October 2025 arXiv papers — page 14

A. Nonato, Pedro D. S. Silva

In this work, we investigate the bi-isotropic effects in the formation and tunability of hybrid surface polaritons in bilayer configurations. We consider a heterostructure composed of a medium with bi-isotropic constitutive relations and an AFM layer. Using the transfer matrix formalism, we derive general expressions for the dispersion relations of surface p

Yasir Ech-Chammakhy, Anas Motii, Anass Rabii, Oussama Azrara

Extracting structured intelligence via Named Entity Recognition (NER) is critical for cybersecurity, but the proliferation of datasets with incompatible annotation schemas hinders the development of comprehensive models. While combining these resources is desirable, we empirically demonstrate that naively concatenating them results in a noisy label space tha

Guillaume Geoffroy

We introduce and develop propositional continuous intuitionistic logic and propositional continuous affine logic via complete algebraic semantics. Our approach centres on AC-algebras, which are algebras $USC(\mathcal{L})$ of sup-preserving functions from $[0,1]$ to an integral commutative residuated complete lattice $\mathcal{L}$ (in the intuitionistic case,

Adam E. Flanders, Yifan Peng, Luciano Prevedello, Robyn Ball

Purpose: The purpose of this study was to determine if an ensemble of multiple LLM agents could be used collectively to provide a more reliable assessment of a pixel-based AI triage tool than a single LLM. Methods: 29,766 non-contrast CT head exams from fourteen hospitals were processed by a commercial intracranial hemorrhage (ICH) AI detection tool. Radiolo

Zach Blunden-Codd, Mohamed Tamaazousti

Numerous mitigation methods exist for quantum noise suppression, making it challenging to identify the optimum approach for a specific application; especially as ongoing advances in hardware tuning and error correction are expected to reduce logical error rates. In order to facilitate the future-proof application-dependent comparison of mitigation methods, w

Dimas Abreu Archanjo Dutra

Variational system identification is a new formulation of maximum likelihood for estimation of parameters of dynamical systems subject to process and measurement noise, such as aircraft flying in turbulence. This formulation is an alternative to the filter-error method that circumvents the solution of a Riccati equation and does not have problems with unstab

Linzhuang Sun, Tianyu Guo, Hao Liang, Yuying Li

Recent advances in Text-to-SQL have achieved strong results in static, single-turn tasks, where models generate SQL queries from natural language questions. However, these systems fall short in real-world interactive scenarios, where user intents evolve and queries must be refined over multiple turns. In applications such as finance and business analytics, u

Sadegh Shirani, Mohsen Bayati

Online social networks have transformed the ways in which political mobilization messages are disseminated, raising new questions about how peer influence operates at scale. Building on the landmark 61-million-person Facebook experiment \citep{bond201261}, we develop an agent-based simulation framework that integrates real U.S. Census demographic distributio

Qishuo Hua, Lyumanshan Ye, Dayuan Fu, Yang Xiao

Karl Marx once wrote that ``the human essence is the ensemble of social relations'', suggesting that individuals are not isolated entities but are fundamentally shaped by their interactions with other entities, within which contexts play a constitutive and essential role. With the advent of computers and artificial intelligence, these contexts are no longer

Gursel Serpen

We are proposing fully parallel and maximally distributed hardware realization of a generic neuro-computing system. More specifically, the proposal relates to the wireless sensor networks technology to serve as a massively parallel and fully distributed hardware platform to implement and realize artificial neural network (ANN) algorithms. A parallel and dist

Erle Zhu, Dazhi Jiang, Yuan Wang, Xujun Li

Data selection is a critical aspect of Reinforcement Learning with Verifiable Rewards (RLVR) for enhancing the reasoning capabilities of large language models (LLMs). Current data selection methods are largely heuristic-based, lacking theoretical guarantees and generalizability. This work proposes a theoretically-grounded approach using influence functions t

Alon Rosenbaum, Yigal David, Eran Kaufman, Gilad Ravid

Large language models (LLMs) are increasingly shaping creative work and problem-solving; however, prior research suggests that they may diminish unassisted creativity. To address this tension, a coach-like LLM environment was developed that embodies divergent and convergent thinking personas as two complementary processes. Effectiveness and user behavior wer

Artur Avkhadiev, Valerio Bertone, Chiara Bissolotti, Matteo Cerutti

We present a first joint extraction of the Collins-Soper kernel (CSK) combining experimental and lattice QCD data in the context of an analysis of transverse-momentum-dependent distributions (TMDs). Based on a neural-network parametrization, we perform a Bayesian reweighting of an existing fits of TMDs using lattice data, as well as a joint TMD fit to lattic

