October 2025 arXiv papers — page 205

Özgür Esentepe

Given a Cohen-Macaulay local ring, the cohomology annihilator ideal and the annihilator of the stable category of maximal Cohen-Macaulay modules are two ideals closely related both with each other and the singularities of the ring. Kimura recently showed that the two ideals agree up to radicals. In this article, we give a sufficient condition for the two ide

S. Peng, L. Hu, W. Zhang, B. Jie

Graph embedding has been widely applied in areas such as network analysis, social network mining, recommendation systems, and bioinformatics. However, current graph construction methods often require the prior definition of neighborhood size, limiting the effective revelation of potential structural correlations in the data. Additionally, graph embedding met

Rui Wang, Liping Chen, Kong Aik Lee, Zhengpeng Zha

Given the speech generation framework that represents the speaker attribute with an embedding vector, asynchronous voice anonymization can be achieved by modifying the speaker embedding derived from the original speech. However, the inconsistency between machine and human perceptions of the speaker attribute within the speaker embedding remains unexplored, l

Hedi Zisling, Ilan Naiman, Nimrod Berman, Supasorn Suwajanakorn

Unsupervised representation learning, particularly sequential disentanglement, aims to separate static and dynamic factors of variation in data without relying on labels. This remains a challenging problem, as existing approaches based on variational autoencoders and generative adversarial networks often rely on multiple loss terms, complicating the optimiza

Frederik Krabbe

Bougerol (1993) and Straumann and Mikosch (2006) gave conditions under which there exists a unique stationary and ergodic solution to the stochastic difference equation $Y_t \overset{a.s.}{=} \Phi_t (Y_{t-1}), t \in \mathbb{Z}$ where $(\Phi_t)_{t \in \mathbb{Z}}$ is a sequence of stationary and ergodic random Lipschitz continuous functions from $(Y,|| \cdot

Shihao Zhu, Bohan Cao, Ziheng Ouyang, Zhen Li

Recent diffusion model research focuses on generating identity-consistent images from a reference photo, but they struggle to accurately control age while preserving identity, and fine-tuning such models often requires costly paired images across ages. In this paper, we propose AgeBooth, a novel age-specific finetuning approach that can effectively enhance t

Andrea Poggio

Let $\Omega \subseteq \mathbb{R}^d$ be open, $A$ a complex uniformly strictly accretive $d\times d$ matrix-valued function on $\Omega$ with $L^\infty$ coefficients, and $V$ a locally integrable function on $\Omega$ whose negative part is subcritical. We consider the operator $\mathscr{L} = -\mathrm{div}(A\nabla) + V$ with mixed boundary conditions on $\Omega

Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li

Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailore

Ruizheng Jiang, Haining Li, Gang Zhao, Qianfan Xing

The abundance patterns of extremely metal-poor stars preserve a fossil record of the Universe's earliest chemical enrichment by the supernova explosions from the evolution of first generation of stars, also referred to as Population III (or Pop III). By applying Bayesian inference to the analysis of abundance patterns of these ancient stars, this study prese

Ailiya Borjigin, Cong He

Time-bound stablecoins are DeFi assets that temporarily tokenize traditional securities during market off-hours, enabling continuous cross-market liquidity. We introduce the Liquidity-of-Time Premium (TLP): the extra return or cost of providing liquidity when the primary market is closed. We build a no-arbitrage pricing model that yields a band for fair valu

Fabrizio Dimino, Abhinav Arun, Bhaskarjit Sarmah, Stefano Pasquali

Large language models (LLMs) are increasingly being used to extract structured knowledge from unstructured financial text. Although prior studies have explored various extraction methods, there is no universal benchmark or unified evaluation framework for the construction of financial knowledge graphs (KG). We introduce FinReflectKG - EvalBench, a benchmark

Dawei Jiao, Mahdi Bayanifar, Alexei Ashikhmin, Olav Tirkkonen

We study the use of triorthogonal codes for universal fault-tolerant quantum computation and propose two methods to circumvent the Eastin-Knill theorem, which prohibits any single quantum error-correcting code from supporting both universality and a transversal gate set. We show that our methods reduce the resource overhead compared with existing fault-toler

David Boetius, Abdelrahman Abdelnaby, Ashok Kumar, Stefan Leue

Learning stable dynamical systems from data is crucial for safe and reliable robot motion planning and control. However, extending stability guarantees to trajectories defined on Riemannian manifolds poses significant challenges due to the manifold's geometric constraints. To address this, we propose a general framework for learning stable dynamical systems

