October 2025 arXiv papers — page 60

Guoqiang Zhao, Juxiang Sun

For a tensor ring $T_R(M)$, we obtain sufficient and necessary conditions to describe all complete projective resolutions and all Gorenstein projective modules. As a consequence, we provide a method for constructing Gorenstein projective modules over $T_R(M)$ from the ones of $R$. Some applications to trivial ring extensions, Morita context rings and triangu

Yuxin Pan, Zhiguang Cao, Chengyang Gu, Liu Liu

Existing neural methods for multi-task vehicle routing problems (VRPs) typically learn unified solvers to handle multiple constraints simultaneously. However, they often underutilize the compositional structure of VRP variants, each derivable from a common set of basis VRP variants. This critical oversight causes unified solvers to miss out the potential ben

Yinglong Zou, Juan Zhai, Chunrong Fang, An Guo

Deep learning (DL) plays a key role in autonomous driving systems. DL models support perception modules, equipped with tasks such as object detection and sensor fusion. These DL models enable vehicles to process multi-sensor inputs to understand complex surroundings. Deploying DL models in autonomous driving systems faces stringent challenges, including real

Federico Danieli, Pau Rodriguez, Miguel Sarabia, Xavier Suau

Recurrent Neural Networks (RNNs) laid the foundation for sequence modeling, but their intrinsic sequential nature restricts parallel computation, creating a fundamental barrier to scaling. This has led to the dominance of parallelizable architectures like Transformers and, more recently, State Space Models (SSMs). While SSMs achieve efficient parallelization

Shengtian Yang, Yue Feng, Yingshi Liu, Jingrou Zhang

Video Anomaly Detection (VAD) aims to locate unusual activities or behaviors within videos. Recently, offline VAD has garnered substantial research attention, which has been invigorated by the progress in large language models (LLMs) and vision-language models (VLMs), offering the potential for a more nuanced understanding of anomalies. However, online VAD h

Zhuojing Tian, Yushu Chen

Transformers have demonstrated strong potential in offline reinforcement learning (RL) by modeling trajectories as sequences of return-to-go, states, and actions. However, existing approaches such as the Decision Transformer(DT) and its variants suffer from redundant tokenization and quadratic attention complexity, limiting their scalability in real-time or

Yu Yang, Zhilu Zhang, Xiang Zhang, Yihan Zeng

Interactive world models that simulate object dynamics are crucial for robotics, VR, and AR. However, it remains a significant challenge to learn physics-consistent dynamics models from limited real-world video data, especially for deformable objects with spatially-varying physical properties. To overcome the challenge of data scarcity, we propose PhysWorld,

Shengjun Fan, Ying Hu, Shanjian Tang

A Backward Stochastic Differential Equation (BSDE) with a Peano-type generator, is known to have infinitely many solutions when the terminal value is vanishing, and is shown to have possibly multiple solutions even when the terminal value is not vanishing but nonnegative. In this paper, we study the uniqueness of adapted solutions of such a BSDE when the ter

Thanh Cong Ho, Farah Kharrat, Abderrazek Abid, Fakhri Karray

With the widespread adoption of wearable devices in our daily lives, the demand and appeal for remote patient monitoring have significantly increased. Most research in this field has concentrated on collecting sensor data, visualizing it, and analyzing it to detect anomalies in specific diseases such as diabetes, heart disease and depression. However, this d

Julian Schulz, Kirankumar Karkihalli Umesh, Sven Enns, Frank Vewinger

Photonic quantum gases explore the physics of open driven-dissipative quantum systems under ambient conditions and thus open access to thermodynamics and transport phenomena in quantum gases in the weakly interacting regime. Here we introduce the technology of 3D micro-printing to create potential landscapes for photonic quantum gases in dye-filled micro cav

Mohammad Amin Zadenoori, Vincenzo De Martino, Jacek Dabrowski, Xavier Franch

[Context and motivation] Large language models (LLMs) show notable results in natural language processing (NLP) tasks for requirements engineering (RE). However, their use is compromised by high computational cost, data sharing risks, and dependence on external services. In contrast, small language models (SLMs) offer a lightweight, locally deployable altern

Davide Mancino, Hasret Ozan Sevim, Oriol Saguillo Gonzalez

This student paper introduces a novel methodology for the detection and analysis of multihop cross-chain arbitrage opportunities, wherein multihop denotes arbitrage sequences involving more than two transactional steps across distinct blockchain networks, executed using sequence-dependent strategies. Utilizing a comprehensive dataset comprising over 2.4 bill

