March 2025 arXiv papers — page 83

C. Gautier, J. Delanoy, G. Gesquière

Digital 3D representations of urban areas, through their growing availability, are a helpful tool to better understand a territory. However, they lack contextual information about, for example, the history or functionality of buildings. On another side, multimedia documents like images, videos or texts usually contain such information. Crossing these two typ

Ajay D. Kshemkalyani, Manish Kumar, Anisur Rahaman Molla, Gokarna Sharma

The dispersion problem has received much attention recently in the distributed computing literature. In this problem, $k\leq n$ agents placed initially arbitrarily on the nodes of an $n$-node, $m$-edge anonymous graph of maximum degree $\Delta$ have to reposition autonomously to reach a configuration in which each agent is on a distinct node of the graph. Di

Francis J. Headley, Alessio Belenchia, Mauro Paternostro, Daniel Braun

Newton's constant is the least well-measured among the fundamental constants of Nature, and, indeed, its accurate measurement has long served an experimental challenge. Levitated mechanical systems are attracting growing attention for their promising applications in sensing and as an experimental platform for exploring the intersection between quantum physic

Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic

In light of the increasing interest in G9a's role in neuroscience, three machine learning (ML) models, that are time efficient and cost effective, were developed to support researchers in this area. The models are based on data provided by PubChem and performed by algorithms interpreted by the scikit-learn Python-based ML library. The first ML model aimed to

ATLAS Collaboration

A search is performed for long-lived heavy neutral leptons (HNLs), produced through the decay of a $W$ boson along with a muon or electron. Two channels are explored: a leptonic channel, in which the HNL decays into two leptons and a neutrino, and a semi-leptonic channel, in which the HNL decays into a lepton and a charged pion. The search is performed with

Qizhi Pei, Lijun Wu, Zhuoshi Pan, Yu Li

Large Language Models (LLMs) have shown impressive progress in mathematical reasoning. While data augmentation is promising to enhance mathematical problem-solving ability, current approaches are predominantly limited to instance-level modifications-such as rephrasing or generating syntactic variations-which fail to capture and leverage the intrinsic relatio

Hazhir Aliahmadi, Aidan Sheedy, Greg van Anders

Revolutionary advances in both manufacturing and computational morphogenesis raise critical questions about design sensitivity. Sensitivity questions are especially critical in contexts, such as topology optimization, that yield structures with emergent morphology. However, analyzing emergent structures via conventional, perturbative techniques can mask larg

Marco Caliari, Fabio Cassini, Lukas Einkemmer, Alexander Ostermann

Splitting the exponential-like $\varphi$ functions, which typically appear in exponential integrators, is attractive in many situations since it can dramatically reduce the computational cost of the procedure. However, depending on the employed splitting, this can result in order reduction. The aim of this paper is to analyze different such split approximati

Moritz Moeller, Sebastian Neumayer, Kateryna Pozharska, Tizian Sommerfeld

We investigate the reconstruction of multivariate functions from samples using sparse recovery techniques. For Square Root Lasso, Orthogonal Matching Pursuit, and Compressive Sampling Matching Pursuit, we demonstrate both theoretically and empirically that they allow us to recover functions from a small number of random samples. In contrast to Basis Pursuit

Wei Zi, Junhong Nie, Xiaoming Sun

The optimization of quantum circuit depth is crucial for practical quantum computing, as limited coherence times and error-prone operations constrain executable algorithms. Measurement and feedback operations are fundamental in quantum computing (e.g., quantum error correction); we develop a framework using them to achieve constant-depth implementations of e

Wenjun Cui, Qiyu Kang, Xuhao Li, Kai Zhao

Neural differential equation models have garnered significant attention in recent years for their effectiveness in machine learning applications.Among these, fractional differential equations (FDEs) have emerged as a promising tool due to their ability to capture memory-dependent dynamics, which are often challenging to model with traditional integer-order a

Beate Sick, Oliver Dürr

The ultimate goal of most scientific studies is to understand the underlying causal mechanism between the involved variables. Structural causal models (SCMs) are widely used to represent such causal mechanisms. Given an SCM, causal queries on all three levels of Pearl's causal hierarchy can be answered: $L_1$ observational, $L_2$ interventional, and $L_3$ co

Swagat R Das, Saumya Gupta, Jessy Jose, Manash Samal

To identify member populations of IC 1396, we employ the random forest (RF) classifier of machine learning technique. Random forest classifier is an ensemble of individual decision trees suitable for large, high-dimensional datasets. The training set used in this work is derived from previous Gaia-based studies, where the member stars are younger than $\sim$