Markus Walker, Daniel Frisch, Uwe D. Hanebeck

Cross-entropy method model predictive control (CEM--MPC) is a powerful gradient-free technique for nonlinear optimal control, but its performance is often limited by the reliance on random sampling. This conventional approach can lead to inefficient exploration of the solution space and non-smooth control inputs, requiring a large number of samples to achiev

Eva Martín del Pico, Josep Lluís Gelpí, Salvador Capella-Gutiérrez

In the ever-changing realm of research software development, it is crucial for the scientific community to grasp current trends to identify gaps that can potentially hinder scientific progress. The adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) principles can serve as a proxy to understand those trends and provide a mechanism to propos

Saugata Ghosh, Dambaru Bhatta, S. M. Mallikarjunaiah

This paper presents a comprehensive computational framework for investigating thermo-elastic fracture in transversely isotropic materials, where classical linear elasticity fails to predict physically realistic behavior near stress concentrations. We address the challenge of unphysical strain singularities at crack tips by employing a strain-limiting theory

Yihan Du, Seo Taek Kong, R. Srikant

The widespread application of large language models (LLMs) raises increasing demands on ensuring safety or imposing constraints, such as reducing harmful content and adhering to predefined rules. While there have been several works studying LLM safety alignment, these works either need to train three models and incur high memory costs, or require prior knowl

Fabrizio Dimino, Krati Saxena, Bhaskarjit Sarmah, Stefano Pasquali

Large Language Models are increasingly adopted in financial applications to support investment workflows. However, prior studies have seldom examined how these models reflect biases related to firm size, sector, or financial characteristics, which can significantly impact decision-making. This paper addresses this gap by focusing on representation bias in op

Reinosuke Kusano, Miguel Yulo Asuncion, Keith Horne

We present a parametric study of the spacetime structures obtainable in Weyl conformal gravity's dyonic Reissner-Nordstr\"{o}m solution. We derive expressions for photon sphere radii and horizons for this metric in terms of the conformal gravity parameters, from which we then determine analytic formulae for extremal limits and Hawking temperatures. Due to th

Yahya Ladghami, Taoufik Ouali

In this paper, we study the thermodynamic behavior of charged AdS black holes in higher-dimensional spacetimes within the framework of conformal holographic extended thermodynamics. This formalism is based on a novel AdS/CFT dictionary in which the conformal rescaling factor of the boundary conformal field theory (CFT) is treated as a thermodynamic parameter

Xinmin Hou, Xiangyang Wang

Let $\mathbb{Z}_q$ denote the cyclic group of order $q$. A $\mathbb{Z}_q$-edge-weighted $K_f$ is the complete graph $K_f$ equipped with a weight function $\omega : E(K_f) \to \mathbb{Z}_q$. A subdivision of a graph $H$ in a $\mathbb{Z}_q$-edge-weighted $K_f$ is called a $q$-divisible subdivision of $H$ if every subdivision path has weight congruent to zero m

Antoine Teissier, Marie Tahon, Nicolas Dugué, Aghilas Sini

Due to the rapid progress of speech synthesis, deepfake detection has become a major concern in the speech processing community. Because it is a critical task, systems must not only be efficient and robust, but also provide interpretable explanations. Among the different approaches for explainability, we focus on the interpretation of latent representations.

J. van der Duin, R. Loll, M. Schiffer, A. Silva

We introduce a new methodology to characterize properties of quantum spacetime in a strongly quantum-fluctuating regime, using tools from topological data analysis. Starting from a microscopic quantum geometry, generated nonperturbatively in terms of dynamical triangulations (DT), we compute the Betti numbers of a sequence of coarse-grained versions of the g

Rémi Delaunay, Christoph Hennersperger, Stefan Wörz

Ultrafast intracardiac echocardiography (ICE) uses unfocused transmissions to capture cardiac motion at frame rates exceeding 1 kHz. While this enables real-time visualization of rapid dynamics, image quality is often degraded by diffraction artifacts, requiring many transmits to achieve satisfying resolution and contrast. To address this limitation, we prop