Nathan Corecco, Batuhan Yardim, Vinzenz Thoma, Zebang Shen

Designing incentives for a multi-agent system to induce a desirable Nash equilibrium is both a crucial and challenging problem appearing in many decision-making domains, especially for a large number of agents $N$. Under the exchangeability assumption, we formalize this incentive design (ID) problem as a parameterized mean-field game (PMFG), aiming to reduce

Lisa Weijler, Sebastian Koch, Fabio Poiesi, Timo Ropinski

Modeling the inherent hierarchical structure of 3D objects and 3D scenes is highly desirable, as it enables a more holistic understanding of environments for autonomous agents. Accomplishing this with implicit representations, such as Neural Radiance Fields, remains an unexplored challenge. Existing methods that explicitly model hierarchical structures often

Giovanni Trappolini, Florin Cuconasu, Simone Filice, Yoelle Maarek

Traditional Information Retrieval (IR) metrics, such as nDCG, MAP, and MRR, assume that human users sequentially examine documents with diminishing attention to lower ranks. This assumption breaks down in Retrieval Augmented Generation (RAG) systems, where search results are consumed by Large Language Models (LLMs), which, unlike humans, process all retrieve

Giulio Barni, Simone Blasi, Eric Madge, Miguel Vanvlasselaer

Cosmological phase transitions are a frequent phenomenon in particle physics models beyond the Standard Model, and the corresponding gravitational wave signal offers a key probe of new physics in the early Universe. Depending on the underlying microphysics, the transition can exhibit either direct or inverse hydrodynamics, leading to a different phenomenolog

Satheeshkumar Veeramani, Zhengxue Zhou, Francisco Munguia-Galeano, Hatem Fakhruldeen

Mobile robotic chemists are a fast growing trend in the field of chemistry and materials research. However, so far these mobile robots lack workflow awareness skills. This poses the risk that even a small anomaly, such as an improperly capped sample vial could disrupt the entire workflow. This wastes time, and resources, and could pose risks to human researc

Xi Zhang, Xiaolin Wu

Table look-up realization of image restoration CNNs has the potential of achieving competitive image quality while being much faster and resource frugal than the straightforward CNN implementation. The main technical challenge facing the LUT-based CNN algorithm designers is to manage the table size without overly restricting the receptive field. The prevaili

Ankur Sinha, Shobhit Arora, Dhaval Pujara

This study presents AutoOpt-11k, a unique image dataset of over 11,000 handwritten and printed mathematical optimization models corresponding to single-objective, multi-objective, multi-level, and stochastic optimization problems exhibiting various types of complexities such as non-linearity, non-convexity, non-differentiability, discontinuity, and high-dime

S. Alekhin, M. V. Garzelli, S. Moch, O. Zenaiev

We determine the strong coupling from high-energy data for the Drell-Yan (DY) process and top-quark hadro-production collected at the Large Hadron Collider and the Tevatron combined with the world data on deep-inelastic scattering (DIS) and fixed-target DY data. The theory description uses results at next-to-next-to-leading order in perturbative QCD in the $

Eisuke Omori, Kairi Takimoto, Atsuko Nagata, Ashif Fathnan

In this study, we introduce a design concept that leverages pulse width variation to enable a reconfigurable intelligent surface (RIS) and to autonomously switch reflection properties between two angles without any active control system. Our RIS alters its beam pattern from a singular specular reflection to another unique singular anomalous reflection when t

Yaoyao Xu, Di Wang, Zihan Zhou, Tianshu Yu

Understanding the dynamic behavior of proteins is critical to elucidating their functional mechanisms, yet generating realistic, temporally coherent trajectories of protein ensembles remains a significant challenge. In this work, we introduce a novel hierarchical autoregressive framework for modeling protein dynamics that leverages the intrinsic multi-scale

Anton Engelmann

The $C_2$-spectrum of Atiyah's Real $K$-theory is denoted by $\mathbf{KR}$ and the $C_2$-spectrum of topological modular forms of level structure $Γ_1(3)$ by $\mathbf{TMF}_1(3)$. In this short note we compute the $C_2$-equivariant stable Adams operations on the $RO(C_2)$-graded homotopy groups of $\mathbf{KR}$ and $\mathbf{TMF}_1(3)$.

