December 2025 arXiv papers — page 45

Partha Sarathi Mondal, Tamas Vicsek, Shradha Mishra

Chaotic transitions in inertial fluids typically proceed through a direct energy cascade from large to small scales. In contrast, active systems, composed of self propelled units, inject energy at microscopic scales and therefore exhibit an inverse cascade, giving rise to distinctly unconventional flow patterns. Here, we investigate an active mixture consist

Akshat Dubey, Aleksandar Anžel, Bahar İlgen, Georges Hattab

Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is driven by increasingly sophisticated model architectures, such as ResNets, Vision Transformers, and Hybrid Convolutional Neural Networks, that offer enhanced performance at the cost of greater complexity. This complexity of

Adrien Pavão

AI-generated images are now pervasive online, yet many people believe they can easily tell them apart from real photographs. We test this assumption through an interactive web experiment where participants classify 20 images as real or AI-generated. Our dataset contains 120 difficult cases: real images sampled from CC12M, and carefully curated AI-generated c

Wanting Sun, Shunan Wei, Donglei Yang

We show that for any integer $r\ge 2$, there exists a constant $c>0$ such that for every sufficiently large integer $n$, every $((r-1)n+1)$-regular graph $G$ on $rn$ vertices has at least $c2^{rn}$ subsets $S\subseteq V(G)$ such that $G[S]$ contains a $K_r$-factor. This confirms a conjecture of Dragani\'c, Keevash and M\"uyesser for large $n$ [Cyclic subsets

Timothy Weber, Cheng Cheng, Harry Thawley, Kylie Catchpole

Fossil gas is sometimes presented as an enabler of variable solar and wind generation beyond 2050, despite being a primary source of greenhouse gas emissions from methane leakage and combustion. We find that balancing solar and wind generation with pumped hydro energy storage eliminates the need for fossil gas without incurring a cost penalty. However, many

Gaurav Rudra Malik, Jeet Sharma, Rohit Kumar Shukla, S. Aravinda

The nonintegrable transverse-field Ising model is a common platform for studying ergodic quantum dynamics. In this work, we introduce a simple variant of the model in which this ergodic behaviour is suppressed by introducing a spatial inhomogeneity in the interaction strengths. For this we partition the chain into two equal segments within which the spins in

Da-Yong Liu, Zhe Sun, Feng Lu, Wei-Hua Wang

A series of experiments revealed that novel bipartite magnetic and superconducting (SC) phases widely exist in the phase diagrams of iron pnictides and chalcogenides. Nevertheless, the origin of the two-dome magnetic and SC phases in iron-based compounds remains unclear. Here we theoretically investigated the electronic structures, magnetic and SC properties

Pabitra Masanta, Ratan Sarkar, Punit Parmananda, Raghunath Chelakkot

We investigate the spontaneous motion of an elliptical Janus particle, driven by Marangoni forces, on a water surface to understand how particle shape and size influence its dynamics. The Janus particle is one-half infused with a substance such as camphor, which lowers the surface tension upon release onto the water surface. The resulting surface tension gra

Pierre Kuna, Erik Hesselmeier-Hüttmann, Phillip Schillinger, Felix Gloistein

Optically addressable spin defects are excellent candidate platform for quantum sensing and quantum network. Nuclear spins coupled to color centers naturally enable long lived quantum memories and local qubits registers. To fully leverage this potential precise characterization of the surrounding nuclear-spin environment augmented with refined DFT models is

Enrique Gaztañaga, K. Sravan Kumar, João Marto

The formulation of quantum field theory in Minkowski spacetime, which emerges from the unification of special relativity and quantum mechanics, is based on treating time as a parameter, assuming a fixed arrow of time, and requiring that field operators commute for spacelike distances. This procedure is questioned here in the context of quantum field theory i

Sarah Alsubaie, Sakhaa Alsaedi, Xin Gao

In 2008, melamine in infant formula forced laboratories across three continents to verify a compound they had never monitored. Non-targeted analysis using LC/GC-HRMS handles these cases. But when findings trigger regulatory action, reproducibility becomes operational: can an independent laboratory repeat the analysis and reach the same conclusion? We assesse

Nishant Gaurav, Adit Akarsh, Ankit Ranjan, Manoj Bajaj

While CodeMem establishes executable code as the optimal representation for agentic procedural memory, the mechanism for autonomously synthesizing this memory from a blank slate remains underexplored. This paper operationalizes the transition of Large Language Models from passive tool-users to active workflow architects. Through a high-fidelity case study of

