November 2025 arXiv papers — page 13

Randa Boukabene, Fatima Benbouzid Si Tayeb

Community detection is a fascinating and rapidly evolving field, but when it comes to analyzing networks with multiple types of interactions, referred to as multilayer networks, there is still a lot of untapped potential. Despite the wide array of methods developed to identify community structures in such networks, this area remains underexplored, leaving pl

Yanrui Xu, Da Zhao

We reduce the $p^2$ block all-one matrices in the generalized block Laplacian spectrum of graphs to $p$ block all-one matrices in the generalized block diagonal Lapalcian spectrum of graphs introduced by Wang and the second author (\textit{Adv. Appl. Math.} 173B (2026)). In this case the matrices are all real symmetric, and hence the spectrum is real, which

Eduardo Marin-Bujedo, Julien A. L. Grondin, Thomas Schiltz, Thomas Corbo

We report the construction and characterization of an experimental setup for producing a cold gas of $^{40}$Ca atoms and excite them to high Rydberg states with a resonant three-photon-excitation scheme. The apparatus comprises four stages, each designed in-house. An oven heated to $\sim 500^\circ$C generates an atomic beam that is collimated by a capillary

C. P. Walther, C. Nigro, D. Elsässer, W. Rhode

Gamma-ray astronomy is able to acquire large data volumes that astronomers use to draw scientific conclusions from. Ensuring the possibility of accessing and utilizing this data also after the lifetime of currently running experiments requires the use of a standardized data format. Following the data standardization format proposed by the gamma-ray astronomy

Jonathan Ethier

Traditional and modern machine learning-based path loss models typically assume a constant prediction variance. We propose a neural network that jointly predicts the mean and link-specific variance by minimizing a Gaussian negative log-likelihood, enabling heteroscedastic uncertainty estimates. We compare shared, partially shared, and independent-parameter a

Dejan Gajic, Lionor Kehrberger

We prove the global leading-order late-time asymptotic behaviour of solutions to inhomogeneous wave equations on dynamical black hole exterior backgrounds that settle down to Schwarzschild backgrounds with arbitrarily small decay rates. In particular, we show that for non-spherically symmetric solutions arising from compactly supported initial data, the late

Jose Moises Araya-Martinez, Adrián Sanchis Reig, Gautham Mohan, Sarvenaz Sardari

Reducing the burden of data generation and annotation remains a major challenge for the cost-effective deployment of machine learning in industrial and robotics settings. While synthetic rendering is a promising solution, bridging the sim-to-real gap often requires expert intervention. In this work, we benchmark a range of domain randomization (DR) and domai

Xiao-yu Chen

Genuine entanglement identification of large scale systems is crucial for quantum computation, quantum communication and quantum learning advantage. In contrast to experiments, where noisy intermediate-scale programmable photonic quantum processors have been developed, theoretically very limited results have been achieved for detecting genuine entanglement o

Wei Shi, Yuan Cao

Transformers have proven highly effective across various applications, especially in handling sequential data such as natural languages and time series. However, transformer models often lack clear interpretability, and the success of transformers has not been well understood in theory. In this paper, we study the capability and interpretability of transform

Yuting Fang, Qouc Le Gia, Flora Salim

Irregularly sampled time series with substantial missing observations are common in healthcare and sensor networks. We introduce SDE-Attention, a family of SDE-RNNs equipped with channel-level attention on the latent pre-RNN state, including channel recalibration, time-varying feature attention, and pyramidal multi-scale self-attention. We therefore conduct

Ole Sönnerborn

We provide a complete characterization of all finite-dimensional quantum systems that saturate the Margolus-Levitin quantum speed limit at arbitrary Uhlmann-Jozsa fidelity. Employing a purification-based approach, we prove that mixed-state saturation occurs precisely when three structural criteria are fulfilled: the state's support is confined to the sum of

Alex Richardson, Azhar Hasan, Gabor Karsai, Jonathan Sprinkle

This paper introduces a data-driven traffic microsimulation framework in CARLA that reconstructs real-world wave dynamics using high-fidelity time-space data from the I-24 MOTION testbed. Calibration of road networks in microsimulators to reproduce ephemeral phenomena such as traffic waves for large-scale simulation is a process that is fraught with challeng

Praveen Gatla, Anushka, Nikita Kanwar, Gouri Sahoo

This article presents the first comprehensive study on designing a baseline extractive question-answering (QA) system for the Hindi tourism domain, with a specialized focus on the Varanasi-a cultural and spiritual hub renowned for its Bhakti-Bhaav (devotional ethos). Targeting ten tourism-centric subdomains-Ganga Aarti, Cruise, Food Court, Public Toilet, Kun