Honghua Chen, Yushi Lan, Yongwei Chen, Xingang Pan

We propose ArtiLatent, a generative framework that synthesizes human-made 3D objects with fine-grained geometry, accurate articulation, and realistic appearance. Our approach jointly models part geometry and articulation dynamics by embedding sparse voxel representations and associated articulation properties, including joint type, axis, origin, range, and p

Vincent Ouazan-Reboul, Ramin Golestanian, Jaime Agudo-Canalejo

Living systems contain intricate biochemical networks whose structure is closely related to their function and allows them to exhibit robust behavior in the presence of external stimuli. Such networks typically involve catalytic enzymes, which can have non-trivial transport properties, in particular chemotaxis-like directed motion along gradients of substrat

Kevin Dijkstra, Carlotta Giannelli, Deepesh Toshniwal

This paper introduces a novel adaptive refinement strategy for Isogeometric Analysis (IGA) using Truncated Hierarchical B-splines (THB-splines). The proposed strategy enhances locally-refined meshes for specific applications, simplifying implementation. We focus on two key applications: an $L^2$-stable local projector for THB-splines via B\'ezier projection

Claudia Piccinini, Aleksander Rodek, Abdulmalik A. Madigawa, Ailton Garcia

Coherent control of quantum emitters is essential for scalable quantum photonic technologies. The recently proposed swing-up of quantum emitter (SUPER) scheme allows efficient and coherent preparation of single photons via off-resonant, red-detuned laser pulses, simplifying laser suppression and enhancing photon collection. We present a systematic study of S

Hao Liang, Shuqing Shi, Yudi Zhang, Biwei Huang

Large-scale networked systems, such as traffic, power, and wireless grids, challenge reinforcement-learning agents with both scale and environment shifts. To address these challenges, we propose GSAC (Generalizable and Scalable Actor-Critic), a framework that couples causal representation learning with meta actor-critic learning to achieve both scalability a

Pei-Zhi Zhuang, Ming-Yue Yang, Fei Ren, Hong-Ya Yue

The inverse Stefan problem, as a typical phase-change problem with moving boundaries, finds extensive applications in science and engineering. Recent years have seen the applications of physics-informed neural networks (PINNs) to solving Stefan problems, yet they still exhibit shortcomings in hyperparameter dependency, training efficiency, and prediction acc

Maneeha Rani, Bhupesh Kumar Mishra, Dhavalkumar Thakker

LLMs have demonstrated highly effective learning, human-like response generation,and decision-making capabilities in high-risk sectors. However, these models remain black boxes because they struggle to ensure transparency in responses. The literature has explored numerous approaches to address transparency challenges in LLMs, including Neurosymbolic AI (NeSy

Abderrazek Abid, Thanh-Cong Ho, Fakhri Karray

As generative AI continues to evolve, Vision Language Models (VLMs) have emerged as promising tools in various healthcare applications. One area that remains relatively underexplored is their use in human activity recognition (HAR) for remote health monitoring. VLMs offer notable strengths, including greater flexibility and the ability to overcome some of th

Linh Anh Nguyen

The problem of minimizing fuzzy interpretations in fuzzy description logics (FDLs) is important both theoretically and practically. For instance, fuzzy or weighted social networks can be modeled as fuzzy interpretations, where individuals represent actors and roles capture interactions. Minimizing such interpretations yields more compact representations, whi

S. Shradha, R. Rosati, H. Lamsaadi, J. Picker

Semiconducting transition metal dichalcogenides (TMDs), such as MoSe$_2$ and WSe$_2$, exhibit unique optical and electronic properties. Vertical stacking of layers of one or more TMDs, to create heterostructures, has expanded the fields of moir\'e physics and twistronics. Bottom-up fabrication techniques, such as chemical vapor deposition, have advanced the

Haruya Shimizu, Masahiro Yukawa

We propose a novel multi-layer neural network architecture that gives a promising neural network empowered optimization approach to the image restoration problem. The proposed architecture is motivated by the recent study of monotone Lipschitz-gradient (MoL-Grad) denoiser (Yukawa and Yamada, 2025) which establishes an ``explainable'' plug-and-play (PnP) fram

Isabella Masina, Mariano Quiros

In the Standard Model, partial unification of the non-Abelian running gauge couplings is achieved at the scale $\mu^{SM}_{32} \approx 2.8 \times 10^{16}$ GeV. Elaborating on this fact, we discuss a simple general parametrization for the new physics corrections leading to full unification at some scale $M_X$. We show that for any new physics model such that t

Sankalpa Timilsina, Susmit Shannigrahi

Distributed cloud environments hosting data-intensive applications often experience slowdowns due to network congestion, asymmetric bandwidth, and inter-node data shuffling. These factors are typically not captured by traditional host-level metrics like CPU or memory. Scheduling without accounting for these conditions can lead to poor placement decisions, lo