Junyu Meng

We consider the geometry of a general polarized K3 surface $(S,h)$ of genus 16 and its Fourier-Mukai partner $(S',h')$. We prove that $S^{[2]}$ is isomorphic to the moduli space $M_{S'}(2,h',7)$ of stable sheaves with Mukai vector $(2,h',7)$ and describe the embeddings of the projectivization of the stable vector bundle of Mukai vector $(2,-h',8)$ over $S'$

Wajdi Hammami, Soumaya Cherkaoui, Jean-Frederic Laprade, Ola Ahmad

Anomaly detection in time-series data is a critical challenge with significant implications for network security. Recent quantum machine learning approaches, such as quantum kernel methods and variational quantum circuits, have shown promise in capturing complex data distributions for anomaly detection but remain constrained by limited qubit counts. We intro

Dipak Meher, Carlotta Domeniconi, Guadalupe Correa-Cabrera

Human smuggling networks are complex and constantly evolving, making them difficult to analyze comprehensively. Legal case documents offer rich factual and procedural insights into these networks but are often long, unstructured, and filled with ambiguous or shifting references, posing significant challenges for automated knowledge graph (KG) construction. E

Rebecca F. Supple, Hannah Worthington, Ben Swallow

Causal discovery is the subfield of causal inference concerned with estimating the structure of cause-and-effect relationships in a system of interrelated variables, as opposed to quantifying the strength or describing the form of causal effects. As interest in causal discovery builds in fields such as ecology, public health, and environmental sciences where

Rasoul Amirzadeh, Dhananjay Thiruvady, Fatemeh Shiri

Large language models (LLMs) continue to advance, with an increasing number of domain-specific variants tailored for specialised tasks. However, these models often lack transparency and explainability, can be costly to fine-tune, require substantial prompt engineering, yield inconsistent results across domains, and impose significant adverse environmental im

A. Suárez Mascareño

The search for life beyond our Solar system has been a long and difficult endeavour. The majority of current efforts are focused on the potential detection of biosignatures. However, their detection and interpretation are extremely challenging. Technosignatures appear as an attractive alternative, given their expected univocal interpretation. In recent years

Hachem Dich, Ramy Abou Rjeily, Gabriela Rath, Mathéo Berthet

Neutrophils play a central role in immunothrombosis through the formation of neutrophil extracellular traps (NETs), a process known as NETosis. Upon stimulation, neutrophils release decondensed chromatin structures enriched with proteolytic enzymes, which contribute to thrombus formation. NETosis is critically dependent on reactive oxygen species (ROS), maki

Shaowei Lan, Qiuhua Liu, Yong Li, Shusu Shi

We present a systematic study of the elliptic flow $v_2$ relative to the participant plane (PP) and reaction plane (RP) in Au+Au collisions at $\sqrt{s_{NN}} = 7.7$-200 GeV using the AMPT model with the string melting version. The ratio $v_{2}^{\text{PP}}$/$v_{2}^{\text{RP}}$ is investigated under different hadronic cascade times (0.6 fm/$c$, 10 fm/$c$, and

Clarissa Sabrina Arlinghaus, Tristan Kenneweg, Barbara Hammer, Günter W. Maier

Large language models (LLMs) such as ChatGPT are increasingly integrated into high-stakes decision-making, yet little is known about their susceptibility to social influence. We conducted three preregistered conformity experiments with GPT-4o in a hiring context. In a baseline study, GPT consistently favored the same candidate (Profile C), reported moderate

Sivajeet Chand, Melih Kilic, Roland Würsching, Sushant Kumar Pandey

Automating the Extract Method refactoring (EMR) remains challenging and largely manual despite its importance in improving code readability and maintainability. Recent advances in open-source, resource-efficient Large Language Models (LLMs) offer promising new approaches for automating such high-level tasks. In this work, we critically evaluate five state-of

Emanuele Palumbo, Alessandro Alocco, Andrea Celotto, Luca Fasolo

In this contribution we present JosephsonCircuitsOptimizer.jl (JCO), a simulation and optimization framework based on the JosephsonCircuits.jl library for Julia. It models superconducting circuits that include Josephson junctions (JJs) and other nonlinear elements within a lumped-element approach, leveraging harmonic balance, a frequency-domain technique tha

Congyuan Duan, Wanteng Ma, Dong Xia, Kan Xu

This paper studies decision-making and statistical inference for two-sided matching markets via matrix completion. In contrast to the independent sampling assumed in classical matrix completion literature, the observed entries, which arise from past matching data, are constrained by matching capacity. This matching-induced dependence poses new challenges for

