October 2025 arXiv papers — page 123

Jungi Lee, Junyong Park, Soohyun Cha, Jaehoon Cho

Reduced-precision data formats are crucial for cost-effective serving of large language models (LLMs). While numerous reduced-precision formats have been introduced thus far, they often require intrusive modifications to the software frameworks or are rather unconventional for widespread adoption across hardware vendors. In this paper, we instead focus on re

L. L. Lage, Tarik. P. Cysne, A. Latgé

Orbital magnetization (OM) in Sierpinski carpet (SC) and triangle (ST) fractal is theoretically investigated by using Haldane model as a prototypical example. The OM calculation is performed following two distinct approaches; employing the definition and local markers formalism. Both methods coincides for all systems analyzed. For the SC, higher fractal gene

F. Herklotz, E. V. Lavrov, T. D. C. Hobson, T. P. Shalvey

We report persistent photoconductivity in $p$-type Sb$_2$Se$_3$ single crystals doped with Cd or Zn, where enhanced conductivity remains for hours after illumination ceases at temperatures below $\sim$25~K. Comparative transport and infrared absorption measurements, including on $n$-type Cl-doped counterparts, reveal strong indications that hole transport in

Song Tang, Peihao Gong, Kunyu Li, Kai Guo

Consistent text-to-image (T2I) generation seeks to produce identity-preserving images of the same subject across diverse scenes, yet it often fails due to a phenomenon called identity (ID) shift. Previous methods have tackled this issue, but typically rely on the unrealistic assumption of knowing all target scenes in advance. This paper reveals that a key so

Andrea Spina

Asymptotic Safety offers a conservative and predictive framework for quantum gravity, based on the existence of a renormalization group fixed point that ensures ultraviolet completeness without introducing new degrees of freedom. Black holes provide a natural arena in which to explore the implications of this scenario, as they probe the strongest gravitation

Jiangyu Han, Ruoyu Wang, Yoshiki Masuyama, Marc Delcroix

Self-supervised models such as WavLM have demonstrated strong performance for neural speaker diarization. However, these models are typically pre-trained on single-channel recordings, limiting their effectiveness in multi-channel scenarios. Existing diarization systems built on these models often rely on DOVER-Lap to combine outputs from individual channels.

S Hitarth, Alessio Mansutti, Guruprerana Shabadi

This paper presents the first study of the complexity of the optimization problem for integer linear-exponential programs which extend classical integer linear programs with the exponential function $x \mapsto 2^x$ and the remainder function ${(x,y) \mapsto (x \bmod 2^y)}$. The problem of deciding if such a program has a solution was recently shown to be NP-

Rekha R Nair, Tina Babu, Alavikunhu Panthakkan, Balamurugan Balusamy

Wind turbine reliability is critical to the growing renewable energy sector, where early fault detection significantly reduces downtime and maintenance costs. This paper introduces a novel ensemble-based deep learning framework for unsupervised anomaly detection in wind turbines. The method integrates Variational Autoencoders (VAE), LSTM Autoencoders, and Tr

Gloria Montaña, Vincent Mathieu, Vanamali Shastry, Łukasz Bibrzycki

The observation of hybrid mesons in photoproduction experiments can provide essential insight into the inner workings of quantum chromodynamics in the strong coupling regime. In particular, the study of final $\eta^{(\prime)}\pi$ states is of great interest due to the presence of the lowest lying hybrid candidate with manifestly exotic quantum numbers, the $

Asen Nachkov, Xi Wang, Luc Van Gool

Recent LLM agents have made great use of chain of thought reasoning and function calling. As their capabilities grow, an important question arises: can this software represent not only a smart problem-solving tool, but an entity in its own right, that can plan, design immediate tasks, and reason toward broader, more ambiguous goals? To study this question, w

Caio V. P. de Brito, Gabriel S. Denicol

Understanding the applicability of fluid-dynamical models to describe the hot and dense matter produced in the early stages of hadronic collisions is a fundamental problem in the field. In particular, it is not clear to what degree this hydrodynamization process requires proximity to a local equilibrium state. In this contribution, we study this problem in k

Dong-Hyeon Kang, Ju-Hyeon Nam, Sang-Chul Lee

Accurate interpretation of 12 lead electrocardiograms (ECGs) is critical for early detection of cardiac abnormalities, yet manual reading is error prone and existing CNN based classifiers struggle to choose receptive field sizes that generalize to the long sequences typical of ECGs. Omni Scale CNN (OS CNN) addresses this by enumerating prime sized kernels in

