emission with limited directionality control. Controlling RL emission direction in compact systems remains a challenge. Here we report directional random lasing achieved by infiltrating dye-doped nematic liquid crystals into a nanostructured s

Elias Döhrer, Nicolas Freches

We prove full convergence of gradient-flows of the arc-length restricted tangent point energies in the Hilbert-case towards critical points. This is done through a {\L}ojasiewicz-Simon gradient inequality for these energies. In order to do so, we prove, that the tangent-point energies are anlytic on the manifold of immersed embeddings and that their Hessian

Yuanheng Mao, Lillian Yang, Stephen Yang, Ethan Shao

Chronic pain is a global health challenge affecting millions of individuals, making it essential for physicians to have reliable and objective methods to measure the functional impact of clinical treatments. Traditionally used methods, like the numeric rating scale, while personalized and easy to use, are subjective due to their self-reported nature. Thus, t

Srijon Ghosh, Arkadiusz Kobus, Stanisław Kurdziałek, Rafał Demkowicz-Dobrzański

We investigate optimal metrological protocols for phase estimation in the presence of correlated dephasing noise, including spin-squeezed states sensing strategies as well as parallel and adaptive protocols optimized using tensor-network based numerical methods. The results are benchmarked against fundamental bounds obtained either via a latest quantum comb

Matteo Francobaldi, Michele Lombardi, Andrea Lodi

Artificial Intelligence systems are increasingly deployed in settings where ensuring robustness, fairness, or domain-specific properties is essential for regulation compliance and alignment with human values. However, especially on Neural Networks, property enforcement is very challenging, and existing methods are limited to specific constraints or local pro

Luiz Schirmer, Tiago Novello, Vinícius da Silva, Guilherme Schardong

\textit{Implicit neural representations} (INRs) have emerged as a promising framework for representing signals in low-dimensional spaces. This survey reviews the existing literature on the specialized INR problem of approximating \textit{signed distance functions} (SDFs) for surface scenes, using either oriented point clouds or a set of posed images. We refe

Geoffroy Peeters, Zafar Rafii, Magdalena Fuentes, Zhiyao Duan

In this paper, we trace the evolution of Music Information Retrieval (MIR) over the past 25 years. While MIR gathers all kinds of research related to music informatics, a large part of it focuses on signal processing techniques for music data, fostering a close relationship with the IEEE Audio and Acoustic Signal Processing Technical Commitee. In this paper,

Giacomo Fidone, Lucia Passaro, Riccardo Guidotti

Online Social Networks (OSNs) widely adopt content moderation to mitigate the spread of abusive and toxic discourse. Nonetheless, the real effectiveness of moderation interventions remains unclear due to the high cost of data collection and limited experimental control. The latest developments in Natural Language Processing pave the way for a new evaluation

Dominik Bullach, Matthew H. L. Honnor

We prove a substantial part of conjectures of Mazur and Tate that refine the conjecture of Birch and Swinnerton-Dyer. Our approach, which also leads to some results even finer than the predictions of Mazur and Tate, is via the `rank-zero component' of the relevant case of the equivariant Tamagawa Number conjecture.

Shoou-Ren Hsiau, Yi-Shen Lin

We consider a double secretary problem which contains $2n$ applicants of $n$ different qualities, two of each quality. As in the classical secretary problem (CSP), the applicants are interviewed sequentially in a random order by a manager and the manager wants to find an optimal stopping rule that maximizes the probability of selecting one of the two best ap

Mi Feng, Liang Tian, Changsong Zhou

Effective vaccine prioritization is critical for epidemic control, yet real outbreaks exhibit memory effects that inflate state space and make long-term prediction and optimization challenging. As a result, many strategies are tuned to short-term objectives and overlook how vaccinating certain individuals indirectly protects others. We develop a general age-

Konrad Reuter, Lennart Thaysen, Bilkay Doruk, Sarah Latus

Endoscopic sinus surgery requires careful preoperative assessment of the skull base anatomy to minimize risks such as cerebrospinal fluid leakage. Anatomical risk scores like the Keros, Gera and Thailand-Malaysia-Singapore score offer a standardized approach but require time-consuming manual measurements on coronal CT or CBCT scans. We propose an automated d

Hua Ye, Siyuan Chen, Haoliang Zhang, Weihao Luo

Large language models (LLMs) demonstrate impressive generalization abilities, yet adapting them effectively across multiple heterogeneous domains remains challenging due to inter-domain interference. To overcome this challenge, we propose a partition-based multi-stage fine-tuning framework designed to exploit inter-domain synergies while minimizing negative

