October 2025 arXiv papers — page 113

T. V. Pavlova

The precise incorporation of a phosphorus atom into a silicon surface is essential for the fabrication of nanoelectronic devices in which the active area is formed from single impurities. The most accurate approach employs scanning tunneling microscopy (STM) lithography, which may be done with atomic precision. However, the accuracy decreases when phosphorus

Mateusz Kędziora, Andrzej Opala, Maciej Zaremba, Helgi Sigurðsson

Controlling the spin degree of freedom of light at the microscale is crucial for advancing photonic information processing. Spin polarized light propagation, combined with strong optical nonlinearities, unlocks new functionalities in compact photonic circuits and active spin optronic devices. Lead halide perovskite exciton polaritons uniquely combine room te

Olof Runborg, Elliot Backman

In this paper we analyse the Waveholtz method, a time-domain iterative method for solving the Helmholtz iteration, in the constant-coefficient case in all of $\mathbb{R}^d$. We show that the difference between a Waveholtz iterate and the outgoing Helmholtz solution satisfies a Helmholtz equation with a particular kind of forcing. For this forcing, we prove a

Yelda Gülfırat, Mehmet Ünver

In this paper, we introduce the concept of the circular complex $q$-rung orthopair fuzzy set (CC$q$-ROFS) as a novel generalization that unifies the existing frameworks of circular complex intuitionistic fuzzy sets (CCIFSs) and complex $q$-rung orthopair fuzzy sets. If $q = 2$, the structure is referred to as a circular complex Pythagorean fuzzy set, and if

Chen Zhang, Patrick Henning, Mahima Yadav, Wenbin Chen

This paper studies the numerical approximation of the ground state of rotating Bose--Einstein condensates, formulated as the minimization of the Gross--Pitaevskii energy functional under a mass conservation constraint. To solve this problem, we consider three Sobolev gradient flow schemes: the $H_0^1$ scheme, the $a_0$ scheme, and the $a_u$ scheme. Convergen

Elisabetta Carlini, Luca Saluzzi

We introduce a fully discrete scheme to solve a class of high-dimensional Mean Field Games systems. Our approach couples semi-Lagrangian (SL) time discretizations with Tensor-Train (TT) decompositions to tame the curse of dimensionality. By reformulating the classical Hamilton-Jacobi-Bellman and Fokker-Planck equations as a sequence of advection-diffusion-re

Andrei-Timotei Ardelean, Patrick Rückbeil, Tim Weyrich

Zero-shot anomaly localization is a rising field in computer vision research, with important progress in recent years. This work focuses on the problem of detecting and localizing anomalies in textures, where anomalies can be defined as the regions that deviate from the overall statistics, violating the stationarity assumption. The main limitation of existin

Pierre Glaser, Steffanie Paul, Alissa M. Hummer, Charlotte M. Deane

We propose a set of kernel-based tools to evaluate the designs and tune the hyperparameters of conditional sequence models, with a focus on problems in computational biology. The backbone of our tools is a new measure of discrepancy between the true conditional distribution and the model's estimate, called the Augmented Conditional Maximum Mean Discrepancy (

Haoran Sun, Yankai Jiang, Zhenyu Tang, Yaning Pan

The foundation of reproducible science lies in protocols that are precise, logically ordered, and executable. The autonomous generation of these protocols through natural language queries could greatly improve the efficiency of the reproduction process. However, current leading large language models (LLMs) often generate incomplete or inconsistent protocols,

T. V. Pavlova, V. M. Shevlyuga

Phosphorus diffusion on a Si(100) surface was studied using scanning tunneling microscopy (STM) at temperatures of 77 and 300 K. The phosphorus source utilized was the PBr$_3$ molecule, which fully dissociates on the surface at 77 K. We observed diffusion of P atoms both along and across the rows of Si dimers. To support the observation of different diffusio

Michael Sebek

We develop observer design over hypercomplex quaternions in a characteristic-polynomial-free framework. Using the standard right-module convention, we derive a right observable companion form and companion polynomial that encode error dynamics through right-eigenvalue similarity classes. We also give an Ackermann-type formula for real-coefficient target poly

Maria Caterina Crocco, Flavio Cognigni, Alessia Sanna, Raffaele Filosa

Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, Xray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of st

Alicia Golden, Michael Kuchnik, Samuel Hsia, Zachary DeVito

Large model training beyond tens of thousands of GPUs is an uncharted territory. At such scales, disruptions to the training process are not a matter of if, but a matter of when -- a stochastic process degrading training productivity. Dynamic runtime variation will become increasingly more frequent as training scales up and as GPUs are operated in increasing