J. van der Duin, R. Loll, M. Schiffer, A. Silva

In a novel application of the tools of topological data analysis (TDA) to nonperturbative quantum gravity, we introduce a new class of observables that allows us to assess whether quantum spacetime really resembles a ``quantum foam" near the Planck scale. The key idea is to investigate the Betti numbers of coarse-grained path integral histories, regularized

Yuhang Zhang, Jiaping Xiao, Chao Yan, Mir Feroskhan

A prevailing approach for learning visuomotor policies is to employ reinforcement learning to map high-dimensional visual observations directly to action commands. However, the combination of high-dimensional visual inputs and agile maneuver outputs leads to long-standing challenges, including low sample efficiency and significant sim-to-real gaps. To addres

Yongqi Leng, Yikun Lei, Xikai Liu, Meizhi Zhong

Agentic Retrieval-Augmented Generation (Agentic RAG) enhances the processing capability for complex tasks through dynamic retrieval and adaptive workflows. Recent advances (e.g., Search-R1) have shown that outcome-supervised reinforcement learning demonstrate strong performance. However, this approach still suffers from inefficient exploration, sparse reward

Deshui Yu, Yizhi Wang, Saihui Jin, Taojie Zhu

Large language models (LLMs) excel on general tasks yet still hallucinate in high-barrier domains such as pathology. Prior work often relies on domain fine-tuning, which neither expands the knowledge boundary nor enforces evidence-grounded constraints. We therefore build a pathology vector database covering 28 subfields and 1.53 million paragraphs, and prese

Vittorio Latorre

In this work, we introduce a new class of non-convex functions, called implicit concave functions, which are compositions of a concave function with a continuously differentiable mapping. We analyze the properties of their minimization by leveraging Fenchel conjugate theory to construct an augmented optimization problem. This reformulation yields a one-to-on

Ayub Ahmadi, Mahdieh Tahmasebi

In financial mathematics, the calculation of the Greeks, especially the delta, is emphasized due to its role in risk management. In this article, we employ Malliavin calculus to determine the delta of European and Asian options, where the underlying asset evolves according to a Hawkes jump-diffusion process. A central feature is that the Hawkes jump intensit

Huynh Anh N. Le, Jong-Hak Woo, Yongquan Xue, Ashraf Ayubinia

We utilize a large sample of $\sim$113,000 galaxies ($z < 0.3$) from the Sloan Digital Sky Survey with high-quality data to compare star formation rates (SFRs) across multiple diagnostic methods and examine their connection to Active Galactic Nuclei (AGNs) strength, indicated by Eddington ratio. Our sample encompassed star-forming (SF), composite, Seyfert, a

Eashan Wadhwa, Shanker Shreejith

Excessive switching activity is a primary contributor to dynamic power dissipation in modern FPGAs, where fine-grained configurability amplifies signal toggling and associated capacitance. Conventional low-power techniques -- gating, clock-domain partitioning, and placement-aware netlist rewrites - either require intrusive design changes or offer diminishing

José Gómez-delaHiz, Mohamed Faten Zhani, Jaime Galán-Jiménez, John Kaippallimalil

The Transport Control Protocol has long been the primary transport protocol for applications requiring performance and reliability over the Internet. Unfortunately, due its retransmission mechanism, TCP incurs high packet delivery delays when segments are lost. To address this issue, previous research proposed to use a novel network function, namely Transpor

Ruiyu Han, Johannes Wiesel

We prove a new sample complexity result for divergence regularized optimal transport. Our bound holds for probability measures on~$\mathbb{R}^d$ with exponential tail decay and for radial cost functions that satisfy a local Lipschitz condition. It is sharp up to logarithmic factors, and captures the intrinsic dimension of the marginal distributions through a

Jihun Lim, Sungwon Lee

To facilitate the widespread adoption of renewable energy, dispatchable, zero-emission power sources are essential for grid stability. This work performs a comprehensive techno-economic analysis of a self-sustainable thermophotovoltaic (TPV) system, an architecture that integrates solar charging to function as a standalone power generation asset. Using theor

Suhwan Choi, Jaeyoon Jung, Haebin Seong, Minchan Kim

Large language models leverage internet-scale text data, yet embodied AI remains constrained by the prohibitive costs of physical trajectory collection. Desktop environments -- particularly gaming -- offer a compelling alternative: they provide rich sensorimotor interactions at scale while maintaining the structured observation-action coupling essential for