Qi Zhu, Yu Yang, Liang Yu, Qing-Shan Jia

The heating, ventilation and air-conditioning (HVAC) system dominates building's energy consumption and meanwhile exhibits substantial operational flexibility that can be exploited for providing grid services. However, the goal is largely hindered by the difficulty to characterize the system's operating flexibility due to the complex building thermal dynamic

Yuntao Dai, Hang Gu, Teng Wang, Qianyu Cheng

Vision-Language-Action (VLA) models have emerged as a unified paradigm for robotic perception and control, enabling emergent generalization and long-horizon task execution. However, their deployment in dynamic, real-world environments is severely hin dered by high inference latency. While smooth robotic interaction requires control frequencies of 20 to 30 Hz

Divya Vijay, Vignesh Ethiraj

As networks evolve toward 5G Standalone and 6G, operators face orchestration challenges that exceed the limits of static automation and Deep Reinforcement Learning. Although Large Language Model (LLM) agents offer a path toward intent-based networking, they introduce stochastic risks, including topology hallucinations and policy non-compliance. To mitigate t

Ze-Sheng Xu, J. Lukas K. König, Andrea Cataldo, Rohan Yadgirkar

Non-Hermitian physics has unlocked a wealth of unconventional wave phenomena beyond the reach of Hermitian systems, with exceptional points (EPs) driving enhanced sensitivity, nonreciprocal transport, and topological behavior unique to non-Hermitian degeneracies. Here, we present a scalable and reconfigurable silicon photonic integrated circuit capable of em

Yuanjian Xu, Yuan Shuai, Jianing Hao, Guang Zhang

Neural Stochastic Differential Equations (Neural SDEs) provide a principled framework for modeling continuous-time stochastic processes and have been widely adopted in fields ranging from physics to finance. Recent advances suggest that Generative Adversarial Networks (GANs) offer a promising solution to learning the complex path distributions induced by SDE

Angjela Davitkova, Sebastian Michel

The use of deep learning for database optimization has gained significant traction, offering improvements in indexing, cardinality estimation, and query optimization. However, acquiring high-quality training data remains a significant challenge. This paper explores the possibility of using generative models, such as GPT, to synthesize training data for learn

Matthias K. Hoffmann, Amine Othmane, Kathrin Flaßkamp

Many engineering tasks require solving families of nonlinear constrained optimization problems, parametrized in setting-specific variables. This is computationally demanding, particularly, if solutions have to be computed across strongly varying parameter values, e.g., in real-time control or for model-based design. Thus, we propose to learn the mapping from

Monica Montardini, Stefan Takacs, Mattia Tani

The construction of robust solvers for linear systems obtained from the discretization of partial differential equations using Isogeometric Analysis is challenging since the condition number of the system matrix not only grows with the reciprocal square of the grid size (for second order problems), but also exponentially with the spline degree. The Fast Diag

Marco A. Iglesias, Michael. E. Causon, Mikhail Y. Matveev, Andreas Endruweit

This work demonstrates that neural operator learning provides a powerful and flexible framework for building fast, accurate emulators of moving boundary systems, enabling their integration into digital twin platforms. To this end, a Deep Operator Network (DeepONet) architecture is employed to construct an efficient surrogate model for moving boundary problem

Letian Dai, Wanru Chen, Quanming Geng, Ying Xu

Lead-free tin-based halide perovskites are attractive for flexible and environmentally benign optoelectronics, but their application is limited by the rapid oxidation of Sn2+ to Sn4+ and poor operational stability. Here, we report a flexible CsSnI3 nanowire photodetector that achieves both high near-infrared photoresponse and long-term stability through syne

Z. Q. Qu, R. Y. Zhou, H. Su, Y. Liang

A new source in solar corona scattering photospheric and chromospheric Fraunhofer spectral lines is detected below a height of one solar radius above solar limb, consisting of tenuous and cool neutral atoms and much fewer once ionized ions. It is demonstrated via maps at the sample Fraunhofer lines within the band from 516.38 to 539.89nm, reconstructed from

R. Amato, M. Bachetti, R. Soria, A. Gúrpide

Ultraluminous X-ray sources (ULXs) represent the closest and most accessible laboratories to study sustained super-Eddington accretion onto compact objects. Over the past decade, the discoveries of coherent pulsations in a few ULXs has proved that these systems can be powered by accreting neutron stars, while the most luminous and distant ones remain strong

Hemant Shukla

LLMs are hitting the scaling wall - compute grows 10-100x while accuracy barely moves. This note quantifies the slowdown and argues that the next leap in AI will come not from bigger models, but from smarter, more efficient ones.

