March 2025 arXiv papers — page 112

Sofie Kölling, Florian R. Westerhof, Alexander Brinkman

A common method of controlling the chemical potential in topological insulators is applying a gate electrode. Simultaneously applying high source-drain bias currents can lead to parasitic effects in such devices. We derive that these parasitic effects lead to a gradient in the Hall effect along the current lead of a Hall bar. Consequently, nonreciprocal effe

Yao Yang

Altermagnets represent a novel class of magnetic materials that integrate the advantages of both ferromagnets and antiferromagnets, providing a rich platform for exploring the physical properties of multiferroic materials.This work demonstrates that $\mathrm{VOX_2}$ monolayers ($\mathrm{X = Cl, Br, I}$) are two-dimensional ferroelectric altermagnets, as conf

Weiyu Guo, Ziyang Chen, Shaoguang Wang, Jianxiang He

Understanding long video content is a complex endeavor that often relies on densely sampled frame captions or end-to-end feature selectors, yet these techniques commonly overlook the logical relationships between textual queries and visual elements. In practice, computational constraints necessitate coarse frame subsampling, a challenge analogous to "finding

Jun Wang, Qiang Zhao

We carry out a combined study of the isospin-violating decay $D_{s}^{*} \to D_{s} \pi^{0}$ and radiative decay $D^*_s\to D_s\gamma$ in an effective Lagrangian approach by taking into account the corrections from the one-loop transitions. By distinguishing the transition mechanisms of the long-distance interactions through the intermediate meson loops from th

Till M. Blaha, Mike M. Kuijper, Radu Pop, Ewoud J. J. Smeur

The inertia tensor is an important parameter in many engineering fields, but measuring it can be cumbersome and involve multiple experiments or accurate and expensive equipment. We propose a method to measure the moment of inertia tensor of a rigid body from a single spinning throw, by attaching a small and inexpensive stand-alone measurement device consisti

S. Rendon Restrepo, U. Ziegler, M. Villenave, O. Gressel

Context. In Class 0/I and the outskirts of Class II circumstellar discs, the self-gravity of gas significantly affects the disc's vertical hydrostatic equilibrium. The contribution of dust, whose measured mass is still uncertain, could influence this equilibrium. Aims. We aim to formulate and solve approximately the equations governing the hydrostatic equili

ATLAS Collaboration

A search for cascade decays of charged sleptons and sneutrinos using final states characterized by three leptons (electrons or muons) and missing transverse momentum is presented. The analysis is based on a dataset with 140 fb$^{-1}$ of proton-proton collisions at a center-of-mass energy of $\sqrt{s}$=13 TeV recorded by the ATLAS detector at the Large Hadron

Prakhar Bhardwaj, Sheethal Bhat, Andreas Maier

Due to the large volume of medical imaging data, advanced AI methodologies are needed to assist radiologists in diagnosing thoracic diseases from chest X-rays (CXRs). Existing deep learning models often require large, labeled datasets, which are scarce in medical imaging due to the time-consuming and expert-driven annotation process. In this paper, we extend

Hyat Huang, Xiao-Pin Rao

Regular black holes without curvature singularity can arise in Einstein gravity with appropriate matter energy-momentum tensor. We show that these regular solutions represent only a special case of a much broader family of black holes with a free mass parameter. The regularity is achieved only at a specific mass value, and any deviation from the fine-tuned p

Moises Diaz, Miguel A. Ferrer, Juan M. Gil, Rafael Rodriguez

Online Signature Verification commonly relies on function-based features, such as time-sampled horizontal and vertical coordinates, as well as the pressure exerted by the writer, obtained through a digitizer. Although inferring additional information about the writers arm pose, kinematics, and dynamics based on digitizer data can be useful, it constitutes a

Bochen Jin

We investigate the limiting behaviour of the path of random bridges treated as random sets in $\mathbb{R}^{d}$ with the Euclidean metric and the dimension $d$ increasing to infinity. The main result states that, in the square integrable case, the limit (in the Gromov-Hausdorff sense) is deterministic, namely, it is $[0,1]$ equipped with the pseudo-metric $\s

Yaxi Chen, Simin Ni, Aleksandra Ivanova, Shaheer U. Saeed

Classical radiomic features have been designed to describe image appearance and intensity patterns. These features are directly interpretable and readily understood by radiologists. Compared with end-to-end deep learning (DL) models, lower dimensional parametric models that use such radiomic features offer enhanced interpretability but lower comparative perf

