October 2025 arXiv papers — page 181

Qiaoyu Tang, Hao Xiang, Le Yu, Bowen Yu

While recent advances in reasoning models have demonstrated cognitive behaviors through reinforcement learning, existing approaches struggle to invoke deep reasoning capabilities in multi-turn agents with long-horizon interactions. We propose DeepMiner, a novel framework that elicits such abilities by introducing high-difficulty training tasks and dynamic co

Alexander Makarovskiy, Mateusz Slysz, Łukasz Grodzki, Dawid Siera

Binary optimisation tasks are ubiquitous in areas ranging from logistics to cryptography. The exponential complexity of such problems means that the performance of traditional computational methods decreases rapidly with increasing problem sizes. Here, we propose a new algorithm for binary optimisation, the Bosonic Binary Solver, designed for near-term photo

Haipeng Liu, Yang Wang, Meng Wang

Text-guided image inpainting aims at reconstructing the masked regions as per text prompts, where the longstanding challenges lie in the preservation for unmasked regions, while achieving the semantics consistency between unmasked and inpainted masked regions. Previous arts failed to address both of them, always with either of them to be remedied. Such facts

Cédrick Austa, Jan Tobias Mühlberg, Jean-Michel Dricot

While interest in the open RISC-V instruction set architecture is growing, tools to assess the security of concrete processor implementations are lacking. There are dedicated tools and benchmarks for common microarchitectural side-channel vulnerabilities for popular processor families such as Intel x86-64 or ARM, but not for RISC-V. In this paper we describe

Andreas Engelhardt, Mark Boss, Vikram Voleti, Chun-Han Yao

We present Stable Video Materials 3D (SViM3D), a framework to predict multi-view consistent physically based rendering (PBR) materials, given a single image. Recently, video diffusion models have been successfully used to reconstruct 3D objects from a single image efficiently. However, reflectance is still represented by simple material models or needs to be

Damir Nurtdinov, Aliaksei Korshuk, Alexei Kornaev, Alexander Maloletov

This study evaluates the performance of classical and modern control methods for real-world Cable-Driven Parallel Robots (CDPRs), focusing on underconstrained systems with limited time discretization. A comparative analysis is conducted between classical PID controllers and modern reinforcement learning algorithms, including Deep Deterministic Policy Gradien

Chenying Liu, Gianmarco Perantoni, Lorenzo Bruzzone, Xiao Xiang Zhu

Multi-label classification (MLC) offers a more comprehensive semantic understanding of Remote Sensing (RS) imagery compared to traditional single-label classification (SLC). However, obtaining complete annotations for MLC is particularly challenging due to the complexity and high cost of the labeling process. As a practical alternative, single-positive multi

Kairan Hong, Jinling Gan, Qiushi Tian, Yanglinxuan Guo

Cryptocurrency markets present unique prediction challenges due to their extreme volatility, 24/7 operation, and hypersensitivity to news events, with existing approaches suffering from key information extraction and poor sideways market detection critical for risk management. We introduce a theoretically-grounded multi-agent cryptocurrency trend prediction

Johan Messchendorp, Mohammad Al-Turany, Volker Friese, Thorsten Kollegger

This Conceptual Design Report (CDR) presents the plans of the computing infrastructure for research at FAIR, Darmstadt, Germany. It presents the computing requirements of the various research groups, the policies for the computing and storage infrastructure, the foreseen FAIR computing model including the open data, software and services policies and archite

Roselyn Nmaju, Fiona Speirits, Sarah Croke

We present a low-depth amplitude encoding method for arbitrary quantum state preparation. Building on the foundation of an existing divide-and-conquer algorithm, we propose a method to disentangle the ancillary qubits from the final state. Our method is measurement-based but deterministic, and offers an alternative approach to existing state preparation algo

Zwidofhela N. Khangale, Stephen B. Potter, David A. H. Buckley, Paul E. Barrett

We present high-resolution, phase-resolved spectroscopic observations of the polar EF Eri, obtained with SALT and the SAAO 1.9-m telescope during its recent emergence from a three-decade-long low state. The average spectrum shows strong emission from the Balmer lines (H$\alpha$ and H$\beta$) and He~\textsc{ii} 4686 \AA{}, along with weaker emission from the

Rafael L. S. Costa, Marcos L. W. Basso, Jonas Maziero, Lucas C. Céleri

We investigate the formulation of work distributions for quantum scalar fields in static curved spacetimes by extending the Ramsey interferometric protocol originally developed in previous works for flat spacetimes. The use of Unruh-DeWitt particle detectors provides a causally consistent framework to define and measure work statistics, avoiding the limitati

M. Lanza_de_Cristoforis, M. Norman

We extend to the context of metric measured spaces, with a measure that satisfies upper Ahlfors growth conditions the validity of (generalized) H\"{o}lder continuity results for the solution of a Fredholm integral equation of the second kind. Here we note that upper Ahlfors growth conditions include also cases of nondoubling measures.