Latent Labs Team, Henry Kenlay, Daniella Pretorius, Jonathan Crabbé

Drug discovery has long sought computational systems capable of designing drug-like molecules directly: developable and non-immunogenic from the start. Here we introduce Latent-X2, a frontier generative model that achieves this goal through zero-shot design of antibodies with strong binding affinities, drug-like properties, and, for the first time for any de

Rishu Garg, Jitender Singh

In this paper, we obtain several new factorization results for certain classes of polynomials having integer coefficients. In doing so, we use the information about prime factorization of the value taken up by such polynomials and their higher order formal derivatives at sufficiently large integer arguments. If a lower bound for the minimum possible degree o

Edith Elkind, Michele Flammini, Giovanna Varricchio

We consider the complexity of maximizing egalitarian welfare in Friends and Enemies Games -- a subclass of hedonic games in which every agent partitions other agents into friends and enemies. We investigate two classic scenarios proposed in the literature, namely, Friends Appreciation ($\mathsf{FA}$) and Enemies Aversion ($\mathsf{EA}$): in the former, each

Jiawei Ge, Jiuxin Cao, Xinyi Li, Xuelin Zhu

Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks

Tobias M. Wolff, Isabelle Krauss, Victor G. Lopez, Matthias A. Müller

In this work, we introduce a sample- and data-based moving horizon estimation framework for linear systems. We perform state estimation in a sample-based fashion in the sense that we assume to have only few, irregular output measurements available. This setting is encountered in applications where measuring is expensive or time-consuming. Furthermore, the st

Michał Marczenko

We study net-baryon number density fluctuations in isospin-symmetric matter near the nuclear liquid-gas phase transition using the parity doublet model. We analyze second-order susceptibilities of net-proton and net-neutron numbers and their correlations. We show that proton-neutron correlations are nontrivial and lead to qualitative differences between net-

Daniele Cardullo, Simone Teglia, Irene Amerini

With the rise of easily accessible tools for generating and manipulating multimedia content, realistic synthetic alterations to digital media have become a widespread threat, often involving manipulations across multiple modalities simultaneously. Recently, such techniques have been increasingly employed to distort narratives of important events and to sprea

Jinghao Shi, Jianing Song

High-resolution remote sensing image semantic segmentation (HRSS) is a fundamental yet critical task in the field of Earth observation. However, it has long faced the challenges of high inter-class similarity and large intra-class variability. Existing approaches often struggle to effectively inject abstract yet strongly discriminative semantic knowledge int

Ramon Codina, Roberto Federico Ausas, Pedro Balbão Bazon, Cristian Guillermo Gebhardt

We consider the primal and dual forms of the optimality conditions for PDE-contrained optimization problems arising in Data-Driven Computational Mechanics when specialized to the reaction-diffusion context. Starting with the continuous setting, we establish well-posedness of such concomitant formulations. Then, we propose stable and consistent finite element

Prasoon Saurabh

Standard topological invariants, such as the Chern number and Berry phase, form the bedrock of modern quantum matter classification. However, we demonstrate that this framework undergoes a \textbf{catastrophic failure} in the presence of essential singularities -- ubiquitous in open, driven, and non-Hermitian systems ("Wild" regime). In these settings, the l

Longqing Ge, Qingdong Cai, Yonghao Zhang, Tianbai Xiao

Computational modeling and simulation of fluid-structure interactions constitute a fundamental cornerstone for advancing aerospace engineering endeavors. This paper addresses the notion and implementation of the immersed boundary method for the discrete velocity model of the Boltzmann equation. The method incorporates the Maxwell gas-surface interaction mode

Tobias M. Wolff, Victor G. Lopez, Matthias A. Müller, Thomas Beckers

Latent force models, a class of hybrid modeling approaches, integrate physical knowledge of system dynamics with a latent force - an unknown, unmeasurable input modeled as a Gaussian process. In this work, we introduce two optimal state estimation frameworks to reconstruct the latent forces and to estimate the states. In contrast to state-of-the-art approach

Xuanyu Hu

Multimodal brain decoding aims to reconstruct semantic information that is consistent with visual stimuli from brain activity signals such as fMRI, and then generate readable natural language descriptions. However, multimodal brain decoding still faces key challenges in cross-subject generalization and interpretability. We propose a BrainROI model and achiev

Maximiliano Hertel, Friedrich M. Philipp, Manuel Schaller, Karl Worthmann

We prove $L^\infty$-error bounds for kernel extended dynamic mode decomposition (kEDMD) approximants of the Koopman operator for stochastic dynamical systems. To this end, we establish Koopman invariance of suitably chosen reproducing kernel Hilbert spaces and provide an in-depth analysis of the pointwise error in terms of the data points. The latter is spli

