October 2025 arXiv papers — page 34

Benjamin D. C. Lowe, Thomas Nordlander, Luca Casagrande, Gary S. Da Costa

We present a detailed chemical analysis study of 16 candidate metal-poor stars, previously identified with 2dF + AAOmega, using X-Shooter spectra and the Korg 1D local thermodynamic equilibrium spectral synthesis code. We confirm the earlier metallicity estimates and reveal six extremely metal-poor ([Fe/H] $< -3$) stars in the current sample. Two of these st

Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei

Generating long, informative, and factual outputs remains a major challenge for Large Language Models (LLMs). Existing benchmarks for long-form generation typically assess real-world queries with hard-to-verify metrics or use synthetic setups that ease evaluation but overlook real-world intricacies. In this paper, we introduce \textbf{LongWeave}, which balan

Mostafa Raeisi Sarkandiz

This article investigates the fundamental factors influencing the rate and manner of Electoral participation with an economic model-based approach. In this study, the structural parameters affecting people's decision making are divided into two categories. The first category includes general topics such as economic and livelihood status, cultural factors and

Megumi Shidatsu, Shogo Kobayashi, Yusuke Sakai, Toshihiro Takagi

We report on the jet structure in SS 433 based on X-ray high resolution spectroscopy with the XRISM/Resolve. The source was observed over 5 days covering both inside and outside an eclipse of the compact object by the companion star. Doppler-shifted, ionized Fe and Ni K emission lines were resolved, as well as lower-energy lines including Si and S K lines. T

S. Han, J. -K. Krogager, C. Ledoux, G. Ma

Quasar absorption systems not only affect the way quasars are selected, but also serve as key probes of galaxies, providing insight into their chemical evolution and interstellar medium (ISM). Recently, a method based on Gaia astrometric measurements has aided the selection of quasars reddened by dust hitherto overlooked. We conducted a spectroscopic study u

Yunxuan Jiang, Silan Hu, Xiaoning Wang, Yuanyuan Zhang

Large language models (LLMs) become increasingly integrated into data science workflows for automated system design. However, these LLM-driven data science systems rely solely on the internal reasoning of LLMs, lacking guidance from scientific and theoretical principles. This limits their trustworthiness and robustness, especially when dealing with noisy and

Qunyi Bie, Hui Fang, Yanping Zhou

Physical experiments and numerical simulations have revealed a remarkable stabilizing phenomenon: a background magnetic field stabilizes and dampens electrically conducting fluids. This paper provides a rigorous mathematical justification of this effect for the $n$-dimensional incompressible magnetohydrodynamic equations with partial diffusion on periodic do

Daria Kravets-Meinke, Hannah Schmid-Petri, Sonja Niemann, Ute Schmid

Generative Large Language Models (gLLMs), such as ChatGPT, are increasingly being used in communication research for content analysis. Studies show that gLLMs can outperform both crowd workers and trained coders, such as research assistants, on various coding tasks relevant to communication science, often at a fraction of the time and cost. Additionally, gLL

Mingyu Jeong, Eunsung Kim, Sehun Park, Andrew Jaeyong Choi

We present NVSim, a framework that automatically constructs large-scale, navigable indoor simulators from only common image sequences, overcoming the cost and scalability limitations of traditional 3D scanning. Our approach adapts 3D Gaussian Splatting to address visual artifacts on sparsely observed floors a common issue in robotic traversal data. We introd

Suvrojit Mitra, G B Kevin Arjun, Sanjay Ghosh

In modern display technology and visualization tools, downscaling images is one of the most important activities. This procedure aims to maintain both visual authenticity and structural integrity while reducing the dimensions of an image at a large scale to fit the dimension of the display devices. In this study, we proposed a new technique for downscaling i

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $(10087\pm44)\times10^{6}$ $J/\psi$ events collected with the BESIII detector, a full angular distribution analysis is carried out on the process $J/\psi\rightarrow\Lambda\bar{\Lambda}\rightarrow n\pi^{0}\bar{p}\pi^{+}+c.c.$ The decay parameters $\alpha_{0}$ for $\Lambda\rightarrow n\pi^{0}$ and $\bar{\alpha}_{0}$ for $\bar{\Lambda}\rightarrow \bar{n}\

Jonas Hein, Lazaros Vlachopoulos, Maurits Geert Laurent Olthof, Bastian Sigrist

Purpose: Surgical scene understanding is key to advancing computer-aided and intelligent surgical systems. Current approaches predominantly rely on visual data or end-to-end learning, which limits fine-grained contextual modeling. This work aims to enhance surgical scene representations by integrating 3D acoustic information, enabling temporally and spatiall