Tuan Minh Ha, Binh Thanh Nguyen, Lam Si Tung Ho

In many areas of systems biology, including virology, pharmacokinetics, and population biology, dynamical systems are commonly used to describe biological processes. These systems can be characterized by estimating their parameters from sampled data. The key problem is how to optimally select sampling points to achieve accurate parameter estimation. Classica

S. V. Aksenov, M. S. Shustin, I. S. Burmistrov

Within the quantum field-theoretical approach describing the evolution of a quadratic Liouvillian in the basis of Keldysh contour coherent states, we investigate the spectral and transport properties of a dissipative superconducting system coupled to normal Fermi reservoirs. We derive a generalization of the Meir-Wingreen formula and Onsager matrix for a sup

Yakov Bloch, Avshalom C. Elitzur

Relativistic time dilation implies that an accelerating excited atom would have its lifetime prolonged in the lab frame. In this paper, we demonstrate a complementary effect: Longer-lived excited atoms turn out to have been accelerated. We propose the following experiment. An excited atom is prepared in a superposition of momenta. Due to relativistic time di

Kristoffer Eggestad, Benjamin A. D. Williamson, Sverre M. Selbach

Among all-inorganic halide perovskites, the only known ferroelectrics are the family of CsGeX$_3$ (X = Cl, Br, I). Here, we study their ferroelectric domain walls (DWs) and common point defects by density functional theory (DFT) calculations and investigate the interplay between DWs and defects. The most stable defects are V$_{\text{X}}$ and V$_{\text{Cs}}$

Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao

Large Language Models (LLMs) have emerged as powerful tools for passage reranking in information retrieval, leveraging their superior reasoning capabilities to address the limitations of conventional models on complex queries. However, current LLM-based reranking paradigms are fundamentally constrained by an efficiency-accuracy trade-off: (1) pointwise metho

Jiacheng Huang, Ning Yu, Xiaoyin Yi

Large language models (LLMs) are increasingly deployed in real-world communication settings, yet their ability to resolve context-dependent ambiguity remains underexplored. In this work, we present EMODIS, a new benchmark for evaluating LLMs' capacity to interpret ambiguous emoji expressions under minimal but contrastive textual contexts. Each instance in EM

Jiajie Lu, Zhenkan Fu, Na Zhao, Long Xing

The rapid progress of generative AI has led to the emergence of new generative models, while existing detection methods struggle to keep pace, resulting in significant degradation in the detection performance. This highlights the urgent need for continuously updating AI-generated image detectors to adapt to new generators. To overcome low efficiency and cata

A. Card, M. R. Stoneking, A. Deller, E. V. Stenson

The objective of the APEX (A Positron-Electron eXperiment) project is to magnetically confine and study electron--positron pair plasmas. For this purpose, a levitated dipole trap (APEX-LD) has been constructed. The magnetically levitated, compact (7.5-cm radius), closed-loop, high-temperature superconducting (HTS) floating (F-)coil consists exclusively of a

A. Parkosidis, S. Toonen, E. Laplace, F. Dosopoulou

Although mass transfer (MT) has been studied primarily in circular binaries, observations show that it also occurs in eccentric systems. We investigate orbital evolution during nonconservative MT in eccentric orbits, a process especially relevant for binaries containing compact objects (COs). We examined four angular momentum loss (AML) modes: Jeans, isotrop

Marc Jayson Baucas, Petros Spachos

Due to the recent shortage of resources in the healthcare industry, Remote Patient Monitoring (RPM) systems arose to establish a convenient alternative for accessing healthcare services remotely. However, as the usage of this system grows with the increase of patients and sensing devices, data and network management becomes an issue. As a result, wireless ar

Muneeb ur Rahman

The catalytic CO2 photoreduction to CO is significantly hindered by the pervasive kinetic bottleneck of *CO-desorption and inefficient charge separation. Surpassing the conventional single photocatalytic strategy, herein, a multi-synergetic 0D/1D S-scheme heterojunction by precisely assembling 0D InVO4 nanoparticles on 1D Cu-doped TbFeO3 (IVO/CTFO). This nan

Chiara Simeoni, Elisa Scanu, Donato Pera, Corrado Mascia

In this work, we introduce a variant of the Gatenby-Gawlinski model for acid-mediated tumor invasion in the one-dimensional experimental setting, accounting for heterogeneous diffusion of the lactic acid across the surrounding healthy tissues. Numerical simulations are performed by employing finite volume schemes on staggered cartesian grids, together with e