Songnan Gu, Hao Jiang, Chongjun Ouyang, Yuanwei Liu

A multiuser uplink transmission framework based on the segmented waveguide-enabled pinching-antenna system (SWAN) is proposed under two operating protocols: segment selection (SS) and segment aggregation (SA). For each protocol, the achievable uplink sum-rate is characterized for both time-division multiple access (TDMA) and non-orthogonal multiple access (N

Tarik Houichime, Abdelghani Souhar, Younes El Amrani

The memory of contemporary Large Language Models is bound by a physical paradox: as they learn, they fill up. The linear accumulation (O(N)) of Key-Value states treats context as a warehouse of static artifacts, eventually forcing a destructive choice between amnesia and latency. We challenge this discrete orthodoxy, proposing that long-term memory is not th

Moussa Labbadi, Ilyasse Lamrani

In this paper, we address the robustness of parabolic-elliptic systems under boundary control. A sliding mode control strategy is proposed to reject matched perturbations. The stability analysis establishes finite-time convergence of the sliding manifold and exponential stability of the closed-loop system. Since the closed-loop system is discontinuous, we al

Thomas Attema, Bor de Kock, Sandesh Manganahalli Jayaprakash, Dimitrios Schoinianakis

In this work, the conventional cryptographic algorithms used in the 5G Core are replaced with post-quantum alternatives and the practical impact of this transition is evaluated. Using a simulation environment, we model the registration and deregistration of varying numbers of user equipments (UEs) and measure the resulting effects on bandwidth consumption an

Shekhar Chandra Pandey, Shilpam Sharma, Pooja Gupta, L. S. Sharath Chandra

The influence of metallic and semiconducting (V, Al, and Si) under-layer induced charge carrier substitution on the superconducting properties of the Ti40V60 alloy thin films are studied and also compared with a pristine reference film without any under-layer. All the films exhibit metallic behavior in the normal state and a superconducting transition at low

Qian-Hui Chen, Alex M. Garcia, Zefeng Li, Kathryn Grasha

Spiral structures are one of the most common features in galaxies, yet their origins and evolution remain debated. Stellar age distributions offer crucial insights into galaxy evolution and star formation, though environmental effects can obscure the intrinsic age patterns. Using the Auriga cosmological gravo-magnetohydrodynamical zoom-in simulations, we inv

Enlin Shen, Tiberiu I. Popescu, Nishwal Gora, Guratinder Kaur

The discovery of ordered magnetism in two-dimensional van der Waals materials at the monolayer limit challenges the Mermin-Wagner theorem, which forbids spontaneous breaking of continuous symmetries in two dimensions at finite temperatures. The persistence of static magnetism in low-dimensions is fundamentally influenced by magnetic anisotropy and the local

Xingbo Du, Loka Li, Duzhen Zhang, Le Song

Memory systems have been designed to leverage past experiences in Large Language Model (LLM) agents. However, many deployed memory systems primarily optimize compression and storage, with comparatively less emphasis on explicit, closed-loop control of memory retrieval. From this observation, we build memory retrieval as an autonomous, accurate, and compatibl

Oikantik Nath, Sahithi Kukkala, Mitesh Khapra, Ravi Kiran Sarvadevabhatla

Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labels and multilingual diversity, making them insufficient for representing complex document layouts. In contrast, human-annotated datasets suc

Sebastiano Guaraldo, Sonia Mazzucchi, Alessio Baldazzi, Stefano Azzini

Quantum entanglement is an essential resource for quantum technologies, and the controlled swap test provides a versatile tool for its detection and quantification. Here, we propose a SWAP-based entanglement witness that applies to arbitrary two-qubit states - both pure and mixed - and provides a lower bound on the concurrence. The method is resource-efficie

Bin Xie, Rui Song, Xuyuan Cai

Anonymous credentials (ACs) are a crucial cryptographic tool for privacy-preserving authentication in decentralized networks, allowing holders to prove eligibility without revealing their identity. However, a major limitation of standard ACs is the disclosure of the issuer's identity, which can leak sensitive contextual information about the holder. Issuer-h

Bingyan Xie, Yongpeng Wu, Wenjun Zhang, Derrick Wing Kwan Ng

The evolution of semantic communications has profoundly impacted wireless video transmission, whose applications dominate driver of modern bandwidth consumption. However, most existing schemes are predominantly optimized for simple additive white Gaussian noise or Rayleigh fading channels, neglecting the ubiquitous multiple-input multiple-output (MIMO) envir