Gabriel O. dos Santos, Esther Colombini, Sandra Avila

In-context learning (ICL) enables Large Language Models (LLMs) to learn tasks from demonstration examples without parameter updates. Although it has been extensively studied in LLMs, its effectiveness in Vision-Language Models (VLMs) remains underexplored. In this work, we present a systematic study of ICL in VLMs, evaluating seven models spanning four archi

Oliver M. Bohle, Maryam Lotfigolian, Andre Laestadius, Erik I. Tellgren

A detailed convex analysis-based formulation of density-functional theory for periodic systems in arbitrary dimensions is presented. The electron-electron interaction is taken to be of Yukawa type, harmonising with underlying function spaces for densities and wave functions. Moreau--Yosida regularisation of the underlying non-interacting density functionals

Xiaoran Guo, Fei Gao, Kaihang Li, Qing Lin

Transverse position reconstruction in a Time Projection Chamber (TPC) is crucial for accurate particle tracking and classification, and is typically accomplished using machine learning techniques. However, these methods often exhibit biases and limited resolution due to incompatibility between real experimental data and simulated training samples. To mitigat

Hunzalah Hassan Bhatti, Firoj Alam

Large Language Models (LLMs) are increasingly used to answer everyday questions, yet their performance on culturally grounded and dialectal content remains uneven across languages. We propose a comprehensive method that (i) translates Modern Standard Arabic (MSA) multiple-choice questions (MCQs) into English and several Arabic dialects, (ii) converts them in

Valia Allori

There are several important philosophical problems to which quantum mechanics is often said to have made significant contributions: - Determinism: quantum theory has been taken to refute determinism; -Free Will: in turn, this is thought to open the door to free will; - The mind-body problem: relatedly, it is sometimes said to shed light on consciousness; - I

Alexandros Chatzinikolaou, Evgenios T. A. Kakariadis, Se-Jin Kim, Ioannis Apollon Paraskevas

We investigate when a map on a selfadjoint operator space $E$ is an embedding, i.e., when its unitisation in the sense of Werner is completely isometric. Combining with results of Russell, of Ng, and of Dessi, the second and the last author, it is shown that this is equivalent to: (a) extending bounded positive functionals on each matrix level with the same

Z. X. Shen, H. Y. Shang, Y. G. Ma, D. Bai

Entanglement is a key resource in quantum information science, yet its properties and applications in nuclear systems remain largely unexplored. Here, using proton-proton scattering as a quantum laboratory, we report the emergence of a near-pure Bell-triplet state at a laboratory energy of 151 MeV and a center-of-mass scattering angle of 90 degrees. In this

Ivan Tolkachev, Daniel R. Mason, Max Boleininger, Pui-Wai Ma

Nanocrystalline materials are promising candidates for future fusion reactor applications, due to their high density of grain boundaries which may serve as sinks for irradiation induced defects. We use molecular dynamics to simulate collision cascades in nanocrystalline iron and compare these to collision cascades in initially defect free single crystals. We

Evandro C. R. Rosa, Jerusa Marchi, Eduardo I. Duzzioni, Rafael de Santiago

Current quantum programming is dominated by low-level, circuit-centric approaches that limit the potential for compiler optimization. This work presents how a high-level programming construct provides compilers with the semantic information needed for advanced optimizations. We introduce a novel optimization that leverages a quantum-specific instruction to a

Declan Nell, Stefano Sanvito, Andrea Droghetti

Understanding non-equilibrium spin transport through 2D ferromagnets is a theoretical challenge, as correlations produce a complex electronic structure with coexisting itinerant and localized electrons. We have developed a fully non-equilibrium ab initio method, combining density functional theory, dynamical mean-field theory, and non-equilibrium Green's fun

Ivica Dimitrovski, Vlatko Spasev, Ivan Kitanovski

Remote sensing applications increasingly rely on deep learning for scene classification. However, their performance is often constrained by the scarcity of labeled data and the high cost of annotation across diverse geographic and sensor domains. While recent vision-language models like CLIP have shown promise by learning transferable representations at scal

Zhiheng Xi, Jixuan Huang, Xin Guo, Boyang Hong

Training critiquing language models to assess and provide feedback on model outputs is a promising way to improve LLMs for complex reasoning tasks. However, existing approaches typically rely on stronger supervisors for annotating critique data. To address this, we propose Critique-RL, an online RL approach for developing critiquing language models without s