Diogo Belloni, Matthias R. Schreiber, Kareem El-Badry

The existence of the binary system SDSS J1257+5428 has been described as paradoxical. Here we investigate under which conditions SDSS J1257+5428 could be understood as a descendant of a cataclysmic variable with an evolved donor star, which is a scenario that has never been explored in detail. We used the BSE code for pre-common-envelope (CE) evolution and t

Stefano Fioravanti, Francesco Giannini, Paolo Frazzetto, Fabio Zanasi

The most common methods in explainable artificial intelligence are post-hoc techniques which identify the most relevant features used by pretrained opaque models. Some of the most advanced post hoc methods can generate explanations that account for the mutual interactions of input features in the form of logic rules. However, these methods frequently fail to

Yulei Wang, Yalin Liu, Yaru Fu, Yujie Qin

Due to their flexibility, aerial vehicles (AVs), such as unmanned aerial vehicles and airships, are widely employed as relays to assist communications between massive ground users (GUs) and satellites, forming an AV-relayed ground-air-satellite solution (GASS). In GASS, the deployment of AVs is crucial to ensure overall performance from GUs to satellites. Th

Ignacio Barros

We exhibit a simple uniruledness criterion for general orthogonal modular varieties in terms of invariants of the corresponding lattice. As an application, we obtain the uniruledness of almost all Nikulin--Vinberg moduli spaces parameterizing projective K3 surfaces of Picard number at least 3 and fixed finite automorphism group.

Piotr Chmielowski

This note outlines an approach to stress testing of covariance of financial time series, in the context of financial risk management. It discusses how the geodesic distance between covariance matrices implies a notion of plausibility of covariance stress tests. In this approach, correlation stress tests span a submanifold of constant determinant of the Fishe

Andrea Pugnana, Riccardo Massidda, Francesco Giannini, Pietro Barbiero

Concept Bottleneck Models (CBMs) are machine learning models that improve interpretability by grounding their predictions on human-understandable concepts, allowing for targeted interventions in their decision-making process. However, when intervened on, CBMs assume the availability of humans that can identify the need to intervene and always provide correct

Vasileios Fragkos, Igor Pikovski

The equivalence of active and passive (EAP) gravitational mass is one of the most fundamental principles of gravity. But in contrast to the usual equivalence of inertial and (passive) gravitational mass, the EAP has not received much attention. Here we revisit this principle and show how it can be used to probe quantum gravity in laboratory-based experiments

Georgios Apostolides, Wei Pan, Jens Kober, Cosimo Della Santina

Achieving controlled jumping behaviour for a quadruped robot is a challenging task, especially when introducing passive compliance in mechanical design. This study addresses this challenge via imitation-based deep reinforcement learning with a progressive training process. To start, we learn the jumping skill by mimicking a coarse jumping example generated b

Zhijie Du, Huoyuan Duan, Roger C E Tan, Yuanhong Wei

An interior penalty DG method is proposed for the steady-state linear partial differential equations of nonnegative characteristic form, suitable for mixed second-order elliptic-parabolic and first-order hyperbolic equations. Due to the different natures of the elliptic, parabolic, and hyperbolic equations. In the new DG method, the averages, jumps and penal

Chia-Yi Hsu, Jia-You Chen, Yu-Lin Tsai, Chih-Hsun Lin

Differentially private (DP) synthetic data has become the de facto standard for releasing sensitive data. However, many DP generative models suffer from the low utility of synthetic data, especially for high-resolution images. On the other hand, one of the emerging techniques in parameter efficient fine-tuning (PEFT) is visual prompting (VP), which allows we

Xin Huang, Shiyao Zhu, Ziyu Wang, Yaping He

We introduce a novel multimodal emotion recognition dataset that enhances the precision of Valence-Arousal Model while accounting for individual differences. This dataset includes electroencephalography (EEG), electrocardiography (ECG), and pulse interval (PI) from 64 participants. Data collection employed two emotion induction paradigms: video stimuli that

Ziyao Guo, Kaipeng Zhang, Michael Qizhe Shieh

Autoregressive models have shown remarkable success in image generation by adapting sequential prediction techniques from language modeling. However, applying these approaches to images requires discretizing continuous pixel data through vector quantization methods like VQ-VAE. To alleviate the quantization errors that existed in VQ-VAE, recent works tend to