Haribandhu Jena, Jyotirmaya Shivottam, Subhankar Mishra

Quantum graph neural networks offer a powerful paradigm for learning on graph-structured data, yet their explainability is complicated by measurement-induced stochasticity and the combinatorial nature of graph structure. In this paper, we introduce QuantumGraphLIME (QGraphLIME), a model-agnostic, post-hoc framework that treats model explanations as distribut

Jorge Sanchez Almeida

It has been proposed that the stellar cores observed in ultra-faint dwarf (UFD) galaxies reflect underlying dark matter (DM) cores that cannot be formed by stellar feedback acting on collisionless cold dark matter (CDM) halos. Assuming this claim is correct, we investigate the constraints that arise if such cores are produced by self-interacting dark matter

Anna Nalpantidi, Dimitris Karlis

We present a bivariate vector valued discrete autoregressive model of order $1$ (BDAR($1$)) for discrete time series. The BDAR($1$) model assumes that each time series follows its own univariate DAR($1$) model with dependent random mechanisms that determine from which component the current status occurs and dependent innovations. The joint distribution of th

Rachel L. Franz, Jacob O. Wobbrock

There are over a hundred virtual reality (VR) locomotion techniques that exist today, with new ones being designed as VR technology evolves. The different ways of controlling locomotion techniques (e.g., gestures, button inputs, body movements), along with the diversity of upper-body motor impairments, can make it difficult for a user to know which locomotio

Haneul Yoo, Jiho Jin, Kyunghyun Cho, Alice Oh

While large language models (LLMs) have achieved notable progress in multilingual settings, their performance remains uneven across languages as LLMs often rely on English-centric latent representations. In this work, we introduce code-switching in-context learning (CSICL), an inference-time mechanism for cross-lingual representational alignment. Rather than

Xavier Buff, Arnaud Chéritat, Guillaume Tahar

A meromorphic connection on the tangent bundle of a Riemann surface induces a complex affine structure on the complement of the poles. Local models for Fuchsian singularities are already known. In this paper, we introduce a complete set of local invariants for a meromorphic connection and provide local models for a complex affine structure in a punctured nei

Yanming Li, Cédric Eichler, Nicolas Anciaux, Alexandra Bensamoun

We propose a system for marking sensitive or copyrighted texts to detect their use in fine-tuning large language models under black-box access with statistical guarantees. Our method builds digital ``marks'' using invisible Unicode characters organized into (``cue'', ``reply'') pairs. During an audit, prompts containing only ``cue'' fragments are issued to t

Parth Pratim Pandey, Sanjay Jain

We present a geometric framework to study the growth-division dynamics of cells and protocells, and demonstrate that self-reproduction emerges only when a system's growth dynamics and division strategy are mutually compatible. Using several commonly used models (the linear Hinshelwood cycle and non-linear coarse-grained models of protocells and bacteria), we

Jike Zhong, Yuxiang Lai, Xiaofeng Yang, Konstantinos Psounis

Recent advances in language modeling have witnessed the rise of highly desirable emergent capabilities, such as reasoning and in-context learning. However, vision models have yet to exhibit comparable progress in these areas. In this paper, we argue that this gap could stem from the lack of semantic and contextual guidance in current vision transformer (ViT)

L. Bruno, A. Botteon, D. Dallacasa, T. Venturi

Complex energy transfer processes in the intracluster medium (ICM) can revive fossil (with spectral ages $\gg100$ Myr) plasma initially generated by radio galaxies. This leads to the re-ignition of faint radio sources with irregular and filamentary morphologies, and ultra-steep ($\alpha \gtrsim 1.5$) synchrotron spectra, which can be more easily detected at

Mostapha Benhenda

We build a smooth Gaussian-Kronecker diffeomorphism, answering a question raised by Anatole Katok in his list of ''Five Most Resistant Problems in Dynamics''.