Arefeh Abbasi, Maurizio Ricci, Pietro Carrara, Moritz Flaschel

We assess the performance of EUCLID, Efficient Unsupervised Constitutive Law Identification and Discovery, a recently proposed framework for automated discovery of constitutive laws, on experimental data. Mechanical tests are performed on natural rubber specimens spanning simple to complex geometries, from which we collect both global, force elongation, and

Guanting Dong, Licheng Bao, Zhongyuan Wang, Kangzhi Zhao

Recently, Agentic Reinforcement Learning (Agentic RL) has made significant progress in incentivizing the multi-turn, long-horizon tool-use capabilities of web agents. While mainstream agentic RL algorithms autonomously explore high-uncertainty tool-call steps under the guidance of entropy, excessive reliance on entropy signals can impose further constraints,

M. Borchiellini, D. Maurin, M. Vecchi

Electron-capture (EC) unstable species in Galactic cosmic rays constrain the time elapsed between nucleosynthesis and acceleration. They have also been advocated as tracers of reacceleration or gas inhomogeneities during their transport. The number of EC-unstable species grows with mass, with an expected EC-decay impact more important for larger atomic numbe

Ziqi Jiang, Yanghao Wang, Long Chen

Aligning features from different modalities, is one of the most fundamental challenges for cross-modal tasks. Although pre-trained vision-language models can achieve a general alignment between image and text, they often require parameter-efficient fine-tuning (PEFT) for further adjustment. Today's PEFT methods (e.g., prompt tuning, LoRA-based, or adapter-ba

Hasan Kamliya Jawahar, Benshuai Lyu, Mahdi Azarpeyvand

An experiment is conducted to investigate the effects of chevrons on installed subsonic jet noise at a Mach number of 0.5 using the NASA SMC000 (round) and SMC006 (chevron) nozzles. The jets are of a diameter D=16.93 mm and placed near a flat plate, with a horizontal separation distance L=6.5D between the plate's trailing edge and the nozzle exit. The vertic

Amir Khan, Shalini Sharma, Tiago de Oliveira Schneider, Markus Meinert

Planar current-induced magnetization switching (CIMS) driven by spin-orbit torque (SOT) requires an in-plane uniaxial magnetic anisotropy (UMA), which can be induced by oblique-angle sputter deposition of the heavy-metal underlayer in heavy-metal/ferromagnet heterostructures. To enhance the SOT efficiency, we employ trilayer heterostructures of (Ta or W)/CoF

Juan García Escudero

We construct algebraic surfaces with a large number of type A singularities. Bivariate polynomials presented in previous works for the construction of nodal surfaces and certain families of Belyi polynomials are used. In some cases explicit expressions in terms of classical Jacobi polynomials are obtained.

Emanuele Antonioni, Stefan Markovic, Anirudha Shankar, Jaime Bernardo

AI systems are continually evolving and advancing, and user expectations are concurrently increasing, with a growing demand for interactions that go beyond simple text-based interaction with Large Language Models (LLMs). Today's applications often require LLMs to interact with external tools, marking a shift toward more complex agentic systems. To support th

Chenyuan Qu, Hao Chen, Jianbo Jiao

Exploring and understanding efficient image representations is a long-standing challenge in computer vision. While deep learning has achieved remarkable progress across image understanding tasks, its internal representations are often opaque, making it difficult to interpret how visual information is processed. In contrast, classical visual descriptors (e.g.

Jordan Stone, Youssef Saleh, Darryl Wright, Jess Riedel

Vacuum decay posits that the universe's apparent vacuum is metastable and could transition to a lower-energy state. According to current physics models, if such a transition occurred in any location, a region of "true vacuum" would propagate outward at near light speed, destroying the accessible universe as we know it by deeply altering the effective physica

Keima Abe, Hayato Muraki, Shuhei Tomoshige, Kenichi Oishi

Medical images like MR scans often show domain shifts across imaging sites due to scanner and protocol differences, which degrade machine learning performance in tasks such as disease classification. Domain harmonization is thus a critical research focus. Recent approaches encode brain images $\boldsymbol{x}$ into a low-dimensional latent space $\boldsymbol{

Saeed Salehi

We offer a new proof (and review some known proofs) of Cantor's Powerset Theorem (1891), which concerns the non-existence of a surjective function from a set onto its powerset.