François t'Serstevens, Roberto Cerina, Gustav Peper

This study investigates affective polarization among Swedish politicians on Twitter from 2021 to 2023, including the September 2022 parliamentary election. Analyzing over 25,000 tweets and employing large language models (LLMs) for sentiment and political classification, we distinguish between positive partisanship (support of allies) and negative partisansh

Yuki Akiyama, Konstantinos Slavakis

This paper introduces novel Bellman mappings (B-Maps) for value iteration (VI) in distributed reinforcement learning (DRL), where agents are deployed over an undirected, connected graph/network with arbitrary topology -- but without a centralized node, that is, a node capable of aggregating all data and performing computations. Each agent constructs a nonpar

Yinon Goldshtein, Gal Perelman, Assaf Schuster, Avi Ostfeld

The integration of Large Language Models (LLMs) into engineering workflows presents new opportunities for making computational tools more accessible. Especially where such tools remain underutilized due to technical or expertise barriers, such as water distribution system (WDS) management. This study introduces LLM-EPANET, an agent-based framework that enabl

Sergey Nemirovskii

The study explores the development of the vortex line density in superfluids under thermal activation. This problem is of interest to both applied and fundamental research, and has been investigated by many authors in various aspects. Despite the important and impressive results obtained, a significant part of the process, namely the kinetics of processes le

Haroune Houamed, Marc Magaña

We investigate the strong convergence of weak solutions to the two-dimensional Quasi-Geostrophic Shallow-Water (QGSW) equation as the inverse Rossby radius tends to zero. In this limit, we recover the Yudovich solution of the incompressible Euler equations. We prove that the vorticity convergence holds in $L^\infty_t L^p_x$, for any finite integrability expo

Ming Li, Jike Zhong, Shitian Zhao, Yuxiang Lai

This paper investigates the role of explicit thinking process in rule-based reinforcement fine-tuning (RFT) for MLLMs. We first propose CLS-RL for MLLM image classification, using verifiable rewards for fine-tuning. Experiments show CLS-RL significantly outperforms SFT and yields a cross-dataset generalization effect. We then rethink and question whether exp

Liane Xu, Amit Singer

Laplacian-based methods are popular for the dimensionality reduction of data lying in $\mathbb{R}^N$. Several theoretical results for these algorithms depend on the fact that the Euclidean distance locally approximates the geodesic distance on the underlying submanifold which the data are assumed to lie on. However, for some applications, other metrics, such

Anna Lindeberg, Bruno J. Schmidt, Manoj Changat, Ameera Vaheeda Shanavas

Directed acyclic graphs (DAGs) are fundamental structures used across many scientific fields. A key concept in DAGs is the least common ancestor (LCA), which plays a crucial role in understanding hierarchical relationships. Surprisingly little attention has been given to DAGs that admit a unique LCA for every subset of their vertices. Here, we characterize s

Peihao Wu, Yongxiang Yao, Wenfei Zhang, Dong Wei

Multimodal remote sensing image (MRSI) matching is pivotal for cross-modal fusion, localization, and object detection, but it faces severe challenges due to geometric, radiometric, and viewpoint discrepancies across imaging modalities. Existing unimodal datasets lack scale and diversity, limiting deep learning solutions. This paper proposes MapGlue, a univer

Andrea Maracani, Savas Ozkan, Sijun Cho, Hyowon Kim

Scaling architectures have been proven effective for improving Scene Text Recognition (STR), but the individual contribution of vision encoder and text decoder scaling remain under-explored. In this work, we present an in-depth empirical analysis and demonstrate that, contrary to previous observations, scaling the decoder yields significant performance gains

Xiao Wang, Hendrik Borras, Bernhard Klein, Holger Fröning

The disparity between the computational demands of deep learning and the capabilities of compute hardware is expanding drastically. Although deep learning achieves remarkable performance in countless tasks, its escalating requirements for computational power and energy consumption surpass the sustainable limits of even specialized neural processing units, in

Ayesha Siddique, Khurram Khalil, Khaza Anuarul Hoque

Approximate deep neural networks (AxDNNs) are promising for enhancing energy efficiency in real-world devices. One of the key contributors behind this enhanced energy efficiency in AxDNNs is the use of approximate multipliers. Unfortunately, the simulation of approximate multipliers does not usually scale well on CPUs and GPUs. As a consequence, this slows d

P. A. Grassi, S. Penati

We generalize the study of higher-form-symmetries to theories with supersymmetry. Using a supergeometry formulation, we find that ordinary higher-form-symmetries nicely combine with supersymmetry to give rise to a much larger spectrum of topological conserved (super)currents. These can be classified as a supersymmetric version of Chern-Weil symmetries, and a