Abhishek Kumar, Peter D. Hodgson, Manus Hayne, Avirup Dasgupta

The memory demands of large-scale deep neural networks (DNNs) require synaptic weight values to be stored and updated in off-chip memory like dynamic random-access memory, which reduces energy efficiency and increases training time. Monolithic crossbar or pseudo-crossbar arrays using analog non-volatile memories, which can store and update weights on-chip, p

David Debot, Giuseppe Marra

Concept Bottleneck Models (CBNMs) are deep learning models that provide interpretability by enforcing a bottleneck layer where predictions are based exclusively on human-understandable concepts. However, this constraint also restricts information flow and often results in reduced predictive accuracy. Concept Sidechannel Models (CSMs) address this limitation

Xin Ma, Daxun Wang, Wenyuan Yang

In this paper, we study topological dynamics on the visual boundary and several combinatorial boundaries associated to $\operatorname{CAT}(0)$ spaces. Through verifying the freeness of Myrberg points on the boundaries, we prove that a large class of these boundary actions are topologically free strong boundary actions. These include certain visual boundary a

M. Torrente, A. Follador, A. Calcante, P. Casati

The study investigates the effects of R. solani inoculation on the germination and early development of Lactuca sativa L. seeds using a low-cost, image-based monitoring system. Multiple cameras were deployed to continuously capture images of the germination process in both infected and control groups. The objective was to assess the impact of the pathogen by

Corentin Cadiou, Harley Katz, Martin P. Rey, Oscar Agertz

We present three cosmological radiation-hydrodynamic zoom simulations of the progenitor of a Milky Way-mass galaxy from the MEGATRON suite. The simulations combine on-the-fly radiative transfer with a detailed non-equilibrium thermochemical network (81 ions and molecules), resolving the cold and warm gas in the circumgalactic medium (CGM) on spatial scales d

Sara Al Sayyed, Aline Roumy, Thomas Maugey

DNA has emerged as a promising alternative for long-term data storage due to its high capacity, durability, and low-energy potential. However, storing data in DNA presents several challenges. First, it requires complex and costly biochemical processes, making efficient compression crucial to reducing DNA synthesis time and cost. Second, these processes are p

Tuan Nguyen, Thi Nguyen, Thu Tran

We construct a left-compressed family F(n, k, G) generated by a collection of generating sets G, and identify conditions on G under which F(n, k, G) is an intersecting family. From this, we obtain a convenient method to construct left-compressed intersecting families. After that, we provide some comparisons between the theorem of Bond and our result. Finally

Shawnak Shivakumar, Jefferson Hernandez

Wuchereria bancrofti, the parasitic roundworm responsible for lymphatic filariasis, permanently disables over 36 million people and places 657 million at risk across 39 countries. A major bottleneck for drug discovery is the lack of functional annotation for more than 90 percent of the W. bancrofti dark proteome, leaving many potential targets unidentified.

Michael Lovemore, Joshua Botha, Bertus van Heerden, Tjaart Kruger

Single-molecule spectroscopy (SMS) is an exceptionally sensitive technique, but its inherently limited photon budget produces noisy data that can readily lead to subjective analyses, fitting errors, and reduced statistical power, obscuring true subpopulations and their dynamics. Here, we present an unbiased, objective method to cluster two-dimensional single

Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos

Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anon

R. Senthamizhan, R. Gopal, V. K. Chandrasekar

We investigate the role of frequency-weighted interactions in a solvable model of one-dimensional (1D) swarmalators confined to a ring, where both spatial and phase couplings are scaled by the heterogeneous natural frequencies of individual agents. Our analysis identifies three distinct collective states: the asynchronous state , the phase-wave state , and t

Taeyeop Lee, Gyuree Kang, Bowen Wen, Youngho Kim

Despite the prevalence of transparent object interactions in human everyday life, transparent robotic manipulation research remains limited to short-horizon tasks and basic grasping capabilities. Although some methods have partially addressed these issues, most of them have limitations in generalization to novel objects and are insufficient for precise long-

Daniel Gonzálbez-Biosca, Josep Cabacas-Maso, Carles Ventura, Ismael Benito-Altamirano

Automated video editing remains an underexplored task in the computer vision and multimedia domains, especially when contrasted with the growing interest in video generation and scene understanding. In this work, we address the specific challenge of editing multicamera recordings of classical music concerts by decomposing the problem into two key sub-tasks:

Cong Zeng, Shengkun Tang, Yuanzhou Chen, Zhiqiang Shen

The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication. This trend has heightened the need for reliable detection methods to distinguish between human-authored and machine-generated content. Existing approaches both zero-shot methods an

Jialu Gao, K J Joseph, Fernando De La Torre

The task of realistically inserting a human from a reference image into a background scene is highly challenging, requiring the model to (1) determine the correct location and poses of the person and (2) perform high-quality personalization conditioned on the background. Previous approaches often treat them as separate problems, overlooking their interconnec

Chenhao Tang, Han Wu, Jie Yang, Wenyan Yang

We generalize Koyama's $7/10$ bound of the error term in the prime geodesic theorems to the principal congruence subgroups for quaternion algebras. Our method avoids the spectral side of the Jacquet--Langlands correspondences, and relates the counting function directly to those for the principal congruence subgroups of Eichler orders of level less than one.