Mohamedou Ould Haye, Anne Philippe

Distinguishing long-memory behaviour from nonstationarity is challenging, as both produce slowly decaying sample autocovariances. Existing stationarity tests either fail to account for long-memory processes or exhibit poor empirical size, particularly near the boundary between stationarity and nonstationarity. We propose a new, parameter-free testing procedu

Prajit Bhaskaran, Tom Viering

Bayesian clustering accounts for uncertainty but is computationally demanding at scale. Furthermore, real-world datasets often contain missing values, and simple imputation ignores the associated uncertainty, resulting in suboptimal results. We present Cluster-PFN, a Transformer-based model that extends Prior-Data Fitted Networks (PFNs) to unsupervised Bayes

María Sanz-Gómez, Víctor Mayoral-Vilches, Francesco Balassone, Luis Javier Navarrete-Lozano

Cybersecurity spans multiple interconnected domains, complicating the development of meaningful, labor-relevant benchmarks. Existing benchmarks assess isolated skills rather than integrated performance. We find that pre-trained knowledge of cybersecurity in LLMs does not imply attack and defense abilities, revealing a gap between knowledge and capability. To

Kyeongan Park, Gwonhak Lee, Minhyeok Kang, Youngjun Park

The energy distribution of a quantum state is essential for accurately estimating a molecule's ground state energy in quantum computing. Directly obtaining this distribution requires full Hamiltonian diagonalization, which is computationally prohibitive for large-scale systems. A more practical strategy is to approximate the distribution from a finite set of

Naillin Guan, Yongle Hu

We present a comprehensive formalization in the Lean4 theorem prover of the Auslander--Buchsbaum--Serre criterion, which characterizes regular local rings as those Noetherian local rings with finite global dimension. Rather than following the well-known proof that computes the projective dimension of the residue field via quotient by regular sequences and us

Baozhe Zhang, Xinwei Chen, Qingcheng Chen, Chao Xu

CoNi-MPC provides an efficient framework for UAV control in air-ground cooperative tasks by relying exclusively on relative states, eliminating the need for global state estimation. However, its lack of environmental information poses significant challenges for obstacle avoidance. To address this issue, we propose a novel obstacle avoidance algorithm, Cooper

Sahil Islam, Mohd. Suhail Rizvi, Anupam Gupta

Embryonic tissues deform across broad spatial and temporal scales and relax stress through active rearrangements. A quantitative link between cell-scale activity, spatial forcing, and emergent tissue-scale mechanics remains incomplete. Here, we use a vertex-based tissue model with active force fluctuations to study how motility controls viscoelastic response

Pekka K. Sinervo, C. M

Systematic uncertainties in high energy physics and astrophysics are often significant contributions to the overall uncertainty in a measurement, in many cases being comparable to the statistical uncertainties. However, consistent definition and practice is elusive, as there are few formal definitions and there exists significant ambiguity in what is defined

Saša Novaković

We are motivated by a result of Alzer and Luca who presented all the integer solutions to the relations $(k!)^n-k^n=(n!)^k-n^k$ and $(k!)^n+k^n=(n!)^k+n^k$. We modify the equations by considering the double factorial instead and present all integer solutions. We also consider some variations of these equations. Furthermore, we study equations of the form $f(

Fang Su, Xue Wang, Xia Pa

This paper focuses on the numerical approximation of random lattice reversible Selkov systems. It establishes the existence of numerical invariant measures for random models with nonlinear noise, using the backward Euler-Maruyama (BEM) scheme for time discretization. The study examines both infinite dimensional discrete random models and their corresponding

Guus Toussaint, Arno Knobbe

Equation Discovery techniques have shown considerable success in regression tasks, where they are used to discover concise and interpretable models (\textit{Symbolic Regression}). In this paper, we propose a new ED-based binary classification framework. Our proposed method EDC finds analytical functions of manageable size that specify the location and shape

Bojan Kuzma, Srdjan Stefanović, Ryotaro Tanaka

It is shown that every linear strong Birkhoff-James isomorphism between unital $C^*$-algebras is a $*$-isomorphism followed by a unitary multiplication. Moreover, as a partial extension of this result to the non-unital case, the form of (possibly nonlinear) strong Birkhoff-James isomorphisms between compact $C^*$-algebras are determined. A nonlinear characte

Shyam Jesalpura, Shengda Zhu, Amir Shaikhha, Antonio Barbalace

Running data analytics queries on serverless (FaaS) workers has been shown to be cost- and performance-efficient for a variety of real-world scenarios, including intermittent query arrival patterns, sudden load spikes and management challenges that afflict managed VM clusters. Alas, existing serverless data analytics works focus primarily on the serverless e