Ling-An Zeng, Guohong Huang, Yi-Lin Wei, Shengbo Gu

We propose ChainHOI, a novel approach for text-driven human-object interaction (HOI) generation that explicitly models interactions at both the joint and kinetic chain levels. Unlike existing methods that implicitly model interactions using full-body poses as tokens, we argue that explicitly modeling joint-level interactions is more natural and effective for

A. M. Morgen, S. S. Balling, M. T. Strøe, T. G. Skov

Loss spectroscopy is a key tool for investigating systems where important system parameters are linked to intrinsic resonant loss processes. We investigate loss processes of impurity atoms embedded in a medium of a Bose-Einstein Condensate close to a Feshbach resonance. In this case, three-body loss processes occur faster than the measurement duration, imped

Willie Aboumrad, Daiwei Zhu, Claudio Girotto, François-Henry Rouet

The solution of large sparse linear systems via factorization methods such as LU or Cholesky decomposition, can be computationally expensive due to the introduction of non-zero elements, or ``fill-in.'' Graph partitioning can be used to reduce the ``fill-in,'' thereby speeding up the solution of the linear system. We introduce a quantum approach to the graph

Eduard Frankford, Daniel Crazzolara, Michael Vierhauser, Niklas Meissner

The increasing demand for programmers has led to a surge in participants in programming courses, making it increasingly challenging for instructors to assess student code manually. As a result, automated programming assessment systems (APASs) have been developed to streamline this process. These APASs support lecturers by managing and evaluating student prog

Pan Liu

Laser chips, the core components of semiconductor lasers, are extensively utilized in various industries, showing great potential for future application. Smoothness emitting surfaces are crucial in chip production, as even imperceptible scratches can significantly degrade performance and lifespan, thus impeding production efficiency and yield. Therefore, non

Mayengbam Kishan Singh, N. Nimai Singh

We study a 3+1 active-sterile neutrino mixings model using an $A_4$ triplet right-handed neutrino $\nu_R$ and a singlet eV-scale sterile neutrino under $A_4\times Z_3 \times Z_2$ discrete symmetry. Four scalar flavons are considered to reproduce neutrino oscillation parameters within the experimental 3$\sigma$ range. The model also studies the effective mass

Xintian Yuan, Yunke Ao, Boqi Chen, Philipp Fuernstahl

Simulating the complex interactions between soft tissues and rigid anatomy is critical for applications in surgical training, planning, and robotic-assisted interventions. Traditional Finite Element Method (FEM)-based simulations, while accurate, are computationally expensive and impractical for real-time scenarios. Learning-based approaches have shown promi

Helge Jørgen Samuelsen

We present a sufficient condition on sets $E$ and $F$ in $\mathbb{R}^d$ to ensure compactness of Fourier concentration operators by introducing the notion of sets which are very thin at infinity. We are able to show that if the sets $E$ and $F$ are both very thin at infinity, then the associated Fourier concentration operator is compact on $L^2(\mathbb{R}^d)

Sebin Lee, Yeonho Cho, Jungjin Lee

Computer-mediated concerts can be enjoyed on various devices, from desktop and mobile to VR devices, often supporting multiple devices simultaneously. However, due to the limited accessibility of VR devices, relatively small audience members tend to congregate in VR venues, resulting in diminished unique social experiences. To address this gap and enrich VR

Siyuan Fan, Wenke Huang, Xiantao Cai, Bo Du

3D human interaction generation has emerged as a key research area, focusing on producing dynamic and contextually relevant interactions between humans and various interactive entities. Recent rapid advancements in 3D model representation methods, motion capture technologies, and generative models have laid a solid foundation for the growing interest in this

Paul Wawerek-López, Navid Mahmoudian Bidgoli, Pascal Frossard, André Kaup

Developing effective 360-degree (spherical) image compression techniques is crucial for technologies like virtual reality and automated driving. This paper advances the state-of-the-art in on-the-sphere learning (OSLO) for omnidirectional image compression framework by proposing spherical attention modules, residual blocks, and a spatial autoregressive conte

Nontapat Wanwieng, Nithiwadee Thaicharoen, Narupon Chattrapiban, Apimook Watcharangkool

We explore the effects of gravitational waves (GWs) on hydrogen's radio spectral lines, focusing on the ground-state hyperfine transition and radiative transitions in highly excited Rydberg states. To analyze GW impacts on hyperfine structure, we derive Maxwell's equations in a gravitational-wave background using linearized gravity and the $3+1$ form

Manisha Lohan

IceCube-Gen2 is a proposed extension to the existing IceCube Neutrino Observatory at the South Pole. It will consist of three components: an in-ice optical array, a surface array on top of the optical array, and a radio array for detecting ultra-high energy neutrinos. Here we study the sensitivity of this future detector to the mass separation of primary cos