Florian Litzinger, Miles Simon

We consider Riemannian manifolds $(M^n,g_0)$, $(M^n,h)$, where $(M^n,h)$ is smooth, complete, with curvature bounded in absolute value by $K_0 < \infty$, and $(1-\varepsilon_0(n)) h \leq g_0 \leq (1+\varepsilon_0(n)) h$ for some small $\varepsilon_0(n)>0$. It was shown by Simon (2002) that a Ricci-DeTurck flow solution $g(t)_{t \in (0,T)}$ related to $g_0$ e

Samuel Mercer, Yves van Gennip

A 1972 theorem by Brezis and Pazy establishes the uniform convergence of nonlinear semigroups generated by $\omega$-accretive operators on a Banach space. Our goal is to expand the setting of this theorem to include nonlinear semigroups that are acting on different Banach spaces. This is useful, for example, to prove discrete-to-continuum convergence for gra

J. C. Loredo, L. Stefan, B. Krogh, R. Jensen

Non-classical states of light play a fundamental role in quantum technology. From photonic quantum computers and simulators, to quantum communication and sensing, quantum states of light enable performing tasks that may outperform their best classical counterparts. Semiconductor quantum dots embedded in photonic nanostructures offer the most advanced classes

Jaime Corsetti, Francesco Giuliari, Davide Boscaini, Pedro Hermosilla

3D functionality segmentation aims to identify the interactive element in a 3D scene required to perform an action described in free-form language (e.g., the handle to ``Open the second drawer of the cabinet near the bed''). Progress has been constrained by the scarcity of annotated real-world data, as collecting and labeling fine-grained 3D masks is prohibi

Koustav Roy, Shahroze Shahab, Saurabh Basu

Engineering dissipative dynamics in open quantum systems is under active focus, especially in topological settings where resilient edge modes are expected to exhibit decay rates distinct from the bulk. In this letter, we propose an efficient dynamical scheme to discern such long-lived excitations. Employing a Floquet-Lindblad framework, we explore how period

Niklas Houba, Giovanni Giarda, Lorenzo Speri

Inferring the number of distinct components contributing to an observation, while simultaneously estimating their parameters, remains a long-standing challenge across signal processing, astrophysics, and neuroscience. Classical trans-dimensional Bayesian methods such as Reversible Jump Markov Chain Monte Carlo (RJMCMC) provide asymptotically exact inference

Ze Cai, Haotian Tang, Shuai Gao, Binbin Zhou

Adverse drug reactions (ADRs) are a major barrier to safe and effective pharmacotherapy and increasingly reflect higher order interactions between drugs, genetic background, and clinical phenotypes. Existing graph based approaches usually predict ADRs as properties of drugs or drug pairs, leaving the causal gene implicit and limiting their value for pharmaco

Lihan Li, Haofeng Zhong, Rui Bu, Mingchao Sun

Existing convolutional learning methods for 3D point cloud data are divided into two paradigms: point-based methods that preserve geometric precision but often face performance challenges, and voxel-based methods that achieve high efficiency through quantization at the cost of geometric fidelity. This loss of precision is a critical bottleneck for tasks such

Guang Liang, Jie Shao, Ningyuan Tang, Xinyao Liu

Native FP8 support in modern hardware is essential for training large Transformers, but is severely hindered by extreme activation outliers. Existing solutions either rely on complex mixed-precision engineering or invasive architectural modifications. This paper fundamentally challenges the conventional wisdom that outliers are data-driven. We demonstrate th

Vincenzo Lipardi, Domenica Dibenedetto, Georgios Stamoulis, Evert van Nieuwenburg

This article proposes a Graph Neural Network (GNN) approach to estimate nonstabilizerness in quantum circuits, measured by the stabilizer R\'enyi entropy (SRE). Nonstabilizerness is a fundamental resource for quantum advantage, and efficient SRE estimations are highly beneficial in practical applications. We address the nonstabilizerness estimation problem t

Hai-Liang Wu, Yue-Feng She

In this paper, by using the arithmetic theory of ternary quadratic forms, we study some refinements on Lagrange's four-square theorem. For example, given positive integers $a,b$ satisfying some algebraic conditions and a positive integer $C\ge3$, we will show that for any sufficiently large integer $n$ with $\ord_2(n)\le C$, there exist non-negative integers