Luis Briceño-Arias, Paulo Gonçalves, Guillaume Lauga, Nelly Pustelnik

Block coordinate descent (BCD) methods are prevalent in large scale optimization problems due to the low memory and computational costs per iteration, the predisposition to parallelization, and the ability to exploit the structure of the problem. The theoretical and practical performance of BCD relies heavily on the rules defining the choice of the blocks to

ATLAS Collaboration

This paper presents differential cross-section measurements of events containing a charged lepton, missing transverse momentum, two $b$-jets, and two light jets, consistent with the $W^{+}W^{-}b\bar{b}$ final state. The analysis is based on 140 fb$^{-1}$ of proton-proton collision data at $\sqrt{s}$ = 13 TeV recorded with the ATLAS detector during Run 2 of t

Qiaoling Chen, Zijun Liu, Peng Sun, Shenggui Li

Adapting large language models (LLMs) via reinforcement learning (RL) is often bottlenecked by the generation stage, which can consume over 75\% of the training time. Speculative decoding (SD) accelerates autoregressive generation in serving systems, but its behavior under RL training remains largely unexplored. We identify three critical gaps that hinder th

Xin Guo, Zhiheng Xi, Yiwen Ding, Yitao Zhai

Self-improvement has emerged as a mainstream paradigm for advancing the reasoning capabilities of large vision-language models (LVLMs), where models explore and learn from successful trajectories iteratively. However, we identify a critical issue during this process: the model excels at generating high-quality trajectories for simple queries (i.e., head data

Junya Shiraishi, Shashi Raj Pandey, Israel Leyva-Mayorga, Petar Popovski

The use of Dynamic Random Access Memory (DRAM) for storing Machine Learning (ML) models plays a critical role in accelerating ML inference tasks in the next generation of communication systems. However, periodic refreshment of DRAM results in wasteful energy consumption during standby periods, which is significant for resource-constrained Internet of Things

Cong Zhou, Haina Wu, Chaoneng Wu, Yitong Zhao

We compared the performance of two post-compression techniques, a gas-filled hollow-core fiber (HCF) and a multi-pass cell (MPC), using a high-power ytterbium-doped fiber laser. The HCF produced 27 fs pulses from 230 fs inputs at >50% efficiency, whereas the MPC achieved 34 fs pulses with significantly higher efficiency (>88%). Both results aligned well with

Arseniy M. Buryakov, Anastasia V. Gorbatova, Pavel Y. Avdeev, Igor Yu. Pashen'kin

Uniaxial spintronic heterostructures constitute compact THz emitters under femtosecond optical excitation, with emission amplitude and polarization governed by the applied magnetic field is presented. We demonstrate here efficient magnetically tunable THz amplitude control in ultrathin, exchange-biased Co/Pt/Co/IrMn spin valve. Terahertz spintronic magnetome

Jonas M. Mikhaeil, Christopher Harshaw

The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While the parallel trends assumption is counterfactual and cannot be tested directly, researchers often examine pre-treatment periods to check whether the time trends are parallel befor

Zihao Guo, Qingyun Sun, Ziwei Zhang, Haonan Yuan

Graph incremental learning (GIL), which continuously updates graph models by sequential knowledge acquisition, has garnered significant interest recently. However, existing GIL approaches focus on task-incremental and class-incremental scenarios within a single domain. Graph domain-incremental learning (Domain-IL), aiming at updating models across multiple g

Ally Nagasawa-Hinck, Peyton Phinehas Wood

In this paper we introduce the notion of a spherical knot mosaic where a knot is represented by tiling the surface of a topological 2-sphere with 11 canonical knot mosaic tiles and show this gives rise to several novel knot (and link) invariants: the spherical mosaic number, spherical tiling number, minimal spherical mosaic tiling number, spherical face numb

Sudip Sinha, Subhasis Sinha, Sushanta Dattagupta

The spin-boson (SB) model is a standard prototype for quantum dissipation, which we generalize in this work, to explore the dissipative effects on a one-dimensional spin-orbit (SO) coupled particle in the presence of a sub-ohmic bath. We analyze this model by extending the well-known variational polaron approach, revealing a localization transition accompani

Jan Elsner, K Nikolas Lausch, Jörg Behler

Oxide-water interfaces govern a wide range of physical and chemical processes fundamental to many fields like catalysis, geochemistry, corrosion, electrochemistry, and sensor technology. Near solid oxide surfaces, water behaves differently than in the bulk, exhibiting pronounced structuring and increased reactivity, typically requiring ab initio-level accura