Lukas Bierling, Davide Pasero, Jan-Henrik Bertrand, Kiki Van Gerwen

We introduce DreamerV3-XP, an extension of DreamerV3 that improves exploration and learning efficiency. This includes (i) a prioritized replay buffer, scoring trajectories by return, reconstruction loss, and value error and (ii) an intrinsic reward based on disagreement over predicted environment rewards from an ensemble of world models. DreamerV3-XP is eval

Guanxiong Luo, Shoujin Huang, Yanlong Yang

We propose self-diffusion, a novel framework for solving inverse problems without relying on pretrained generative models. Traditional diffusion-based approaches require training a model on a clean dataset to learn to reverse the forward noising process. This model is then used to sample clean solutions -- corresponding to posterior sampling from a Bayesian

S. A. Pirogov, A. N. Rybko, D. D. Pervouchine, E. N. Petrova

The net of N ``physical'' neurons is considered as a dynamical system. These neurons form a complete graph. The state of any neuron is its electric potential. The potential linearly increases until reaches its maximal value. Then it falls to zero and the neuron sends spikes to all other neurons. Having got a spike any neuron changes its state by some given f

Jietian Liu, Peter Seiler

This paper proposes a robust regret control framework in which the performance baseline adapts to the realization of system uncertainty. The plant is modeled as a discrete-time, uncertain linear time-invariant system with real-parametric uncertainty. The performance baseline is the optimal non-causal controller constructed with full knowledge of the disturba

Hoang Ly, Emina Soljanin, Michael Schleppy

Maximum-likelihood (ML) decoding for arbitrary block codes remains fundamentally hard, with worst-case time complexity-measured by the total number of multiplications-being no better than straightforward exhaustive search, which requires $q^{k} n$ operations for an $[n,k]_q$ code. This paper introduces a simple, code-agnostic framework that reduces the worst

Seyedmoein Mohsenimofidi, Matthias Galster, Christoph Treude, Sebastian Baltes

GenAI-based coding assistants have disrupted software development. The next generation of these tools is agent-based, operating with more autonomy and potentially without human oversight. Like human developers, AI agents require contextual information to develop solutions that are in line with the standards, policies, and workflows of the software projects t

Whie Jung, Semin Kim, Junee Kim, Seunghoon Hong

Human intelligence effortlessly interprets visual scenes along a rich spectrum of semantic dimensions. However, existing approaches to language-grounded visual concept learning are limited to a few predefined primitive axes, such as color and shape, and are typically explored in synthetic datasets. In this work, we propose a scalable framework that adaptivel

Ryuho Miyamoto, Hiroki Wakatsuchi

Waveform-selective metasurfaces offer unprecedented control over electromagnetic waves on the basis of pulse width. However, existing circuit models fail to capture the power-dependent behaviors of these metasurfaces, thereby limiting their use in practical applications. Here, for the first time, we present analytical equivalent circuit models that accuratel

Alessandro Martini, Andrea Miani, Marco Drago, Claudia Lazzaro

The most general searches for gravitational wave transients (GWTs) rely on data analysis methods that do not assume prior knowledge of the signal waveform, direction, or arrival time on Earth. These searches provide data-driven signal reconstructions that are crucial both for testing available emission models and for discovering yet-to-be-uncovered sources.

T. M. Aliev, S. Bilmis, M. Savci

We study the semileptonic decay of its SU(3) partner, the $\Lambda_c \to \Lambda \ell^+ \nu_\ell$ ($\ell = e, \mu$) transition, within the framework of light-cone QCD sum rules (LCSR) by using the distribution amplitudes of heavy $\Lambda_c$ baryon. The numerical analysis is performed using two different sets of $\Lambda_c$ baryon light-cone distribution amp

Camila Kolling, Vy Ai Vo, Mariya Toneva

Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Testing hypotheses on how representational changes occur in biological systems is challenging, but large language models (LLMs) offer a scalable alternative. Building on LLMs' in-context learning, we adapt a c

Kyungjun Min, Kyumin Cho, Junhwan Jang, Seokhyeong Kang

Large Language Models (LLMs) are used for Register-Transfer Level (RTL) code generation, but they face two main challenges: functional correctness and Power, Performance, and Area (PPA) optimization. Iterative, feedback-based methods partially address these, but they are limited to local search, hindering the discovery of a global optimum. This paper introdu

Yue Feng, Jinwei Hu, Qijia Lu, Jiawei Niu

We propose the Multi-modal Untrimmed Video Retrieval task, along with a new benchmark (MUVR) to advance video retrieval for long-video platforms. MUVR aims to retrieve untrimmed videos containing relevant segments using multi-modal queries. It has the following features: 1) Practical retrieval paradigm: MUVR supports video-centric multi-modal queries, expres