Iman Seyedi, Antonio Candelieri, Enza Messina, Francesco Archetti

Urban transportation networks are inherently vulnerable to disruptions that affect connectivity and passenger mobility. Traditional graph_theoretic metrics, such as betweenness and degree centrality, offer insights into local network structure but often fail to capture global structural distortions resulting from link failures. On the other hand, global indi

Alexis Forestier, Gunnar Weck, Sandra Ninet, Gaston Garbarino

Using synchrotron x-ray diffraction in laser-heated diamond anvil cells, we report the observation of an hexagonal close-packed (hcp) phase of water ice at high pressure and temperature conditions. Above 200 GPa and 1800 K, the hcp phase becomes dominant upon entering the superionic regime, as evidenced by anomalous thermal expansion. Observations are consis

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

Accurate prediction of rock thermal conductivity under in-situ conditions is essential for characterizing subsurface heat flow. This study presents a numerical framework based on the Lattice Element Method (LEM) for simulating the effective thermal conductivity of polymineralic rocks under coupled pressure-temperature conditions. The model resolves interacti

Deniz Gorur, Antonio Rago, Francesca Toni

Judgmental forecasting is the task of making predictions about future events based on human judgment. This task can be seen as a form of claim verification, where the claim corresponds to a future event and the task is to assess the plausibility of that event. In this paper, we propose a novel multi-agent framework for claim verification, whereby different a

Shangyu Xing, Siyuan Wang, Chenyuan Yang, Xinyu Dai

Reinforcement Learning with Verifiable Rewards (RLVR), particularly with algorithms like Group Relative Policy Optimization (GRPO), has proven highly effective in enhancing the reasoning capabilities of large language models. However, a critical bottleneck in current pipelines lies in the limited diversity of sampled trajectories during group rollouts. Homog

Edilberto O. Silva

We present a unified, SI-consistent framework to constrain minimal SME coefficients $a_\mu$ and $b_\mu$ using magnetically confined two-dimensional electron systems under a uniform magnetic field. Working in the nonrelativistic (Schr\"odinger--Pauli) limit with effective mass, we derive the radial problem for cylindrical geometries and identify how spatial c

DongJae Kim, Yaejin Lee, Minsu Park, Eunil Park

Stance detection has emerged as an area of research in the field of artificial intelligence. However, most research is currently centered on the target-dependent stance detection task, which is based on a person's stance in favor of or against a specific target. Furthermore, most benchmark datasets are based on English, making it difficult to develop models

Begüm Ateşli, Oğul Esen, Miroslav Grmela, Michal Pavelka

This manuscript introduces novel approaches to three phenomena. First, we extend the algebraic formulation of kinetic theory within the contact framework by making explicit the gauge freedom, thereby obtaining a formulation in which the phase-space volume itself becomes an additional dynamical variable. Second, we develop a new and simpler geometric formulat

Emilio N. M. Cirillo, Matteo Colangeli, Claudio Giberti, Lamberto Rondoni

Inspired by recent studies on deterministic oscillator models, we introduce a stochastic one-dimensional model for a chain of interacting particles. The model consists of $N$ oscillators performing continuous-time random walks on the integer lattice $\mathbb{Z}$ with exponentially distributed waiting times. The oscillators are bound by confining forces to tw

Jiayu Liu, Wei Dai, Zhenya Huang, Ning Miao

Despite the strong reasoning ability of large language models~(LLMs), they are prone to errors and hallucinations. As a result, how to check their outputs effectively and efficiently has become a critical problem in their applications. Existing checking methods heavily rely on external resources, such as trained verifiers (e.g., process/outcome reward models

Tjark Bantelmann

We model equivariant infinite loop spaces indexed on incomplete universes via suitable equivariant analogs of $\Gamma$-spaces. The choice of universe dictates a transfer system which in turn dictates the Segal condition on equivariant $\Gamma$-spaces. Equivariant $\Gamma$-spaces themselves come in different but equivalent guises interpolating between categor

Robin Schmöcker, Alexander Dockhorn, Bodo Rosenhahn

One weakness of Monte Carlo Tree Search (MCTS) is its sample efficiency which can be addressed by building and using state and/or action abstractions in parallel to the tree search such that information can be shared among nodes of the same layer. The primary usage of abstractions for MCTS is to enhance the Upper Confidence Bound (UCB) value during the tree

Daniel Parrochia

Scientific cosmology has now reached its period of maturity with the establishment of a standard model, which is the theory of an expanding universe. The question of whether this expansion resolves itself, in the past, into a singularity identifiable with an absolute beginning, or whether the universe in which we are is only one of the multiple possible univ

Carlos Caro, Francisco Gamez

Motivated by the emerging control of Berry-curvature textures in altermagnets, we explore a two-terminal configuration where a topological-insulator film is interfaced with two altermagnetic electrodes whose crystalline phases can be rotated independently. The proximity coupling imprints each momentum-dependent of the altermagnet spin texture onto the Dirac

Tomasz R. Bielecki, Jacek Jakubowski, Mariusz iewȩgłowski, Anatoliy Swishchuk

We give functional laws of large numbers for a class of marked Hawkes processes and marked compound Hawkes processes with a general mark space. Our results provide some complement to those presented previously in the literature. As an example we provide an application to analysis of time limit of an insurance ruin process.