Pei Peng, MingKun Xie, Hang Hao, Tong Jin

Object-context shortcuts remain a persistent challenge in vision-language models, undermining zero-shot reliability when test-time scenes differ from familiar training co-occurrences. We recast this issue as a causal inference problem and ask: Would the prediction remain if the object appeared in a different environment? To answer this at inference time, we

Bojana Femić

We introduce Para and coPara double categories for double categories. They rely on a horizontal action $\crta\ot$ of a horizontally monoidal double category $\Mm$ on a double category $\Dd$. We prove a series of properties, most importantly, we characterize monoidality of $\coPara_\Mm(\Dd)$ in the way that it extends monoidality of $\Dd$ as: lax monoidality

Yuanting Fan, Jun Liu, Xiaochen Chen, Bin-Bin Gao

Few-shot anomaly detection (FSAD) methods identify anomalous regions with few known normal samples. Most existing methods rely on the generalization ability of pre-trained vision-language models (VLMs) to recognize potentially anomalous regions through feature similarity between text descriptions and images. However, due to the lack of detailed textual descr

Xiaolin He, Cenlin Duan, Yingjie Qi, Xiao Ma

Computing-in-Memory (CIM) architectures have emerged as a promising solution for accelerating Deep Neural Networks (DNNs) by mitigating data movement bottlenecks. However, realizing the potential of CIM requires specialized dataflow optimizations, which are challenged by an expansive design space and strict architectural constraints. Existing optimization ap

Ansgar Denner, Robert Franken, Christoph Haitz, Daniele Lombardi

We present a calculation of next-to-leading-order electroweak corrections to the vector-boson scattering (VBS) process resulting in leptonically decaying W and Z bosons in association with two jets at the LHC. The VBS process is computed for both polarised and unpolarised intermediate bosons, exploiting the pole approximation and the separation of helicity s

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, A. G. Abac

We search for gravitational-wave background signals produced by various early Universe processes in the Advanced LIGO O4a dataset, combined with the data from the earlier O1, O2, and O3 (LIGO-Virgo) runs. The absence of detectable signals enables powerful constraints on fundamental physics. We derive gravitational-wave background energy density upper limits

Danial Ebrat, Sepideh Ahmadian, Luis Rueda

Recommender systems often struggle with data sparsity and cold-start scenarios, limiting their ability to provide accurate suggestions for new or infrequent users. This paper presents a Graph Attention Network (GAT) based Collaborative Filtering (CF) framework enhanced with Large Language Model (LLM) driven context aware embeddings. Specifically, we generate

Vinicius F. Lisboa, Pedro R. Dieguez, Kyrylo Simonov, Roberto M. Serra

Allowing the order of quantum operations to exist in superposition is known to open new routes for thermodynamic tasks. We investigate a quantum heat engine where energy exchanges are driven by generalized measurements, and the sequence of these operations is coherently controlled in a superposition of causal orders. Our analysis explores how initial correla

Andres Stump, Jeremy R. Green

Obtaining hadronic two-point functions is a central step in spectroscopy calculations in lattice QCD. This requires solving the Dirac equation repeatedly, which is computationally demanding. The distillation method addresses this difficulty by using the lowest eigenvectors of the spatial Laplacian to construct a subspace in which the Dirac operator can be fu

Damaris Meier, Noa Vikman, Stefan Wenger

We prove a monotone Sobolev extension theorem for maps to Jordan domains with rectifiable boundary in metric surfaces of locally finite Hausdorff 2-measure. This is then used to prove a uniformization result for compact metric surfaces by minimizing energy in the class of monotone Sobolev maps.

Fang Liu, Simiao Liu, Yinghao Zhu, Xiaoli Lian

Identifying and addressing security issues during the early phase of the development lifecycle is critical for mitigating the long-term negative impacts on software systems. Code review serves as an effective practice that enables developers to check their teammates' code before integration into the codebase. To streamline the generation of review comments,

Jiahui Zou, Andrey Vasnev, Wendun Wang, Xinyu Zhang

Forecast combination and model averaging have become popular tools in forecasting and prediction, both of which combine a set of candidate estimates with certain weights and are often shown to outperform single estimates. A data-driven method to determine combination/averaging weights typically optimizes a criterion under certain weight constraints. While a

Szabolcs Borsányi, Zoltán Fodor, Jana N. Guenther, Piyush Kumar

Charting the phase diagram of Quantum Chromodynamics (QCD) at large density is a challenging task due to the complex action problem in lattice simulations. Through simulations at imaginary baryon chemical potential $\mu_B$ we observe that, if the strangeness neutrality condition is imposed, both the strangeness chemical potential $\mu_S/\mu_B$ and the strang