Aidan Dakhama, W. B. Langdon, Hector D. Menendez, Karine Even-Mendoza

We present GreenMalloc, a multi objective search-based framework for automatically configuring memory allocators. Our approach uses NSGA II and rand_malloc as a lightweight proxy benchmarking tool. We efficiently explore allocator parameters from execution traces and transfer the best configurations to gem5, a large system simulator, in a case study on two a

Xueguang Xie, Shu Yan, Shiwen Jia, Siyu Yang

While data-driven methods offer significant promise for modeling complex materials, they often face challenges in generalizing across diverse physical scenarios and maintaining physical consistency. To address these limitations, we propose a generalizable framework called Physics-Embedded Conditional Neural Constitutive Laws for Elastoplastic Materials, whic

Jieyuan Zhang, Xiaolong Zhou, Shuai Wang, Wenjie Wei

Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computational models have greatly advanced, SNNs struggle to achieve competitive performance in visual long-sequence modeling tasks. In artificial neural networks, the effective receptive fi

Whie Jung, Dong Hoon Lee, Seunghoon Hong

Recent disentangled representation learning (DRL) methods heavily rely on factor specific strategies-either learning objectives for attributes or model architectures for objects-to embed inductive biases. Such divergent approaches result in significant overhead when novel factors of variation do not align with prior assumptions, such as statistical independe

Mojtaba Eshghie, Gabriele Morello, Matteo Lauretano, Alexandre Bartel

Smart contract vulnerabilities cost billions of dollars annually, yet existing automated analysis tools fail to generate deployable defenses. We present FLAMES, a novel automated approach that synthesizes executable runtime guards as Solidity "require" statements to harden smart contracts against exploits. Unlike prior work that relies on vulnerability label

Victor P. Ruban

A simplified mathematical model is suggested to describe the dynamics of a quasi-monochromatic optical wave in the bulk of an effectively isotropic metamaterial with averaged dielectrical permittivity near zero (ENZ medium), in the presence of a weak spatial nonuniformity, Kerr nonlinearity as well as linear gain due to external pumping. The model is a vecto

Ravindra Aribowo Tarunokusumo, Rafael Fernandes Cunha

Test-time scaling methods have seen a rapid increase in popularity for its computational efficiency and parameter-independent training to improve reasoning performance on Large Language Models. One such method is called budget forcing, a decoding intervention strategy which allocates extra compute budget for thinking and elicits the inherent self-correcting

Emmanuelle Augeraud-Véron, Daria Ghilli, Fausto Gozzi, Marta Leocata

The aim of this paper is to formulate and study a stochastic model for the management of environmental assets in a geographical context where in each place the local authorities take their policy decisions maximizing their own welfare, hence not cooperating each other. A key feature of our model is that the welfare depends not only on the local environmental

Zhiying Jiang, Ruhao Yan, Zengxi Zhang, Bowei Zhang

Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. Firs

Hermine Landt, Benjamin D. Boizelle, Michael S. Brotherton, Laura Ferrarese

The AGN Space Telescope and Optical Reverberation Mapping 2 (STORM 2) campaign targeted Mrk 817 with intensive multi-wavelength monitoring and found its soft X-ray emission to be strongly absorbed. We present results from 157 near-IR spectra with an average cadence of a few days. Whereas the hot dust reverberation signal as tracked by the continuum flux does

Marco Caliari, Fabio Cassini

In this manuscript, we propose matrix- and tensor-oriented methods for the numerical solution of the multidimensional evolutionary space-fractional complex Ginzburg--Landau equation. After a suitable spatial semidiscretization, the resulting system of ordinary differential equations is time integrated with stiff-resistant schemes. The needed actions of speci

Lucas Federspiel, Jorge Arrieta, Marco Polin, Francoise Argoul

The unicellular microalga Chlamydomonas reinhardtii (CR) is well known for its bi-flagellated swimming in response to light stimuli. This work aims to study the resynchronization of CR flagella after a high light intensity stimulus, known as photoshock. The synchronization is estimated thanks to a quantity defined as the Phase Synchronization Index (PSI). Th

Alexander Pluska, Sagar Malhotra

Local convergence has emerged as a fundamental tool for analyzing sparse random graph models. We introduce a new notion of local convergence, color convergence, based on the Weisfeiler-Leman algorithm. Color convergence fully characterizes the class of random graphs that are well-behaved in the limit for message-passing graph neural networks. Building on thi