Zeng Wang, Minghao Shao, Mohammed Nabeel, Prithwish Basu Roy

Large language models (LLMs) offer significant potential for coding, yet fine-tuning (FT) with curated data is essential for niche languages like Verilog. Using proprietary intellectual property (IP) for FT presents a serious risk, as FT data can be leaked through LLM inference. This leads to a critical dilemma for design houses: seeking to build externally

Chandan Tankala, Dheeraj M. Nagaraj, Anant Raj

Gradient flow in the 2-Wasserstein space is widely used to optimize functionals over probability distributions and is typically implemented using an interacting particle system with $n$ particles. Analyzing these algorithms requires showing (a) that the finite-particle system converges and/or (b) that the resultant empirical distribution of the particles clo

Kento Tsubouchi, Yosuke Mitsuhashi, Ryuji Takagi, Nobuyuki Yoshioka

Symmetry inherent in quantum states has been widely used to reduce the effect of noise in quantum error correction and a quantum error mitigation technique known as symmetry verification. However, these symmetry-based techniques exploit symmetry in quantum states rather than quantum channels, limiting their application to cases where the entire circuit share

Aikaterini Niklanovits, Kirill Simonov, Shaily Verma, Ziena Zeif

The classical theorem due to Gy\H{o}ri and Lov\'{a}sz states that any $k$-connected graph $G$ admits a partition into $k$ connected subgraphs, where each subgraph has a prescribed size and contains a prescribed vertex, as long as the total size of target subgraphs is equal to the size of $G$. However, this result is notoriously evasive in terms of efficient

Erik Daxberger, Nina Wenzel, David Griffiths, Haiming Gang

Multimodal large language models (MLLMs) excel at 2D visual understanding but remain limited in their ability to reason about 3D space. In this work, we leverage large-scale high-quality 3D scene data with open-set annotations to introduce 1) a novel supervised fine-tuning dataset and 2) a new evaluation benchmark, focused on indoor scenes. Our Cubify Anythi

Jing Li, Yihang Fu, Falai Chen

Boundary representation (B-rep) of geometric models is a fundamental format in Computer-Aided Design (CAD). However, automatically generating valid and high-quality B-rep models remains challenging due to the complex interdependence between the topology and geometry of the models. Existing methods tend to prioritize geometric representation while giving insu

Kedi Chen, Zhikai Lei, Fan Zhang, Yinqi Zhang

Large language models make remarkable progress in reasoning capabilities. Existing works focus mainly on deductive reasoning tasks (e.g., code and math), while another type of reasoning mode that better aligns with human learning, inductive reasoning, is not well studied. We attribute the reason to the fact that obtaining high-quality process supervision dat

Hao Yin, Guangzong Si, Zilei Wang

Multimodal large language models (MLLMs) improve performance on vision-language tasks by integrating visual features from pre-trained vision encoders into large language models (LLMs). However, how MLLMs process and utilize visual information remains unclear. In this paper, a shift in the dominant flow of visual information is uncovered: (1) in shallow layer

Hao Yin, Guangzong Si, Zilei Wang

Contrastive decoding strategies are widely used to mitigate object hallucinations in multimodal large language models (MLLMs). By reducing over-reliance on language priors, these strategies ensure that generated content remains closely grounded in visual inputs, producing contextually accurate outputs. Since contrastive decoding requires no additional traini

MacAulay Harvey, Richard Cisek, Sarry Al-Turk, Harry. E. Ruda

Second harmonic generation microscopy (SHG) is a powerful imaging modality which has found applications in investigating both biological and synthetic nanostructures. Like all optical microscopy techniques, the resolution of SHG is limited to approximately half the wavelength of the excitation light. Because of this several groups have proposed techniques to

Zeng Wang, Minghao Shao, Jitendra Bhandari, Likhitha Mankali

Large Language Models (LLMs) have revolutionized code generation, achieving exceptional results on various established benchmarking frameworks. However, concerns about data contamination - where benchmark data inadvertently leaks into pre-training or fine-tuning datasets - raise questions about the validity of these evaluations. While this issue is known, li

José Raimundo Carvalho, Marcelino Guerra

We evaluated one of the most common policing strategies in Brazil: the allocation of police blitzes. This place-based focused deterrence intervention has well-defined assignments, and 3,423 interventions were precisely recorded in Fortaleza-CE, Brazil, between 2012 and 2013. Our analysis takes advantage of the high spatiotemporal daily data resolution coming