Xinrui Huang, Fan Xiao, Dongming He, Anqi Gao

Oral and maxillofacial radiology plays a vital role in dental healthcare, but radiographic image interpretation is limited by a shortage of trained professionals. While AI approaches have shown promise, existing dental AI systems are restricted by their single-modality focus, task-specific design, and reliance on costly labeled data, hindering their generali

Sebastian Onder, Philipp Gaggl, Jürgen Burin, Andreas Gsponer

We present the design and simulation of a 30 $\mathrm{\mu m}$ thick 4H-SiC Low Gain Avalanche Diode (LGAD) optimized for high-voltage operation. A 2.4 $\mathrm{\mu m}$ thick epitaxially grown gain layer enables controlled internal amplification up to 1 kV reverse bias, while maintaining full depletion below 500 V. Electrical characteristics, including I-V, C

Jiangong Chen, Xia Lei, Yuchen Zhang, Kaitao Meng

Beamforming with a sufficient number of antennas is one of the most significant technologies for both Multi-user (MU) Multiple-input Multiple-output (MIMO) communication and MIMO radar sensing in Integrated Sensing and Communication (ISAC) systems. However, its performance suffers from limited Degrees of Freedom (DoFs) in conventional hybrid beamforming syst

Mehdy Lefkir, Simon Vaughan, Mike Goad, Daniela Huppenkothen

Accreting supermassive black holes at the centres of galaxies are the engine of active galactic nuclei (AGN). X-ray light curves of unabsorbed AGN show dramatic random variability on timescales ranging from seconds to years. The power spectrum of the fluctuations is usually well-modelled with a power law that decays as $1/f$ at low frequencies, and which ben

Cheng Cui, Ting Sun, Suyin Liang, Tingquan Gao

In this report, we propose PaddleOCR-VL, a SOTA and resource-efficient model tailored for document parsing. Its core component is PaddleOCR-VL-0.9B, a compact yet powerful vision-language model (VLM) that integrates a NaViT-style dynamic resolution visual encoder with the ERNIE-4.5-0.3B language model to enable accurate element recognition. This innovative m

Mikhail Skopenkov, Khusrav Yorov

This work is on surfaces with a constant ratio of principal curvatures. These CRPC surfaces generalize minimal surfaces but are much more challenging to construct. We propose a construction of a family of such surfaces containing a given minimal surface without flat points. This leads to a partial solution of Plateau's problem for CRPC surfaces. We obtain an

Miheer Salunke, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar

Auditory sensory overload affects 50-70% of individuals with Autism Spectrum Disorder (ASD), yet existing approaches, such as mechanistic models (Hodgkin Huxley type, Wilson Cowan, excitation inhibition balance), clinical tools (EEG/MEG, Sensory Profile scales), and ML methods (Neural ODEs, predictive coding), either assume fixed parameters or lack interpret

Yunze Tong, Didi Zhu, Zijing Hu, Jinluan Yang

In text-to-image generation, different initial noises induce distinct denoising paths with a pretrained Stable Diffusion (SD) model. While this pattern could output diverse images, some of them may fail to align well with the prompt. Existing methods alleviate this issue either by altering the denoising dynamics or by drawing multiple noises and conducting p

Qurrat Ul Ain, Atif Aftab Ahmed Jilani, Zunaira Shafqat, Nigar Azhar Butt

Defective surgical instruments pose serious risks to sterility, mechanical integrity, and patient safety, increasing the likelihood of surgical complications. However, quality control in surgical instrument manufacturing often relies on manual inspection, which is prone to human error and inconsistency. This study introduces SurgScan, an AI-powered defect de

Jia Zhang, Guo-Bao Zhang, Li-Ying Zhu, Sheng-Bang Qian

The classification of X-ray binaries into high- and low-mass types has historically lacked a unified, data-driven quantitative criterion, and large-scale statistical studies of the donor star population have been limited. In this work, we address this gap by compiling data for 3,964 XRBs and deriving a plentiful set of physical parameters (mass, radius, age,

Eliezer da Silva, Arto Klami, Diego Mesquita, Iñigo Urteaga

Selecting the latent dimensions (ranks) in tensor factorization is a central challenge that often relies on heuristic methods. This paper introduces a rigorous approach to determine rank identifiability in probabilistic tensor models, based on prior predictive moment matching. We transform a set of moment matching conditions into a log-linear system of equat

Evangelos Lamprou, Julian Dai, Grigoris Ntousakis, Martin C. Rinard

Software supply-chain attacks are an important and ongoing concern in the open source software ecosystem. These attacks maintain the standard functionality that a component implements, but additionally hide malicious functionality activated only when the component reaches its target environment. Lexo addresses such stealthy attacks by automatically learning

Keita Akutagawa, Shinsuke Imada, Munehito Shoda

Magnetic reconnection is a multiscale phenomenon where fluid- and particle-scale processes interact. The particle-in-cell (PIC) method, capable of resolving kinetic (particle-scale) physics, is extensively used to study the kinetic effects in magnetic reconnection. Meanwhile, because of the high computational cost, PIC simulations cannot capture the interact