Kai Jiang, Lei Zhang, Xiangcheng Zheng, Tiejun Zhou

We propose the nullspace-preserving high-index saddle dynamics (NPHiSD) method for degenerating multiple solution systems in constrained and unconstrained settings. The NPHiSD efficiently locates high-index saddle points and provides parent states for downward searches of lower-index saddles, thereby constructing the solution landscape systematically. The NP

Vladimir U. Nazarov, Tchavdar N. Todorov, E. K. U. Gross

The recent discovery that electrons in nano-scale conductors can act like a highly viscous liquid has triggered a surge of research activities investigating consequences of this surprising fact. Here we demonstrate that the electronic viscosity has an enormous influence on the operation of a prototypical AC-current-driven nano-motor. The design of this proto

Yao Liu

Accurate and robust weather forecasting remains a fundamental challenge due to the inherent spatio-temporal complexity of atmospheric systems. In this paper, we propose a novel self-supervised learning framework that leverages spatio-temporal structures to improve multi-variable weather prediction. The model integrates a graph neural network (GNN) for spatia

Anna Kneselová

We will focus on studying the ball measure of non-compactness $\alpha(T)$ for various particular instances of embedding operators in sequence spaces. Our first main goal is to find necessary and sufficient conditions for an identity operator to be maximally non-compact. Next, we will focus on studying Lorentz sequence spaces $\ell^{p,q}$ and their basic prop

Zhiwei Zhai, Wenjing Yan, Ying-Jun Angela Zhang

Decentralized bilevel optimization has garnered significant attention due to its critical role in solving large-scale machine learning problems. However, existing methods often rely on prior knowledge of problem parameters-such as smoothness, convexity, or communication network topologies-to determine appropriate stepsizes. In practice, these problem paramet

Richard Koebe, Noah Saibel, Juan Miguel Lopez Alcaraz, Simon Schäfer

Hypotension in critically ill ICU patients is common and life-threatening. Escalation to catecholamine therapy marks a key management step, with both undertreatment and overtreatment posing risks. Most machine learning (ML) models predict hypotension using fixed MAP thresholds or MAP forecasting, overlooking the clinical decision behind treatment escalation.

Luca Consolini, Mattia Laurini, Marco Locatelli

In this paper we address the speed planning problem for a vehicle along a predefined path. A weighted sum of two conflicting objectives, energy consumption and travel time, is minimized. After deriving a non-convex mathematical model of the problem, we prove that the feasible region of this problem is a lattice. Moreover, we introduce a feasibility-based bou

Martin Bicher, Maximilian Viehauser, Daniele Giannandrea, Hannah Kastinger

GEPOC, short for Generic Population Concept, is a collection of models and methods for analysing population-level research questions. For the valid application of the models for a specific country or region, stable and reproducible data processes are necessary, which provide valid and ready-to-use model parameters. This work contains a complete description o

Juntian Zhang, Song Jin, Chuanqi Cheng, Yuhan Liu

The limited capacity for fine-grained visual perception presents a critical bottleneck for Vision-Language Models (VLMs) in real-world applications. Addressing this is challenging due to the scarcity of high-quality data and the limitations of existing methods: supervised fine-tuning (SFT) often compromises general capabilities, while reinforcement fine-tuni

Wenhao Wang, Peizhi Niu, Zhao Xu, Zhaoyu Chen

Large Language Models (LLMs) increasingly rely on external tools to perform complex, realistic tasks, yet their ability to utilize the rapidly expanding Model Contextual Protocol (MCP) ecosystem remains limited. Existing MCP research covers few servers, depends on costly manual curation, and lacks training support, hindering progress toward real-world deploy

Boyi Ding, Elia Pizzati, Joop Schaye, Joseph F. Hennawi

Cosmological hydrodynamical simulations are essential tools for studying the formation and evolution of galaxies and their central supermassive black holes. While they reproduce many key observed properties of galaxies, their limited volumes have hindered comprehensive studies of the AGN and quasar populations. In this work, we leverage the FLAMINGO simulati