Jun-jie Shi, Yao-hui Zhu

Unlike ordinary conductors and semiconductors, which conduct electricity through individual electrons, superconductors usually conduct electricity through electron pairs, known as Cooper pairs. Even after 4 decades of intense study, no one knows what holds electrons together in high-$T_c$ cuprates. Here, targeting the critical challenge of pairing mechanism

Lennart Spode, Ola Hartmann, Gunnar Karl Pálsson

We demonstrate the feasibility of using optical transmission to determine concentration-dependent hydrogen diffusion coefficients and activation energies of thin metallic glass films over a wide range of temperatures and concentrations. The hydrogen concentration's temporal and spatial profiles are simultaneously extracted without requiring a metal-insulator

Alexander Pugachev, Alena Fenogenova, Vladislav Mikhailov, Ekaterina Artemova

Recent advances in large language models (LLMs) have introduced the novel paradigm of using LLMs as judges, where an LLM evaluates and scores the outputs of another LLM, which often correlates highly with human preferences. However, the use of LLM-as-a-judge has been primarily studied in English. In this paper, we evaluate this framework in Russian by introd

Babangida Sani, Aakansha Soy, Sukairaj Hafiz Imam, Ahmad Mustapha

The advancement of large language models (LLMs) has allowed them to be proficient in various tasks, including content generation. However, their unregulated usage can lead to malicious activities such as plagiarism and generating and spreading fake news, especially for low-resource languages. Most existing machine-generated text detectors are trained on high

Sébastien Bouchard, Arnaud Labourel, Andrzej Pelc

A mobile agent has to find an inert target in some environment that can be a graph or a terrain in the plane. This task is known as treasure hunt. We consider deterministic algorithms for treasure hunt in trees. Our goal is to establish the impact of different kinds of initial knowledge given to the agent on the cost of treasure hunt, defined as the total nu

Shima Shabani, Michael Breuß

Line search methods are a prominent class of iterative methods to solve unconstrained minimization problems. These methods produce new iterates utilizing a suitable step size after determining proper directions for minimization. In this paper we propose a semi-monotone line search technique based on the Goldstein quotient for dealing with convex non-smooth o

Huaqiu Li, Xiaowan Hu, Haoqian Wang

Real-world low-light images often suffer from complex degradations such as local overexposure, low brightness, noise, and uneven illumination. Supervised methods tend to overfit to specific scenarios, while unsupervised methods, though better at generalization, struggle to model these degradations due to the lack of reference images. To address this issue, w

Jeffrey Chen, Rohan Chandra

Fully decentralized, safe, and deadlock-free multi-robot navigation in dynamic, cluttered environments is a critical challenge in robotics. Current methods require exact state measurements in order to enforce safety and liveness e.g. via control barrier functions (CBFs), which is challenging to achieve directly from onboard sensors like lidars and cameras. T

Csaba Kozma, Jonathan Horsley, Gerard Hall, Callum Simpson

Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it is unknown if both types of abnormalities are important in supporting seizures. Here, we test if surgical removal of GM and/or SWM abnormalities relates to post-surgical seizure outcome in people with temporal l

Cypres Verbeeck, Nikolaos Sfakianakis

Integer-order differential operators were originally used to describe local and isotropic effects, in both space and time. However, in fields like biology, the modelling of complex phenomena with spatial heterogeneity necessitates more advanced approaches. The fractional calculus framework provides powerful tools for developing models that better capture the

Ulrich Heber, Maximilian Halenke, Aakash Bhat, Veronika Schaffenroth

We report the discovery of the young B6V run-away star LAMOST J083323.18+430825.4, 2.5\,kpc above the Galactic plane. Its atmospheric parameters and chemical composition are determined from LAMOST spectra, indicating normal composition. Effective temperature (Teff=14,500) and gravity (log g=3.79) suggest that the star is close to terminating hydrogen burning

Utku Erdogan, Gabriel Lord

In this paper, we develop numerical methods for solving Stochastic Differential Equations (SDEs) with solutions that evolve within a hypercube $D$ in $\mathbb{R}^d$. Our approach is based on a convex combination of two numerical flows, both of which are constructed from positivity preserving methods. The strong convergence of the Euler version of the method

Qiuqi Li, Chang Liu, Yifei Yang

Dynamic mode decomposition (DMD) is a widely used data-driven algorithm for predicting the future states of dynamical systems. However, its standard formulation often struggles with poor long-term predictive accuracy. To address this limitation, we propose a localized DMD (LDMD) framework that improves prediction performance by integrating DMD's strong linea