Nathan Roos, Ekaterina Iakovleva, Ani Gjergji, Vito Paolo Pastore

Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains poorly understood. Although previous research has viewed bias amplification as an inherent characteristic of diffusion models, this work provides the first analysis of how sampling

Onintze Zaballa, Verónica Álvarez, Santiago Mazuelas

Simultaneous load forecasting across multiple entities (e.g., regions, buildings) is crucial for the efficient, reliable, and cost-effective operation of power systems. Accurate load forecasting is a challenging problem due to the inherent uncertainties in load demand, dynamic changes in consumption patterns, and correlations among entities. Multi-task learn

Baoli Yin, Guoyu Zhang, Yang Liu, Hong Li

This work proposes a novel approach for designing high-order energy-decaying schemes for Maxwell's equations in Havriliak-Negami dispersive media. It is shown that conventional convolution quadrature (CQ) methods, which rely directly on the generating function of linear multistep methods, cannot generate completely monotonic sequences beyond first-order accu

Behnam Farnaghinejad, Antonio Porsia, Annachiara Ruospo, Alessandro Savino

Security in modern RISC-V processors demands more than functional correctness: It requires resilience to side-channel attacks. This paper evaluates the vulnerability of the side channel of the CVA6 RISC-V core by analyzing software-based AES encryption uses an RTL-level power profiling framework called VeriSide. This work represents that this design's Correl

Hossein Tahmasbi, Andreas Knüpfer, Thomas D. Kühne, Hossein Mirhosseini

The rapid emergence of universal Machine Learning Interatomic Potentials (uMLIPs) has transformed materials modeling. However, a comprehensive understanding of their generalization behavior across configurational space remains an open challenge. In this work, we introduce a benchmarking framework to evaluate both the equilibrium and far-from-equilibrium perf

Imtiaz Ur Rehman, Moussa Labbadi, Amine Abadi, Lew Lew Yan Voon

A robust tracking control strategy is designed to empower wheeled mobile robots (WMRs) to track predetermined routes while operating in diverse fields and encountering disturbances like strong winds or uneven path conditions, which affect tracking performance. Ensuring the applicability of this tracking method in real-world scenarios is essential. To accompl

Jun Hu, Pengzhan Jin, Weijun Zhang

We propose the Manifold Function Encoder (MFE) for identifying different functions defined on different manifolds. Both a manifold in Euclidean space and a function defined on this manifold can be viewed as bounded linear functionals on a suitable space of continuous functions. From this perspective, we treat manifold functions as elements of the dual space.

Imtiaz Ur Rehman, Moussa Labbadi, Amine Abadi, Lew Lew Yan Voon

This paper develops a robust safety-critical control method for nonlinear strictfeedback systems with mismatched disturbances. Using a state transformation and a linear time-varying disturbance observer, the system is converted into a form that enables safe control design. The approach ensures forward invariance of the safety set and also applies to disturba

Letizia Milli, Laura Pollacci, Riccardo Guidotti

The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework that extends the classical SEIZ (Susceptible-Exposed-Infected-Skeptic) epidemic model to capture the dynamics of toxic m

Qijun Qin, Ziqi Zhang, Yihan Zhong, Feng Huang

Due to the limitations of a single autonomous vehicle, Cellular Vehicle-to-Everything (C-V2X) technology opens a new window for achieving fully autonomous driving through sensor information sharing. However, real-world datasets supporting vehicle-infrastructure cooperative navigation in complex urban environments remain rare. To address this gap, we present

Stephan Durr, Stylianos Gregoriou, Giannis Koutsou

We propose a practical formulation of the overlap Dirac operator in lattice QCD that employs the diagonal Kenney-Laub rational iterates - expressed via their partial fraction decomposition - to approximate the matrix sign function. We investigate this approximation using the Brillouin operator as kernel, in addition to the standard Wilson Dirac operator. Num

Maxime Herda, Marc Pegon, Isabelle Tristani

In this paper, we provide a result of exponential stability for several dissipative linear kinetic equations with heavy-tailed equilibria. The approach, inspired by the so-called $L^2$-hypocoercivity method, is robust enough to provide estimates that are uniform in the anomalous diffusion limit. Moreover, it is able to deal with bounded domains with periodic

Sung Park, Daeho Yoon, Jungmin Lee

As artificial intelligence (AI) systems become increasingly embedded in everyday life, the ability of interactive agents to express empathy has become critical for effective human-AI interaction, particularly in emotionally sensitive contexts. Rather than treating empathy as a binary capability, this study examines how different levels of empathic expression