Datao Tang, Hao Wang, Yudeng Xin, Hui Qiao

Remote sensing vision tasks require extensive labeled data across multiple, interconnected domains. However, current generative data augmentation frameworks are task-isolated, i.e., each vision task requires training an independent generative model, and ignores the modeling of geographical information and spatial constraints. To address these issues, we prop

Laura Selicato, Flavia Esposito, Andersen Ang

Nonconvex and nonsmooth bi-level optimization poses critical theoretical challenges, while arising in several applications. In this work, we develop a method for nonconvex, nonsmooth bi-level optimization and introduce Binno, a first-order method that builds on proximal-gradient updates within the the proximal alternate minimization framework with descent co

Stefan Kraft, Andreas Theissler, Vera Wienhausen-Wilke, Gjergji Kasneci

Artificial intelligence (AI) systems increasingly match or surpass human experts in biomedical signal interpretation. However, their effective integration into clinical practice requires more than high predictive accuracy. Clinicians must discern \textit{when} and \textit{why} to trust algorithmic recommendations. This work presents an application-grounded u

Arshdeep Singh, Vinayak Abrol, Mark D. Plumbley

Conventional Convolutional Neural Networks (CNNs) in the real domain have been widely used for audio classification. However, their convolution operations process multi-channel inputs independently, limiting the ability to capture correlations among channels. This can lead to suboptimal feature learning, particularly for complex audio patterns such as multi-

Jonas Deré, Joren Matthys

If $g\in G$ is a non-trivial element in a residually finite group, then there exists by definition a finite group $Q$ and a homomorphism $\varphi: G \to Q$ such that $\varphi(g) \neq e$. The residual finiteness growth $\text{RF}_G$ of a finitely generated residually finite group $G$ estimates the size of $Q$ in terms of the word norm $\|g\|$ of the element $

Xiaotian Fan, Xingyu Zhou, Le Liang, Shi Jin

Deep generative models offer a powerful alternative to conventional channel estimation by learning the complex prior distribution of wireless channels. Capitalizing on this potential, this paper proposes a novel channel estimation algorithm based on latent diffusion models (LDMs), termed posterior sampling with latent diffusion for channel estimation (PSLD-C

J. Maíz Apellániz

I present some of the highlights of the Gaia mission on massive stars and discuss what the fourth data release (DR4) will bring in late 2026. In the first part of the contribution I describe the different types of Gaia products available now and for DR4 and their caveats. In the second part I present the most significant results on massive stars regarding pa

Michał Zegrodnik, Waseem Akbar, Andrzej Biborski, Louk Rademaker

The recent discovery of unconventional superconductivity has pointed to twisted WSe$_2$ bilayer as a versatile platform for studying the correlated and topological phases of matter. Here we analyze the effect of the displacement field and electron interactions on the formation of a topological paired state in twisted WSe$_2$. Our approach is based on the eff

Erik Lindell, Arthur Soulié

We prove twisted homological stability for handlebody mapping class groups. Using the categorical framework developed by Randal-Williams and Wahl, we establish that the homology of the handlebody groups stabilises with respect to both genus and the number of marked boundary discs, for all coefficient systems of finite degree. Our first main theorem refines a

Carlos Arranz-Simón, Alexander Ostermann

Exponential Runge-Kutta methods are a well-established tool for the numerical integration of parabolic evolution equations. However, these schemes are typically developed under the assumption of homogeneous boundary conditions. In this paper, we extend classical convergence results to the case of non-homogeneous boundary conditions. Since non-homogeneous bou

Bence Borda, Jean-Claude Cuenin

We prove general upper estimates for the distance between two Borel probability measures in Wasserstein metric in terms of the Fourier transforms of the measures. We work in compact manifolds including the torus, the Euclidean unit sphere, compact Lie groups and compact homogeneous spaces, and treat the Wasserstein metric $W_p$ in the full range $1 \le p \le

Dong Bok Lee, Aoxuan Silvia Zhang, Byungjoo Kim, Junhyeon Park

In this paper, we address the problem of \emph{cost-sensitive} hyperparameter optimization (HPO) built upon freeze-thaw Bayesian optimization (BO). Specifically, we assume a scenario where users want to early-stop the HPO process when the expected performance improvement is not satisfactory with respect to the additional computational cost. Motivated by this

Zhenjie Liu, Hironao Miyatake, Joop Schaye, Matthieu Schaller

Assembly bias, which is the variation in halo clustering at fixed mass driven by formation history, has long been predicted by numerical simulations but remains difficult to confirm observationally. Previous studies have reported evidence for halo assembly bias by dividing samples according to galaxy stellar mass using various methods. In this work, we prese