Andrew Goodall, Florent Jouve, Jean-Sébastien Sereni

We study algebraic properties of the Tutte polynomial of a matroid and its generalizations to other combinatorially defined bivariate polynomial invariants. Merino, de Mier and Noy showed that the Tutte polynomial of a connected matroid is irreducible, and Bohn, Cameron and M{\"u}ller conjectured the stronger property that the Galois/monodromy group of the T

Anwen Lu, Mingxin Liu, Yiping Jiao, Hongyi Gong

Primary liver malignancies are widely recognized as the most heterogeneous and prognostically diverse cancers of the digestive system. Among these, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) emerge as the two principal histological subtypes, demonstrating significantly greater complexity in tissue morphology and cellular archite

Mina Farahani, Jiří Čapek, Tomáš Kozák

High-Power Impulse Magnetron Sputtering (HiPIMS) offers higher ionized flux fractions at the cost of lower deposition rates compared to conventional DCMS. A fine optimization of the deposition conditions is crucial for specific applications. Chopped or multi-pulse HiPIMS (segmenting pulses into shorter micropulses) has been proposed to mitigate ion back-attr

Yuchen Fan

We report the discovery of a multicritical point that extends the liquid-gas paradigm to systems with competing symmetry-breaking orders. Using large-scale Monte Carlo simulations of a frustrated bilayer Ising antiferromagnet with tunable couplings, we map out a rich finite-temperature phase diagram hosting three ordered phases separated by both continuous a

C. W. James, J. Hoffmann, J. X. Prochaska, M. Glowacki

The intrinsic width and scattering distributions of fast radio bursts (FRBs) inform on their emission mechanism and local environment, and act as a source of detection bias and, hence, an obfuscating factor when performing FRB population and cosmological studies. Here, we utilise a sample of 29 FRBs with measured high-time-resolution properties and known red

Zheng-Han Huang, Jing-Yuan Lai, Yu-Shu G. Wu

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is cre

Manolis Mylonas, Charalampia Zerva, Evlampios Apostolidis, Vasileios Mezaris

In this work, we present a method and two large-scale datasets for Script-Driven Multimodal Video Summarization. The proposed method, SD-MVSum, builds on our earlier SD-VSum method for script-driven video summarization, which considered just the visual content of the video. SD-MVSum takes into account, in addition to the visual modality, the relevance of the

Eliana Masha, Daniel Bemmerer, Axel Boeltzig, Konrad Schmidt

For almost three decades it has been known that the study of astro-physically important nuclear reactions between stable nuclei requires the use of low-background, underground accelerator laboratories. The Felsenkeller shallow-underground laboratory in Dresden, shielded by a 45 m thick rock cover, hosts a 5 MV Pelletron ion accelerator with an external sputt

Yiping Ma, Shiyu Hu, Buyuan Zhu, Yipei Wang

Reproducing cognitive development, group interaction, and long-term evolution in virtual classrooms remains a core challenge for educational AI, as real classrooms integrate open-ended cognition, dynamic social interaction, affective factors, and multi-session development rarely captured together. Existing approaches mostly focus on short-term or single-agen

Saja Al-Dabet, Sherzod Turaev, Nazar Zaki, Arif O. Khan

Ocular-induced abnormal head posture (AHP) is a compensatory mechanism that arises from ocular misalignment conditions, such as strabismus, enabling patients to reduce diplopia and preserve binocular vision. Early diagnosis minimizes morbidity and secondary complications such as facial asymmetry; however, current clinical assessments remain largely subjectiv

Vanessa Jacquier, Wioletta M. Ruszel

We investigate the Ising model on finite subgraphs of the hyperbolic lattice under minus boundary conditions and in the presence of a positive external field $h$. Interpreting the boundary as frozen or cold wall conditions, we show that, for small values of $h$, the system exhibits metastable behaviour. Our result is very surprising, since non-amenable graph

Jean-Michel Poggi, Bruno Portier, Emma Thulliez

This article focuses on the use of Geographically Weighted Regression (GWR) method to correct air quality low-cost sensors measurements. Those sensors are of major interest in the current era of high-resolution air quality monitoring at urban scale, but require calibration using reference analyzers. The results for NO2 are provided along with comments on the