Kausthubh Manda, Raghuram Bharadwaj Diddigi

We study offline multitask reinforcement learning in settings where multiple tasks share a low-rank representation of their action-value functions. In this regime, a learner is provided with fixed datasets collected from several related tasks, without access to further online interaction, and seeks to exploit shared structure to improve statistical efficienc

Weihan Zhang, Zijun Gao

Understanding how much each variable contributes to an outcome is a central question across disciplines. A causal view of explainability is favorable for its ability in uncovering underlying mechanisms and generalizing to new contexts. Based on a family of causal explainability quantities, we develop methods for their estimation and inference. In particular,

Jixiao Yang, Jinyu Chen, Zixiao Huang, Chengda Xu

Federated learning across multi-cloud environments faces critical challenges, including non-IID data distributions, malicious participant detection, and substantial cross-cloud communication costs (egress fees). Existing Byzantine-robust methods focus primarily on model accuracy while overlooking the economic implications of data transfer across cloud provid

Xiangxuan Ren, Zhongdao Wang, Pin Tang, Guoqing Wang

3D object detection is fundamental for safe and robust intelligent transportation systems. Current multi-modal 3D object detectors often rely on complex architectures and training strategies to achieve higher detection accuracy. However, these methods heavily rely on the LiDAR sensor so that they suffer from large performance drops when LiDAR is absent, whic

Linuk Perera

This research introduces a novel quantitative methodology tailored for quantitative finance applications, enabling banks, stockbrokers, and investors to predict economic regimes and market signals in emerging markets, specifically Sri Lankan stock indices (S&P SL20 and ASPI) by integrating Environmental, Social, and Governance (ESG) sentiment analysis with m

Thomas Barthel

Extending corresponding results for matrix product states [Verstraete and Cirac, PRB 73, 094423 (2006); Schuch et al. PRL 100, 030504 (2008)], it is shown how the approximation error of tree tensor network states (TTNS) can be bounded using Schmidt spectra or R\'{e}nyi entanglement entropies of the target quantum state. Conversely, one obtains bounds on TTNS

Philipp Schröer, Darion Haase, Joost-Pieter Katoen

This paper focuses on effective user diagnostics generated during the deductive verification of probabilistic programs. Our key principle is based on providing slices for (1) error reporting, (2) proof simplification, and (3) preserving successful verification results. By formally defining these different notions on HeyVL, an existing quantitative intermedia

Tao Ye, Hongbin Ren, Chongbing Zhang, Haoran Chen

Given the complexity of underwater environments and the variability of water as a medium, underwater images are inevitably subject to various types of degradation. The degradations present nonlinear coupling rather than simple superposition, which renders the effective processing of such coupled degradations particularly challenging. Most existing methods fo

Yinan Ni, Xiao Yang, Yuqi Tang, Zhimin Qiu

The serverless computing paradigm offers compelling advantages for deploying Large Language Model (LLM) inference services, including elastic scaling and pay-per-use billing. However, serving multiple fine-tuned LLMs via Low-Rank Adaptation (LoRA) in serverless environments faces critical challenges: reactive adapter loading causes significant cold start lat

Ya-Min Quan, Shi-Qing Jia, Xiang-Long Yu, Hai-Qing Lin

Detecting Kitaev interactions in real materials remains challenge, as conventional experimental techniques often have difficulty distinguishing fractionalized excitations from other normal contributions. Terahertz two-dimensional coherent spectroscopy (2DCS) offers a novel approach for probing many-body phenomena, such as exotic excitations in quantum magnet

Raziel Itzhak, Alex Hayat, Ilya Goykhman

Single-photon emitters are essential building blocks for quantum communication and photonic quantum technologies. However, realizing scalable, on-chip SPEs on a CMOS-compatible platform remains a significant challenge. Here, we propose and theoretically demonstrate a scalable approach to exciton confinement in two-dimensional semiconductors via local dielect

Zhe Sun, Kunlun Wu, Chuanjian Fu, Zeming Song

As artificial intelligence (AI) rapidly advances, especially in multimodal large language models (MLLMs), research focus is shifting from single-modality text processing to the more complex domains of multimodal and embodied AI. Embodied intelligence focuses on training agents within realistic simulated environments, leveraging physical interaction and actio

Xu Yang, Wei Yang, Kaibo He, Bo Yang

Wheeled-legged robots combine the efficiency of wheels with the versatility of legs, but face significant energy optimization challenges when navigating diverse environments. In this work, we present a hierarchical control framework that integrates predictive power modeling with residual reinforcement learning to optimize omnidirectional locomotion efficienc