Yefeng Wu, Shan Wan, Ling Wu, Yecheng Zhao

Accurate detection of tea leaf pests and diseases in real plantations remains challenging due to complex backgrounds, variable illumination, and frequent occlusions among dense branches and leaves. Existing detectors often suffer from missed detections and false positives in such scenarios. To address these issues, we propose DAONet-YOLOv8, an enhanced YOLOv

Milad Abdollahzadeh, Abdul Raheem, Zilong Zhao, Uzair Javaid

Tabular instruction tuning has emerged as a promising research direction for improving LLMs understanding of tabular data. However, the majority of existing works only consider question-answering and reasoning tasks over tabular data, leaving tabular data generation largely unnoticed. In this work, for the first time, we explore the efficacy of instruction t

Suratna Das, Umang Kumar, Swagat S. Mishra, Varun Sahni

Warm Inflation is a scenario in which the inflaton field dissipates its energy during inflation to maintain a subdominant constant radiation bath. Two of its remarkable features are (i) inflation can be realized even by very steep potentials and (ii) such a scenario doesn't call for a separate post-inflation reheating phase. We exploit the first feature to s

Rohit Kishan Ray, Carlo Danieli, Alexei Andreanov, Sergej Flach

We develop a systematic framework for constructing all-bands-flat (ABF) lattice Hamiltonians that explicitly break time-reversal symmetry (TRS). By threading magnetic flux through disconnected polygonal plaquettes and applying local entangling unitary transformations, we map plaquettes onto families of ABF models in one, two, and three dimensions. This proce

Nikola Sekulovski, František Bartoš, Don van den Bergh, Giuseppe Arena

Model uncertainty is a central challenge in statistical models for binary outcomes such as logistic regression, arising when it is unclear which predictors should be included in the model. Many methods have been proposed to address this issue for logistic regression, but their relative performance under realistic conditions remains poorly understood. We ther

Eduardo Sergio Oliveros-Mata, Oleksandr V. Pylypovskyi, Eleonora Raimondo, Rico Illing

Complex and even chaotic dynamics, though prevalent in many natural and engineered systems, has been largely avoided in the design of electromechanical systems due to concerns about wear and controlability. Here, we demonstrate that complex dynamics might be particularly advantageous in soft robotics, offering new functionalities beyond motion not easily ach

Jose Moises Araya-Martinez, Gautham Mohan, Kenichi Hayakawa Bolaños, Roberto Mendieta

Early-stage visual quality inspection is vital for achieving Zero-Defect Manufacturing and minimizing production waste in modern industrial environments. However, the complexity of robust visual inspection systems and their extensive data requirements hinder widespread adoption in semi-controlled industrial settings. In this context, we propose a pose-agnost

Tomoshige Nakamura, Hiroshi Shiraishi

Quantile Regression Forests (QRF) are widely used for non-parametric conditional quantile estimation, yet statistical inference for variable importance measures remains challenging due to the non-smoothness of the loss function and the complex bias-variance trade-off. In this paper, we develop a asymptotic theory for variable importance defined as the differ

Alexander Turoczy, Young-San Lin

We study the online multi-level aggregation problem with deadlines (MLAP-D) introduced by Bienkowski et al. (ESA 2016, OR 2020). In this problem, requests arrive over time at the vertices of a given vertex-weighted tree, and each request has a deadline that it must be served by. The cost of serving a request equals the cost of a path from the root to the ver

Enhong Cheng, Zheng Lian, Zezhou Chen, Li-Jun Lang

Landau-Zener tunneling (LZT) is a fundamental dynamical phenomenon, ubiquitous in various quantum systems. Here, we propose a time-varying electric circuit to address the question of whether the quantum LZT can occur in classical systems. Although the underlying differential equation of motion is quite different from the Schr\"odinger equation and the instan

Chao Wang, Minlan Li, Chang Liu

Building upon the eco-evolutionary game dynamics framework established by Tilman et al., we investigate stochastic fluctuations in a two-strategy system incorporating environmental feedback mechanisms, where the payoff matrix exhibits population size dependence. We adopt a systematic approach which is the so-called $\Omega$-expansion. When the stochastic fac

Suehyun Kim, Kwonsang Lee

Causal inference in longitudinal datasets has long been challenging due to dynamic treatment adoption and confounding by time-varying covariates. Prior work either fails to account for heterogeneity across treatment adoption cohorts and treatment timings or relies on modeling assumptions. In this paper, we develop a novel design-based framework for inference

Loic Remolif, Ivan Iorsh

We consider a quantum ring placed in a gyrotropic cavity characterized by the energy splitting between the left and right circularly polarized modes. We show that despite the absence of constant magnetic field penetrating through the ring, in the regime of the ultrastrong light matter coupling, the total current in the ground state changes discontinuously wi