Robin Requadt, Housen Li, Axel Munk

We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial structure. This general theory encompasses important examples such as the squared W

Sheriff Issaka, Keyi Wang, Yinka Ajibola, Oluwatumininu Samuel-Ipaye

Despite representing nearly one-third of the world's languages, African languages remain critically underserved by modern NLP technologies, with 88\% classified as severely underrepresented or completely ignored in computational linguistics. We present the African Languages Lab (All Lab), a comprehensive research initiative that addresses this technological

Shozo Saeki, Minoru Kawahara, Hirohisa Aman

Deep metric learning (DML) aims to learn a neural network mapping data to an embedding space, which can represent semantic similarity between data points. Hyperbolic space is attractive for DML since it can represent richer structures, such as tree structures. DML in hyperbolic space is based on pair-based loss or unsupervised regularization loss. On the oth

Naoto Shiraishi, Ryuji Takagi

Quantum thermodynamics investigates how robust the second law of thermodynamics serves as the unique fundamental law in the small quantum world. To tackle this problem, the quantum coherence constitutes a major difficulty of investigations, which provides severe constraints hindering the recovery of a single thermodynamic potential. Here we solve this long-s

Ruimeng Hu, Daniel Ralston, Xu Yang, Haosheng Zhou

We study a continuous-time stochastic Stackelberg game in which a leader seeks to accomplish a primary objective while inferring a hidden parameter of a rational follower. The follower solves an entropy-regularized tracking problem and responds to the leader's trajectory with a randomized policy. Anticipating this response, the leader designs informative con

Yasushi Kasahara

Under the natural action of the pure mapping class group of a surface of genus at least three, we show that any global fixed point in the low-dimensional deformation space of the surface group corresponds to the trivial representation. A key observation is that such a global fixed point gives rise to a linear representation of the pure mapping class group of

Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli, Gabriele Lagani

In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This system, referred to as biological reservoir computing (BRC), replaces artificial recurrent units with the spontaneous and evoked activity of living neurons. A multi-electrode array (

Ji Zhou, Marco Clementi, Samantha Sbarra, Ozan Yakar

By employing a pump-probe technique for enhanced spectral mapping of the dynamics in nonlinear frequency conversion, we demonstrate that photo-induced second-harmonic generation (SHG) in silicon nitride (Si3N4) microresonators can persist when transitioning from the preferred doubly resonant condition--where the resonances of the optical harmonics are requir

Alexander Murphy, Michal Danilowski, Soumyajit Chatterjee, Abhirup Ghosh

Test-Time Adaptation (TTA) methods are often computationally expensive, require a large amount of data for effective adaptation, or are brittle to hyperparameters. Based on a theoretical foundation of the geometry of the latent space, we are able to significantly improve the alignment between source and distribution-shifted samples by re-centering target dat

Shuai Liu, Long Wang, Ataru Tanikawa, Weiwei Wu

GW231123 is a binary black hole merger whose primary component lies within or above the pair-instability mass gap, while the secondary component falls within this gap. The standard theory of stellar evolution is significantly challenged by this event. We investigate the formation of candidate progenitors of GW231123 in Population III (Pop III) star clusters.

Sara Mandelli, Diego Vila-Portela, David Vázquez-Padín, Paolo Bestagini

Over the years, the forensics community has proposed several deep learning-based detectors to mitigate the risks of generative AI. Recently, frequency-domain artifacts (particularly periodic peaks in the magnitude spectrum), have received significant attention, as they have been often considered a strong indicator of synthetic image generation. However, stat

Sebastiano Randino, Lorenzo Schena, Nicolas Coudou, Emanuele Garone

This work presents a nonlinear system identification framework for modeling the power extraction dynamics of wind turbines, including both freestream and waked conditions. The approach models turbine dynamics using data-driven power coefficient maps expressed as combinations of compact radial basis functions and polynomial bases, parameterized in terms of ti

Tianhao Zhu, Dahu Feng, Erhu Feng, Yubin Xia

With the widespread adoption of Large Language Models (LLMs), the demand for high-performance LLM inference services continues to grow. To meet this demand, a growing number of AI accelerators have been proposed, such as Google TPU, Huawei NPU, Graphcore IPU, and Cerebras WSE, etc. Most of these accelerators adopt multi-core architectures to achieve enhanced