Tenyo Takahashi

Many logical properties are known to be undecidable for normal modal logics, with few exceptions such as consistency and coincidence with $\mathsf{K}$. This paper shows that the property of being a union-splitting in $\mathsf{NExt}\mathsf{K}$, the lattice of normal modal logics, is decidable, thus answering the open problem [WZ07, Problem 2]. This is done by

David J. Dougan, Matti Dorsch, Laura J. A. Scott, Niall E. McElroy

High abundances of various lead (Pb) species have been identified in the spectra of many Asymptotic Giant Branch (AGB) stars and O- and B-type subdwarfs (sdO/B). Additional atomic data relating to Pb, and in particular photoionization cross sections, are needed to allow a greater understanding of the origin of these observed Pb abundances, and hence discern

Tytti Kärki, Senna Luntama, Yasamin Modabber, Saila Pönkä

Biological tissues exhibit complex behaviors with their dynamics often resembling inert soft matter such as liquids, polymers, colloids, and liquid crystals. These analogies enable physics-based approaches for investigations of emergent behaviors in biological processes. A well-studied case is the spreading of cellular aggregates on solid surfaces, where the

Mitja Kovac, Rok Spruk

This paper provides the first causal evidence on the long-run economic dividends of Arab-Israeli peace treaties. Using synthetic control and difference-in-differences estimators, we analyze 1978 Camp David Accords and 1994 peace treaty between Jordan and Israel. Both cases reveal large and lasting gains. By 2011, real GDP of Egypt exceeded its synthetic coun

Ali Kashefi, Tapan Mukerji

Vision Mamba has recently received attention as an alternative to Vision Transformers (ViTs) for image classification. The network size of Vision Mamba scales linearly with input image resolution, whereas ViTs scale quadratically, a feature that improves computational and memory efficiency. Moreover, Vision Mamba requires a significantly smaller number of tr

Andrew Adair, Dafydd Owen-Newns, Giovanni Donati, Joshua Robertson

Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting increasing research interest as they promise ultrafast, energy-efficient operation with low-crosstalk and high bandwidth.

Daniel Owusu Adu, Yongxin Chen

We extend flow matching to ensembles of linear systems in both deterministic and stochastic settings. Averaging over system parameters induces memory leading to a non-Markovian interpolation problem for the stochastic case. In this setting, a control law that achieves the distributional controllability is characterized as the conditional expectation of a Vol

Juheon Choi, Juyong Lee, Jian Kim, Chanyoung Kim

When working on digital devices, people often face distractions that can lead to a decline in productivity and efficiency, as well as negative psychological and emotional impacts. To address this challenge, we introduce a novel Artificial Intelligence (AI) assistant that elicits a user's intention, assesses whether ongoing activities are in line with that in

Haoyuan Li, Mathias Funk, Aaqib Saeed

Federated Learning (FL) offers a powerful paradigm for training models on decentralized data, but its promise is often undermined by the immense complexity of designing and deploying robust systems. The need to select, combine, and tune strategies for multifaceted challenges like data heterogeneity and system constraints has become a critical bottleneck, res

Xingjian Wu, Xiangfei Qiu, Hanyin Cheng, Zhengyu Li

Time Series Forecasting has made significant progress with the help of Patching technique, which partitions time series into multiple patches to effectively retain contextual semantic information into a representation space beneficial for modeling long-term dependencies. However, conventional patching partitions a time series into adjacent patches, which cau

Jingyao Liu, Chen Huang, Zhizhao Guan, Wenqiang Lei

The rapid advancement in large language models (LLMs) has demonstrated significant potential in End-to-End Software Development (E2ESD). However, existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols, hindering a true understanding of current framework capabilities. To address these limitations

Marcin Korecki, Cesare Carissimo

This paper investigates the concept of Labour as an expression of `timenergy' - a fusion of time and energy - and its entanglement within the system of Capital. We define Labour as the commodified, quantifiable expansion of timenergy, in contrast to Capital, which is capable of accumulation and abstraction. We explore Labour's historical evolution, its coerc

Petr Kim, Georgii Makoian

This work studies circle-geometry methods through their application to a main theorem about circles tangent twice to a conic. The authors investigate the Sharygin point -- a point lying in the pencil of two non-intersecting circles -- and explore its properties. These properties are applied to solve several olympiad problems, such as problems from MGO 2024 a

Qin Yi, Zeping Sui, Zilong Liu

This paper studies the error rate performance and low-complexity receiver design for zero-padded affine frequency division multiplexing (ZP-AFDM) systems. By exploiting the unique ZP-aided lower triangular structure of the time domain (TD) channel matrix, we propose a novel low-complexity minimum mean square error (MMSE) detector and a maximum ratio combinin