Zhen Sun, Lei Tan, Yunhang Shen, Chengmao Cai

Multimodal person re-identification (Re-ID) aims to match pedestrian images across different modalities. However, most existing methods focus on limited cross-modal settings and fail to support arbitrary query-retrieval combinations, hindering practical deployment. We propose FlexiReID, a flexible framework that supports seven retrieval modes across four mod

Antoine Bourgois, Thierry Poibeau

While coreference resolution is attracting more interest than ever from computational literature researchers, representative datasets of fully annotated long documents remain surprisingly scarce. In this paper, we introduce a new annotated corpus of three full-length French novels, totaling over 285,000 tokens. Unlike previous datasets focused on shorter tex

Paul Sievers, George Skretas, Georg Tennigkeit

Network redesign problems ask for modifications to the edges of a given graph to satisfy certain properties. In temporal graphs, where edges are only active at certain times, we are sometimes only allowed to modify when the edges are going to be active. In practice, we might not even be able to perform all of the necessary modifications at once; changes must

Palki Gakkhar, D Angom, B K Mani

We employ an all-particle multireference Fock-space relativistic coupled-cluster (FSRCC) theory to study the $5s^2{\;^1}S_0 - 5s5p{\;^3P^o_0}$ clock transition in both Fermionic and Bosonic isotopes of Sr. We compute the excitation energies, E1 and M1 transition amplitudes, hyperfine reduced matrix elements, and isotope shifts using FSRCC theory. Further, we

Lavanya Umapathy, Patricia M Johnson, Tarun Dutt, Angela Tong

Temporal context in medicine is valuable in assessing key changes in patient health over time. We developed a machine learning framework to integrate diverse context from prior visits to improve health monitoring, especially when prior visits are limited and their frequency is variable. Our model first estimates initial risk of disease using medical data fro

Félix Cache, Yoann Baron, Baptiste Lefaucher, Jean-Baptiste Jager

We demonstrate single spin spectroscopy of a fluorescent tumbling defect in silicon called the G center, behaving as a pseudo-molecule randomly reorienting itself in the crystalline matrix. Using high-resolution spin spectroscopy, we reveal a fine magnetic structure resulting from the spin principal axes jumping between discrete orientations in the crystal.

Harkaitz Goyena, Peter M. Atkinson, Unai Pérez-Goya, M. Dolores Ugarte

Spatio-Temporal Image Fusion (STIF) methods usually require sets of images with matching spatial and spectral resolutions captured by different sensors. To facilitate the application of STIF methods, we propose and compare two different standardization approaches. The first method is based on traditional upscaling of the fine-resolution images. The second me

João C. Neves, Bernardo R. Marques, Cristóvão S. Dias, Nuno A. M. Araújo

Folding is emerging as a promising manufacturing process to transform flat materials into functional structures, offering efficiency by reducing the need for welding, gluing, and molding, while minimizing waste and enabling automation. Designing target shapes requires not only to determine cuts and folds, but also folding pathways. Simple combinatorics is im

Enrique Arévalo Rodríguez, Marc Meléndez Schofield, Jorge Cuadra, Ferry Prins

Research on energy transport has advanced in recent years with the emergence of transient microscopy techniques that allow for imaging of carriers with high spatial and temporal resolution. In this context, transient scattering microscopy (TScM), has emerged as an alternative to traditional techniques. However, the sensitivity of TScM to different carriers c

Björn Schäfer

We study C*-algebras generated by two partitions of unity with orthogonality relations governed by hypercubes $Q_n$ for $n \in \mathbb{N} \setminus \{0\}$. These "hypercube C*-algebras'' are special cases of bipartite graph C*-algebras which have been investigated by the author in a previous work. We prove that the hypercube C*-algebras $C^\ast(Q_n)$ are sub

Simon Thorne, Advait Sarkar

Large Language Models (LLMs), such as ChatGPT, are increasingly leveraged for generating both traditional software code and spreadsheet logic. Despite their impressive generative capabilities, these models frequently exhibit critical issues such as hallucinations, subtle logical inconsistencies, and syntactic errors, risks particularly acute in high stakes d

Dingguo Zheng, Ofer Kfir

The quantum coupling between free-electrons and photons enables applying quantum optics techniques in electron microscopy. Here, we formulate the elastic electron-photon quantum coupling and its possible implications. Our analysis shows that when an electron traverses the field of an optical cavity, it induces a phase shift onto its confined photonic mode, w