Marko Čechovič, Natália Komorníková, Dominik Macháček, Ondřej Bojar

Speech processing and translation technology have the potential to facilitate meetings of individuals who do not share any common language. To evaluate automatic systems for such a task, a versatile and realistic evaluation corpus is needed. Therefore, we create and present a corpus of cross-lingual dialogues between individuals without a common language who

Zhenlei Ye, Xiaobing Sun, Sicong Cao, Lili Bo

The advances of large language models (LLMs) have paved the way for automated software vulnerability repair approaches, which iteratively refine the patch until it becomes plausible. Nevertheless, existing LLM-based vulnerability repair approaches face notable limitations: 1) they ignore the concern of locations that need to be patched and focus solely on th

Martin Averseng, Nicolas Frantz, Frédéric Hérau, Nicolas Raymond

We consider a non-self adjoint operator of the form $-h^2 \Delta + i(V(x) + \alpha(x)y)$ on the upper half plane $y > 0$ with Dirichlet boundary conditions on $\{y = 0\}$ with $V \geq 0$, $V$ admitting a non-degenerate minimum at $x = 0$ and $\alpha'(0) = 0$. We study its eigenfunctions associated to the smallest eigenvalues in magnitude in the semiclassical

Heekang Song, Wan Choi

In distributed computing systems, reducing the communication load during the data shuffling phase is a critical challenge, as excessive inter-node transmissions are a major performance bottleneck. One promising approach to alleviate this burden is Embedded Index Coding (EIC), which exploits cached data at user nodes to encode transmissions more efficiently.

Diogo Ramos, Bruno Coutinho, Duarte Magano

The systematic discovery of effective drug combinations is a challenging problem in modern pharmacology, driven by the combinatorial growth of potential pairings and dosage configurations. Network medicine, modeling diseases and drugs as interconnected modules of the human protein-protein interactome, has emerged as a new paradigm for understanding disease m

Huizheng Wang, Taiquan Wei, Hongbin Wang, Zichuan Wang

Large language models (LLMs) rely on self-attention for contextual understanding, demanding high-throughput inference and large-scale token parallelism (LTPP). Existing dynamic sparsity accelerators falter under LTPP scenarios due to stage-isolated optimizations. Revisiting the end-to-end sparsity acceleration flow, we identify an overlooked opportunity: cro

Claudia Menini, Blas Torrecillas

This paper continues the research we developed in \cite{MT1} and \cite% {MT2}. The main aim of this paper is to investigate separability conditions for a cowreath $(A\otimes H^{op},H,\psi )$ constructed by using the $8$% -dimensional Clifford algebra $A=Cl(\alpha ,\beta _{1},\beta _{2},\gamma _{1},\gamma _{2},\lambda )$ considered as an $H$-comodulo algebra

Huanrong Liu, Qingan Li, Shan Zhang, Rui Su

Enhancing the kinetic stability of glasses often necessitates deepening thermodynamic stability, which typically compromises ductility due to increased structural rigidity. Decoupling these properties remains a critical challenge for functional applications. Here, we demonstrate that pattern engineering in metallic glasses (MGs) enables unprecedented kinetic

Olena Atlasiuk, Vladimir Mikhailets, Jari Taskinen

We study a wide class of linear inhomogeneous boundary-value problems for $r$th order ODE-systems depending on a parameter $\mu$ in a general metric space $\mathcal M$. The solutions belong to the Sobolev spaces $(W^{n+r}_p)^m$, $n\in\mathbb{N}\cup\{0\}$, $m, r \in \mathbb{N}$, $1\leq p\leq \infty$. The boundary conditions are of a most general form $By=c$,

Yueping Shi, Ping Hu

A (directed) linear forest is a (di)graph whose components are (directed) paths. The linear arboricity $la(F)$ of a (di)graph $F$ is the minimum number of (directed) linear forests required to decompose its edges. Akiyama, Exoo, and Harary (1980) proposed the Linear Arboricity Conjecture that $la(G) \leq \left\lceil \frac{\Delta+1}{2}\right\rceil$ for any gr

Zhaoyang Jia, Jiahao Li, Bin Li, Houqiang Li

Most existing image compression approaches perform transform coding in the pixel space to reduce its spatial redundancy. However, they encounter difficulties in achieving both high-realism and high-fidelity at low bitrate, as the pixel-space distortion may not align with human perception. To address this issue, we introduce a Generative Latent Coding (GLC) a