Ayse Karagenc, Mehmet Acikgoz, Serkan Araci

In this paper, we introduce a new class of polynomials, called probabilistic q-Bernstein polynomials, alongside their generating function. Assuming Y is a random variable satisfying moment conditions, we use the generating function of these polynomials to establish new relations. These include connections to probabilistic Stirling numbers of the second kind

V. Kholoimov, B. Kishor Jashal, A. Oyanguren, V. Svintozelskyi

A new algorithm has been developed at LHCb which is able to reconstruct and select very displaced vertices in real-time at the first level of the trigger (HLT1). It makes use of the Upstream Tracker (UT) and the Scintillator Fiber detector (SciFi) of LHCb and it is executed on GPUs inside the Allen framework. In addition to an optimized strategy, it utilizes

Lucas Bickmann, Lucas Plagwitz, Antonius Büscher, Lars Eckardt

Electrocardiogram data, one of the most widely available biosignal data, has become increasingly valuable with the emergence of deep learning methods, providing novel insights into cardiovascular diseases and broader health conditions. However, heterogeneity of electrocardiogram formats, limited access to deep learning model weights and intricate algorithmic

Stephen Cantrell, Mark Pollicott

We study counting limit laws that compare length functions on infinite graphs. We then apply these results to flat surfaces to obtain a statistical comparison between the geometric length and the number of singularities visited by geodesic paths.

Václav Truhlařík, Tomáš Pivoňka, Michal Kasarda, Libor Přeučil

Uniform and variable environments still remain a challenge for stable visual localization and mapping in mobile robot navigation. One of the possible approaches suitable for such environments is appearance-based teach-and-repeat navigation, relying on simplified localization and reactive robot motion control - all without a need for standard mapping. This wo

Baohao Liao, Christian Herold, Seyyed Hadi Hashemi, Stefan Vasilev

As large language models (LLMs) scale, model compression is crucial for edge deployment and accessibility. Weight-only quantization reduces model size but suffers from performance degradation at lower bit widths. Moreover, standard finetuning is incompatible with quantized models, and alternative methods often fall short of full finetuning. In this paper, we

Tu Lingjun, Sun Hao, Yi Huaiqian, Zeng Li

The generation of attosecond X-ray pulses has garnered significant attention within the X-ray free-electron laser (FEL) community due to their potential for ultrafast time-resolved studies. Such pulses enable the investigation of electron dynamics with unprecedented temporal resolution, opening new avenues in fields such as quantum control and ultrafast spec

Rodrigo Capucha, Karim Elyaouti, Milada Margarete Mühlleitner, Johann Plotnikov

We present the C++ program RelExt for Standard Model (SM) extensions that feature a Dark Matter (DM) candidate. The tool allows to efficiently scan the parameter spaces of these models to find parameter combinations that lead to relic density values which are compatible with the measured value within the uncertainty specified by the user. The code computes t

Yiwei Xu, Yifei Yu, Wentian Gan, Tengfei Wang

3D Gaussian Splatting (3DGS) achieves high-fidelity rendering with fast real-time performance, but existing methods rely on offline training after full Structure-from-Motion (SfM) processing. In contrast, this work introduces Gaussian on-the-fly Splatting (abbreviated as On-the-Fly GS), a progressive framework enabling near real-time 3DGS optimization during

Haruko Nakao, Tai-Yu Ma, Richard D. Connors, Francesco Viti

Electrifying demand-responsive transport systems need to plan the charging infrastructure carefully, considering the trade-offs of charging efficiency and charging infrastructure costs. Earlier studies assume a fully electrified fleet and overlook the planning issue in the transition period. This study addresses the joint fleet size and charging infrastructu

Simone Faro, Francesco Pio Marino, Gabriele Messina

Quantum computing leverages the principles of quantum mechanics to perform computations far beyond the capabilities of classical systems, particularly in fields such as cryptography and optimization. However, current quantum programming languages often require low-level implementation, posing significant barriers for many developers due to their steep learni

Jeonghun Oh, Herve Hugonnet, Weisun Park, YongKeun Park

Optical microscopy has been employed to derive salient characteristics of an object in various fields, including cell biology, flow cytometry, biopsy, and neuroscience. In particular, measuring the phase of light scattered from an object aroused great interest by allowing retrieving quantitative parameters such as refractive index, an intrinsic property of a

Runyu Jiao, Alice Fasoli, Francesco Giuliari, Matteo Bortolon

Performing robotic grasping from a cluttered bin based on human instructions is a challenging task, as it requires understanding both the nuances of free-form language and the spatial relationships between objects. Vision-Language Models (VLMs) trained on web-scale data, such as GPT-4o, have demonstrated remarkable reasoning capabilities across both text and