Nelson Martins-Ferreira

The original Lawvere condition asserts that every reflexive graph admits a unique natural structure of internal groupoid. This property was identified by P. T. Johnstone, following a question by A. Carboni and a suggestion by F. W. Lawvere, and it plays a central role in the characterization of naturally Mal'tsev categories. A broad and conceptually rich gen

Shiqin Tang, Yining Dong, S. Joe Qin

In traditional multivariate data analysis, dimension reduction and regression have been treated as distinct endeavors. Established techniques such as principal component regression (PCR) and partial least squares (PLS) regression traditionally compute latent components as intermediary steps -- although with different underlying criteria -- before proceeding

Jordi Fornt, Pau Fontova-Musté, Adrian Gras, Omar Lahyani

Voltage overscaling, or undervolting, is an enticing approximate technique in the context of energy-efficient Deep Neural Network (DNN) acceleration, given the quadratic relationship between power and voltage. Nevertheless, its very high error rate has thwarted its general adoption. Moreover, recent undervolting accelerators rely on 8-bit arithmetic and cann

Chunlei Li, Angelica Piccirillo, Olga Polverino, Ferdinando Zullo

In 2019, Trombetti and Zhou introduced a new family of $\mathbb{F}_{q^n}$-linear Maximum Rank Distance (MRD) codes over $\mathbb{F}_{q^{2n}}$. For such codes we propose a new syndrome-based decoding algorithm. It is well known that a syndrome-based decoding approach relies heavily on a parity-check matrix of a linear code. Nonetheless, Trombetti-Zhou codes a

Hoang Khang Phan, Nhat Tan Le

Psychological stress is a widespread issue that significantly impacts student well-being and academic performance. Effective remote stress recognition is crucial, yet existing methods often rely on wearable devices or GPS-based clustering techniques that pose privacy risks and lack of human understandable explanations. In this study, we introduce a novel, en

Zhenxiong Tan, Zeqing Wang, Xingyi Yang, Songhua Liu

We introduce Vision Bridge Transformer (ViBT), a large-scale instantiation of Brownian Bridge Models designed for conditional generation. Unlike traditional diffusion models that transform noise into data, Bridge Models directly model the trajectory between inputs and outputs, creating an efficient data-to-data translation paradigm. By scaling these models t

Martin Mocko, Jakub Ševcech, Daniela Chudá

Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware clustering. Despite the efforts of the community, malware cluster

Miguel Hoyuelos

A thermodynamic framework that predicts the thermal conductivity $\lambda$ of simple fluids beyond the dilute-gas limit is introduced. By generalizing the transition-rate approach of particles on a lattice to conserved quantities in continuous space, an expression for the ratio $\lambda/\lambda_{\rm id}$ is derived, where $\lambda_{\rm id}$ is the dilute-gas

Ye Gu, He Li, Tinus Pannier, Shengpu Niu

Meeting the escalating demands of data transmission and computing, driven by artificial intelligence (AI), requires not only faster optical transceivers but also advanced integration technologies that can seamlessly combine photonic and electronic components. Traditional approaches struggle to overcome the parasitic limitations arising from fabricating those

Chính T. Hoàng, Ramin Javadi, Nicolas Trotignon

Determining the complexity of colouring ($4K_1, C_4$)-free graph is a long open problem. Recently Penev showed that there is a polynomial-time algorithm to colour a ($4K_1, C_4, C_6$)-free graph. In this paper, we will prove that if $G$ is a ($4K_1, C_4, P_6$)-free graph that contains a $C_6$, then $G$ has bounded clique-width. To this purpose, we use a new

Minsung Kim, Sungjoon Kim

We explore 3d $ \mathcal{N}=4 $ theories arising from twisted compactification of 4d $ \mathcal{N}=2 $ $ (G, G') $ Argyres-Douglas superconformal field theories (SCFTs), together with the 2d vertex operator algebras (VOAs) supported on the holomorphic boundary of their topologically twisted sector. Starting from the Coulomb branch BPS spectra of the $ (G,G')

Yuchen Shi, Huaxin Pei, Yi Zhang, Danya Yao

Multi-Agent Reinforcement Learning (MARL) holds significant promise for enabling cooperative driving among Connected and Automated Vehicles (CAVs). However, its practical application is hindered by a critical limitation, i.e., insufficient fault tolerance against observational faults. Such faults, which appear as perturbations in the vehicles' perceived data