Baizhou Lin, Yuetong Fang, Renjing Xu, Rishad Shafik

The Tsetlin Machine (TM) has recently attracted attention as a low-power alternative to neural networks due to its simple and interpretable inference mechanisms. However, its performance on speech-related tasks remains limited. This paper proposes TsetlinKWS, the first algorithm-hardware co-design framework for the Convolutional Tsetlin Machine (CTM) on the

Leen Decin, Owen Vermeulen, Mats Esseldeurs, Florian Driessen

[abbreviated] Long Secondary Periods (LSPs) are observed in about one third of pulsating red giants yet remain unexplained. Four key observational constraints anchor the discussion: (i) a roughly 30 percent occurrence rate in semi-regular variable AGB stars (SRVs), with a much lower rate or absence in regularly pulsating Mira-type AGB stars (Miras), (ii) abo

Anjali Bhagat, Tanmay Kulkarni, Urban Larsson, Divya Murali

Subtraction games have a rich literature as normal-play combinatorial games (e.g., Berlekamp, Conway, and Guy, 1982). Recently, the theory has been extended to zero-sum scoring play (Cohensius et al. 2019). Here, we take the approach of cumulative self-interest games, as introduced in a recent framework preprint by Larsson, Meir, and Zick. By adapting standa

Matteo Calafà, Yuanxin Xia, Cheol-Ho Jeong

We present a novel neural network architecture for the efficient prediction of sound fields in two and three dimensions. The network is designed to automatically satisfy the Helmholtz equation, ensuring that the outputs are physically valid. Therefore, the method can effectively learn solutions to boundary-value problems in various wave phenomena, such as ac

Pietro Bongini, Valentina Molinari, Andrea Costanzo, Benedetta Tondi

Synthetic image source attribution is a challenging task, especially in data scarcity conditions requiring few-shot or zero-shot classification capabilities. We present a new training-free one-shot attribution method based on image resynthesis. A prompt describing the image under analysis is generated, then it is used to resynthesize the image with all the c

John Larkin, Brendan D. McKay, Fang Tian

Let $G$ be a uniformly chosen simple (labelled) random graph with given degree sequence $\boldsymbol{d}$ and let $X,Y,L$ be edge-disjoint graphs on the same vertex set as $G$. We investigate the probability that $X \subseteq G$ and that $G \cap Y = \emptyset$ both conditioned on the event $G \cap L = \emptyset$. We improve upon known bounds of these probabil

Christof Wetterich

Wave guides for classical electromagnetic fields can realize the discrete quantum evolution of the wave function for a system of qubits. Phase shifts, switches and beam splits allow for the construction of arbitrary quantum gates. They can act at once on a large number of qubits. For this correlation based photonic quantum computer the channels of the wave g

Luca Pezzini, Fabio Bacchini, Andrei N. Zhukov, Giuseppe Arrò

In-situ observations of the fast solar wind in the inner-heliosphere show that minor ions and ion sub-populations often exhibit distinct drift velocities. Both alpha particles and proton beams stream at speeds that rarely exceed the local Alfv\'{e}n speed relative to the core protons, suggesting the presence of instabilities that constrain their maximum drif

Junlin Mu, Hantao Huang, Jihang Zhang, Minghui Yu

Large Language Models capable of handling extended contexts are in high demand, yet their inference remains challenging due to substantial Key-Value cache size and high memory bandwidth requirements. Previous research has demonstrated that KV cache exhibits low-rank characteristics within the hidden dimension, suggesting the potential for effective compressi

Omkar Kulkarni, Rohitash Chandra

Financial fraud detection is critical for maintaining the integrity of financial systems, particularly in decentralised environments such as cryptocurrency networks. Although Graph Convolutional Networks (GCNs) are widely used for financial fraud detection, graph Transformer models such as Graph-BERT are gaining prominence due to their Transformer-based arch

Maximilian Bloor, Max Mowbray, Ehecatl Antonio Del Rio Chanona, Calvin Tsay

Sequential decision making under uncertainty is central to many Process Systems Engineering (PSE) challenges, where traditional methods often face limitations related to controlling and optimizing complex and stochastic systems. Reinforcement Learning (RL) offers a data-driven approach to derive control policies for such challenges. This paper presents a sur

Andrzej Biborski, Michał Zegrodnik

Strong evidence of unconventional superconductivity has been very recently reported experimentally in twisted transition metal dichalcogenide bilayer and gathered a significant amount of interest. Here we consider the Hubbard model on a triangular lattice describing the hole-doped moir\'e superlattice emerging in WSe$_{2}$/WSe$_{2}$ twisted homobilayer in th