Shunyu An, Miao Wang, Yongchao Li, Dong Wan

This paper proposes Co-TAP (T: Triple, A: Agent, P: Protocol), a three-layer agent interaction protocol designed to address the challenges faced by multi-agent systems across the three core dimensions of Interoperability, Interaction and Collaboration, and Knowledge Sharing. We have designed and proposed a layered solution composed of three core protocols: t

Wenxia Qu, Wenston J. T. Zang

Gaussian polynomial, which is also known as $q$-binomial coefficient, is one of the fundamental concepts in the theory of partitions. Zeilberger provided a combinatorial proof of Gaussian polynomial, which is called Algorithm Z by Andrews and Bressoud. In this paper, we provide a new bijection on Gaussian polynomial, which leads to a refinement of Algorithm

Ricardo D. Matheus, Elmer M. Gennaro, Marcelo T. Yamashita

We examine more than a decade of quota policy at Unesp, analyzing Physics, Biology, and Pedagogy as representative programs of distinct assessment styles. Quotas show little impact in Physics, where the admission barrier is low, and in Pedagogy, where high pass rates make it difficult to differentiate students, but they reveal systematic differences in Biolo

Mu Li, Yin Wang, Zhiying Leng, Jiapeng Liu

Human interaction is inherently dynamic and hierarchical, where the dynamic refers to the motion changes with distance, and the hierarchy is from individual to inter-individual and ultimately to overall motion. Exploiting these properties is vital for dual-human motion generation, while existing methods almost model human interaction temporally invariantly,

Hassan Saoud

We propose a composite Lyapunov framework for nonlinear autonomous systems that ensures strict decay through a pair of differential inequalities. The approach yields integral estimates, quantitative convergence rates, vanishing of dissipation measures, convergence to a critical set, and semistability under mild conditions, without relying on invariance princ

Eleni Bougioukou, Anastasios Petropoulos, Nikolaos Toulgaridis, Theodoros Chatzimichail

In-memory computing technology is used extensively in artificial intelligence devices due to lower power consumption and fast calculation of matrix-based functions. The development of such a device and its integration in a system takes a significant amount of time and requires the use of a real-time emulation environment, where various system aspects are ana

Jason Bohne, Pawel Polak, David Rosenberg, Brian Bloniarz

Direct Preference Optimization (DPO) has recently emerged as a simple and effective alternative to reinforcement learning from human feedback (RLHF) for aligning large language models (LLMs) with user preferences. However, existing DPO formulations rely on a single monolithic model, which limits their expressivity in multi-task settings and their adaptabilit

Marta Emili Garcia Segura, Stephen Hailes, Mirco Musolesi

Large Language Models (LLMs) are increasingly being deployed as autonomous agents in real-world environments. As these deployments scale, multi-agent interactions become inevitable, making it essential to understand strategic behavior in such systems. A central open question is whether LLM agents, like reinforcement learning agents, can shape the learning dy

Abderrahman Ait-Ali, Anders Peterson

Accurate and timely travel information is an asset for enhancing passenger travel experience during normal traffic, and for mitigating the discomforts during disruptions. With longer and more frequent disruptions as well as increasing ridership, traffic delays can incur substantial costs for passengers and other transport stakeholders, e.g., operators and in

Jianlyu Chen, Junwei Lan, Chaofan Li, Defu Lian

In this paper, we introduce ReasonEmbed, a novel text embedding model developed for reasoning-intensive document retrieval. Our work includes three key technical contributions. First, we propose ReMixer, a new data synthesis method that overcomes the triviality problem prevalent in previous synthetic datasets, enabling large-scale production of 82K high-qual

Maria Titova, Kun Zhang

This paper studies a game in which an informed sender with state-independent preferences uses verifiable messages to convince a receiver to choose an action from a finite set. We characterize the equilibrium outcomes of the game and compare them with commitment outcomes in information design. We provide conditions under which a commitment outcome is an equil

Ed Segal, Wei Tseu

We reinvestigate the problem of describing the Fourier-Mukai kernel for the derived equivalence associated to a stratified Mukai flop. For the case of Grassmannians of planes we give a very simple geometric construction of the kernel, using the framework of matrix factorizations.