Mohammad Reza Sorouhesh, Mayam Golriz, Bozorg Panbehkar

We introduce the \emph{Generalized Latin Square Graph} $\Gamma(S)$ of a finite semigroup $S$. Since we record global factorization multiplicities and local alternative counts, we define three counting invariants $N_S,N_R,N_C$. This gives that we have a simple degree formula \[ \text{deg}(v)=2n-3+Q(v),\qquad Q(v)=N_S(s_k)-2N_R(v)-2N_C(v). \] We show that $\Ga

Lorenzo Briganti, Walter Eduard van Rossem, Andrea Miglio, Angela Bragaglia

Blue straggler stars (BSSs) are exotic objects, which, being the results of processes such as mass transfer, mergers, or collisions, are considered key objects in the study of their host clusters' dynamics. While many studies on astrometric, spectroscopic, and photometric properties of BSSs in clusters have been conducted, there are few works in the literatu

Yibo Meng, Lyumanshan Ye, Eve He, Zhe Yan

This study investigates the evolving attitudes of philosophy scholars towards the participation of generative AI based Intelligent User Interfaces (IUIs) in philosophical discourse. We conducted a three year (2023--2025) mixed methods longitudinal study with 16 philosophy scholars and students. Qualitative data from annual interviews reveal a three stage evo

Feng Xi, Dehui Yang

Existing near-field channel estimation methods for extremely large-scale MIMO (XL-MIMO) typically discretize angle and range parameters jointly, resulting in large polar-domain codebooks. This paper proposes a novel framework that formulates near-field channel estimation as a gridless super-resolution problem, eliminating the need for explicitly constructed

Runyu Jiao, Matteo Bortolon, Francesco Giuliari, Alice Fasoli

Successful robotic grasping in cluttered environments not only requires a model to visually ground a target object but also to reason about obstructions that must be cleared beforehand. While current vision-language embodied reasoning models show emergent spatial understanding, they remain limited in terms of obstruction reasoning and accessibility planning.

R. Giménez Conejero, Andreas Lind, Aurélio Menegon

Understanding how singularities behave under small perturbations is a central theme in singularity theory. In this paper we establish sufficient conditions for families of analytic function-germs on a germ of a complex analytic space to admit an embedded topological trivialization. Our results extend previous work of the third author and collaborators, movin

Wenna Lai, Haoran Xie, Guandong Xu, Qing Li

Aspect sentiment quad prediction (ASQP) is inherently challenging to predict a structured quadruple with four core sentiment elements, including aspect term (a), aspect category (c), opinion term (o), and sentiment polarity (s). Prior methods relying on marker-based prediction struggle with modeling the intricate relationships among elements and experience s

Sebastião Alves de Jesus Filho, Gustavo Di Giovanni Bernardo, Paulo Henrique Ribeiro Gabriel, Bruno Bogaz Zarpelão

Given the constant growth and increasing sophistication of cyberattacks, cybersecurity can no longer rely solely on traditional defense techniques and tools. Proactive detection of cyber threats has become essential to help security teams identify potential risks and implement effective mitigation measures. Cyber Threat Intelligence (CTI) plays a key role by

Yuma S. Furuta, Mindaugas Karčiauskas, Kazunori Kohri, Alejandro Sáez

We show that resonant processes during multi-field inflation can generate a large curvature perturbation on small scales. This perturbation naturally leads to the formation of primordial black holes that may constitute dark matter, as well as to the production of stochastic induced gravitational waves in the deci-Hz band. Such waves are within reach of futur

Yihan Wu, Mario Caserta, Tommaso Chiarotti, Nicola Marzari

We study the electronic structure and dynamical correlations in antiferromagnetic BiFeO$_3$, a prototypical room-temperature multiferroic, using a variety of static and dynamical first-principles methods. Conventional static Hubbard corrections (DFT+$U$, DFT+$U$+$V$) incorrectly predict a deep-valence Fe $3d$ peak (around $-7\,\text{eV}$) in antiferromagneti

Waleed Qaisar, Gregory Taroyan

We prove a Tannaka duality statement for geometric stacks in the setting of analytic stacks modelled on globally finitely presented Stein spaces. The key ingredient is the theory of liquid vector spaces and liquid quasicoherent sheaves of Clausen-Scholze. As an application, we reconstruct the topological fundamental group of any complex algebraic variety fro

Vladimir Kulbatov, Jan Lang, Cornelia Schneider, Jan Vybíral

The seminal work of Daubechies, DeVore, Foucart, Hanin, and Petrova introduced in 2022 a sequence of univariate piece-wise linear functions, which resemble the classical Fourier basis and which, at the same time, can be easily reproduced by artificial neural networks with ReLU activation function. We give an alternative way how to calculate the inner product