Michael A. Garrett, Kathryn Denning, Leslie I. Tennen, Carol Oliver

The International Academy of Astronautics (IAA) SETI Committee has long provided guiding principles for responding to a potential detection of a SETI signal. The foundational Declaration of Principles Concerning Activities Following the Detection of Extraterrestrial Intelligence, first formulated in 1989, has been widely recognised by the international scien

Takayuki Goto, Mizuki Miyajima, Takashi Kambe

We report $^{23}$Na-NMR study on a candidate one-dimensional quantum spin system NaO$_2$. The Knight shift, linewidth, and spin-lattice relaxation rate $1/T_1$ were investigated down to 0.3 K under fields up to 16 T. The results reveal the opening of a spin gap of $\Delta(10.1 {\rm T}) \simeq$ 38 K below $T_{\rm S3} =$ 40 K, consistent with a spin-Peierls-li

Matt Grenander, Shay B. Cohen, Mark Steedman

Seq2seq coreference models have introduced a new paradigm for coreference resolution by learning to generate text corresponding to coreference labels, without requiring task-specific parameters. While these models achieve new state-of-the-art performance, they do so at the cost of flexibility and efficiency. In particular, they do not efficiently handle incr

Andrejs Sorstkins, Omer Tariq, Muhammad Bilal

This paper proposes a reversible learning framework to improve the robustness and efficiency of value based Reinforcement Learning agents, addressing vulnerability to value overestimation and instability in partially irreversible environments. The framework has two complementary core mechanisms: an empirically derived transition reversibility measure called

Eva-Maria Maier, Alexander Fottner, Sonja Greven, Almond Stöcker

We present a structured additive regression approach to model conditional densities given scalar covariates, where only samples of the conditional distributions are observed. This links our approach to distributional regression models for scalar data. The model is formulated in a Bayes Hilbert space -- preserving nonnegativity and integration to one under su

Norihisa Namura, Riccardo Muolo, Hiroya Nakao

The Kuramoto model is the simplest case of globally coupled phase oscillators with a purely sinusoidal fundamental-harmonic phase coupling function, whose dynamical properties have been extensively studied. While coupled phase oscillators are derived from weakly interacting limit-cycle oscillators via phase reduction, this procedure does not necessarily yiel

Sergey K. Ivanov, Yaroslav V. Kartashov, Vladimir V. Konotop

We develop a theory of two-dimensional Bloch-Landau-Zener (BLZ) oscillations of wavepackets in incommensurate moir\'e lattices under the influence of a weak linear gradient. Unlike periodic systems, aperiodic lattices lack translational symmetry and therefore do not exhibit a conventional band-gap structure. Instead, they feature a mobility edge, above which

Keshab Chandra Bakshi, Satyajit Guin, Guruprasad

Given two distinct complex Hadamard matrices belonging to the same equivalence class generated by the tensor products of Fourier matrices, we show that if the corresponding Hadamard subfactors are conjugate, then their intersection is a factor with finite Jones index. We compute the index of the intersection explicitly and determine its relative commutant. F

J. M. Camacho-Sánchez, M. Lorite-Díez, Y. Fan, J. I. Jiménez-González

This study investigates the potential for drag-reduction of low-mechanical-order, self-adaptive control systems, consisting of hinged flaps attached along the edges of the rectangular base of a canonical blunt body. Comparative experiments are conducted in a wind tunnel under crosswind conditions at a Reynolds number of $Re = 2.13 \times 10^5$. The flaps, ma

Ioannis Zachos, Zhihao Zhao

We construct the Bruhat-Tits stratification of the ramified unitary splitting Rapoport-Zink space, with the level being the stabilizer of a vertex lattice. To determine certain local properties of the Bruhat-Tits strata, we develop a theory of the strata splitting models. To study their global structure, we establish an explicit isomorphism between the Bruha

Sara Piloneta, Alexey Vladimirov

We evaluate the complete set of kinematic power corrections (KPCs) to the leading power (LP) term of the transverse momentum dependent (TMD) factorization theorem for semi-inclusive deep-inelastic scattering (SIDIS) with a polarized target. This formulation restores the contributions of twist-two TMD distributions to all structure functions, including those

Ella B Schneider, Oscar G Lloyd-Willard, Kristian Stockbridge, Mark Ludlow

Deterministic placement of single dopants is essential for scalable quantum devices based on group-V donors in silicon. We demonstrate a non-destructive, high-efficiency method for detecting individual ion implantation events using secondary electrons (SEs) in a focused ion beam (FIB) system. Using low-energy Sb ions implanted into undoped silicon, we achiev