Juno C. L. Chan, Lorena Magaña Zertuche, Jose María Ezquiaga, Rico K. L. Lo

Gravitational waves (GWs) can be distorted by intervening mass distributions while propagating, leading to frequency-dependent modulations that imprint a distinct signature on the observed waveforms. Bayesian inference for GW lensing with conventional sampling methods is costly, and the problem is exacerbated by the rapidly growing GW catalog. Moreover, asse

Weilong Huang, Srikanth Raj Chetupalli, Mhd Modar Halimeh, Oliver Thiergart

Beamforming with desired directivity patterns using compact microphone arrays is essential in many audio applications. Directivity patterns achievable using traditional beamformers depend on the number of microphones and the array aperture. Generally, their effectiveness degrades for compact arrays. To overcome these limitations, we propose a neural directio

Mikkel Hviid Thorn

We extend the matrix representation of magnetic pseudo-differential operators in a tight Gabor frame from [arXiv:1804.05220, arXiv:2212.12229] to asymmetrical quantizations and smooth symbols dominated by a tempered weight (and not just decay/growth properties in the momentum variables). This leads to new results regarding the symbol calculus of such operato

Emir Bilgili, Nicholas Taormina, Yang Li, Adrian Diaz

This work quantifies the effect of misfit and threading dislocations on the surface energies of PbTe-PbSe interfaces, with the defect structures of the interfaces being obtained from atomistic and multiscale simulations of their manufacturing processes. Simulation results show that direct bonding produces semi-coherent interfaces with two-dimensional misfit

Xin Liu, Qiyang Song, Qihang Zhou, Haichao Du

Large language models (LLMs) increasingly support multilingual understanding and generation. Meanwhile, efforts to interpret their internal mechanisms have emerged, offering insights to enhance multilingual performance. While multi-head self-attention (MHA) has proven critical in many areas, its role in multilingual capabilities remains underexplored. In thi

J. T. Lunardi, S. Salamanca, J. Negro, L. M. Nieto

We investigate the planar Dirac equation with the most general time-independent contact (singular) potential supported on a circumference. Taking advantage of the radial symmetry, the problem is effectively reduced to a one-dimensional one (the radial), and the contact potential is addressed in a mathematically rigorous way using a distributional approach th

Zhiyuan Zhai, Yuan Gao, Wei Ni, Xiaojun Yuan

Low-altitude economy (LAE) is rapidly emerging as a key driver of innovation, encompassing economic activities taking place in airspace below 500 meters. Unmanned aerial vehicles (UAVs) provide valuable tools for logistics collection within LAE systems, offering the ability to navigate through complex environments, avoid obstacles, and improve operational ef

Cédric Aïd

In this paper, we study extensions of valuations over algebraic field extensions without the use of the Axiom of Choice. We show a bijection between the extensions of a valuation and the maximal ideals of the relative integral closure of its valuation ring. In the case of a finite extension, we show that these maximal ideals exist. We conclude with an elemen

Hanlin Cai, Houtianfu Wang, Haofan Dong, Kai Li

Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this paradigm, federated fine-tuning (FFT) serves as a key enabler that allows distributed LLM agents to co-train an intelligent global LLM without centralizing local datasets. However, t

Olavi Nevanlinna, Tiina Vesanen

In multicentric representation of piecewise holomorphic functions one combines Lagrange interpolation at roots of a polynomial $p$ with convergent power series of $p$ as the "coefficients" multiplying the Lagrange basis polynomials. When these power series are truncated one obtains Hermite interpolation polynomials. In this paper we first review different ap

Tao Hao, Ying Hu, Jiaqiang Wen

This paper studies the mean-field backward stochastic Volterra integral equations (mean-field BSVIEs) and associated particle systems. We establish the existence and uniqueness of solutions to mean-field BSVIEs when the generator $g$ is of linear growth or quadratic growth with respect to $Z$, respectively. Moreover, the propagation of chaos is analyzed for

Manfred Buchacher

These notes collect results about algebraic correspondences and adapt them to the setting of correspondences on projective lines. The focus lies on finite orbits of algebraic correspondences. The main result is a field theoretic characterization of the (in)finiteness of the number of finite orbits.