Chen Li, Peiji Yang, Yicheng Zhong, Jianxing Yu

Recent advances in Speech Large Language Models (Speech LLMs) have led to great progress in speech understanding tasks such as Automatic Speech Recognition (ASR) and Speech Emotion Recognition (SER). However, whether these models can achieve human-level auditory perception, particularly in terms of their ability to comprehend latent intentions and implicit e

Deyu Li, Xinyuan Liao, Shaowei Chen, Shuai Zhao

Motor condition monitoring is essential for ensuring system reliability and preventing catastrophic failures. However, data-driven diagnostic methods often suffer from sparse fault labels and severe class imbalance, which limit their effectiveness in real-world applications. This paper proposes a motor condition monitoring framework that leverages the genera

Yoonhee Lee, Daniel Dobesch, Patrick Stender, Ute Tuttlies

In this study, the dynamic oxidation state changes of pure Pt and 50at% PtPd alloy catalysts were investigated during a temperature ramp from 80 to 450 C under a reactive gas mixture of 500 ppm NO and 3 % Oxygen in Nitrogen atmosphere. These changes are closely correlated with variations in NO conversion efficiency. Sharp tip specimens were prepared from Pt

Ashish Chandra, Mohit Tawarmalani

This paper presents a novel two-stage optimization framework designed to model integrated quantile functions, which leads to the formulation of a bilinear optimization problem (P). A specific instance of this framework offers a new approach to minimizing the Value-at-risk (Var) and the Conditional Value-at-risk (CVar), thus providing a broader perspective on

Neemesh Yadav, Francesco Ortu, Jiarui Liu, Joeun Yook

Large Language Models (LLMs) are trained to refuse to respond to harmful content. However, systematic analyses of whether this behavior is truly a reflection of its safety policies or an indication of political censorship, that is practiced globally by countries, is lacking. Differentiating between safety influenced refusals or politically motivated censorsh

Hoang Khang Phan, Khang Le, Tu Nhat Khang Nguyen, Anh Van Dao

Monitoring physical exercises is vital for health promotion, with automated systems becoming standard in personal health surveillance. However, sensor placement variability and unconstrained movements limit their effectiveness. This study proposes the team "3KA"'s one-sensor workout activity recognition method using feature extraction and data augmentation i

Liyi Chen, Ruihuang Li, Guowen Zhang, Pengfei Wang

Text-driven 3D editing enables user-friendly 3D object or scene editing with text instructions. Due to the lack of multi-view consistency priors, existing methods typically resort to employing 2D generation or editing models to process each view individually, followed by iterative 2D-3D-2D updating. However, these methods are not only time-consuming but also

Tong Wu, Weibin Wu, Zibin Zheng

Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great potential in many fields, such as writing, research, and programming. Today, the number of GPTs has reached three millions, with the range of specific expert domains becoming increasingly diverse. However, given th

Raul Quintero-Monsebaiz, Per Hyldgaard, Elsebeth Schröder

Understanding photophysical properties of DNA is important: It can help us elucidate and probe the impact of charges and free radicals in the cellular environment. For example, a photoemission at a given nucleobase means that we both charge it and place an electron right next to a neighboring part of the genetic code. Inverse photoemission means that we trap

Masaki Kawamura, Nakamasa Inoue, Rintaro Yanagi, Hirokatsu Kataoka

Contrastive vision-language pre-training frameworks such as CLIP have demonstrated impressive zero-shot performance across a range of vision-language tasks. Recent studies have shown that aligning individual text tokens with specific image patches or regions enhances fine-grained compositional understanding. However, it remains challenging to capture composi

Joner Assolin, Diego Kreutz, Leandro Bertholdo

The growing presence of IoT devices in academic environments has increased operational complexity and exposed security weaknesses, especially in academic institutions without unified policies for registration, monitoring, and incident response involving IoT. This work presents IoTEdu, an integrated platform that combines access control, incident detection, a

Hiroshi Yamauchi, Satoshi Kanno, Yuki Sato, Hiroyuki Tezuka

Topological data analysis (TDA) characterizes complex dynamics through global invariants, but classical computation becomes prohibitive for high-dimensional data. We reinterpret time-domain dynamics as the eigenvalue spectrum of a supersymmetric (SUSY) Hamiltonian and thereby estimate topological descriptors through quantum spectroscopy. While zero modes cor

M. E. Mosquera, K. J. Fushimi, M. J. de L. Domínguez Romero

We reexamined the framework used to determine the dark matter mass of a subhalo using the gravitational effects of its passage upon the stellar halo of a host. In particular, we aim to include different density distribution functions for the perturber and a non homogeneous background due to the host's halo gravitational potential. We have used a sample of K