Denise G. Yudovich, Kai E. Yang, Xudong Sun

Stellar flares occasionally present a $\textit{peak-bump}$ light curve morphology, consisting of an initial impulsive phase followed by a gradual late phase. Analyzing this specific morphology can uncover the underlying physics of stellar flare dynamics, particularly the plasma heating-evaporation-condensation process. While previous studies have mainly exam

Rasmus Svejstrup Nielsen, Ángel Labordet Álvarez, Yvonne Tomm, Galina Gurieva

Chalcogenide perovskites have emerged as a promising class of materials for the next generation of optoelectronic applications, with BaZrS$_\text{3}$ attracting significant attention due to its wide bandgap, earth-abundant composition, and thermal and chemical stability. However, previous studies have consistently reported weak and ambiguous photoluminescenc

Fatemeh Amerehi, Patrick Healy

Efforts to address declining accuracy as a result of data shifts often involve various data-augmentation strategies. Adversarial training is one such method, designed to improve robustness to worst-case distribution shifts caused by adversarial examples. While this method can improve robustness, it may also hinder generalization to clean examples and exacerb

Jinxing Zhao, Yu Guo, Fei He

The $k$-partite entanglement, which focus on at most how many particles in the global system are entangled but separable from other particles, is complementary to the $k$-entanglement that reflects how many splitted subsystems are entangled under partitions of the systems in characterizing multipartite entanglement. Very recently, the theory of the complete

Shiyong Liu, Xiao Tang, Zhihao Li, Yingfan He

In large-scale scene reconstruction using 3D Gaussian splatting, it is common to partition the scene into multiple smaller regions and reconstruct them individually. However, existing division methods are occlusion-agnostic, meaning that each region may contain areas with severe occlusions. As a result, the cameras within those regions are less correlated, l

Chunfeng Cui, Liqun Qi

An M-eigenvalue of a nonnegative biquadratic tensor is referred to as an M$^+$-eigenvalue if it has a pair of nonnegative M-eigenvectors. If furthermore that pair of M-eigenvectors is positive, then that M$^+$-eigenvalue is called an M$^{++}$-eigenvalue. A nonnegative biquadratic tensor has at least one M$^+$ eigenvalue, and the largest M$^+$-eigenvalue is b

Urban Jezernik, Matevž Miščič

We study a random walk on the Lie algebra $\mathfrak{sl}_2(\mathbf{F}_p)$ where new elements are produced by randomly applying adjoint operators of two generators. Focusing on the generic case where the generators are selected at random, we analyze the limiting distribution of the random walk and the speed at which it converges to this distribution. These qu

Antoine Honet, Michaël Sarrazin

In the present work, we introduce a new interpretation of exciton binding energies in two-dimensional (2D) materials using concepts from brane physics. We adapt the Dvali-Gabadadze-Porrati-Shifman mechanism to a (2+1)-dimensional brane in a (3+1)-D spacetime, deriving an effective electromagnetic potential on the brane. Using this potential, we develop a hyd

D. Maurin, L. Audouin, E. Berti, P. Coppin

Cosmic-ray physics in the GeV-to-TeV energy range has entered a precision era thanks to recent data from space-based experiments. However, the poor knowledge of nuclear reactions, in particular for the production of antimatter and secondary nuclei, limits the information that can be extracted from these data, such as source properties, transport in the Galax

Sergey N. Tumanov

The aim of this paper is to find necessary and sufficient conditions for sectoriality and compactness of the resolvent for Sturm--Liouville operators with complex-valued potentials of the class $q\in W_{2,loc}^{-1}(\mathbb{R}_+)$ in terms of its generalized antiderivatives $s\in L_{2,loc}(\mathbb{R}_+)$.

Yiyan Su, Ruiqing Sun, Yajing Wang, Yanfeng xi

Background: Tumour budding is an independent predictor of metastasis and prognosis in colorectal cancer and is a vital part of the pathology specification report. In a conventional pathological section observation process, pathologists have to repeatedly switch from 10x objective to 20x objective several times to localize and image the target region. Besides

Louis-Adrien Dufrène, Quentin Lampin, Guillaume Larue

This study investigates the problem of learning linear block codes optimized for Belief-Propagation decoders significantly improving performance compared to the state-of-the-art. Our previous research is extended with an enhanced system design that facilitates a more effective learning process for the parity check matrix. We simplify the input dataset, restr