Laurent Stolovitch, Xiaojun Wu

In this article, we give completely new examples of embedded complex manifolds the germ of neighborhood of which is holomorphically equivalent to a germ of neighborhood of the zero section in its normal bundle. The first set of examples is composed of connected abelian complex Lie groups, embedded in some complex manifold $M$. These are non compact manifolds

Qiyu Huang, Jingwen Deng, Zishuo Lin, Mahdi Azarpeyvand

The present study investigates the aerodynamic and aeroacoustic characteristics of a propeller operating under varying rotational speeds (RPM) and heights ("H" ), with a particular focus on the effects of upstream obstruction modelled as a tall building. Unlike previous studies that primarily examined rotor noise under axial inflow conditions, this work expl

Yi-Ting Hong, Stefano Rini, Luca Barletta

Polar codes with large kernels can achieve improved error exponents but are challenging to design with low decoding complexity. This work investigates kernel construction under recursive maximum likelihood decoding (RMLD) using a reinforcement learning framework based on the Gumbel AlphaZero algorithm. The proposed method efficiently explores the design spac

Jiayi Luo, Qingyun Sun, Beining Yang, Haonan Yuan

Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates significantly, while existing robust graph learning technolog

Vadim V. Bobylev, Anisa T. Bajkova, Anton A. Smirnov

For a sample of masers, the basic kinematic equations were solved by including the Galactic rotation parameters and the peculiar velocity of the Sun as the unknown variables. Based on spectral analysis, the following estimates were obtained: $|f|_{R,\theta}=(7.0,5.1)\pm(1.2,1.4)$ km s$^{-1}$ and the corresponding wavelengths $\lambda_{R,\theta}=(1.9,1.7)\pm(

A. Empey, R. Garcia Lopez, A. Natta, C. F. Manara

The dipper subclass of YSOs are characterised by frequent dips in their light curves. Irregular dippers do not show periodic signatures and have dips accounting for significant proportions of their photospheric flux. Given the short timescales on which these dips occur, their driving mechanisms are linked to the inner circumstellar disc dynamics. We present

Saïd Maanan, Azzouz Dermoune, Ahmed El Ghini

This paper introduces a unified family of smoothed quantile estimators that continuously interpolate between classical empirical quantiles and the sample mean. The estimators q(z, h) are defined as minimizers of a regularized objective function depending on two parameters: a smoothing parameter h $\ge$ 0 and a location parameter z $\in$ R. When h = 0 and z $

Zeliang Zong, Kai Zhang, Zheyang Li, Wenming Tan

Large Language Models (LLMs) have demonstrated remarkable proficiency in language comprehension and generation; however, their widespread adoption is constrained by substantial bandwidth and computational demands. While pruning and low-rank approximation have each demonstrated promising performance individually, their synergy for LLMs remains underexplored.

Basile Husquinet, Julie Vitorino, Olli Sipilä, Paola Caselli

Neon (Ne) is the fifth most abundant element in the Universe. Because it is chemically inert, it has never been considered in astrochemical models that studied molecular evolution. In the cold dark environments of pre-stellar cores, where the temperatures are below 10 K, Ne can condense onto the surface of interstellar grains. We investigated the effect of N

Wenjie Chen, Li Zhuang, Ziying Luo, Yu Liu

Personalized treatment outcome prediction based on trial data for small-sample and rare patient groups is a critical task in precision medicine. However, the high cost and scarcity of trial data limit the prediction performance. To address this issue, we propose a cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages a

Rhodri Guerrier, Adam W. Harley, Dima Damen

Recent advances in foundational 3D reconstruction models, such as DUSt3R and MASt3R, have shown great potential in 2D and 3D correspondence in static scenes. In this paper, we propose to adapt them for the task of point tracking through 3D grounded correspondence. We first demonstrate that these models are competitive point trackers when focusing on static p

Xuesong Wang, Xinyan Xie, Mo Li, Zhaoqian Liu

Semantic communication focuses on conveying the task-relevant meaning rather than exact bitwise recovery. For image transmission with a generative receiver, relying only on text descriptions can be insufficient to preserve instance-specific visual evidence, whereas sending dense latent representations can incur substantial overhead. This paper presents a rec

Shihab Aaqil Ahamed, Udaya S. K. P. Miriya Thanthrige, Ranga Rodrigo, Muhammad Haris Khan

Test-time prompt tuning (TPT) has emerged as a promising technique for adapting large vision-language models (VLMs) to unseen tasks without relying on labeled data. However, the lack of dispersion between textual features can hurt calibration performance, which raises concerns about VLMs' reliability, trustworthiness, and safety. Current TPT approaches prima