Chenyu Liu, Zhaoyang Zhang, Zirui Chen, Zhaohui Yang

Split learning (SL) offloads main computing tasks from multiple resource-constrained user equippments (UEs) to the base station (BS), while preserving local data privacy. However, its computation and communication processes remain sequential, resulting in limited system efficiency. To overcome this limitation, this paper applies pipeline parallelism (PP) of

Nursultan Amanzhol, Jurn-Gyu Park

The growing deployment of Vision Transformers (ViTs) on energy-constrained devices requires evaluation methods that go beyond accuracy alone. We present a two-stage pipeline for assessing ViT energy efficiency that combines device-agnostic model selection with device-related measurements. We benchmark 13 ViT models on ImageNet-1K and CIFAR-10, running infere

Takayuki R. Saitoh, Yutaka Hirai, Michiko S. Fujii, Yuki Isobe

The James Webb Space Telescope reveals anomalous nitrogen enrichment (high N/O ratios) in compact, star-forming galaxies, such as GN-z11 at $z\sim10$. The origin of this chemical signature provides an insight into the early star and galaxy formation processes, yet remains unclear. We performed high-resolution cosmological zoom-in simulations of massive galax

Selma Dahms, Luca Torresi, Shahbaz Tareq Bandesha, Jan Hansmann

Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coa

Xianyu Tan, Xi Zhang, Mark S. Marley, Yifan Zhou

Large-amplitude variations are commonly observed in the atmospheres of directly imaged exoplanets and brown dwarfs. VHS 1256B, the most variable known planet-mass object, exhibits a near-infrared flux change of nearly 40%, with red color and silicate features revealed in recent JWST spectra, challenging current theories. Using a general circulation model, we

Anders Vestergaard Nørskov, Kasper Jørgensen, Alexander Neergaard Zahid, Morten Mørup

Event-related potentials (ERP) are measurements of brain activity with wide applications in basic and clinical neuroscience, that are typically estimated using the average of many trials of electroencephalography signals (EEG) to sufficiently reduce noise and signal variability. We introduce EEG2ERP, a novel uncertainty-aware autoencoder approach that maps a

Diego J. Cirilo-Lombardo, Norma G. Sanchez

A precise physical description and understanding of the classical dual content of quantum theory is necessary in many disciplines today: from concepts and interpretation to quantum technologies and computation. In this paper we investigate Quantum Entanglement with the new approach APL Quantum 2, 016104 (2025) on dual Classicalization. Thus, the results of t

Saumya Shah, Patrick Rebentrost

The computational cost of simulating quantum many-body systems can often be reduced by taking advantage of physical symmetries. While methods exist for specific symmetry classes, a general algorithm to find the full permutation symmetry group of an arbitrary Pauli Hamiltonian is notably lacking. This paper introduces a new method that identifies this symmetr

Bertrand Meyer

Vibe coding, the much-touted use of AI techniques for programming, faces two overwhelming obstacles: the difficulty of specifying goals ("prompt engineering" is a form of requirements engineering, one of the toughest disciplines of software engineering); and the hallucination phenomenon. Programs are only useful if they are correct or very close to correct.

Huangsen Cao, Qin Mei, Zhiheng Li, Yuxi Li

The rapid progress of visual generative models has made AI-generated images increasingly difficult to distinguish from authentic ones, posing growing risks to social trust and information integrity. This motivates detectors that are not only accurate but also forensically explainable. While recent multimodal approaches improve interpretability, many rely on

Hana Kataoka, Jialong Li, Yutaka Matsuno

As LLMs reshape software development, integrating LLM-augmented practices into SE education has become imperative. While existing studies explore LLMs' educational use in introductory programming or isolated SE tasks, their impact in more open-ended Project-Based Learning (PBL) remains unexplored. This paper introduces a two-year longitudinal study comparing

Sanne M. van Essen, Harleigh C. Seyffert

Wave impact loads on maritime structures can cause casualties, damage, pollution and operational delays. Consequently, their extreme values should be accounted for in the design of these structures. However, this is challenging, as wave impact events are both rare and highly complex, requiring both high-fidelity simulations and long analysis durations to rel

Teng Long, Yibo Deng, Xuekai Ma, Chunling Gu

Neuromorphic computing is at the basis of the recent progress in artificial intelligence. But the progress is accompanied with increasing demands in computational resources and power supply. Reservoir neuromorphic computing uses a non-linear physical system to replace a part of a large neural network. The advantages can include reduced power consumption and