Shi Yin Hong, Uttamasha Oyshi, Quan Mai, Gibson Nkhata

Emotional support (ES) systems alleviate users' mental distress by generating strategic supportive dialogues based on diverse user situations. However, ES systems are limited in their ability to generate effective ES dialogues that include timely context and interpretability, hindering them from earning public trust. Driven by cognitive models, we propose Mi

Likai Tang, Niruth Bogahawatta, Yasod Ginige, Jiarui Xu

Large Language Models (LLMs) are acquiring a wider range of capabilities, including understanding and responding in multiple languages. While they undergo safety training to prevent them from answering illegal questions, imbalances in training data and human evaluation resources can make these models more susceptible to attacks in low-resource languages (LRL

Hubert Szolc, Mateusz Wasala, Remigiusz Mietla, Kacper Iwicki

The use of unmanned aerial vehicles (UAVs) for smart agriculture is becoming increasingly popular. This is evidenced by recent scientific works, as well as the various competitions organised on this topic. Therefore, in this work we present a system for automatic fruit counting using UAVs. To detect them, our solution uses a vision algorithm that processes s

Max van Meer, Tim van Meijel, Emile van Halsema, Edwin Verschueren

Piezo-stepper actuators enable accurate positioning through the sequential contraction and expansion of piezoelectric elements, generating a walking motion. The aim of this paper is to reduce velocity ripples caused by parasitic effects, due to hysteresis in the piezoelectric material and mechanical misalignments, through suitable feedforward control. The pr

Tobias Østmo Hermansen, Manuela Zucknick, Zhi Zhao

An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge can produce non-interpretable results. We propose a penalized semiparametric Bayesian Cox model with graph-structured selection priors for s

Fernando Gómez-Ortiz, Aldo H. Romero, Eric Bousquet

We present an algorithm that integrates pseudosymmetry search with first-principles calculations to systematically identify achiral parent structures and establish potential chiral displacive transitions linking them to their corresponding chiral phases within the 22 enantiomorphic space groups. This approach enables a robust exploration of structural relati

Lorién López-Villellas, Carl Christian Kjelgaard Mikkelsen, Juan José Galano-Frutos, Santiago Marco-Sola

All-atom, force field-based molecular dynamics simulations are essential tools in computational chemistry, enabling the prediction and analysis of biomolecular systems with atomic-level resolution. However, as system sizes and simulation timescales increase, so does the associated computational cost. To extend simulated time using the same resources, a commo

Shaolin Su, Josep M. Rocafort, Danna Xue, David Serrano-Lozano

As super-resolution (SR) techniques advance, we observe a growing distrust of evaluation metrics in recent SR research. An inconsistency often emerges between certain evaluation criteria and human perceptual preference. Although current SR research employs varying metrics to evaluate SR performance, it remains underexplored how robust and reliable these metr

Zhicheng Zhao, Jinquan Yan, Chenglong Li, Xiao Wang

Optical remote sensing image dehazing presents significant challenges due to its extensive spatial scale and highly non-uniform haze distribution, which traditional single-image dehazing methods struggle to address effectively. While Synthetic Aperture Radar (SAR) imagery offers inherently haze-free reference information for large-scale scenes, existing SAR-

Fabian Lehmann, Jonathan Bader, Friedrich Tschirpke, Ninon De Mecquenem

Scientific workflows process extensive data sets over clusters of independent nodes, which requires a complex stack of infrastructure components, especially a resource manager (RM) for task-to-node assignment, a distributed file system (DFS) for data exchange between tasks, and a workflow engine to control task dependencies. To enable a decoupled development

Loïc Béthencourt, Nicolas Fournier

We establish the fractional diffusion limit of the kinetic scattering equation with diffusive boundary condition in a strongly convex bounded domain $\mathcal{D}\subset\mathbb{R}^d$. According to the nature of the boundary condition, two types of fractional heat equations may arise at the limit, corresponding to two types of isotropic stable processes reflec

László Csató, Karel Devriesere, Dries Goossens, András Gyimesi

Starting in the 2024/25 season, the Union of European Football Associations (UEFA) has fundamentally changed the format of its club competitions: the group stage has been replaced by a league phase played by 36 teams in an incomplete round robin format. This makes ranking the teams based on their results challenging because teams play against different sets

Yihong Luo, Tianyang Hu, Weijian Luo, Kenji Kawaguchi

This paper addresses the challenge of achieving high-quality and fast image generation that aligns with complex human preferences. While recent advancements in diffusion models and distillation have enabled rapid generation, the effective integration of reward feedback for improved abilities like controllability and preference alignment remains a key open pr