Lukas Möller, Simon Stellmer

We report on laser cooling and magneto-optical trapping of atomic zinc. The atoms are cooled using the 213.9\,nm $^1$S$_0$ $\rightarrow$ $^1$P$_1$ transition, making this the shortest wavelength employed for magneto-optical trapping thus far. We demonstrate trapping of all stable isotopes of zinc, including the fermionic isotope $^{67}$Zn, which features a v

Simon Zhamkochyan, Sergey Abrahamyan, Amur Margaryan, Hayk Elbakyan

In this paper, we present the design and preliminary performance evaluation of a new heavy-ion detector for direct measurements of heavy {\Lambda} hypernuclei lifetime. The detector employs the previously developed 10 picosecond resolution Radio Frequency (RF) Timer, which converts the temporal information of incident particles into spatial coordinates of se

Asger Tornquist, David Schrittesser

Let $x$ denote a Laver real over $L$. We prove that in $L[x]$ there is a $\Pi^1_1$ infinite mad family. Since $\Pi^1_1$ and $\Sigma^1_2$ sets are Laver measurable in $L[x]$, this shows that there are examples of well-behaved classical pointclasses $\Gamma$, namely $\Gamma=\Pi^1_1$ and $\Gamma=\Sigma^1_2$, where $\Gamma$-uniformization and ``all sets in $\Gam

Sankalpa Timilsina, Susmit Shannigrahi

Scientific communities are increasingly using geographically distributed computing platforms. The current methods of compute placement predominantly use logically centralized controllers such as Kubernetes (K8s) to match tasks to available resources. However, this centralized approach is unsuitable in multi-organizational collaborations. Furthermore, workflo

Javier López-de-la-Cruz, Susana Merchán, Felipe Rivero, Javier Rodrigo

In this paper we investigate the asymptotic behavior of some SIR models incorporating demography, bounded random transmission coefficient and a time-dependent vaccination strategy targeting the susceptible population. In this setting, we establish the existence and uniqueness of non-negative global solution of the models and derive conditions under which eit

Zain Ul Abideen Tariq, Mahmood Al-Zubaidi, Uzair Shah, Marco Agus

HIKMA Semi-Autonomous Conference is the first experiment in reimagining scholarly communication through an end-to-end integration of artificial intelligence into the academic publishing and presentation pipeline. This paper presents the design, implementation, and evaluation of the HIKMA framework, which includes AI dataset curation, AI-based manuscript gene

Vivian S. Medeiros, Giovanni B. Dessy, Thiago Boaventura, Marcelo Becker

Collapsing terrains, often present in search and rescue missions or planetary exploration, pose significant challenges for quadruped robots. This paper introduces a robust locomotion framework for safe navigation over unstable surfaces by integrating terrain probing, load-bearing analysis, motion planning, and control strategies. Unlike traditional methods t

Rohit Goswami

Estimating reaction rates and chemical stability is fundamental, yet efficient methods for large-scale simulations remain out of reach despite advances in modeling and exascale computing. Direct simulation is limited by short timescales; machine-learned potentials require large data sets and struggle with transition state regions essential for reaction rates

Junda Wang, Minghui Hu, Ning Li, Abdulaziz Al-Ali

Class incremental learning (CIL) requires an agent to learn distinct tasks consecutively with knowledge retention against forgetting. Problems impeding the practical applications of CIL methods are twofold: (1) non-i.i.d batch streams and no boundary prompts to update, known as the harsher online task-free CIL (OTCIL) scenario; (2) CIL methods suffer from me

Xi Zhang, Hanwei Zhu, Yan Zhong, Jiamang Wang

In this work, we propose a novel framework to enable diffusion models to adapt their generation quality based on real-time network bandwidth constraints. Traditional diffusion models produce high-fidelity images by performing a fixed number of denoising steps, regardless of downstream transmission limitations. However, in practical cloud-to-device scenarios,

Weixu Su, Shenxing Zhang

Associated to a holomorphic quadratic differential is a unit ball of the measured lamination space. The Thurston volume of the unit ball defines a function on the moduli space. We show that the volume function is not proper and characterize when it tends to infinity. We prove that the volume function is $p$-integrable for any $0<p<1$.