Kerem Delikoyun, Qianyu Chen, Win Sen Kuan, John Tshon Yit Soong

Emergency departments worldwide face rising patient volumes, workforce shortages, and variability in triage decisions that threaten the delivery of timely and accurate care. Current triage methods rely primarily on vital signs, routine laboratory values, and clinicians' judgment, which, while effective, often miss emerging biological signals that could impro

Jixin Zhang

We propose an Attention Enhanced Join-Graph Neural Networks(Attn-JGNN) model for solving #SAT problems, which significantly improves the solving accuracy. Inspired by the Iterative Join Graph Propagation (IJGP) algorithm, Attn-JGNN uses tree decomposition to encode the CNF formula into a join-graph, then performs iterative message passing on the join-graph,

Jianguo Chen, Jinlong Lei, Biqiang Mu, Yiguang Hong

Inverse game theory is utilized to infer the cost functions of all players based on game outcomes. However, existing inverse game theory methods do not consider the learner as an active participant in the game, which could significantly enhance the learning process. In this paper, we extend inverse game theory to active inverse methods. For Stackelberg games

Junhao Cai, Yuhan Li, Yunqiao Liu, Benlong Wang

Laser-induced cavitation in liquids originates from optical breakdown processes that depend sensitively on both laser-plasma dynamics and the chemical microenvironment of the solvent. Herein, we experimentally decouple the effects of ionic strength and ion specificity on cavitation inception in aqueous electrolytes spanning neutral, acidic, and alkaline regi

Alcides Buss, Damián Ferraro

We resolve key open questions regarding approximation properties and their permanence for Fell bundles over locally compact groups. Specifically, we establish the equivalence between the B\'edos--Conti approximation property (BCAP) and the Exel--Ng positive approximation property (AP), completely removing the necessity of assuming nuclearity on the unit fibe

Kyle Stanley, Nicole Lazar, Matthew Reimherr

Many analyses of functional magnetic resonance imaging (fMRI) examine functional connectivity (FC), or the statistical dependencies among distant brain regions. These analyses are typically exploratory, guiding future confirmatory research. In this work, we present an approach based on factor analysis (FA) that is well-suited to studying FC. FA is appealing

Martin Bicher, Dominik Brunmeir, Claire Rippinger, Christoph Urach

The Generic Population Concept - Agent-Based Model, henceforth short, GEPOC ABM, is one of the models within GEPOC, a generic concept to model a country's population and its dynamics using causal modelling approaches. The model is well established and had already proven its worth in various use cases from evaluation of MMR vaccination rates to SARS-CoV-2 epi

Mohammad Soltaninezhad, Yashar Rouzbahani, Jhonatan Contreras, Rohan Chippalkatti

Lightweight deep learning models offer substantial reductions in computational cost and environmental impact, making them crucial for scientific applications. We present a lightweight CycleGAN for modality transfer in fluorescence microscopy (confocal to super-resolution STED/deconvolved STED), addressing the common challenge of unpaired datasets. By replaci

Osmar M. Guerra-Alvarado, N. van der Marel, P. Nazari, J. Di Francesco

Aims. Studying protostellar objects in their earliest stages, particularly during the Class 0 phase, provides key insight into the beginnings of planet formation and dust evolution. Disentangling the various components, however, is particularly challenging. High spatial and spectral resolution observations of molecular line emission with the Atacama Large Mi

Xiaotian Wang, Takehito Utsuro, Masaaki Nagata

Document alignment is necessary for the hierarchical mining (Ba\~n\'on et al., 2020; Morishita et al., 2022), which aligns documents across source and target languages within the same web domain. Several high precision sentence embedding-based methods have been developed, such as TK-PERT (Thompson and Koehn, 2020) and Optimal Transport (OT) (Clark et al., 20

Sami Belguesmia, Mohand Saïd Allili, Assia Hamadene

DeepFake technology has advanced significantly in recent years, enabling the creation of highly realistic synthetic face images. Existing DeepFake detection methods often struggle with pose variations, occlusions, and artifacts that are difficult to detect in real-world conditions. To address these challenges, we propose a multi-view architecture that enhanc

Yi Tao, Zhen Gao, Zhuoran Li, Ziwei Wan

The integrated sensing and communications (ISAC) can achieve the sharing of hardware and spectrum resources, enabling efficient data transmission and environmental sensing. This fusion is particularly important for unmanned aerial vehicle (UAV) swarms, as it enhances the overall performance, flexibility, and efficiency of such systems. To facilitate the coll

Mahtab Vafaeefar, Conall Quinn, Ted J. Vaughan

In early years of life, the cranium rapidly changes in size and shape to accommodate brain growth, primarily driven by mechanical stress from brain expansion. Developmental disorders such as premature fusion of sutures in craniosynostosis, disrupts normal growth process, leading to abnormal skull shapes. Thus, understanding the interplay between biomechanica