M. Abbrescia, M. De Serio, S. Colafranceschi, B. Liberti

In recent years, the Resistive Cylindrical Chamber (RCC) has been introduced as a novel gaseous detector, extending the well-established Resistive Plate Chambers (RPCs) to the case of cylindrical electrode geometry. Preliminary experimental studies have highlighted several promis- ing features of this configuration, motivating the need for further systematic

Yu Watanabe

Sub-GeV dark matter (DM) with $s$-channel resonant self-scattering provides a promising framework for addressing small-scale structure problems. However, models that also account for the observed relic abundance through the same resonance are strongly constrained by current $\gamma$-ray observations, since the associated signals are significantly enhanced. T

Jian Luo

Over the past decade, the field of high-entropy ceramics (HECs) has expanded rapidly to encompass a broad range of oxides, borides, silicides, and other ceramic solid solutions. In 2020, we proposed extending HECs to compositionally complex ceramics (CCCs), where non-equimolar compositions and the presence of long- or short-range order, although reducing con

Elena Guardo, Graham Keiper, Adam Van Tuyl

Let $I_\mathbb{X}$ be the bihomogeneous ideal of a finite set of points $\mathbb{X} \subseteq \mathbb{P}^1 \times \mathbb{P}^1$. The purpose of this note is to consider ``splittings'' of the ideal $I_\mathbb{X}$, that is, finding ideals $J$ and $K$ such that $I_\mathbb{X} = J+K$, where $J$ and $K$ have prescribed algebraic or geometric properties. We show th

Jean-Marc Schlenker

We review recent progress on two closely related sets of questions concerning convex co-compact hyperbolic manifolds, or convex domains in those manifolds, such as their convex core. The first set of questions is to what extent the hyperbolic metric on such a manifold is uniquely determined by either of two possible geometric data on their boundary. The seco

Gabriel Athènes, Adam Woolfe, Thierry Mora, Aleksandra M. Walczak

The specific region of an antibody responsible for binding to an antigen, known as the paratope, is essential for immune recognition. Accurate identification of this small yet critical region can accelerate the development of therapeutic antibodies. Determining paratope locations typically relies on modeling the antibody structure, which is computationally i

Yao Zhang, Yuchen Song, Shengnan Li, Yan Shi

The rapid development of Generative Artificial Intelligence (GenAI) has catalyzed a transformative technological revolution across all walks of life. As the backbone of wideband communication, optical networks are expecting high-level autonomous operation and zero-touch management to accommodate their expanding network scales and escalating transmission band

Nolwenn Bernard, Krisztian Balog

Research and development on conversational recommender systems (CRSs) critically depends on sound and reliable evaluation methodologies. However, the interactive nature of these systems poses significant challenges for automatic evaluation. This paper critically examines current evaluation practices and identifies two key limitations: the over-reliance on st

Surajit Das, Raghunathan Ramakrishnan

Accurate prediction of nuclear magnetic resonance (NMR) shielding with machine learning (ML) models remains a central challenge for data-driven spectroscopy. We present atomic variants of the Coulomb matrix (aCM) and bag-of-bonds (aBoB) descriptors, and extend them using radial basis functions (RBFs) to yield smooth, per-atom representations (aCM-RBF, aBoB-R

Junghee Ryu, Jinhyoung Lee, Hoon Ryu

We extend the generic Bell inequalities suggested by Son, Lee, and Kim [Phys. Rev. Lett. 96, 060406 (2006)] to incorporate multiple observables for tripartite systems and introduce a geometric methodology for calculating classical upper bounds of the inequalities. Our method transforms the problem of finding the classical upper bounds into identifying constr

Zhiyuan Ren, Tao Zhang, Wenchi Chen

In distributed multi-agent systems, correctness is often entangled with operational policies such as scheduling, batching, or routing, which makes systems brittle since performance-driven policy evolution may break integrity guarantees. This paper introduces the Deterministic Causal Structure (DCS), a formal foundation that decouples correctness from policy.

Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar, Saif Eddin Jabari

The Monte Carlo-type Neural Operator (MCNO) introduces a framework for learning solution operators of one-dimensional partial differential equations (PDEs) by directly learning the kernel function and approximating the associated integral operator using a Monte Carlo-type approach. Unlike Fourier Neural Operators (FNOs), which rely on spectral representation