Hong Yi Lin, Chunhua Liu, Haoyu Gao, Patanamon Thongtanunam

State-of-the-art large language models (LLMs) have demonstrated impressive code generation capabilities but struggle with real-world software engineering tasks, such as revising source code to address code reviews, hindering their practical use. Code review comments are often implicit, ambiguous, and colloquial, requiring models to grasp both code and human

Mert Yildiz, Alexey Rolich, Andrea Baiocchi

While scheduling and dispatching of computational workloads is a well-investigated subject, only recently has Google provided publicly a vast high-resolution measurement dataset of its cloud workloads. We revisit dispatching and scheduling algorithms fed by traffic workloads derived from those measurements. The main finding is that mean job response time att

Xiangyu Li, Wanshu Fan, Yue Shen, Cong Wang

High-fidelity imaging is crucial for the successful safety supervision and intelligent deployment of vision-based measurement systems (VBMS). It ensures high-quality imaging in VBMS, which is fundamental for reliable visual measurement and analysis. However, imaging quality can be significantly impaired by adverse weather conditions, particularly rain, leadi

Jonáš Kříž, Vojtěch Vonásek

The asymptotically optimal version of Rapidly-exploring Random Tree (RRT*) is often used to find optimal paths in a high-dimensional configuration space. The well-known issue of RRT* is its slow convergence towards the optimal solution. A possible solution is to draw random samples only from a subset of the configuration space that is known to contain config

Shibo Jie, Yehui Tang, Kai Han, Zhi-Hong Deng

Transformer-based large language models (LLMs) have already achieved remarkable results on long-text tasks, but the limited GPU memory (VRAM) resources struggle to accommodate the linearly growing demand for key-value (KV) cache as the sequence length increases, which has become a bottleneck for the application of LLMs on long sequences. Existing KV cache co

Paul Caucal, Marcos Guerrero Morales, Edmond Iancu, Farid Salazar

Within the Colour Glass Condensate effective theory, we demonstrate that back-to-back dijet correlations in dilute-dense collisions involving a small-$x$ quark from the nuclear target can be factorised in terms of universal transverse momentum dependent distributions (TMDs) for the sea quarks. Two building blocks are needed to construct all these TMDs at the

Alex-Razvan Ispas, Charles-Elie Simon, Fabien Caspani, Vincent Guigue

Large Language Models are prompting us to view more NLP tasks from a generative perspective. At the same time, they offer a new way of accessing information, mainly through the RAG framework. While there have been notable improvements for the autoregressive models, overcoming hallucination in the generated answers remains a continuous problem. A standard sol

A. Pezzotta, A. Eggemeier, G. Gambardella, L. Finkbeiner

We introduce an extension of the evolution mapping framework to cosmological models that include massive neutrinos. The original evolution mapping framework exploits a degeneracy in the linear matter power spectrum when expressed in ${\rm Mpc}$ units, which compresses its dependence on cosmological parameters into those that affect its shape and a single ext

Jiwoo Son, Zhikai Zhao, Federico Berto, Chuanbo Hua

The practical deployment of Neural Combinatorial Optimization (NCO) for Vehicle Routing Problems (VRPs) is hindered by a critical sim-to-real gap. This gap stems not only from training on oversimplified Euclidean data but also from node-based architectures incapable of handling the node-and-edge-based features with correlated asymmetric cost matrices, such a

Shenbin Qian, Constantin Orăsan, Diptesh Kanojia, Félix do Carmo

Evaluating machine translation (MT) of user-generated content (UGC) involves unique challenges such as checking whether the nuance of emotions from the source are preserved in the target text. Recent studies have proposed emotion-related datasets, frameworks and models to automatically evaluate MT quality of Chinese UGC, without relying on reference translat

A. Ashimbekova, J. Vaubaillon, P. Koten

As of today, there is no official definition of a meteor cluster. It is usually identified as a large number of meteors sharing a similar radiant and velocity, all occurring within a few seconds. Only eight clusters have been reported so far, from single-camera or camera network observations. We aim to provide an overview of meteor clusters to help define wh

Jin-Tian Zhang, Cheng-Ge Liu, Qing Ai

Quantum battery (QB) is an application of quantum thermodynamics which uses quantum effects to store and transfer energy, overcoming the limitations of classical batteries and potentially improving performance. However, due to the interaction with the external environment, it will lead to decoherence and thus reduce the lifetime of QBs. Here, we propose supp