Jianguo Chen, Zhengqin Liu, Jinlong Lei, Peng Yi

With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the influence of HVs, necessitating accurate HV trajectory predi

Kamel Lahssini, Guerric le Maire, Nicolas Baghdadi, Ibrahim Fayad

Quantifying aboveground biomass (AGB) is essential in the context of global climate change. Canopy height, which is related to AGB, can be mapped using machine learning models trained with multi-source spatial data and GEDI measurements. In this study, a comparative analysis of canopy height estimates derived from two models is presented: a U-Net deep learni

Marcin Hajduk, Timothy Shimwell, Glenn White, Marijke Haverkorn

Context. Planetary nebulae are shells ejected by low- and intermediate-mass stars. The slow wind ejected by the asymptotic giant branch star is compressed by a fast stellar wind to produce an expanding gaseous shell surrounding a hot bubble. The shell is a source of thermal radio emission which shows a spectral index between -0.1 and 2. Only two planetary ne

Hao Peng, Haochen Liu, Chuhao Li, Hehu Xie

Attaining a reliable complete basis set (CBS) limit remains a significant challenge in ab initio correlated electronic-structure calculations. Building on our previous work for atoms and diatomic molecules, we present a finite-element (FE) Delta Sternheimer approach for numerically accurate random phase approximation (RPA) calculations applicable to general

Syed Mohammad Sualeh Ali

This paper delves into the intricate world of Urdu poetry, exploring its thematic depths through a lens of polysemy. By focusing on the nuanced differences between three seemingly synonymous words (pyaar, muhabbat, and ishq) we expose a spectrum of emotions and experiences unique to the Urdu language. This study employs a polysemic case study approach, metic

Alexander Doudkin, Anton Voelker, Friedrich von Borries

Creative services teams increasingly rely on large language models (LLMs) to accelerate ideation, yet production systems often converge on homogeneous outputs that fail to meet brand or artistic expectations. Art of X developed persona-conditioned LLM agents -- internally branded as "Sparks" and instantiated through a library of role-inspired system prompts

Eduard Andrei Cristea, Petter Molnes, Jingyue Li

Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools that attribute malware to the specific software vulnerabilities it exploits are largely lacking. Understanding the connection between malware and the vulnerabilities it targets is crucial for analyzing past threats

Rachmad Vidya Wicaksana Putra, Aadithyan Rajesh Nair, Muhammad Shafique

Speech disorders can significantly affect the patients capability to communicate, learn, and socialize. However, existing speech therapy solutions (e.g., therapist or tools) are still limited and costly, hence such solutions remain inadequate for serving millions of patients worldwide. To address this, state-of-the-art methods employ neural network (NN) algo

Vinicius Moraes de Jesus, Andre Georghton Cardoso Pacheco

The widespread adoption of wearable devices such as smartwatches and fitness trackers has fueled the demand for reliable physiological and movement data collection tools. However, challenges such as limited access to large, high-quality public datasets and a lack of control over data collection conditions hinder the development of robust algorithms. This wor

Xiaoming Zhu, Xu Huang, Qinghongbing Xie, Zhi Deng

Generating artistic and coherent 3D scene layouts is crucial in digital content creation. Traditional optimization-based methods are often constrained by cumbersome manual rules, while deep generative models face challenges in producing content with richness and diversity. Furthermore, approaches that utilize large language models frequently lack robustness

Edward Tansley, Estelle Massart, Coralia Cartis

Understanding feature learning is an important open question in establishing a mathematical foundation for deep neural networks. The Neural Feature Ansatz (NFA) states that after training, the Gram matrix of the first-layer weights of a deep neural network is proportional to some power $\alpha>0$ of the average gradient outer product (AGOP) of this network w

Zhiwei Liu, Bo Che, Hailin Zhang, Linhong Deng

Airway mucus is a complex gel with an anisotropic three-dimensional network structure. As a crucial component of the respiratory defense barrier, it plays a vital role in maintaining airway hydration and supporting the function of airway epithelial cells. Through linear and nonlinear rheological mechanisms such as ciliary motion and coughing, airway mucus ex

Josef Jon, Ondřej Bojar

In this paper, we present our submission for the token prediction task of EvaCun 2025. Our sys-tems are based on LLMs (Command-R, Mistral, and Aya Expanse) fine-tuned on the task data provided by the organizers. As we only pos-sess a very superficial knowledge of the subject field and the languages of the task, we simply used the training data without any ta