Julian Koellermeier, Michael Redle, Manuel Torrilhon

Originally introduced to describe a transition region in stars, the magnetic rotating shallow water (MRSW) model is now used in many solar physics and geophysical applications. Derived from the 3-D incompressible magnetohydrodynamic system, the shallow nature of these applications motivates depth-averaging of both the velocities and magnetic fields. This is

Jin-Seop Lee, SungJoon Lee, SeongJun Jung, Boyang Li

Video Temporal Grounding (VTG) aims to localize a temporal segment in a video corresponding to a natural language query. However, existing VTG models assume that a relevant segment always exists, causing them to always predict a target segment even when the query is irrelevant to the video. While recent approaches attempt to handle irrelevant queries, they c

Chaoyun Wang, Quanxin Huang, I-Chao Shen, Takeo Igarashi

Document rectification in real-world scenarios poses significant challenges due to extreme variations in camera perspectives and physical distortions. Driven by the insight that complex transformations can be decomposed and resolved progressively, we introduce a novel multi-stage framework that progressively reverses distinct distortion types in a coarse-to-

Andrea Del Vecchio, Jan Eysermans, Loukas Gouskos, George Iakovidis

The expected precision at the FCC-ee on the product $σ\times\mathcal{B}(H\rightarrow b\bar{b}, c\bar{c},s\bar{s},gg)$ of Higgs boson production cross sections times branching ratios of hadronic decays is presented. This study provides the first comprehensive determination of all major hadronic Higgs decay modes in a combined fit at future $e^+ e^-$ colliders

Mian Ibad Ali Shah, Marcos Eduardo Cruz Victorio, Maeve Duffy, Enda Barrett

The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perfor

Yipeng Zou, Jiangshui Zhang, Dingyuan Wei, Yaoting Yan

To date, the Galactic interstellar radial $^{32}$S/$^{34}$S gradient has only been studied with the CS isotopologs, which may be affected by uncertainties due to the use of a single tracer. As another abundant S-bearing molecules, SO and its isotopomer $^{34}$SO could be considered as tracers of the $^{32}$S/$^{34}$S ratio. We present the first systematic ob

Yuheng Chen, Teng Hu, Jiangning Zhang, Zhucun Xue

Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video models rely solely on textual conditions, lacking general fine-grained controllability over video generation. To address this challenge, we propose InstanceV, a video generation framewo

A. Arkhutsik, P. Kats, O. Voskresenskaya

The first, second and third Born approximations of the Mott scattering cross section are considered. The relative error of all three Born approximations averaged by angles and energies is calculated for the first 30 elements of the Mendeleev periodic table of elements and also of the second and third-Born approximation for the first hundred elements of the M

Riko Kelter, Samuel Pawel

Sequential trial design is an important statistical approach to increase the efficiency of clinical trials. Bayesian sequential trial design relies primarily on conducting a Monte Carlo simulation under the hypotheses of interest and investigating the resulting design characteristics via Monte Carlo estimates. This approach has several drawbacks, namely that

Enrico Saccon, Davide De Martini, Matteo Saveriano, Edoardo Lamon

We present a novel framework that integrates Large Language Models (LLMs) with automated planning and formal verification to streamline the creation and use of Markov Decision Processes (MDP). Our system leverages LLMs to extract structured knowledge in the form of a Prolog knowledge base from natural language (NL) descriptions. It then automatically constru

Ard Kastrati, Luca Lanzendörfer, Riccardo Rigoni, John Staib Matilla

EEG and audio are inherently distinct modalities, differing in sampling rate, channel structure, and scale. Yet, we show that pretrained neural audio codecs can serve as effective starting points for EEG compression, provided that the data are preprocessed to be suitable to the codec's input constraints. Using DAC, a state-of-the-art neural audio codec as ou

Ard Kastrati, Josua Bürki, Jonas Lauer, Cheng Xuan

We introduce a unified benchmarking framework focused on evaluating EEG-based foundation models in clinical applications. The benchmark spans 11 well-defined diagnostic tasks across 14 publicly available EEG datasets, including epilepsy, schizophrenia, Parkinson's disease, OCD, and mild traumatic brain injury. It features minimal preprocessing, standardized

David Leeftink, Roman Doll, Heleen Visserman, Marco Post

Laser dicing of semiconductor wafers is a critical step in microelectronic manufacturing, where multiple sequential laser passes precisely separate individual dies from the wafer. Adapting this complex sequential process to new wafer materials typically requires weeks of expert effort to balance process speed, separation quality, and material integrity. We p