Alessio Berti, Željka Bošnjak, Alberto Castro-Tirado, Stefano Covino

Gamma-ray bursts (GRBs) are one of the main targets for the observations of the MAGIC telescopes. As a result of the effort in improving the sensitivity of the instrument and the automatic follow-up strategy, MAGIC detected two GRBs in the very-high-energy (VHE, $E>100$ GeV) range, namely GRB 190114C and GRB 201216C. In GRB 190114C ($z=0.42$), the data colle

Huaxiang Lü, Michael Röckner

In this paper, we investigate the stochastic differential equation on $\mathbb{R}^d,d\geq2$: \begin{align*} \dif X_t&=v(t,X_t)\dif t+\sqrt{2} \dif W_t. \end{align*} For any finite collection of initial probability measures $\{\mu^i_0\}_{1\leq i\leq M}$ on $\mathbb{R}^d$ and $\frac{d}{p}+\frac{1}{r}>1$, we construct a divergence-free drift field $v\in L_t^rL^

Frederik J. Zuiderveen Borgesius, Wilfred Steenbruggen

In the European Union, the General Data Protection Regulation (GDPR) provides comprehensive rules for the processing of personal data. In addition, the EU lawmaker intends to adopt specific rules to protect confidentiality of communications, in a separate ePrivacy Regulation. Some have argued that there is no need for such additional rules for communications

Joost Poort, Frederik J. Zuiderveen Borgesius

Online stores can present a different price to each customer. Such algorithmic personalised pricing can lead to advanced forms of price discrimination based on the characteristics and behaviour of individual consumers. We conducted two consumer surveys among a representative sample of the Dutch population (N=1233 and N=1202), to analyse consumer attitudes to

Jannek Ulm, Kevin Du, Vésteinn Snæbjarnarson

Large language models (LLMs) are trained on huge amounts of textual data, and concerns have been raised that the limits of such data may soon be reached. A potential solution is to train on synthetic data sampled from LLMs. In this work, we build on this idea and investigate the benefits of contrastive decoding for generating synthetic corpora. In a controll

Dominick Banasik, Varsha Dani, Fabien Dufoulon, Aayush Gupta

The maximal independent set (MIS) is one of the most fundamental problems in distributed computing, and it has been studied intensively for over four decades. This paper focuses on the MIS problem in the Radio Network model, a standard model widely used to model wireless networks, particularly ad hoc wireless and sensor networks. Energy is a premium resource

Saeid Azam

We investigate the notions of \emph{localization} and \emph{filtration} in the context of extended affine Lie algebras. Our primary objective is to develop a localization theory that facilitates the construction of meaningful local substructures, particularly local affine Lie subalgebras. These subalgebras play a crucial role in understanding the global stru

Mohammed Almutairi, Charles Chiang, Haoze Guo, Matthew Belcher

Enabling users to create their own simulations offers a powerful way to study team dynamics and performance. We introduce VirTLab, a system that allows researchers and practitioners to design interactive, customizable simulations of team dynamics with LLM-based agents situated in 2D spatial environments. Unlike prior frameworks that restrict scenarios to pre

Eleni Bougioukou, Theodore Antonakopoulos

In-Memory Computing (IMC) represents a paradigm shift in deep learning acceleration by mitigating data movement bottlenecks and leveraging the inherent parallelism of memory-based computations. The efficient deployment of Convolutional Neural Networks (CNNs) on IMC-based hardware necessitates the use of advanced task allocation strategies for achieving maxim

Roberto Ognibene, Bozhidar Velichkov

This survey synthesizes the current state of the art on the regularity theory for solutions to the optimal partition problem. Namely, we consider non-negative, vector-valued Sobolev functions whose components have mutually disjoint support, and which are either local minimizers of the Dirichlet energy or, more generally, critical points satisfying a system o

Jingyu Zhang, Haozhu Wang, Eric Michael Smith, Sid Wang

Harnessing the power of LLMs requires a delicate dance between being helpful and harmless. This creates a fundamental tension between two competing challenges: vulnerability to adversarial attacks that elicit unsafe content, and a tendency for overrefusal on benign but sensitive prompts. Current approaches often navigate this dance with safeguard models that