Jiayuan Bai, Xuan-guang Pan, Chongyang Tao, Shuai Ma

Text-to-SQL is a pivotal task that bridges natural language understanding and structured data access, yet it remains fundamentally challenging due to semantic ambiguity and complex compositional reasoning. While large language models (LLMs) have greatly advanced SQL generation though prompting, supervised finetuning and reinforced tuning, the shift toward te

Dongjun Kim, Chanhee Park, Chanjun Park, Heuiseok Lim

The instruction-following capabilities of large language models (LLMs) are pivotal for numerous applications, from conversational agents to complex reasoning systems. However, current evaluations predominantly focus on English models, neglecting the linguistic and cultural nuances of other languages. Specifically, Korean, with its distinct syntax, rich morph

Tingyu Lin, Marco Peer, Florian Kleber, Robert Sablatnig

This paper presents ClapperText, a benchmark dataset for handwritten and printed text recognition in visually degraded and low-resource settings. The dataset is derived from 127 World War II-era archival video segments containing clapperboards that record structured production metadata such as date, location, and camera-operator identity. ClapperText include

Abdelilah Ganmati, Karim Afdel, Lahcen Koutti

We present a practical match-on-card design for face verification in which compact 64/128-bit templates are produced off-card by PCA-ITQ and compared on-card via constant-time Hamming distance. We specify ISO/IEC 7816-4 and 14443-4 command APDUs with fixed-length payloads and decision-only status words (no score leakage), together with a minimal per-identity

Yitong Li, Ralph Buchert, Benita Schmitz-Koep, Timo Grimmer

Positron emission tomography (PET) with 18F-Fluorodeoxyglucose (FDG) is an established tool in the diagnostic workup of patients with suspected dementing disorders. However, compared to the routinely available magnetic resonance imaging (MRI), FDG-PET remains significantly less accessible and substantially more expensive. Here, we present SiM2P, a 3D diffusi

Sibo Xiao

We introduce the Strategic Doubly Robust (SDR) estimator, a novel framework that integrates strategic equilibrium modeling with doubly robust estimation for causal inference in strategic environments. SDR addresses endogenous treatment assignment arising from strategic agent behavior, maintaining double robustness while incorporating strategic considerations

Han Ye, Guoding Liu, Xiongfeng Ma

Quantum gate benchmarking is unavoidably influenced by state preparation and measurement errors. Randomized benchmarking addresses this challenge by employing group twirling to regularize the noise channel, then provides a characterization of quantum channels that is robust to these errors through exponential fittings. In practice, local twirling gates are p

Linda Cook, Ross J. Kang, Eileen Robinson, Gabriëlle Zwaneveld

Given a graph $G$, let $\Delta_2(G)$ denote the maximum number of neighbors any two distinct vertices of $G$ have in common. Vu (2002) proposed that, provided $\Delta_2(G)$ is not too small as a proportion of the maximum degree $\Delta(G)$ of $G$, the chromatic number of $G$ should never be too much larger than $\Delta_2(G)$. We make a first approach towards

Jinliang Liu, Jiale Bai, Shaoning Zeng

Large language models (LLMs) still struggle with multi-hop reasoning over knowledge-graphs (KGs), and we identify a previously overlooked structural reason for this difficulty: Transformer attention heads naturally specialize in distinct semantic relations across reasoning stages, forming a hop-aligned relay pattern. This key finding suggests that multi-hop

Shujing Ruan, Guangzhen Gao, Jianing Zhang, Haotian Wang

Whispering gallery mode (WGM) microcavities feature ultrahigh Q-factors and small mode volumes, offering strong light-matter interactions for sensing applications. However, unmodified surfaces are weakly responsive togas-phase refractive index changes, limiting trace gas detection. In this work, we propose a novel dissipative sensing scheme based on a non-fu

Mohamamd Mazhari

In this work, we have constructed anisotropic bosonic dark-matter star (DMS) solutions in the context of a regularized four-dimensional Einstein$-$Gauss$-$Bonnet (4D EGB) gravity theory. Using dimensional regularization, we solve modified Tolman$-$Oppenheimer$-$Volkoff equations for a self-interacting complex scalar field in the dilute polytropic regime, $p_

Sushil Bohara, Amedeo Roberto Esposito

Variational Inference (VI) provides a scalable framework for Bayesian inference by optimizing the Evidence Lower Bound (ELBO), but convergence analysis remains challenging due to the objective's non-convexity and non-smoothness in Euclidean space. We establish a novel theoretical framework for analyzing VI convergence by exploiting the exponential family str