L. -A. Hühn, C. P. Dullemond

Growing observational evidence suggests that Class II protoplanetary disks may undergo substantial interactions with their environment in the form of late infall. This mass inflow predominantly manifests itself in the form of so-called streamers: filaments and arcs of gas connecting large-scale, extended gas structures to disk scales. Prevalent late infall h

Eliseo Luongo

Recently, in \cite{glogic2025non}, it has been shown that the focusing power nonlinearity heat equation \begin{equation}\label{Eq:Heat_abstract}\tag{NLH} \partial_t u -\Delta u = |u|^{p-1}u, \quad p>1, \end{equation} in dimensions $d \geq 3$ has non-unique local solutions in $L^q(\mathbb{R}^d)$ for $q < d(p-1)/2$ provided that $p < p_{JL}$, where $p_{JL}$ de

Taewon Kim, Mehedi Hasan, Yu Sung Choi, Jae Woong Yoon

Microresonators are essential in integrated photonics, enabling optical filters, modulators, sensors, and frequency converters. Their spectral response is governed by bus-to-resonator coupling, typically classified as under-, critical-, or over-coupling. Conventional single-bus designs inevitably link the conditions for critical coupling, a transmission zero

Duaa Abdullah, Jasem Hamoud

This paper covers a variety of mathematical folk puzzles, including geometric (Tangrams, dissection puzzles), logic, algebraic, probability (Monty Hall Problem, Birthday Paradox), and combinatorial challenges (Eight Queens Puzzle, Tower of Hanoi). It also explores modern modifications, such as digital and gamified approaches, to improve student involvement a

Sadia Afroz, Zixuan Feng, Tyler Menezes, Katie Kimura

Generative AI (GenAI) tools are increasingly being adopted in software development as productivity aids, since there is evidence that GenAI tools can improve individual aspects of productivity. However, productivity is multidimensional; accelerating one aspect of work may simply shift effort to another. In this paper, we investigate how GenAI adoption affect

V. M. Gordeeva, Yanmeng Lei, Xiyin Ye, G. A. Bobkov

We predict the realization of ultrastrong coupling between magnons of antiferromagnets and photons in superconductor/antiferromagnet/superconductor heterostructures at terahertz frequencies, from both quantum and classical perspectives. The hybridization of the two magnon modes with photons strongly depends on the applied magnetic field: at zero magnetic fie

Maksymilian Manko

In arXiv:2503.19532 new examples of ribbon Hopf algebras based on the construction due to Nenciu were presented. This piece serves as a sequel where we study the representation theory of these new examples of ribbon Hopf algebras. We classify indecomposable projective and simple modules, find the Krull-Schmidt decomposition of the adjoint representation of N

Jiyu Guo, Shuo Yang, Yiming Huang, Yancheng Long

Data augmentation using generative models has emerged as a powerful paradigm for enhancing performance in computer vision tasks. However, most existing augmentation approaches primarily focus on optimizing intrinsic data attributes -- such as fidelity and diversity -- to generate visually high-quality synthetic data, while often neglecting task-specific requ

Jingyi Tian, Le Wang, Sanping Zhou, Sen Wang

Learning generalizable robotic manipulation policies remains a key challenge due to the scarcity of diverse real-world training data. While recent approaches have attempted to mitigate this through self-supervised representation learning, most either rely on 2D vision pretraining paradigms such as masked image modeling, which primarily focus on static semant

Zhaotong Yang, Yi Chen, Yanying Li, Shengfeng He

Recent deep models for image shadow removal often rely on attention-based architectures to capture long-range dependencies. However, their fixed attention patterns tend to mix illumination cues from irrelevant regions, leading to distorted structures and inconsistent colors. In this work, we revisit shadow removal from a sequence modeling perspective and exp

Ziqi Ma, Sao Mai Nguyen, Philippe Xu

Emergent symbolic representations are critical for enabling developmental learning agents to plan and generalize across tasks. In this work, we investigate whether large language models (LLMs) can translate human natural language instructions into the internal symbolic representations that emerge during hierarchical reinforcement learning. We apply a structu

Jan Lange, Guoyun Zhang

We provide new logarithmic lower bounds for the torsion order of a very general complete intersection in projective space as well as a very general hypersurface in products of projective spaces and Grassmannians, in particular we prove their retract irrationality.