Ralf Fröberg

Very little is known on the Hilbert series of graded algebras $\mathbb C[x_1,\ldots,x_n]/(g_1,\ldots,g_r)$, $r>n$, $g_i$ generic form of degree $e_i$, in general. One instance when the series is known, is for $n+1$ forms in $n$ variables, \cite{St}. Of course even less is known about Betti numbers. There are some general results on the Betti table by Pardue

E. Calvello, J. A. Carrillo, F. Hoffmann, P. Monmarché

Estimating the state of a dynamical system from partial and noisy observations is a ubiquitous problem in a large number of applications, such as probabilistic weather forecasting and prediction of epidemics. Particle filters are a widely adopted approach to the problem and provide provably accurate approximations of the statistics of the state, but they per

Tianyi Wei, Yifan Zhou, Dongdong Chen, Xingang Pan

The integration of Rotary Position Embedding (RoPE) in Multimodal Diffusion Transformer (MMDiT) has significantly enhanced text-to-image generation quality. However, the fundamental reliance of self-attention layers on positional embedding versus query-key similarity during generation remains an intriguing question. We present the first mechanistic analysis

Anna A. Zarudnyaya, Gleb V. Segal, Andrey P. Morozov, Lev O. Luchnikov

CsSnI3 perovskite is a promising thin-film semiconductor with high intrinsic conductivity for various device applications (thermoelectric, photovoltaics, etc.). Stoichiometric CsSnI3 owns high-density of defects and structural imperfections affecting device performance. In this work, we made an investigation on A-site cation engineering to evaluate the corre

Garazi Retegui, Alan E. Gelfand, Jaione Etxeberria, María Dolores Ugarte

Disease mapping attempts to explain observed health event counts across areal units, typically using Markov random field models. These models rely on spatial priors to account for variation in raw relative risk or rate estimates. Spatial priors introduce some degree of smoothing, wherein, for any particular unit, empirical risk or incidence estimates are eit

Belur Ravindra, Deepangkar Sarkar, Shantikumar Singh Ningombam, Stanzin Tundup

This study analyzes twelve years of wind speed and direction data collected at the proposed National Large Solar Telescope (NLST) site near Pangong Tso, Merak village, Leh-Ladakh. A weather station from Campbell Scientific Instruments, installed in 2008, has been continuously monitoring meteorological parameters, including wind speed and direction. The data

Dong Chen, Boyue Zhao, Yi Zhang, Meng Zhao

Efficient modal feature fusion strategy is the key to achieve accurate segmentation of brain glioma. However, due to the specificity of different MRI modes, it is difficult to carry out cross-modal fusion with large differences in modal features, resulting in the model ignoring rich feature information. On the other hand, the problem of multi-modal feature r

Mats Faulborn, Indira Sen, Max Pellert, Andreas Spitz

Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis. For all of these applications and others, political biases in these models can affect their performance. Several researchers have attempted to study political bias in language models us

Jason Lynch, Tzu-Yu Peng, Jing-Wei Yang, Ben R. Conran

Hyperbolic superlattices are used for sub-wavelength focusing, cloaking, and optical thermal management. Typically, these superlattices are constructed of layers of noble metals and insulators. Despite these systems displaying excellent optical performance, the poor thermal stability of noble metals prevents their application in high-temperature environments

Talip Tolga Sarı, Gökhan Seçinti, Angelo Trotta

In large-scale UAV swarms, dynamically executing machine learning tasks can pose significant challenges due to network volatility and the heterogeneous resource constraints of each UAV. Traditional approaches often rely on centralized orchestration to partition tasks among nodes. However, these methods struggle with communication bottlenecks, latency, and re

Tian Yang, Xi Chen, Xiao-Yun Xu, Yang Yang

We report the spatial distribution and physical characteristics of molecular clouds in the G24 region, which is located near the intersection of the Milky Way's Galactic bar with the Norma arm and the 3 kpc arm. Utilizing molecular line data from the Milky Way Imaging Scroll Painting (MWISP) project, including $^{12}$CO, $^{13}$CO, and C$^{18}$O, along with

Djamel Eddine Khelladi, Charly Reux, Mathieu Acher

Large language model (LLM)-based test generation has gained attention in software engineering, yet most studies evaluate LLMs' ability to generate unit tests in a single attempt for a given language, missing the opportunity to leverage LLM diversity for more robust testing. This paper introduces PolyTest, a novel approach that enhances test generation by exp