Usman Ali, Ali Zia, Waqas Ali, Umer Ramzan

Reliable induction motor (IM) fault diagnosis is vital for industrial safety and operational continuity, mitigating costly unplanned downtime. Conventional approaches often struggle to capture complex multimodal signal relationships, are constrained to unimodal data or single fault types, and exhibit performance degradation under noisy or cross-domain condit

Marwa Ennaceur, Amel Jadlaoui

We establish operator-norm bounds for discrete Hodge Laplacians on weighted flag complexes of a fixed dimension $n$, whose $k$-simplices are the $(k+1)$-cliques of a weighted graph; essential self-adjointness on natural cores follows, with no completeness or curvature assumption. Dual up/down degrees give Schur-type bounds in every degree. At top degree a un

Sibo Xiao, Jinyuan Fu, Zhongle Xie, Lidan Shou

Accelerating the inference of large language models (LLMs) has been a critical challenge in generative AI. Speculative decoding (SD) substantially improves LLM inference efficiency. However, its utility is limited by a fundamental constraint: the draft and target models must share the same vocabulary, thus limiting the herd of available draft models and ofte

Antoine Bricmont

This article addresses the construction and analysis of the Green's function for the Neumann boundary value problem associated with the operator $-\Delta + a$ on a smooth bounded domain $\Omega \subset \mathbb{R}^N$ ($N \geq 3$) with $a\in L^\infty(\Omega)$. Under the assumption that $-\Delta + a$ is coercive, we obtain the existence, uniqueness, and qualita

Qiyu Wu, Shuyang Cui, Satoshi Hayakawa, Wei-Yao Wang

Multimodal retrieval, which seeks to retrieve relevant content across modalities such as text or image, supports applications from AI search to contents production. Despite the success of separate-encoder approaches like CLIP align modality-specific embeddings with contrastive learning, recent multimodal large language models (MLLMs) enable a unified encoder

Ivan Kartashov, Mariia Pushkareva, Iakov Karandashev

This paper introduces SpikeFit, a novel training method for Spiking Neural Networks (SNNs) that enables efficient inference on neuromorphic hardware, considering all its stringent requirements: the number of neurons and synapses that can fit on a single device, and lower bit-width representations (e.g., 4-bit, 8-bit). Unlike conventional compressing approach

D. Galán-Diéguez, S. R. Berlanas, A. Herrero, M. Abdul-Masih

Context. Cygnus OB2, in the Cygnus X complex -- one of the most active star-forming regions of the Galaxy -- hosts hundreds of O- and B-type stars at different evolutionary stages. This association provides a unique laboratory to study massive star evolution and dynamics. However, despite extensive studies, the absence of a fast-rotating group ($v\sin{i}>200

Ming-Yuan Gao, Yue-Wei Song, Ren-Hui Chen, Yin-Hai Li

Difference-frequency generation (DFG) is a powerful technique for generating widely tunable infrared radiation. However, conventional phase-matching schemes may require tuning multiple parameters-such as the wavelengths, crystal temperature, crystal angle, and poling period-to achieve wide tunability, which increases the complexity of practical operation. In

Alessandro Magalotti, Andrea Alimenti, Emilio Bellingeri, Cristina Bernini

We present first preliminary surface impedance measurements on Tl-1223 films in dc magnetic fields, in view of potential applications for the next generation Future Circular Collider (FCC-hh) at CERN. The Tl-1223 samples were produced through laser ablation, with nominal thickness of 1 {\mu}m and grown on a thick LaAl2O3 substrate. The presence of Tl-1212 ph

Alessandro Magalotti, Andrea Alimenti, Valeria Braccini, Giuseppe Celentano

In this work, we have grown $\sim$100 nm thick pristine FeSe films by pulsed laser deposition. The films were structurally characterized with X-ray diffraction and their surface morphology checked through atomic force microscopy. Microwave measurements, performed with a dielectric loaded resonator tuned at the frequency of 8 GHz, allowed the characterization

Abhay Kumar Dwivedi, Shanu Saklani, Soumya Dutta

The extensive adoption of Deep Neural Networks has led to their increased utilization in challenging scientific visualization tasks. Recent advancements in building compressed data models using implicit neural representations have shown promising results for tasks like spatiotemporal volume visualization and super-resolution. Inspired by these successes, we

SeongKu Kang, Jianxun Lian, Dongha Lee, Wonbin Kweon

Recommender systems suffer from biases that cause the collected feedback to incompletely reveal user preference. While debiasing learning has been extensively studied, they mostly focused on the specialized (called counterfactual) test environment simulated by random exposure of items, significantly degrading accuracy in the typical (called factual) test env