Ziad Akram Ali Hammouri, Yating Zou, Rahul Ghosal, Juan C. Vidal

The receiver operating characteristic (ROC) curve and its summary measure, the Area Under the Curve (AUC), are well-established tools for evaluating the efficacy of biomarkers in biomedical studies. Compared to the traditional ROC curve, the covariate-adjusted ROC curve allows for individual evaluation of the biomarker. However, the use of machine learning m

Aleksis Pirinen, Delia Fano Yela, Smita Chakraborty, Erik Källman

Grazing shapes both agricultural production and biodiversity, yet scalable monitoring of where grazing occurs remains limited. We study seasonal grazing detection from Sentinel-2 L2A time series: for each polygon-defined field boundary, April-October imagery is used for binary prediction (grazed / not grazed). We train an ensemble of CNN-LSTM models on multi

Tomonari Mizoguchi

We study the $S=1/2$ square-kagome lattice Heisenberg antiferromagnet with the trimarized modulation. In the trimerized limit, each trimer hosts the four-fold degenearte ground states characterized by the spin and chirality degrees of freedom. We find that, within the first-order perturbation theory with respect to the inter-trimer coupling, the effective Ha

Gye-Hyeon Kim, Tae Hyun Jung, Seungjoon Sun, Jung Kyu Lee

Although ferroelectric systems inherently exhibit binary switching behavior, recent advances in analog memory device have spurred growing interest in achieving continuous memory states. In this work, we demonstrate ferroelectric amplitude switching at the mesoscopic scale in compositionally graded Ba1-xSrxTiO3 heterostructures, enabling continuous modulation

Zhiyuan Hu, Fakhriyya Mammadova, Julián Tachella, Michael Unser

Phase retrieval is a nonlinear inverse problem that arises in a wide range of imaging modalities, from electron microscopy to Fourier ptychography. In particular, the reconstruction is facilitated when the sensing matrix is i.i.d. random, enabling strong theoretical guarantees and efficient reconstruction algorithms. However, its applicability is restricted

Sujai Hiremath, Dominik Janzing, Philipp Faller, Patrick Blöbaum

Causal discovery algorithms often perform poorly with limited samples. While integrating expert knowledge (including from LLMs) as constraints promises to improve performance, guarantees for existing methods require perfect predictions or uncertainty estimates, making them unreliable for practical use. We propose the Guess2Graph (G2G) framework, which uses e

Riccardo Basei, Francesco Pase, Francesco Lucchini, Francesco Toso

Efficient modeling of High Temperature Superconductors (HTSs) is crucial for real-time quench monitoring; however, full-order electromagnetic simulations remain prohibitively costly due to the strong nonlinearities. Conventional projection-based reduced-order modeling pipelines for nonlinear problems, such as Proper Orthogonal Decomposition (POD)-Discrete Em

Roy Urbach, Elad Schneidman

Brains learn to represent information from a large set of stimuli, typically by weak supervision. Unsupervised learning is therefore a natural approach for exploring the design of biological neural networks and their computations. Accordingly, redundancy reduction has been suggested as a prominent design principle of neural encoding, but its ``mechanistic''

S. V. Anishchenko, P. V. Molchanov

The theory of coherent transition radiation produced by a relativistic electron beam during its extraction from a microtron is established. Expressions for the beam form factor, spectral-angular and angular distribution of coherent transition radiation are obtained in explicit form. Estimates of microwave noise caused by coherent transition radiation are giv

Ioannis Apergis, Daniel Bayliss, Leonidas Asimakoulas, Paul Chote

Scientific CMOS cameras are becoming increasingly prevalent in modern observational astronomy. We assess the ability of CMOS image sensors technology to perform high-precision photometry with a detailed laboratory characterization of the Marana 4.2BV-11 CMOS camera. We characterise the camera in the Fastest Frame Rate (FFR) and High Dynamic Range (HDR) modes

Tadashi Udagawa

The tt*-equation (topological-anti-topological fusion equation) was introduced by S. Cecotti and C. Vafa for describing massive deformation of supersymmetric conformal field theories. B. Dubrovin formulated the tt*-equation as a flat bundle, called tt*-structure. In this paper, we construct a tt*-structure for the quantum cohomology of the Grassmannian of co

Jüri Lember, Raul Kangro, Kristi Kuljus

We consider a new method for estimating the parameters of univariate Gaussian mixture models. The method relies on a nonparametric density estimator $\hat{f}_n$ (typically a kernel estimator). For every set of Gaussian mixture components, $\hat{f}_n$ is used to find the best set of mixture weights. That set is obtained by minimizing the $L_2$ distance betwee