A. N. Zubkov

We develop a fragment of the theory of Duflo-Serganova functor over a field of odd characteristic. We elaborate a method of computing the symmetry supergroup $\widetilde{\mathbb{G}_x}$ of this functor, recently introduced by A.Sherman, for a wide class of supergroups $\mathbb{G}$, and apply it to the case when $\mathbb{G}$ is $\mathrm{GL}(m|n)$ or $\mathrm{Q

F. L. Rommel, B. C. N. Proudfoot, B. J. Holler, J. L. Ortiz

Stellar occultations are an ideal way to characterize the physical and orbital properties of trans-Neptunian binary systems. In this research note, we detail the prediction and observation of a stellar occultation observed with NASA's IRTF on March 16$^{\mathrm{th}}$, 2025 (UT), with drop-outs from both the dwarf planet Haumea and its smaller satellite Namak

M. Littich, L. Doria, P. Brand, P. Achenbach

We report on a measurement of elastic electron scattering on argon performed with a novel cryogenic gas-jet target at the Mainz Microtron accelerator MAMI. The luminosity is estimated with the thermodynamical parameters of the target and by comparison to a calculation in distorted-wave Born approximation. The cross section, measured at new momentum transfers

Caicheng Wang, Zili Wang, Shuyou Zhang, Yongzhe Xiang

Pipe routing is a highly complex, time-consuming, and no-deterministic polynomial-time hard (NP-hard) problem in aeroengine design. Despite extensive research efforts in optimizing constant-curvature pipe routing, the growing demand for free-form pipes poses new challenges. Dynamic design environments and fuzzy layout rules further impact the optimization pe

Felix Russo, Thomas Pohl

Continuous time crystals, i.e., nonequilibrium phases with a spontaneously broken continuous time-translational symmetry, have been studied and recently observed in the long-time dynamics of open quantum systems. Here, we investigate a lattice of interacting three-level particles and find two distinct time-crystal phases that cannot be described within mean-

Ruiqi Yang, Jianxu Wang, Wei Yuan, Xun Wang

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a hybrid model capable of extracting and learning meaningful features from gene sequences, such as k-mer frequencies and ph

Niña Zambale Simon, Miguel Revilla, Nathaniel Hermosa

We propose a method to detect exoplanets based on their host star's intensity centroid after it passes thru a vortex filter. Based on our calculations with planets in face-on orbits, exoplanets with relative proximity to their host stars and with low mass ratios ($m_p/m_s$) can have discernible signals that can be amplified by the topological charge $\ell$ o

Darío Slaifstein, Gautham Ram Chandra Mouli, Laura Ramirez-Elizondo, Pavol Bauer

In the context of building electrification, the operation of distributed energy resources integrating multiple energy carriers (electricity, heat, mobility) poses a significant challenge due to the nonlinear device dynamics, uncertainty, and computational issues. As such, energy management systems seek to decide the power dispatch in the best way possible. T

Jinan Zhao

Recently Hollands, Wald and Zhang proposed a new formula for the entropy of a dynamical black hole. We lift this construction to the dynamical cosmological event horizon of an asymptotically de Sitter spacetime. By introducing a nontrivial correction term in the formula for the entropy, we generalize Gibbons and Hawking's "first law of event horizons" to non

Hongrui Jin

The rise of TikTok has brought forth novel ways to create and consume media content, accelerated by technologies such as hyper-individualised algorithms and easy-to-use video production tools. Despite its popularity, scholars and politicians alike have raised many concerns on the legitimacy and ethics of TikTok regarding its services, and its collected data.

Alina Wettstein, Diddo Diddens, Andreas Heuer

Relevant information about the nature of the dynamics of ions in electrolytes can be obtained by studying the nonlinear dependence on an applied electric field. Here we use molecular dynamics (MD) simulations to study the field effects for a polymer electrolyte, i.e. a mixture of PEO with Li-TFSI salt, for a range of different temperatures and salt contents.

Adam Herout, Vojtěch Bartl, Martin Gaens, Oskar Tvrďoch

Malleable Glyph is a new visualization problem and a public challenge. It originated from UX research (namely from research on card sorting UX), but its applications can be diverse (UI, gaming, information presentation, maps, and others). Its essence is: carrying as much information in a defined planar and static area as possible. The information should allo

Shiyang Zhou, Haijin Zeng, Yunfan Lu, Tong Shao

Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS). Although existing learning-based methods that leverage long-range dependency modeling have achieved promising results, their complexity severely limits deployment on mobile devices for real-world applications. To address t