Sébastien Thuau, Siba Haidar, Rachid Chelouah

Deep learning-based video surveillance increasingly demands privacy-preserving architectures with low computational and environmental overhead. Federated learning preserves privacy but deploying large vision-language models (VLMs) introduces major energy and sustainability challenges. We compare three strategies for federated violence detection under realist

Marcin Podhajski, Jan Dubiński, Franziska Boenisch, Adam Dziedzic

Current graph neural network (GNN) model-stealing methods rely heavily on queries to the victim model, assuming no hard query limits. However, in reality, the number of allowed queries can be severely limited. In this paper, we demonstrate how an adversary can extract a GNN with very limited interactions with the model. Our approach first enables the adversa

Kenta Yoshimura, Shunsuke Yasunaga, Daisuke Jido, Hiroyuki Fujioka

Antiprotonic atoms have served as a pivotal tool for investigating the properties of baryon-baryon interactions, including their spin dependence. Examining the spin-orbit splittings induced by their strong interactions also could help clarify the nature of the $\bar{p}$-nucleus interactions and their fraction mediated by scalar and vector mesons. Although th

Giusy Giulia Tuccari, Lorenzo Giammei, Andrea Giovanni Nuzzolese, Misael Mongiovì

Author Name Disambiguation (AND) is a long-standing challenge in bibliometrics and scientometrics, as name ambiguity undermines the accuracy of bibliographic databases and the reliability of research evaluation. This study addresses the problem of cross-source disambiguation by linking academic career records from CercaUniversit\`a, the official registry of

Mengqi Li, Lixin Li, Wensheng Lin, Zhu Han

Emerging 6G wireless systems suffer severe performance degradation in challenging environments like high-speed trains traversing dense urban corridors and Unmanned Aerial Vehicles (UAVs) links over mountainous terrain. These scenarios exhibit non-Gaussian, non-stationary channels with heavy-tailed fading and abrupt signal fluctuations. To address these chall

Meiyun Wang, Charin Polpanumas

We propose AdaRec, a few-shot in-context learning framework that leverages large language models for an adaptive personalized recommendation. AdaRec introduces narrative profiling, transforming user-item interactions into natural language representations to enable unified task handling and enhance human readability. Centered on a bivariate reasoning paradigm

Chenxi Luoa, Zhuangzhuang Zhaoa, Zhaohong Denga, Te Zhangb

Label learning is a fundamental task in machine learning that aims to construct intelligent models using labeled data, encompassing traditional single-label and multi-label classification models. Traditional methods typically rely on logical labels, such as binary indicators (e.g., "yes/no") that specify whether an instance belongs to a given category. Howev

Linji Long, Jinjiang Li, Min Zhang, Yankun Sui

In this paper, it is proved that, for $\gamma\in(\frac{317}{320},1)$, every sufficiently large odd integer can be written as the sum of nine cubes of primes, each of which is of the form $[n^{1/\gamma}]$. This result constitutes an improvement upon the previous result of Akbal and G\"{u}lo\u{g}lu [1].

Ruiqi Lyu, Alistair Turcan, Bryan Wilder

Responding to disease outbreaks requires close surveillance of their trajectories, but outbreak detection is hindered by the high noise in epidemic time series. Aggregating information across data sources has shown great denoising ability in other fields, but remains underexplored in epidemiology. Here, we present LRTrend, an interpretable machine learning f

Lynn Greschner, Meike Bauer, Sabine Weber, Roman Klinger

The convincingness of an argument does not only depend on its structure (logos), the person who makes the argument (ethos), but also on the emotion that it causes in the recipient (pathos). While the overall intensity and categorical values of emotions in arguments have received considerable attention in the research community, we argue that the emotion an a

Gottfried Haider, Jie Zhang

LLMscape is an interactive installation that investigates how humans and AI construct meaning under shared conditions of uncertainty. Within a mutable, projection-mapped landscape, human participants reshape the world and engage with multiple AI agents, each developing incomplete and provisional accounts of their environment. Exhibited in Shanghai and contin

Florent Foucaud, Atrayee Majumder, Tobias Mömke, Aida Roshany-Tabrizi

In the PATH COVER problem, one asks to cover the vertices of a graph using the smallest possible number of (not necessarily disjoint) paths. While the variant where the paths need to be pairwise vertex-disjoint, which we call PATH PARTITION, is extensively studied, surprisingly little is known about PATH COVER. We start filling this gap by designing a linear

Mehmet Turker Takci, James Day, Meysam Qadrdan

The rapid growth of data centres poses an evolving challenge for power systems with high variable renewable energy. Traditionally operated as passive electrical loads, data centres, have the potential to become active participants that provide flexibility to the grid. However, quantifying and utilising this flexibility have not yet been fully explored. This