Carlos Galindo, Fernando Hernando, Helena Martín-Cruz

We introduce homothetic-BCH codes. These are a family of $q^2$-ary classical codes $\mathcal{C}$ of length $\lambda n_1$, where $\lambda$ and $n_1$ are suitable positive integers such that the punctured code $\mathcal{B}$ of $\mathcal{C}$ in the last $\lambda n_1 - n_1$ coordinates is a narrow-sense BCH code of length $n_1$. We prove that whenever $\mathcal{

Henghui Du, Guangyao Li, Chang Zhou, Chunjie Zhang

In recent years, numerous tasks have been proposed to encourage model to develop specified capability in understanding audio-visual scene, primarily categorized into temporal localization, spatial localization, spatio-temporal reasoning, and pixel-level understanding. Instead, human possesses a unified understanding ability for diversified tasks. Therefore,

Sk Mujaffar Hossain, Namitha Anna Koshi, Seung-Cheol Lee, G. P Das

Accurate prediction of the voltage of battery materials plays a pivotal role in the advancement of energy storage technologies and the rational design of high-performance cathode materials. In this work, we present a deep neural network (DNN) model, built using PyTorch, to estimate the average voltage of cathode materials across Li-ion, Na-ion, and other alk

Batool Ilyasi, Naslim Neelamkodan, Kazuki Tokuda, Susmita Barman

The star-forming region N66, as a host of the majority of OB stars in the Small Magellanic Cloud, provides a unique opportunity to enhance our understanding of the triggers of high-mass star formation. We investigate the properties of the molecular cloud in N66 using the $^{12}$CO(1-0) data obtained with the Atacama Large Millimeter/submillimeter Array. A cl

Pranav Suryadevara

The growth of machine learning (ML) workloads has underscored the importance of efficient memory hierarchies to address bandwidth, latency, and scalability challenges. HERMES focuses on optimizing memory subsystems for RISC-V architectures to meet the computational needs of ML models such as CNNs, RNNs, and Transformers. This project explores state-of-the-ar

Zheng Wang, Zihui Wang, Zheng Wang, Xiaoliang Fan

Federated learning (FL) is emerging as a promising technique for collaborative learning without local data leaving their devices. However, clients' data originating from diverse domains may degrade model performance due to domain shifts, preventing the model from learning consistent representation space. In this paper, we propose a novel FL framework, Federa

Maria de Lluc Planas, Antoni Ramos-Buades, Cecilio García-Quirós, Héctor Estellés

We introduce IMRPhenomTEHM, a new phenomenological time-domain model for eccentric aligned-spin binary black holes. Building upon the accurate quasi-circular IMRPhenomTHM model, IMRPhenomTEHM integrates the eccentric post-Newtonian (PN) dynamics and introduces eccentric corrections into the waveform multipoles up to 3PN, including spin effects. The model inc

Partha Nandi, Tiasha Bhattacharyya, A. S. Majumdar, Graeme Pleasance

We present a general open-quantum-systems framework to model decoherence induced by stochastic Planck-scale fluctuations of spacetime, focusing on the kappa-Minkowski noncommutative geometry as a representative quantum-gravity scenario. Treating the deformation parameter as Gaussian white noise, we derive a Lindblad-type master equation applicable to arbitra

Harshal Kausadikar, Tanvi Kale, Onkar Susladkar, Sparsh Mittal

In medieval India, the Marathi language was written using the Modi script. The texts written in Modi script include extensive knowledge about medieval sciences, medicines, land records and authentic evidence about Indian history. Around 40 million documents are in poor condition and have not yet been transliterated. Furthermore, only a few experts in this do

Ignacio Bajo, Saïd Benayadi, Hassan Oubba

We show that there are no symmetric non-zero biderivations on perfect Lie algebras of finite dimension over a field of characteristic zero. We show that this is equivalent to show that every symmetric biderivation on a finite-dimensional perfect Lie algebra over such a field with values in a finite-dimensional module vanishes identically. This answers an ope

Zeyi Huang, Utkarsh Ojha, Yuyang Ji, Donghyun Lee

When a human undertakes a test, their responses likely follow a pattern: if they answered an easy question $(2 \times 3)$ incorrectly, they would likely answer a more difficult one $(2 \times 3 \times 4)$ incorrectly; and if they answered a difficult question correctly, they would likely answer the easy one correctly. Anything else hints at memorization. Do