Chris Fields, James F. Glazebrook, Antonino Marcianò, Emanuele Zappala

The existence and practical utility of operational protocols that certify entanglement raises the question of whether operational protocols exist that certify the absence of entanglement, i.e. that certify separability. We show, within a purely topological, interpretation-independent representation, that such protocols do not exist. Classicality is therefore

Jiyang Qiu, Xinbei Ma, Yunqing Xu, Zhuosheng Zhang

The rapid deployment of large language model (LLM)-based agents in real-world applications has raised serious concerns about their trustworthiness. In this work, we reveal the security and robustness vulnerabilities of these agents through backdoor attacks. Distinct from traditional backdoors limited to single-step control, we propose the Chain-of-Trigger Ba

Milan Krticka, Aaron Couture

The Maxwellian Average Cross Section (MACS) is usually calculated with help of the statistical codes that do not take into account fluctuations of individual resonance parameters. The actual MACS can substantially deviate from its expectation value. This work focuses on description of various sources and aspects of these fluctuations. Simulated resonance seq

Konrad Löhr, Shuzhou Yuan, Michael Färber

Large Language Models (LLMs) are increasingly integral to information dissemination and decision-making processes. Given their growing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereot

Boris Perrot, Jan Boroński, Alex Clark

Motivated by the question whether a round disk can be realized as the rotation set of a torus diffeomorphism, we study the roundness of rotation sets of a parametric family of torus diffeomorphisms $F_\rho$, where the parameter $\rho$ ranges over irrational numbers in $(0,1)$. Each $F_\rho$ is a Kwapisz-like diffeomorphism with a 2-dimensional non-polygonal

Ding-hui Xu, Zheng Liu, Chang-shui Yu

In precision force sensing of multi-mechanical mode optomechanical systems, coherent interference can decouple certain degenerate vibrational modes from the cavity field, leading to incomplete information regarding the measured signal. In this paper, we propose a scheme to enhance and control the detection bandwidth in optomechanical force sensing by exploit

Elchanan Mossel, Amnon Schreiber

We investigate a quantitative variant of the classic Two Doors logic puzzle, in which the answer space is no longer binary, for example when the goal is to recover a numerical fact (such as one's true weight) rather than choose between two doors. The puzzle retains the original structure: one agent always tells the truth, the other always lies. Our central c

Lei Li, Guoliang Lv, Chunhua Zhu, Sufen Guo

Gravitational wave detectors are observing an increasing number of binary black hole (BBH) mergers, revealing a bimodal mass distribution of BBHs, which hints at diverse formation histories for these systems. Using the rapid binary population synthesis code MOBSE, we simulate a series of population synthesis models that include chemically homogeneous evoluti

Keshvi Tuteja, Gregor Olenik, Roman Mishchuk, Yu-Hsiang Tsai

Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplicity and ease of use. Yet high performance sparse kernels in Python remain limited in functionality, especially on modern CPU and GPU architectures. We present pyGinkgo, a lightweigh

Rishi C, Neelam Panwar, Thomas J. Haworth, Yan Sun

Radiative feedback from massive stars plays a central role in the evolution of molecular clouds and the interstellar medium. This paper presents a multi-wavelength analysis of the bright-rimmed cloud, BRC 44, which is located at the periphery of the Hii region Sh2-145 and is excited by the massive stars in the region. We use a combination of archival and new

Pengcheng Deng, Kening Liu, Mengxi Zhou, Mingxi Li

Genomic Selection (GS) uses whole-genome information to predict crop phenotypes and accelerate breeding. Traditional GS methods, however, struggle with prediction accuracy for complex traits and large datasets. We propose DPCformer, a deep learning model integrating convolutional neural networks with a self-attention mechanism to model complex genotype-pheno

Quentin Renau, Amjad Ullah, Emma Hart

This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environme

Zipo Jibao, Yingyi Fu, Xinyang Chen, Guoting Chen

Recent research demonstrates that linear models achieve forecasting performance competitive with complex architectures, yet methodologies for enhancing linear models remain underexplored. Motivated by the hypothesis that distinct time series instances may follow heterogeneous linear mappings, we propose the Classification Auxiliary Trend-Seasonal Decoupling

Mariana Fernandez-Espinosa, Kai Zhang, Jad Bendarkawi, Ashley Ponce

Developing speaking proficiency in a second language can be cognitively demanding and emotionally taxing, often triggering fear of making mistakes or being excluded from larger groups. While current learning tools show promise for speaking practice, most focus on dyadic, scripted scenarios, limiting opportunities for dynamic group interactions. To address th