Zihao Fu, Ryan Brown, Shun Shao, Kai Rawal

Text-to-image diffusion models, such as Stable Diffusion, have demonstrated remarkable capabilities in generating high-quality and diverse images from natural language prompts. However, recent studies reveal that these models often replicate and amplify societal biases, particularly along demographic attributes like gender and race. In this paper, we introdu

Alejandro Lopez-Montes, Robert Seifert, Astrid Delker, Guido Boening

In this work we explored the use of patient specific reinforced learning to generate 3D activity maps from two 2D planar images (anterior and posterior). The solution of this problem remains unachievable using conventional methodologies and is of particular interest for dosimetry in nuclear medicine where approaches for post-therapy distribution of radiophar

Jaesik Yoon, Hyeonseo Cho, Sungjin Ahn

Monte Carlo Tree Diffusion (MCTD) integrates diffusion models with structured tree search to enable effective trajectory exploration through stepwise reasoning. However, MCTD remains fundamentally limited by training trajectory lengths. While periodic replanning allows plan concatenation for longer plan generation, the planning process remains locally confin

Jenny Kunz

Many Swedish benchmarks are translations of US-centric benchmarks and are therefore not suitable for testing knowledge that is particularly relevant, or even specific, to Sweden. We therefore introduce a manually written question-answering benchmark specifically targeted at Sweden-related personalities and events, many of which receive very limited coverage

Shunda Yin, Qiuyan Zhou, Yuxiang Xi, Weiyin Deng

Non-Hermitian physics characterized by complex band spectra has established a new paradigm in condensed matter systems and metamaterials. Recently, non-Hermitian gain and nonreciprocity are deliberately introduced to valley manipulation, leading to various phenomena beyond the Hermitian scenarios, such as the amplified topological whispering gallery modes as

Valentin Boussot, Cédric Hémon, Jean-Claude Nunes, Jean-Louis Dillenseger

We participated in the SynthRAD2025 challenge (Tasks 1 and 2) with a unified pipeline for synthetic CT (sCT) generation from MRI and CBCT, implemented using the KonfAI framework. Our model is a 2.5D U-Net++ with a ResNet-34 encoder, trained jointly across anatomical regions and fine-tuned per region. The loss function combined pixel-wise L1 loss with IMPACT-

Daniel Schleich, Jan Quenzel, Sven Behnke

In recent years, consumer-grade UAVs have been widely adopted by first responders. In general, they are operated manually, which requires trained pilots, especially in unknown GNSS-denied environments and in the vicinity of structures. Autonomous flight can facilitate the application of UAVs and reduce operator strain. However, autonomous systems usually req

Anupam Pani, Yanchao Yang

Eye gaze offers valuable cues about attention, short-term intent, and future actions, making it a powerful signal for modeling egocentric behavior. In this work, we propose a gaze-regularized framework that enhances VLMs for two key egocentric understanding tasks: fine-grained future event prediction and current activity understanding. Unlike prior approache

Aditya Mitra, Sibi Chakkaravarthy Sethuraman

Authentication systems have evolved a lot since the 1960s when Fernando Corbato first proposed the password-based authentication. In 2013, the FIDO Alliance proposed using secure hardware for authentication, thus marking a milestone in the passwordless authentication era [1]. Passwordless authentication with a possession-based factor often relied on hardware

Mariam Arustashvili, Krisztian Balog

The use of natural language (NL) user profiles in recommender systems offers greater transparency and user control compared to traditional representations. However, there is scarcity of large-scale, publicly available test collections for evaluating NL profile-based recommendation. To address this gap, we introduce SciNUP, a novel synthetic dataset for schol

Xinyu Zhou, Tongxin Pan, Lingyi Hong, Pinxue Guo

UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantly emphasize speed and lack exploration in semantic awareness, which hinders the search region from extracting accurate localization information from the template. The limitation re

Wen-Long Sang, Feng Feng, Yu Jia, Zhewen Mo

We report the calculation of the process $e^+ e^- \to J/\psi J/\psi$ up to next-to-next-to-leading order (NNLO) at a center-of-mass (CM) energy of $\sqrt{s}=10.58$ GeV. We employ an improved NRQCD factorization approach, decomposing the amplitude into photon-fragmentation and non-fragmentation components. The fragmentation contribution is determined using th

Mojtaba Mozhganfar, Pooya Jamshidi, Seyyed Ali Aghamiri, Mohsen Ghasemi

Live streaming plays a major role in today's digital platforms, supporting entertainment, education, social media, etc. However, research in this field is limited by the lack of large, publicly available datasets that capture real-time viewer behavior at scale. To address this gap, we introduce YTLive, a public dataset focused on YouTube Live. Collected thro

Dmitry Kudryavtsev

We investigate whether semigroups with a given property which are also locally embeddable into finite semigroups can be locally embedded into finite semigroups with the same property, obtaining a positive answer for completely simple and Clifford semigroups (similarly to group and inverse semigroup cases studied previously) and a negative answer for $\J$-tri