Massimo Bartoletti, Riccardo Marchesin, Roberto Zunino

Maximal Extractable Value (MEV) refers to a class of attacks to decentralized applications where the adversary profits by manipulating the ordering, inclusion, or exclusion of transactions in a blockchain. Decentralized Finance (DeFi) protocols are a primary target of these attacks, as their logic depends critically on transaction sequencing. To date, MEV at

Gaofeng Fan, Yu Meng, Chuan Liu, Zhaofeng Liu

We present a systematic lattice calculation of the $D_s \to \phi \ell \nu_\ell$ semileptonic decay using (2+1)-flavor Wilson-clover fermion configurations generated by the CLQCD collaboration. Seven gauge ensembles with different lattice spacings, from $0.052~\text{fm}$ to $0.105~\text{fm}$, and different pion masses, from about $210~\text{MeV}$ to $320~\tex

Xiang Chen, Ce Xu, Jianing Zhou

This paper investigates a class of special Berndt-type integral calculations where the integrand contains only hyperbolic cosine functions. The research approach proceeds as follows: Firstly, through contour integration methods, we transform the integral into a Ramanujan-type hyperbolic infinite series. Subsequently, we introduce a $\theta$-parameterized aux

Theophilus Gera, Manoj Kumar Patel, Ashok Ji Gupta

We investigate endoartinian modules, which satisfy the descending chain condition on endoimages, and establish new characterizations that unify classical and generalized chain conditions. Over commutative rings, endoartinianity coincides with rings satisfying the strongly ACCR* with dim(R) = 0 and strongly DCCR* conditions. For principally injective rings, t

Elismar R. Oliveira

In this paper we discuss a new method to blend fractal attractors using the code map for the IFS formed by the Hutchinson--Barnsley operators of a finite family of hyperbolic IFSs. We introduce a parameter called blending coefficient to measure the similarity between the blended set and each one of the original attractors. We also introduce a discrete approx

Andrew Darlington, Cindy Tsang

Let $L/K$ be any finite separable extension with normal closure $\widetilde{L}/K$. An extension $L'/K$ is said to be $\textit{parallel to $L/K$}$ if $L'$ is an intermediate field of $\widetilde{L}/K$ with $[L':K]=[L:K]$. We study the following question -- Given that $L/K$ admits a Hopf--Galois structure of type $N$, does it imply that every extension paralle

Paolo Foschi

The use of the Preconditioned Conjugate Gradient (PCG) method for computing the Generalized Least Squares (GLS) estimator of the General Linear Model (GLM) is considered. The GLS estimator is expressed in terms of the solution of an augmented system. That system is solved by means of the PCG method using an indefinite preconditioner. The resulting method ite

Xiaoyu Xue, Yuni Lai, Chenxi Huang, Yulin Zhu

The emergence of graph foundation models (GFMs), particularly those incorporating language models (LMs), has revolutionized graph learning and demonstrated remarkable performance on text-attributed graphs (TAGs). However, compared to traditional GNNs, these LM-empowered GFMs introduce unique security vulnerabilities during the unsecured prompt tuning phase t

Ricardo Z. Ferreira, M. C. David Marsh, Eike Ravensburg

We provide a comprehensive analysis of the phenomenology of axion-like particles (ALPs) produced in core-collapse supernovae (ccSNe) through interactions with electrons and muons, both of which have a non-negligible abundance in the SN plasma. We identify and calculate six significant ALP-production channels, two of which are loop-level processes involving p

Lucas Vicente García-Consuegra, Azadeh Maleknejad

We formulate a stochastic generalisation of the Schwinger effect, extending pair production to statistically fluctuating gauge-field backgrounds. Our approach captures realistic field configurations that are transient, inhomogeneous, and stochastic, as commonly encountered in cosmological and high-energy astrophysical settings. Using the effective action for

Shang-Fu Chen, Co Yong, Shao-Hua Sun

Imitation learning (IL) aims to learn a policy from expert demonstrations and has been applied to various applications. By learning from the expert policy, IL methods do not require environmental interactions or reward signals. However, most existing imitation learning algorithms assume perfect expert demonstrations, but expert demonstrations often contain i

Adem Ait, Gwendal Jouneaux, Javier Luis Cánovas Izquierdo, Jordi Cabot

The stakeholders involved in software development are becoming increasingly diverse, with both human contributors from varied backgrounds and AI-powered agents collaborating together in the process. This situation presents unique governance challenges, particularly in Open-Source Software (OSS) projects, where explicit policies are often lacking or unclear.