Michal Koren, Or Peretz, Tai Dinh, Philip S. Yu

Sequential decisions in volatile, high-stakes settings require more than maximizing expected return; they require principled uncertainty management. This paper presents the Uncertainty-Aware Markov Decision Process (UAMDP), a unified framework that couples Bayesian forecasting, posterior-sampling reinforcement learning, and planning under a conditional value

Daniel Pressensé, Elisavet Kozyri

This paper presents TracE2E, a middleware written in Rust, that can provide both data explainability and compliance across multiple nodes. By mediating inputs and outputs of processes, TracE2E records provenance information and enforces data-protection policies (e.g., confidentiality, integrity) that depend on the recorded provenance. Unlike existing approac

Tim Hagen, Niklas Deckers, Felix Wolter, Harrisen Scells

Many causal claims, such as "sugar causes hyperactivity," are disputed or outdated. Yet research on causality extraction from text has almost entirely neglected counterclaims of causation. To close this gap, we conduct a thorough literature review of causality extraction, compile an extensive inventory of linguistic realizations of countercausal claims, and

Alexander Kleshchev, Lucia Morotti, Pham Huu Tiep

Let $\mathbb{F}$ be an algebraically closed field and $G$ be an almost quasi-simple group. An important problem in representation theory is to classify the subgroups $H<G$ and $\mathbb{F} G$-modules $L$ such that the restriction $L\downarrow_H$ is irreducible. This problem is a natural part of the program of describing maximal subgroups in finite classical g

Yunlong Deng, Boyang Sun, Yan Li, Lingjing Kong

Due to their inherent complexity, reasoning tasks have long been regarded as rigorous benchmarks for assessing the capabilities of machine learning models, especially large language models (LLMs). Although humans can solve these tasks with ease, existing models, even after extensive pre-training and post-training at scale, still fail to perform reasoning rel

Yu Zeng, Mehdi Ghaffarzadeh, Mohsen Ghasemi, Dongfang Yang

For an irreducible complex character \(\chi\) of a finite group \(G\), the \emph{codegree} of \(\chi\) is defined as the ratio \(|G : \ker(\chi)| / \chi(1)\), where \(\ker(\chi)\) represents the kernel of \(\chi\). In this paper, we provide a detailed characterization of finite groups of non-prime power order that have exactly four irreducible character co-d

Wiktor Jan Hoffmann, Sonia Laguna, Moritz Vandenhirtz, Emanuele Palumbo

Concept Bottleneck Models (CBMs) are interpretable models that predict the target variable through high-level human-understandable concepts, allowing users to intervene on mispredicted concepts to adjust the final output. While recent work has shown that modeling dependencies between concepts can improve CBM performance, especially under interventions, such

Nicolas Espinosa-Dice, Kiante Brantley, Wen Sun

Reinforcement learning (RL) is a powerful paradigm for learning to make sequences of decisions. However, RL has yet to be fully leveraged in robotics, principally due to its lack of scalability. Offline RL offers a promising avenue by training agents on large, diverse datasets, avoiding the costly real-world interactions of online RL. Scaling offline RL to i

Jiakang Chen

Partial differential equations (PDEs) underpin models across science and engineering, yet analytical solutions are atypical and classical mesh-based solvers can be costly in high dimensions. This dissertation presents a unified comparison of three mesh-free neural PDE solvers, physics-informed neural networks (PINNs), the deep Ritz method (DRM), and weak adv

Justus Viga, Penelope Mueck, Alexander Löser, Torben Weis

In the shipping industry, fuel consumption and emissions are critical factors due to their significant impact on economic efficiency and environmental sustainability. Accurate prediction of ship fuel consumption is essential for further optimization of maritime operations. However, heterogeneous methodologies and limited high-quality datasets hinder direct c

Fernando Gómez-Ortiz, Louis Bastogne, Xu He, Philippe Ghosez

PbTiO$_3$/SrTiO$_3$ artificial superlattices recently emerged as a prototypical platform for the emergence and study of polar topologies. While previous studies mainly focused on the polar textures inherent to the ferroelectric PbTiO$_3$ layers, the oxygen octahedra rotations inherent to the paraelectric SrTiO$_3$ layers have attracted much little attention.