Lei Chen, Hao Li, Yuxin Zhang, Chao Li

Text-driven image style transfer has seen remarkable progress with methods leveraging cross-modal embeddings for fast, high-quality stylization. However, most existing pipelines assume a \emph{single} textual style prompt, limiting the range of artistic control and expressiveness. In this paper, we propose a novel \emph{multi-prompt style interpolation} fram

Zahra Khalila, Arbi Haza Nasution, Winda Monika, Aytug Onan

Accurate and contextually faithful responses are critical when applying large language models (LLMs) to sensitive and domain-specific tasks, such as answering queries related to quranic studies. General-purpose LLMs often struggle with hallucinations, where generated responses deviate from authoritative sources, raising concerns about their reliability in re

Francisco Plaza, Lucila Kraiselburd

In this paper, we conduct a statistical analysis of various cosmological models within the framework of f (R) gravity theories, motivated by persistent challenges in modern cosmology, such as the unknown mechanisms driving the late-time accelerated expansion of the universe. We begin by presenting a comprehensive formulation of these theories and discussing

Feiyang Li, Yingjian Chen, Haoran Liu, Rui Yang

Large Language Models (LLMs) have shown remarkable progress in medical question answering (QA), yet their effectiveness remains predominantly limited to English due to imbalanced multilingual training data and scarce medical resources for low-resource languages. To address this critical language gap in medical QA, we propose Multilingual Knowledge Graph-base

Burabigul Yakup, Yi-Fang Ren, Mamat Ali Bake, Yusuf Turek

This paper considers an indirect driving model of a cavity QED system in which the left cavity wall consists of a large ensemble of two-level atoms driven by a classical laser field at a specific resonant frequency, inducing an effective drive for the optomechanical system. We investigate the effects of the atomic ensemble on the output intensity squeezing s

Jingwen Li, Aravind Chandrasekar, Mariana Rocha, Chao Li

We present a novel approach for controllable, region-specific style editing driven by textual prompts. Building upon the state-space style alignment framework introduced by \emph{StyleMamba}, our method integrates a semantic segmentation model into the style transfer pipeline. This allows users to selectively apply text-driven style changes to specific segme

Benedykt Pawlus, Bogdan Smolka, Jolanta Kawulok, Michal Kawulok

Assessing smile genuineness from video sequences is a vital topic concerned with recognizing facial expression and linking them with the underlying emotional states. There have been a number of techniques proposed underpinned with handcrafted features, as well as those that rely on deep learning to elaborate the useful features. As both of these approaches h

Yue Xie, Kai-fung Chu, Xing Wang, Fumiya Iida

Soft robotics holds transformative potential for enabling adaptive and adaptable systems in dynamic environments. However, the interplay between morphological and control complexities and their collective impact on task performance remains poorly understood. Therefore, in this study, we investigate these trade-offs across tasks of differing difficulty levels

Bo Wu

Income inequality has been an important social issue that has attracted widespread attention. Taking the Zero Hunger Program in Brazil as a case study, this study analyzes the impact of policy changes on the income distribution of the Brazilian population during the implementation of the program using a breakpoint regression approach. The data for the study

Jiangyi Wang, Na Zhao

Active learning has emerged as a promising approach to reduce the substantial annotation burden in 3D object detection tasks, spurring several initiatives in outdoor environments. However, its application in indoor environments remains unexplored. Compared to outdoor 3D datasets, indoor datasets face significant challenges, including fewer training samples p

Xin Yue Yu, Zishen Guan, William Dallaway, Ushak Rahaman

In this paper, we investigate the effects of $\nu_\tau$ and $\bar{\nu}_\tau$ detection at the DUNE far detector on the experiment's sensitivity to Non-Standard Interactions (NSI) in neutrino propagation. We show that the strongest observable NSI effect in the $\nu_\tau$ and $\bar{\nu}_\tau$ appearance probabilities arises from $\epsilon_{\mu\tau}$. We have s

Runze You, Shi Pu

We study a distributed learning problem in which $n$ agents, each with potentially heterogeneous local data, collaboratively minimize the sum of their local cost functions via peer-to-peer communication. We propose a novel algorithm, \emph{Spanning Tree Push-Pull} (STPP), which employs two spanning trees extracted from a general communication graph to distri

Marwa Saad, Marek Bucki, Sonia Bujok, Dominika Pawcenis

The thermal degradation of unplasticized poly(vinyl chloride), PVC, was comprehensively investigated through the application of spectroscopic techniques, as well as contact angle measurements (CA), dynamic mechanical analysis (DMA), and size-exclusion chromatography (SEC). To study the effect of relative humidity (RH) on the deterioration of unplasticized PV