Robin Zbinden, Nina van Tiel, Gencer Sumbul, Chiara Vanalli

Species Distribution Models (SDMs) play a vital role in biodiversity research, conservation planning, and ecological niche modeling by predicting species distributions based on environmental conditions. The selection of predictors is crucial, strongly impacting both model accuracy and how well the predictions reflect ecological patterns. To ensure meaningful

Richard Biegler-König, Daniel Oeltz

In power markets, Green Power Purchase Agreements have become an important contractual tool of the energy transition from fossil fuels to renewable sources such as wind or solar radiation. Trading Green PPAs exposes agents to price risks and weather risks. Also, developed electricity markets feature the so-called cannibalisation effect : large infeeds induce

Yu-Hong Shen, Chuan-Yu Wu, Yi-Ru Yang, Yen-Ling Tai

We investigate the use of Multimodal Large Language Models (MLLMs) with in-context learning for closed-loop task planning in instruction-following manipulation. We identify four essential requirements for successful task planning: quantity estimation, reachability analysis, relative positioning, and collision avoidance. However, existing benchmarks fail to s

Alessandra Fumagalli, Tiago Castro, Stefano Borgani, Milena Valentini

Ongoing and upcoming wide-field surveys at different wavelengths will measure the distribution of galaxy clusters with unprecedented precision, demanding accurate models for the two-point correlation function (2PCF) covariance. In this work, we assess a semi-analytical framework for the cluster 2PCF covariance that employs three nuisance parameters to accoun

Rishika Kohli, Shaifu Gupta, Manoj Singh Gaur

User profiling, the practice of collecting user information for personalized recommendations, has become widespread, driving progress in technology. However, this growth poses a threat to user privacy, as devices often collect sensitive data without their owners' awareness. This article aims to consolidate knowledge on user profiling, exploring various appro

Nassim Ali Ousalah, Anis Kacem, Enjie Ghorbel, Emmanuel Koumandakis

Compact and efficient 6DoF object pose estimation is crucial in applications such as robotics, augmented reality, and space autonomous navigation systems, where lightweight models are critical for real-time accurate performance. This paper introduces a novel uncertainty-aware end-to-end Knowledge Distillation (KD) framework focused on keypoint-based 6DoF pos

Kris Oosthoek, Kelvin Lubbertsen, Georgios Smaragdakis

This study empirically analyzes the transaction activity of Bitcoin addresses linked to Russian intelligence services, which have liquidated over 7 Bitcoin (BTC), i.e., equivalent to approximately US$300,000 based on the exchange rate at the time. Our investigation begins with an observed anomaly in transaction outputs featuring the Bitcoin Script operation

Etienne Gauthier, Francis Bach, Michael I. Jordan

Conformal prediction is a powerful framework for distribution-free uncertainty quantification. The standard approach to conformal prediction relies on comparing the ranks of prediction scores: under exchangeability, the rank of a future test point cannot be too extreme relative to a calibration set. This rank-based method can be reformulated in terms of p-va

Andris P. Stikuts, Seemant Mishra, Artem Ryabov, Philipp Maass

Shapiro steps are quantized plateaus in the velocity-force or velocity-torque curve of a driven system, when its speed remains constant despite an increase in the driving force. For microscopic particles driven across a sinusoidal potential, integer Shapiro steps have been observed. By driving a single colloidal particle across a time-modulated, non-sinusoid

Haofeng Chen, Bedrich Himmel, Bin Li, Xiaojie Wang

Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EIT-based tactile sensors face significant challenges during surface bending. Deformation alters the baseline impedance distribution and couples with touch-induced conductivity varia

Ruiqi Song, Xianda Guo, Yanlun Peng, Qinggong Wei

Conventional end-to-end autonomous driving methods often rely on explicit global scene representations, which typically consist of 3D object detection, online mapping, and motion prediction. In contrast, human drivers selectively attend to task-relevant regions and implicitly reason over the broader traffic context. Motivated by this observation, we introduc

Ching Wong, Giusi Moffa, Jack Kuipers

The evaluation of G-Wishart normalising constants is a core component for Bayesian analyses for Gaussian graphical models, but remains a computationally intensive task in general. Based on empirical evidence, Roverato [Scandinavian Journal of Statistics, 29:391--411 (2002)] observed and conjectured that such constants can be simplified and rewritten in terms

Gabriele Berton, Kevin Musgrave, Carlo Masone

Image retrieval is the task of finding images in a database that are most similar to a given query image. The performance of an image retrieval pipeline depends on many training-time factors, including the embedding model architecture, loss function, data sampler, mining function, learning rate(s), and batch size. In this work, we run tens of thousands of tr