Pablo Navarrete, Aleksi Vuorinen

In these conference proceedings, we discuss recent progress in high-order perturbative studies of the thermodynamic and transport properties of dense quark matter. Special emphasis is placed on the introduction of a promising new computational tool, thermal Loop Tree Duality, which enables pushing the existing weak-coupling calculations to higher perturbativ

Qiang Yang, Xiuying Chen, Changsheng Ma, Rui Yin

The global impact of the COVID-19 pandemic has highlighted the need for a comprehensive understanding of public sentiment and reactions. Despite the availability of numerous public datasets on COVID-19, some reaching volumes of up to 100 billion data points, challenges persist regarding the availability of labeled data and the presence of coarse-grained or i

Yang-Yang Xu, Qiong Xiao, Jun-Hao Cheng, Wen-Yu Zhang

Nuclear excitation by electron capture (NEEC) in $^{229}$Th holds significant potential for precise nuclear state manipulation. In this study, we thoroughly investigate NEEC in $^{229}\text{Th}^{q+}$ ions by integrating quantum numbers ($n, l, j$) effects and analyzing key parameters (e.g., resonance energy $E_r$, cross section $\sigma$, resonance strength $

Xuhao Hu, Peng Wang, Xiaoya Lu, Dongrui Liu

Previous research has shown that LLMs finetuned on malicious or incorrect completions within narrow domains (e.g., insecure code or incorrect medical advice) can become broadly misaligned to exhibit harmful behaviors, which is called emergent misalignment. In this work, we investigate whether this phenomenon can extend beyond safety behaviors to a broader sp

Balint Pato, June Vanlerberghe, Kenneth R. Brown

Calculating the quantum weight enumerator polynomial (WEP) is a valuable tool for characterizing quantum error-correcting (QEC) codes, but it is computationally hard for large or complex codes. The Quantum LEGO (QL) framework provides a tensor network approach for WEP calculation, in some cases offering superpolynomial speedups over brute-force methods, prov

Davide Dal Martello

Aiming for a revival of the theory of crystallographic complex reflection groups, we compute (minimal) Coxeter-like reflection presentations for the infinite families of those non-genuine groups which satisfy Steinberg's fixed point theorem. These new presentations behave \`{a} la Coxeter, encoding many of the group's properties at a glance, and their signat

Eduard Looijenga

Let $C$ be a smooth projective curve over an algebraically closed field $k$ of characteristic zero. We prove that a Lagrangian supplement of $H^0(C, \Omega_C)$ in the de Rham cohomology group $H^1_{dR}(C)$ determines and is determined by a particular type of symmetric bidifferential on $C^2$ (its polar divisor must be twice the diagonal and have biresidue on

Matteo Gregorini, Chiara Boldrini, Lorenzo Valerio

Artificial Intelligence has achieved remarkable advancements in recent years, yet much of its progress relies on identifying increasingly complex correlations. Enabling causality awareness in AI has the potential to enhance its performance by enabling a deeper understanding of the underlying mechanisms of the environment. In this paper, we introduce DODO, an

S. Courtin, M. Heine, E. Monpribat, J. Nippert

Fusion reactions with light nuclei play an essential role in understanding the energy production, the nucleosynthesis of chemical elements and the evolution of massive stars. The measurement of key fusion reactions at stellar energies is thus of interest, but highly challenging since the associated cross sections are extremely small, of the sub-nanobarn rang

Jonah A. Quirk, Carol E. Tanner, D. S. Elliott

We report a new method of two-pathway coherent control using three narrow-band cw laser sources, phase locked in an optical phase-lock loop, to maintain the high degree of optical coherence required for the coherent control process. In addition, we derive expressions for two-photon transition amplitudes and demonstrate their dependence on the polarization of

Soham Ghosh, Saloni Bhogale, Sameer K. Deshpande

By allowing the effects of $p$ covariates in a linear regression model to vary as functions of $R$ additional effect modifiers, varying-coefficient models (VCMs) strike a compelling balance between interpretable-but-rigid parametric models popular in classical statistics and flexible-but-opaque methods popular in machine learning. But in high-dimensional set

Shaohua Zhang, Yuan Lin, Hang Li

The remarkable success of large language models (LLMs) stems from their ability to consolidate vast amounts of knowledge into the memory during pre-training and to retrieve it from the memory during inference, enabling advanced capabilities such as knowledge memorization, instruction-following and reasoning. However, the mechanisms of memory retrieval and co

Lirui Guo, Michael G. Burke, Wynita M. Griggs

Shared Autonomous Vehicles (SAVs) are likely to become an important part of the transportation system, making effective human-SAV interactions an important area of research. This paper introduces a dataset of 200 human-SAV interactions to further this area of study. We present an open-source human-SAV conversational dataset, comprising both textual data (e.g