Giovanni Montani, Luis A. Escamilla, Nakia Carlevaro, Eleonora Di Valentino

We propose a revised cosmological scenario that extends the $\Lambda$ Cold Dark Matter ($\Lambda$CDM) framework by incorporating metric $f(R)$ gravity in the Jordan frame. In this model, the dark energy component arises from a non-minimally coupled scalar field, decomposed into a smooth background (set to unity to recover General Relativity) and a rapidly va

Francesco Della Pietra, Giuseppina di Blasio, Giuseppe Riey

We prove an existence result for Robin boundary value problems modeled on \[ \begin{cases} \Delta u + |\nabla u|^2 + \lambda f(x) = 0 & \text{in } \Omega \\ \frac{\partial u}{\partial \nu} + \beta u = 0 & \text{on } \partial\Omega \end{cases} \] where $\Omega$ is a bounded, sufficiently smooth open set in $\mathbb R^N$, $f(x)$ belongs to the Marcinkiewicz sp

Anar Dosi

In the paper we propose topological homology framework of noncommutative complex analytic geometries of Fr\'echet algebras, and investigate the related functional calculus and spectral mapping properties. It turns out that an ideal analytic geometry of a Fr\'echet algebra A can be described in terms of a \v{C}ech category over A. The functional calculus prob

Anastasiia Zbandut

This paper measures price differences between Hegic option quotes on Arbitrum and a model-based benchmark built on Black--Scholes model with regime-sensitive volatility estimated via a two-regime MS-AR-(GJR)-GARCH model. Using option-level feasible GLS, we find benchmark prices exceed Hegic quotes on average, especially for call options. The price spread ris

David Dolžan

Let $R$ be a finite commutative local principal ring of cardinality $q^n$, where $q = p^r$ for an odd prime $p$ and integer $r$ with $R/J(R) \simeq GF(q)$. We determine the number of elements in the quaternion ring $H(R)$ that can be expressed as a product of at least $2n-1$ nilpotent elements, and show by example that this bound is sharp.

Teqiang Zou, Hongliang Zeng, Yuxuan Nong, Yifan Li

Most Vision-Language-Action (VLA) systems integrate a Vision-Language Model (VLM) for semantic reasoning with an action expert generating continuous action signals, yet both typically run at a single unified frequency. As a result, policy performance is constrained by the low inference speed of large VLMs. This mandatory synchronous execution severely limits

Shuide Wen, Yu Sun, Beier Ku, Zhi Gao

Background: The House-Tree-Person (HTP) drawing test, introduced by John Buck in 1948, remains a widely used projective technique in clinical psychology. However, it has long faced challenges such as heterogeneous scoring standards, reliance on examiners subjective experience, and a lack of a unified quantitative coding system. Results: Quantitative experime

Mohamad Maassarani

For $R_1,R_2,R_3,\dots$ a family of non isomorphic rings (or algebras) having each only 2 idempotents ($1$ and $0$), we classify up to isomorphism the rings (or algebras) obtained by taking products of powers of the different $R_i$. We show that the automorphism groups of such rings (or algebras) split naturally into the product of wreath products $Aut( R_n)

Liang Wang

Multipath TCP is widely adopted to enhance connection quality-of-service by leveraging multiple network pathways on modern devices. However, the evolution of its core congestion control is hindered by the OS kernel, whose monolithic design imposes high development overhead and lacks the resource flexibility required for data-driven methods. Furthermore, inhe

Zhenxin Lei, Yangyang Dong, Bokai Kou, Mengqi Feng

We design a convolutional neural network (CNN) incorporating channel attention and spatial attention mechanisms to predict atmospheric parameters of hot subdwarfs. The experimental dataset comprises spectra at nine distinct signal-to-noise ratio (SNR) levels, with each SNR level containing 11 396 synthetic spectra and 945 observed spectra. The trained deep l

Chaoyi Ruan, Yiliang Wang, Ziji Shi, Jialin Li

Multi-agent systems have extended the capability of agentic AI. Instead of single inference passes, multiple agents perform collective reasoning to derive high quality answers. However, existing multi-agent orchestration relies on static heuristic workflows such as fixed loop limits and barrier synchronization. These ad-hoc approaches waste computational res

David Dolžan

Let $R$ be a finite commutative local principal ring, and let $H(R)$ denote the corresponding quaternion ring. We show that an element of $H(R)$ is a product of idempotents if and only if it can be expressed as a product of two idempotents. Moreover, we obtain an explicit formula for the number of elements of $H(R)$ admitting such a factorization.