Marvin Plein, Carsten F. Dormann, Andreas Christen

Urban weather station networks (WSNs) are widely used to monitor urban weather and climate patterns and aid urban planning. However, maintaining WSNs is expensive and labor-intensive. Here, we present a step-wise station removal procedure to thin an existing WSN in Freiburg, Germany, and analyze the ability of WSN subsets to reproduce air temperature and hum

Hyunsoo Park, Aron Walsh

Discovering functional crystalline materials entails navigating an immense combinatorial design space. While recent advances in generative artificial intelligence have enabled the sampling of chemically plausible compositions and structures, a fundamental challenge remains: the objective misalignment between likelihood-based sampling in generative modelling

Francesco Zigliotto

We consider cardinality-constrained optimization of set functions over the nodes of a graph. The standard greedy algorithm selects each node according to its immediate marginal contribution, a local criterion that may fail to anticipate the synergies within the final set. We introduce past-aware game-theoretic centrality (PAGTC), which evaluates a candidate

Zhijian Gong, Wenjia Dong, Xueyuan Xu, Fulin Wei

As a critical mental health disorder, depression has severe effects on both human physical and mental well-being. Recent developments in EEG-based depression analysis have shown promise in improving depression detection accuracies. However, EEG features often contain redundant, irrelevant, and noisy information. Additionally, real-world EEG data acquisition

Matteo Pettenó, Alessandro Ilic Mezza, Alberto Bernardini

Recent advances in latent diffusion models have demonstrated state-of-the-art performance in high-dimensional time-series data synthesis while providing flexible control through conditioning and guidance. However, existing methodologies primarily rely on musical context or natural language as the main modality of interacting with the generative process, whic

Thomas Steinecker, Alexander Bienemann, Denis Trescher, Thorsten Luettel

Reinforcement learning (RL) has shown promise in robotics, but deploying RL on real vehicles remains challenging due to the complexity of vehicle dynamics and the mismatch between simulation and reality. Factors such as tire characteristics, road surface conditions, aerodynamic disturbances, and vehicle load make it infeasible to model real-world dynamics ac

Xiaotian Li, Jinjiang Li, Min Zhang

Vinogradov's three primes theorem indicates that, for every sufficiently large odd integer $N$, the equation $N=p_1+p_2+p_3$ is solvable in prime variables $p_1,p_2,p_3$. In this paper, it is proved that Vinogradov's three primes theorem still holds with three prime variables constrained in the intersection of multiple Piatetski-Shapiro sequences.

Lu Haipeng, Yu Mei

In this paper, we consider the following indefinite fully fractional heat equation involving the master operator . Under certain assumptions of the indefinite nonlinearity and its weight, we prove that there is no positive bounded solution, which is based on the monotonicity of the solution along the first direction that is proved by employing the method of

Bas Dorsman, Tuomo Salmi, Anna L. Watts, Mason Ng

Pulse profile modelling is a relativistic ray-tracing technique that has provided constraints on parameters, with a focus on mass and radius, of five rotation-powered millisecond pulsars. While the technique can also be applied to accretion-powered millisecond pulsars (AMPs), this requires accounting for the X-rays from the accretion disc and has only been a

Giuseppe M. Sarda, Nimish Shah, Abubakr Nada, Debjyoti Bhattacharjee

Vortex, a newly proposed open-source GPGPU platform based on the RISC-V ISA, offers a valid alternative for GPGPU research over the broadly-used modeling platforms based on commercial GPUs. Similarly to the push originating from the RISC-V movement for CPUs, Vortex can enable a myriad of fresh research directions for GPUs. However, as a young hardware platfo

Arpit Jain, Boyang Zheng, Sawani Datta, Kanchan Ulman

Atomically thin silver (Ag) films offer unique opportunities in plasmonic, quantum optics, and energy harvesting, yet conventional growth methods struggle to achieve structural control at the monolayer limit. Here, we demonstrate phase-selective synthesis of large-area, crystalline 2D Ag films via defect-engineered confinement heteroepitaxy (CHet) at the epi

Benjamin Doerr, Martin S. Krejca, Milan Stanković

Together with the NSGA-II, the SPEA2 is one of the most widely used domination-based multi-objective evolutionary algorithms. For both algorithms, the known runtime guarantees are linear in the population size; for the NSGA-II, matching lower bounds exist. With a careful study of the more complex selection mechanism of the SPEA2, we show that it has very dif