Ziqi Ma, Changda Tian, Yue Gao

In recent years, there has been growing interest in developing robots and autonomous systems that can interact with human in a more natural and intuitive way. One of the key challenges in achieving this goal is to enable these systems to manipulate objects and tools in a manner that is similar to that of humans. In this paper, we propose a novel approach for

Jack Merullo, Srihita Vatsavaya, Lucius Bushnaq, Owen Lewis

We characterize how memorization is represented in transformer models and show that it can be disentangled in the weights of both language models (LMs) and vision transformers (ViTs) using a decomposition based on the loss landscape curvature. This insight is based on prior theoretical and empirical work showing that the curvature for memorized training poin

Jason M. Pittman, Anton Phillips, Yesenia Medina-Santos, Brielle C. Stark

In aphasia research, Speech-Language Pathologists (SLPs) devote extensive time to manually coding speech samples using Correct Information Units (CIUs), a measure of how informative an individual sample of speech is. Developing automated systems to recognize aphasic language is limited by data scarcity. For example, only about 600 transcripts are available i

Jihao Luo, Zesong Fei, Xinyi Wang, Le Zhao

Unmanned aerial vehicles (UAVs) are emerging as key enablers for low-altitude wireless network (LAWN), particularly when terrestrial networks are unavailable. In such scenarios, the environmental topology is typically unknown; hence, designing efficient and safe UAV trajectories is essential yet challenging. To address this, we propose a digital twin (DT)-as

Madhurima Panja, Dhiman Das, Tanujit Chakraborty, Arnob Ray

Understanding and forecasting precipitation events in the Arctic maritime environments, such as Bear Island and Ny-{\AA}lesund, is crucial for assessing climate risk and developing early warning systems in vulnerable marine regions. This study proposes a probabilistic machine learning framework for modeling and predicting the dynamics and severity of precipi

Marcin Łobejko, Tanmoy Biswas, Michał Horodecki

The catalytic extension of a discrete two-stroke engine employs a cyclic auxiliary system - the catalyst - that remains decoupled from the baths and performs no work, yet enhances power and efficiency beyond the corresponding non-catalytic counterpart. Theoretical models of discrete engines are relatively easy to analyze but remain challenging for experiment

Benjamin Plummer, Corina Cirstea

Traces form a coarse notion of semantic equivalence between states of a process, and have been studied coalgebraically for various types of system. We instantiate the finitary coalgebraic trace semantics framework of Hasuo et al. for controller-versus-environment games, encompassing both nondeterministic and probabilistic environments. Although our choice of

Cui Yakun, Peng Qi, Fushuo Huo, Hang Du

The advent of multi-modal large language models (MLLMs) has greatly advanced research on video fake news detection (VFND) tasks. Existing benchmarks typically focus on the detection accuracy, while failing to provide fine-grained assessments for the entire detection process. To address these limitations, we introduce {POVFNDB (Process-oriented Video Fake New

Jiarui Ji, Zehua Zhang, Zhewei Wei, Bin Tong

Large language models (LLMs) have shown promise in simulating human-like social behaviors. Social graphs provide high-quality supervision signals that encode both local interactions and global network structure, yet they remain underutilized for LLM training. To address this gap, we propose Graphia, the first general LLM-based social graph simulation framewo

Syed Zohaib Hassan, Pål Halvorsen, Miriam S. Johnson, Pierre Lison

Large Language Models (LLMs), predominantly trained on adult conversational data, face significant challenges when generating authentic, child-like dialogue for specialized applications. We present a comparative study evaluating five different LLMs (GPT-4, RUTER-LLAMA-2-13b, GPTSW, NorMistral-7b, and NorBloom-7b) to generate age-appropriate Norwegian convers

Ruiqi Zhang, Ensieh Sharifnia, Simon H. Tindemans

As a consequence of the high variability of load demand and renewable generation, long-term and high-resolution inputs are required for power system expansion planning, making the problem intractable in real-world applications. Time series aggregation (TSA), which captures representative patterns, reduces temporal complexity while providing similar planning

Suresh Govindarajan, Akhila Sadanandan, Jagannath Santara

We revisit (3,0) and (3,3) admissible solutions obtained using the MLDE method. We show that all $(3,0)$ solutions can be written in terms of a universal formula involving the ${}_3F_2$ hypergeometric function that takes into account the monodromy at the elliptic points. We construct $(3,3)$ admissible solutions from (3,0) CFTs using a duality due to Bantay

Ahmad Ghannam, Naif Alharthi, Faris Alasmary, Kholood Al Tabash

In this work, we tackle the Diacritic Restoration (DR) task for Arabic dialectal sentences using a multimodal approach that combines both textual and speech information. We propose a model that represents the text modality using an encoder extracted from our own pre-trained model named CATT. The speech component is handled by the encoder module of the OpenAI