Junhao Zhan, Christian L. Reichardt

We examine the potential improvements in constraints on the dark energy equation of state parameter $w$ and matter density $\Omega_M$ from using clustering information along with number counts for future samples of thermal Sunyaev-Zel'dovich selected galaxy clusters. We quantify the relative improvement from including the clustering power spectrum informatio

Thomas Katraouras, Dimitrios Rafailidis

Image quality is a critical factor in delivering visually appealing content on web platforms. However, images often suffer from degradation due to lossy operations applied by online social networks (OSNs), negatively affecting user experience. Image restoration is the process of recovering a clean high-quality image from a given degraded input. Recently, mul

Youwan Mahé, Elise Bannier, Stéphanie Leplaideur, Elisa Fromont

Unsupervised anomaly detection (UAD) based on deep generative modelling has been increasingly explored for identifying pathological brain abnormalities without requiring voxel-level annotations. By learning the distribution of healthy anatomy and generating pseudo-healthy reconstructions, these methods aim to localise deviations in a pathology-agnostic manne

Karine Beauchard, Rémi Carles, Eugenio Pozzoli

We consider Schr{\"o}dinger equations with logarithmic nonlinearity and bilinear controls, posed on $\mathbb{T}^d$ or $\mathbb{R}^d$. We prove their small-time global $L^2$-approximate controllability. The proof consists in extending to this nonlinear framework the approach introduced by the first and third authors in \cite{beauchard-pozzoli2} to control the

Sven Jacob, Weijia Shao, Gjergji Kasneci

Video-based object detection plays a vital role in safety-critical applications. While deep learning-based object detectors have achieved impressive performance, they remain vulnerable to adversarial attacks, particularly those involving universal perturbations. In this work, we propose a minimally distorted universal adversarial attack tailored for video ob

Ling Zhang, Xianliang Yang, Juwon Yu, Park Cheonyoung

Fine-tuning large pretrained language models is a common approach for aligning them with human preferences, but noisy or off-target examples can dilute supervision. While small, well-chosen datasets often match the performance of much larger ones, systematic and efficient ways to identify high-value training data remain underexplored. Many current methods re

Askhat Mukanov, Erlan Nursultanov

We introduce new classes of general monotone sequences and study their properties. For functions whose Fourier coefficients belong to these classes, we establish Hardy-Littlewood-type theorems.

Tung Phung, Heeryung Choi, Mengyan Wu, Christopher Brooks

Timely and high-quality feedback is essential for effective learning in programming courses; yet, providing such support at scale remains a challenge. While AI-based systems offer scalable and immediate help, their responses can occasionally be inaccurate or insufficient. Human instructors, in contrast, may bring more valuable expertise but are limited in ti

Ching-Yang Pan, Shi-Kai Lin, Yu-An Chen, Pei-hsun Jiang

This work provides a detailed mapping of various mechanisms of surface-trap-induced gate leakage in GaN HEMTs across a temperature range from room to cryogenic levels. Two-dimensional variable-range hopping is observed at small gate bias. Under higher reverse gate bias, the leakage is dominated by the Poole--Frenkel emission above 220 K, but gradually transi

Wenyu Zhu, Chengzhu Li, Xiaohe Tian, Yifan Wang

Molecular optimization is a central task in drug discovery that requires precise structural reasoning and domain knowledge. While large language models (LLMs) have shown promise in generating high-level editing intentions in natural language, they often struggle to faithfully execute these modifications-particularly when operating on non-intuitive representa

Tao Huang, Huayi Wang, Junli Ren, Kangning Yin

Humanoid robots are envisioned to adapt demonstrated motions to diverse real-world conditions while accurately preserving motion patterns. Existing motion prior approaches enable well adaptability with a few motions but often sacrifice imitation accuracy, whereas motion-tracking methods achieve accurate imitation yet require many training motions and a test-

Reid T. Johnson, Michelle D. Pain, Jordan D. West

We present Natural Language Tools (NLT), a framework that replaces programmatic JSON tool calling in large language models (LLMs) with natural language outputs. By decoupling tool selection from response generation, NLT eliminates task interference and format constraints that degrade tool call performance. When evaluated across 10 models and 6,400 trials spa

Akihiro Minamide, Youichi Yanase

CeRh$_{2}$As$_{2}$, a heavy fermion superconductor discovered in 2021, exhibits two distinct superconducting phases under a $c$-axis magnetic field. This unconventional phase diagram has been attributed to the local inversion symmetry breaking at the Ce sites. At low magnetic fields, a conventional even-parity spin-singlet superconducting state is realized,