Ichiro Takahashi, Tomoki Morokuma, Masaomi Tanaka, Mahito Sasada

We report our near-infrared (NIR) follow-up observations of the gravitational wave (GW) event S240422ed using the Subaru Telescope/MOIRCS. S240422ed was initially classified as a black hole-neutron star merger with $>$ 99% probability of electromagnetic wave emission. We started follow-up observations 7.8 hours after the event. Over two nights, we observed 2

Shilei Li, Dawei Shi, Makoto Iwasaki, Yan Ning

The nominal performance of mechanical systems is often degraded by unknown disturbances. A two-degree-of-freedom control structure can decouple nominal performance from disturbance rejection. However, perfect disturbance rejection is unattainable when the disturbance dynamic is unknown. In this work, we reveal an inherent trade-off in disturbance estimation

V. Gladkova

This paper establishes lower bounds for two kinds of arithmetic regularity partitions, building on constructions of Green [arXiv:math/0310476v2] and Hosseini, Lovett, Moshkovitz, and Shapira [arXiv:1405.4409]. The first kind occurs in the so-called strong arithmetic regularity lemma due to Bhattcharrya, Fischer, and Lovett [arXiv:1201.0330v2, Theorem 4.9], w

Roel Boumans, Lisa Cramer, Sascha van de Poll, Henria Vermeulen

Healthcare professionals have limited time to support patients and their relatives, but their information needs are high. Therefore, the Radboud University together with the Canisius Wilhelmina Hospital hospital developed a speaking virtual hu-man avatar which, contrary to many avatars, uses a Large Language Model (LLM) enhanced with Retrieval Augmented Gene

Zehao Ni, Yonghao He, Lingfeng Qian, Jilei Mao

In the context of imitation learning, visuomotor-based diffusion policy learning is one of the main directions in robotic manipulation. Most of these approaches rely on point clouds as observation inputs and construct scene representations through point clouds feature learning, which enables them to achieve remarkable accuracy. However, the existing literatu

Josh Cudby, Sergii Strelchuk

Parameterized complexity enables the practical solution of generally intractable NP-hard problems when certain parameters are small, making it particularly useful in real-world applications. The study of string problems in this framework has been particularly fruitful, yielding many state-of-the-art classical algorithms that run efficiently in certain parame

Muhammad Rizwan Kamal, Mohammad Z. Rahman, Amil Aligayev, Min Liu

This article reports a concept of heat-induced topological modifications of non-layered WO 3 followed by successful synthesis of oxygen-vacant more-porous nanosheets with exposed active (002) facet. Experimental measurements and Density Functional Theory (DFT) calculations have revealed that the photoexcited electrons are found to accumulate preferentially o

Aditya Vir

This work presents a systematic investigation of custom convolutional neural network architectures for satellite land use classification, achieving 97.23% test accuracy on the EuroSAT dataset without reliance on pre-trained models. Through three progressive architectural iterations (baseline: 94.30%, CBAM-enhanced: 95.98%, and balanced multi-task attention:

Dmitrii S. Kalashnikov, Gleb S. Seleznev, Andrei Kudriashov, Ian Babich

We investigate the Josephson diode effect in an asymmetric SQUID consisting of a sinusoidal Josephson junction formed by a Bi$_2$Te$_2$Se flake and a superconducting Nb nanobridge with a linear and multivalued current-phase relation (CPR). Current-voltage characteristics were measured both in the absence (dc regime) and presence (ac regime) of external micro

Weicen Dong, Haoxin Wang, Matteo Baggioli, Yi Liu

In recent years, the interplay between quantum magnetism and topology has attracted growing interest, both for its fundamental importance and its technological potential. Topological magnons, quantized spin excitations with nontrivial band topology, hold particular promise for spintronics, offering routes to robust, low-dissipation devices for next-generatio

Chao Liu, Yi Cao, Qi Xia, Amil Aligayev

Laccase is a natural green catalyst and utilized in pollution treatment. Nevertheless, its practical application is constrained by limitations including high cost, poor stability, and difficulties in recovery. Herein, with inspiration from catalytic mechanism of natural laccase, we designed and prepared a bimetallic metal-organic framework, namely, CoNi-MOF,

Mazhar Hussain Danish, Amil Aligayev, Zahir Muhammad, Tao Chen

Tungsten diselenide (WSe2) emerges as a promising thermoelectric (TE) candidate due to its high thermopower (S), cost-effectiveness, and environmentally friendly characteristics. However, pristine WSe2 exhibits limited electrical conductivity (sigma), a low power factor (PF), and high lattice thermal conductivity (k_L), which restrict its overall TE performa