Shaked Zychlinski, Yuval Kainan

Large Language Models (LLMs) are susceptible to jailbreak attacks where malicious prompts are disguised using ciphers and character-level encodings to bypass safety guardrails. While these guardrails often fail to interpret the encoded content, the underlying models can still process the harmful instructions. We introduce CPT-Filtering, a novel, model-agnost

Piyushkumar Patel

While large language models have transformed how we interact with AI systems, they have a critical weakness: they confidently state false information that sounds entirely plausible. This "hallucination" problem has become a major barrier to using these models where accuracy matters most. Existing solutions either require retraining the entire model, add sign

Yujie Zhang

Junior indie game developers in distributed, part-time teams lack production frameworks suited to their specific context, as traditional methodologies are often inaccessible. This study introduces the CIGDI (Co-Intelligence Game Development Ideation) Framework, an alternative approach for integrating AI tools to address persistent challenges of technical deb

Hongliang Lai, Mengyu Luo, Jie Zhang

This paper resolves an open problem posed by Schweizer and Sklar in 1983. We establish that the binary operation $\tauTL$ is a triangle function on $\Delp$ if and only if the following three conditions hold: (a) $L$ is a continuous t-conorm on $[0, \infty]$ satisfying $(LCS)$; (b) $T$ is a t-norm on $[0, 1]$; and (c) $T$ is weakly left continuous, with left

Konark Jain, Nick Firoozye, Jonathan Kochems, Philip Treleaven

We study the optimal Market Making problem in a Limit Order Book (LOB) market simulated using a high-fidelity, mutually exciting Hawkes process. Departing from traditional Brownian-driven mid-price models, our setup captures key microstructural properties such as queue dynamics, inter-arrival clustering, and endogenous price impact. Recognizing the realistic

Benedetto Bozzini, Massimo Frittelli, Anotida Madzvamuse, Ivonne Sgura

It is well known that phase formation by electrodeposition yields films of poorly controllable morphology. This typically leads to a range of technological issues in many fields of electrochemical technology. Presently, a particularly relevant case is that of high-energy density next-generation batteries with metal anodes, that cannot yet reach practical cyc

Jiaqi Lin, Zhen-Ya Zheng, Bin Ma, Lin Long

Optical variability is a key observational probe for studying the accretion dynamics and central engine physics of Active Galactic Nuclei (AGNs). The quality and completeness of light curves have a direct impact on variability studies, particularly for faint AGNs and high-redshift AGNs. To improve the quality of long-term light curves for AGNs, we bin and st

Lin Long, Zhen-ya Zheng, Ning Jiang, Chun Xu

Amid rapid advances in time-domain astronomy, multi-wavelength (e.g., optical and infrared) time-domain studies of quasars remain scarce. Here we present a systematic analysis of four quasars initially selected by their Ks-band variability amplitudes in the VISTA Variables in the V\'{\i}a L\'actea Survey (VVV/VVVX). For these objects, we obtain complementary

Zhijun Li, Zhengyun You

In the decays of $\eta_c\to\gamma\gamma$ and $J/\psi\to\gamma\eta_c$, there are discrepancies between the theoretical calculations and the PDG experimental values, referred to as the charmonium QCD puzzle. We observe the decay $\eta_c\to\gamma\gamma$ in $J/\psi\to\gamma\eta_c$ using $(2712.4\pm14.3)\times10^{6}$ $\psi(3686)$ events collected with the BESIII

Yucen Wang, Fengming Zhang, De-Chuan Zhan, Li Zhao

Adapting pretrained video generation models into controllable world models via latent actions is a promising step towards creating generalist world models. The dominant paradigm adopts a two-stage approach that trains latent action model (LAM) and the world model separately, resulting in redundant training and limiting their potential for co-adaptation. A co

Haitao Ma, Yantong Li, Yingchun Kang, Bing Yu

Quantum catalysts enable transformations that otherwise would be forbidden, offering a pathway to surpass conventional limits in quantum information processing. Among them, embezzling catalysts stand out for achieving near-perfect performance while tolerating only minimal disturbance, bridging the gap between ideal and practical catalysis. Yet, this superior

Mihály Dobos-Kovács, Levente Bajczi, András Vörös

Constrained Horn Clauses (CHCs) are widely adopted as intermediate representations for a variety of verification tasks, including safety checking, invariant synthesis, and interprocedural analysis. This paper introduces CHCVERIF, a portfolio-based CHC solver that adopts a software verification approach for solving CHCs. This approach enables us to reuse matu

Levente Bajczi, Milán Mondok, Vince Molnár

Theta is a verification framework that has participated in the CHC-COMP competition since 2023. While its core approach -- based on transforming constrained Horn clauses (CHCs) into control-flow automata (CFAs) for analysis -- has remained mostly unchanged, Theta's verification techniques, design trade-offs, and limitations have remained mostly unexplored in