Alexander Hellwig, Nico Jansen, Bernhard Rumpe

In Software Language Engineering, there is a trend towards reusability by composing modular language components. However, this reusability is severely inhibited by a gap in integrating whitespace-sensitive and whitespace-insensitive languages. There is currently no consistent procedure for seamlessly reusing such language components in both cases, such that

Alice Di Bella, Toni Seibold, Tom Brown, Massimo Tavoni

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustr

Chaohua Duan, Zhen Xue

This paper investigates the inverse scattering problem for the magnetic Schr\"odinger equation. We first establish the well-posedness of the direct problem through a variational approach under physically meaningful assumptions on the magnetic and electric potentials. Our main results demonstrate that a single far-field measurement uniquely determines the sup

Diego García-Zamora, Álvaro Labella, José Rui Figueira

Pairwise comparison methods, such as Fuzzy Preference Relations and Saaty's Multiplicative Preference Relations, are widely used to model expert judgments in multi-criteria decision-making. However, their application is limited by the high cognitive load required to complete $m(m-1)/2$ comparisons, the risk of inconsistency, and the computational complexity

Menghao Qu, Yingrui Zhang

Pappe, Paul, and Schilling introduced two combinatorial statistics, depth and ddinv, associated with classical Dyck paths, and proved that the distributions of (area, depth) and (dinv, ddinv) are $q,t$-symmetric by constructing an involution on plane trees. They also provided a new formula for the original $q,t$-Catalan polynomials $C_{n}(q,t)$. We observe t

Ioannis Emmanouil, Olympia Talelli

The stable category of modules over the algebra of a finite group with coefficients in a field is a compactly generated tensor triangulated category, that has been studied extensively in representation theory. In this paper, we provide a plethora of infinite groups G, for which the category of kG-modules (where k is a commutative coherent ring of finite glob

Cui-Qun Chen, Wenyuan Qiu, Zhihui Luo, Meng Wang

The recent discovery of high-$T_c$ superconductivity in Ruddlesden-Popper (RP) nickelates has motivated extensive efforts to explore higher $T_c$ superconductors. Here, we systematically investigate Nd-doped La$_3$Ni$_2$O$_7$ using density functional theory (DFT) and renormalized mean-field theory (RMFT). DFT calculations reveal that both the lattice constan

Rashid Mushkani

Artificial intelligence systems increasingly mediate knowledge, communication, and decision making. Development and governance remain concentrated within a small set of firms and states, raising concerns that technologies may encode narrow interests and limit public agency. Capability benchmarks for language, vision, and coding are common, yet public, audita

Chao Wen, Qiang Sun, Chao Zhang

In 1983, Bouchet conjectured that every flow-admissible signed graph admits a nowhere-zero 6-flow. We verify this conjecture for the class of flow-admissible signed graphs possessing a spanning even Eulerian subgraph, which includes as a special case all signed graphs with a balanced Hamiltonian circuit. Furthermore, we show that this result is sharp by citi

Yuzheng Cai, Siqi Cai, Yuchen Shi, Zihan Xu

Recent advances in Large Language Model (LLM) agents have demonstrated their promising general capabilities. However, their performance in specialized real-world domains often degrades due to challenges in effectively integrating external tools and specific prompting strategies. While methods like agentic reinforcement learning have been proposed to address

Abdou Majeed Alidou, Júlia Baligács, Jan Hązła

A recent line of work studies models of opinion exchange where agent opinions about $d$ topics are tracked simultaneously. The opinions are represented as vectors on the unit $(d-1)$-sphere, and the update rule is based on the overall correlation between the relevant vectors. The update rule reflects the assumption of biased assimilation, i.e., a pair of opi

Yi Lu, Jianing Wang, Linsen Guo, Wei He

Recent trends in test-time scaling for reasoning models (e.g., OpenAI o1, DeepSeek-R1) have led to remarkable improvements through long Chain-of-Thought (CoT). However, existing benchmarks mainly focus on immediate, single-horizon tasks, failing to adequately evaluate models' ability to understand and respond to complex, long-horizon scenarios. To address th

Fernando Gómez-Ortiz, Louis Bastogne, Philippe Ghosez

Magnetic spin topological textures recently found their electrical counterparts in polar topologies emerging from the condensation of inhomogeneous polar atomic distortions. Here, we further extend the concept to other non-polar atomic degrees of freedom. Taking SrTiO$_3$ as a prototypical example, we investigate from second-principles atomistic simulations,