Jingcheng Deng, Liang Pang, Zihao Wei, Shicheng Xu

Latent reasoning offers a computation-efficient alternative to Chain-of-Thought but often suffers from performance degradation due to distributional misalignment and ambiguous chain definitions. Ideally, latent reasoning should function as a superposition of multiple reasoning paths. To realize this, we introduce Latent-SFT, a unified framework addressing ch

Fei Wu, Chen Wu

This study investigates the effects of strong magnetic fields on antikaon condensation in neutron star matter using the extended FSUGold model model. It is found that the presence of strong magnetic fields alters the threshold density of antikaon condensation significantly, which means the threshold density of antikaon condensation is shifted to higher densi

Ignacio Serna

Modern face recognition models embed identities on a unit hypersphere, where identity variation forms tight clusters. Conversely, shared semantic attributes can often be effectively approximated as linear directions in the latent space. Existing bias evaluation methods rely on predefined attribute labels, synthetic counterfactuals, or proximity-based cluster

Yushu Qin, Marcos L. L. Sartori, Shengyu Duan, Emre Ozer

This paper introduces the first implementation of digital Tsetlin Machines (TMs) on flexible integrated circuit (FlexIC) using Pragmatic's 600nm IGZO-based FlexIC technology. TMs, known for their energy efficiency, interpretability, and suitability for edge computing, have previously been limited by the rigidity of conventional silicon-based chips. We develo

Mahdis Ghodrati

In this work, based on an analogy with holographic confining geometries and using complexified fields, we build a holographic toy model of third order photonic exceptional points (EPs) of ternary coupled microrings with gain and loss, which makes an open, non-Hermitian quantum system. In our model, we discuss the Ferrell-Glover-Tinkham sum rule for various c

Rares Dolga, Lucas Maystre, Tudor Berariu, David Barber

Subword tokenization methods like Byte Pair Encoding (BPE) are widely used in large language models due to their balance of vocabulary compactness and representational power. However, they suffer from inefficiencies in representing rare words and require large embedding matrices. Character-level models address these issues but introduce performance bottlenec

Giulia Lanzillotta, Felix Sarnthein, Gil Kur, Thomas Hofmann

The concept of knowledge distillation (KD) describes the training of a student model from a teacher model and is a widely adopted technique in deep learning. However, it is still not clear how and why distillation works. Previous studies focus on two central aspects of distillation: model size, and generalisation. In this work we study distillation in a thir

Meir Ariel

This paper presents a novel post-quantum cryptosystem based on high-memory masked convolutional codes. Unlike conventional code-based schemes that rely on block codes with fixed dimensions and limited error-correction capability, our construction offers both stronger cryptographic security and greater flexibility. It supports arbitrary plaintext lengths with

Ashutosh Bajpai, Tanmoy Chakraborty

The increasing acceptance of large language models (LLMs) as an alternative to knowledge sources marks a significant paradigm shift across various domains, including time-sensitive fields such as law, healthcare, and finance. To fulfill this expanded role, LLMs must not only be factually accurate but also demonstrate consistency across temporal dimensions, n

Warisa Sritriratanarak, Paulo Garcia

The consciousness standing for artificial intelligence divides opinions across epistemological positions. Whether or not machines can be conscious, and whether we can ascertain the truth of such a proposition for any given case, has consequential ethical implications. This challenge is exacerbated by the lack of consensus on the nature of consciousness. We a

Wachiraphan Charoenwet, Patanamon Thongtanunam, Van-Thuan Pham, Christoph Treude

Many software projects employ manual code review to gatekeep defects and vulnerabilities in the code before integration. However, reviewers often work under time pressure and rely primarily on static inspection, leaving the dynamic aspects of the program unexplored. Dynamic analyses could reveal such behaviors, but they are rarely integrated into reviews. Am

Giorgos Nikolaou, Tommaso Mencattini, Donato Crisostomi, Andrea Santilli

Transformer components such as non-linear activations and normalization are inherently non-injective, suggesting that different inputs could map to the same output and prevent exact recovery of the input from a model's representations. In this paper, we challenge this view. First, we prove mathematically that transformer language models mapping discrete inpu

Heeseong Shin, Byeongho Heo, Dongyoon Han, Seungryong Kim

While pre-trained visual representations have significantly advanced imitation learning, they are often task-agnostic as they remain frozen during policy learning. In this work, we explore leveraging pre-trained text-to-image diffusion models to obtain task-adaptive visual representations for robotic control, without fine-tuning the model itself. However, we