Salvador Lucas

We consider sets/relations/computations defined by *Elementary Inference Systems* I, which are obtained from Smullyan's *elementary formal systems* using Gentzen's notation for inference rules, and proof trees for atoms P(t_1,...,t_n), where predicate P represents the considered set/relation/computation. A first-order theory Th(I), actually a set of definite

Gregoire Maire, Thomas Genet

We are interested in proving satisfiability of Constrained Horn Clauses (CHCs) over Algebraic Data Types (ADTs). We propose to prove satisfiability by building a tree automaton recognizing the Herbrand model of the CHCs. If such an automaton exists then the model is said to be regular, i.e., the Herbrand model is a regular set of atoms. Kostyukov et al. have

Volodymyr Sushch

In this paper, we develop a geometric, structure-preserving semi-discrete formulation of Maxwell's equations in both three- and two-dimensional settings within the framework of discrete exterior calculus. This approach preserves the intrinsic geometric and topological structures of the continuous theory while providing a consistent spatial discretization. We

Jiangdong Ai, Gregory Gutin, Fankang He, Anders Yeo

Huang, Ma, Shapira, Sudakov and Yuster (Comb. Prob. Comput. 2013) proved that every Eulerian digraph of average out-degree $d$ has a directed cycle of length at least $\sqrt{d}.$ We improve the lower bound from $\sqrt{d}$ to $\sqrt{2d}-3/2.$

Zhijun Li, Zhengyun You

BESIII experiment has collected a large data sample of charmonium, charm mesons, hyperons, and other light mesons. These data provide a unique opportunity to explore the dark sector beyond the Standard Model, particularly for dark sectors that couple to charm quarks or other light quarks, and for dark sectors with masses in the $\tau-c$ energy region. We pre

Dong Huang, Mingzhe Du, Jie M. Zhang, Zheng Lin

Test oracle generation in non-regression testing is a longstanding challenge in software engineering, where the goal is to produce oracles that can accurately determine whether a function under test (FUT) behaves as intended for a given input. In this paper, we introduce Nexus, a novel multi-agent framework to address this challenge. Nexus generates test ora

Shiyao Sang

This paper challenges a prevailing epistemological assumption in End-to-End Autonomous Driving: that high-performance planning necessitates high-fidelity world reconstruction. Inspired by cognitive science, we propose the Mental Bayesian Causal World Model (MBCWM) and instantiate it as the Tokenized Intent World Model (TIWM), a novel cognitive computing arch

Min Zhang, Hao Chen, Hao Chen, Wenqi Zhang

With the rapid development of large language models (LLMs), various LLM-based works have been widely applied in educational fields. However, most existing LLMs and their benchmarks focus primarily on the knowledge dimension, largely neglecting the evaluation of cultivation capabilities that are essential for real-world educational scenarios. Additionally, cu

David Donaire-Montaño, Matilde García-Valdecasas Ojeda, Nicolás Tacoronte, Juan José Rosa-Cánovas

Land surface models (LSMs) play a crucial role in characterizing land-atmosphere interactions by providing boundary conditions to regional climate models (RCMs). This is particularly true over the Iberian Peninsula (IP), where a water-limited regime governs much of the territory. We optimize the configuration of the Noah land surface model with multiparamete

Yingjia Wang, Ting Qiao, Xing Liu, Chongzuo Li

The rapid advancement of deep neural networks (DNNs) heavily relies on large-scale, high-quality datasets. However, unauthorized commercial use of these datasets severely violates the intellectual property rights of dataset owners. Existing backdoor-based dataset ownership verification methods suffer from inherent limitations: poison-label watermarks are eas

Ari Meir Brodsky, Assaf Rinot, Shira Yadai

We give two consistent constructions of trees $T$ whose finite power $T^{n+1}$ is sharply different from $T^n$: 1. An $\aleph_1$-tree $T$ whose interval topology $X_T$ is perfectly normal, but $(X_T)^2$ is not even countably metacompact. 2. For an inaccessible $\kappa$ and a positive integer $n$, a $\kappa$-tree such that all of its $n$-derived trees are Sou

Kaushiki Mukherjee, Nirman Ganguly

Network nonlocality, a recently noted form of nonlocality has been shown to have distinctive features, marking a significant departure from the notion of standard Bell nonlocality in the context of quantum correlations. On a pragmatic front, it has gained significant importance as researchers worldwide actively engage in the study on quantum networks. Howeve