Michael Lampis

We show that if k-SUM is hard, in the sense that the standard algorithm is essentially optimal, then a variant of the SETH called the Primal Treewidth SETH is true. Formally: if there is an $\varepsilon>0$ and an algorithm which solves SAT in time $(2-\varepsilon)^{tw}|\phi|^{O(1)}$, where $tw$ is the width of a given tree decomposition of the primal graph o

Kiran Smelser, Kaviru Gunaratne, Jacob Miller, Stephen Kobourov

Complex, high-dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visualizing these datasets is with two-dimensional scatter plots that visually capture some properties of the data. Because visually determining the accuracy of these plots is challenging

Siddharth Agarwal, Maria A. Rodriguez, Rajkumar Buyya

The rapid adoption of serverless computing necessitates a deeper understanding of its underlying operational mechanics, particularly concerning request routing, cold starts, function scaling, and resource management. This paper presents Serv-Drishti, an interactive, open-source simulation tool designed to demystify these complex behaviours. Serv-Drishti simu

Rakesh Kumar Sahoo, Paridhi Choudhary, Manoranjan Sinha

Increase in the number of space exploration missions has led to the accumulation of space debris, posing risk of collision with the operational satellites. Addressing this challenge is crucial for the sustainability of space operations. To plan a safe trajectory in the presence of moving space debris, an integrated approach of artificial potential field and

Jannik Zenner, Karl Ulrich Schreiber, Simon Stellmer

Large Sagnac interferometers in the form of active ring lasers have emerged as unique rotation sensors in the geosciences, where their sensitivity allows to detect geodetic and seismological signals. The passive laser gyroscope variant, however, is still at a stage of development, and thus far, only the Pound-Drever-Hall frequency stabilization technique has

Pratik Rai

We study the martingale optimal transport problem with state-dependent trading frictions and develop a geometric and duality framework extending from the one time-step to the multi-marginal setting. Building on the left-monotone structure of frictionless MOT (Beiglb\"ock and Juillet, Ann. Probab., 2016; Henry-Labord\`ere and Touzi, Finance Stoch., 2016; Beig

Thomas Boch, Caroline Bot, Pierre Fernique

We present a technique for creating background-corrected HiPS (Hierarchical Progressive Surveys) from VPHAS+ images using the Montage toolkit. By combining advanced background correction methods and HiPS generation workflows, we produced high-quality color HiPS from the VST Photometric H-alpha Survey of the Southern Galactic Plane and Bulge (VPHAS+). These H

Haoran Yu, Yi Shi

Text-to-image diffusion models have shown great potential for image editing, with techniques such as text-based and object-dragging methods emerging as key approaches. However, each of these methods has inherent limitations: text-based methods struggle with precise object positioning, while object dragging methods are confined to static relocation. To addres

Natalie Carl, Tobias Pfandzelter, David Bermbach

Serverless computing provides just-in-time infrastructure provisioning with rapid elasticity and a finely-grained pricing model. As full control of resource allocation is in the hands of the cloud provider and applications only consume resources when they actually perform work, we believe that serverless computing is uniquely positioned to maximize energy ef

Feng Hong, Yu Huang, Zihua Zhao, Zhihan Zhou

Real-world datasets for deep learning frequently suffer from the co-occurring challenges of class imbalance and label noise, hindering model performance. While methods exist for each issue, effectively combining them is non-trivial, as distinguishing genuine tail samples from noisy data proves difficult, often leading to conflicting optimization strategies.

Johann Schmidt, Sebastian Stober

Fine-grained visual classification (FGVC) tasks, such as insect and bird identification, demand sensitivity to subtle visual cues while remaining robust to spatial transformations. A key challenge is handling geometric biases and noise, such as different orientations and scales of objects. Existing remedies rely on heavy data augmentation, which demands powe

Jiaan Luo, Feng Hong, Qiang Hu, Xiaofeng Cao

Long-tailed recognition is ubiquitous and challenging in deep learning and even in the downstream finetuning of foundation models, since the skew class distribution generally prevents the model generalization to the tail classes. Despite the promise of previous methods from the perspectives of data augmentation, loss rebalancing and decoupled training etc.,

Eleonora Mancini, Joan Serrà, Paolo Torroni, Yuki Mitsufuji

Audio-based lyrics matching can be an appealing alternative to other content-based retrieval approaches, but existing methods often suffer from limited reproducibility and inconsistent baselines. In this work, we introduce WEALY, a fully reproducible pipeline that leverages Whisper decoder embeddings for lyrics matching tasks. WEALY establishes robust and tr