Hashir Kuniyil, Asad Ali, Saif Al-Kuwari

Spatial correlations of photon pairs from spontaneous parametric down-conversion (SPDC) underpin quantum imaging and entanglement certification. We present the first systematic study of spectral filter bandwidth effects on transverse spatial correlations in bulk Type-I BBO for degenerate and non-degenerate configurations. In the far field, the degenerate con

Rodrigo Piera, Gianluca De Santis, Agustin Sanchez, Yury Kurochkin

Quantum Random Number Generators provide true physical randomness based on quantum processes, essential for cryptographic and scientific applications. However, practical implementations face challenges in robustness and verifiability: ensuring that the entropy source remains secure and stable over time, and enabling independent confirmation of randomness qua

Tomasz Matulewicz

The isospin symmetry, originating from similar masses of $u$ and $d$ quarks, if exact would result in equal numbers of charged ($K^+$ and $K^{-}$) and neutral ($K^0$ and $\overline{K}^0$) mesons produced in collisions of isospin-symmetric atomic nuclei. The charged and neutral $K$ meson production in Ar+Sc collisions at a center-of-mass energy of 11.9 GeV pe

Edoardo Suerra, Samuele Altilia, Stefano Olivares, Alessandro Ferraro

A synchronously pumped optical parametric oscillator (SPOPO) operating at 93 MHz is used to generate squeezed states at 1035 nm. The system features a counter-propagating beam at the same wavelength as the quantum state, which simultaneously actively stabilizes the cavity and, after transmission, acts as the local oscillator for homodyne detection. By derivi

Nuno Saavedra, Alexandra Mendes, João F. Ferreira

CI/CD pipelines are widely used in software development, yet their environmental impact, particularly carbon and water footprints (CWF), remains largely unknown to developers, as CI service providers typically do not disclose such information. With the growing environmental impact of cloud computing, understanding the CWF of CI/CD services has become increas

Lele Liu, Bo Ning

Confirming a conjecture of Elphick and Edwards and strengthening a spectral theorem of Wilf, Nikiforov proved that for any $K_{r+1}$-free graph $G$, $\lambda(G)^2 \leq 2 (1 - 1/r) m$, where $\lambda(G)$ is the spectral radius of $G$, and $m$ is the number of edges of $G$. This result was later improved in \cite{LiuN26}, where it was shown that for any graph

Cristina Bertone, Francesca Cioffi, Paolo Lella

Let $\mathbb{K}$ be a field and $A$ a Noetherian $\mathbb{K}$-algebra. In a paper of 2020, M. Albert, C. Bertone, M. Roggero and W. M. Seiler proved that, given a quasi-stable module $U \subset R^m$ with $R=\mathbb{K}[x_0,\dots,x_n]$, any submodule $M\subseteq (R\otimes A)^m$ generated by a marked basis over $U$ admits a special free resolution described in

Wagner F. Balthazar, Quinn M. B. Palmer, Alex. E. Jones, Jake F. F. Bulmer

The Feynman path integral formalism has inspired the development of memory-efficient and parallelizable classical algorithms for simulating quantum computers. We adapt this approach for the calculation of probability amplitudes of linear-optical boson sampling experiments, which involve Fock-state inputs, linear optical circuits, and photo-detection at the o

Ivan Razvorotnev, Marina Munkhoeva, Evgeny Frolov

Sequential recommendation models must navigate sparse interaction data popularity bias and conflicting objectives like accuracy versus diversity While recent contrastive selfsupervised learning SSL methods offer improved accuracy they come with tradeoffs large batch requirements reliance on handcrafted augmentations and negative sampling that can reinforce p

Guanxing Lu, Rui Zhao, Haitao Lin, He Zhang

Reinforcement learning (RL) is widely used to produce robust robotic manipulation policies, but fine-tuning vision-language-action (VLA) models with RL can be unstable due to inaccurate value estimates and sparse supervision at intermediate steps. In contrast, imitation learning (IL) is easy to train but often underperforms due to its offline nature. In this

Yuki Omiya, Yuto Ichinohe, Kazuhiro Nakazawa, Hisamitsu Awaki

We present high-resolution X-ray spectroscopy of the merging galaxy cluster Abell 3667 with \textit{XRISM}/Resolve. Two observations, targeting the cluster X-ray core and the prototypical cold front, were performed with exposures of 105 ks and 276 ks, respectively. We find that the gas in the core is blueshifted by $v_z\sim-200$ km s$^{-1}$ relative to the b

Su Ruibo

We derive explicit a priori consistency error estimates for a standard finite element discretization of the Poisson equation on convex domains, where the domain is approximated by an internal convex polyhedron. The obtained explicit estimates depend only on global geometric parameters and are applicable to general convex domains and arbitrary families of sim