March 2025 arXiv papers — page 74

Yu-Hsi Chen

Detecting and tracking multiple unmanned aerial vehicles (UAVs) in thermal infrared video is inherently challenging due to low contrast, environmental noise, and small target sizes. This paper provides a straightforward approach to address multi-UAV tracking in thermal infrared video, leveraging recent advances in detection and tracking. Instead of relying o

Clément Cosco, Shuta Nakajima, Ofer Zeitouni

We study the maximum $\phi_N^*$ of the partition function of the two dimensional (subcritical) Gaussian directed polymer over an $\sqrt N \times \sqrt N$ box. We show that $\phi_N^*/\log N$ converges towards a constant $\sigma^*$, which we identify to be the same as for the maximum of a branching random walk with a slowly varying variance profile as studied

Janis Nötzel, Pere Munar-Vallespir

We study the hypothesis testing problem of distinguishing between correlated thermal noise and uncorrelated thermal noise of the same average energy on $K$ detectors in asymptotic asymmetric hypothesis testing. We compare the performance of heterodyne or homodyne detection with classical post-processing, the most general quantum strategy (involving any arbit

Jingjing Xiao, Ying Liu, Nianyu Yi

This paper aims to develop an efficient adaptive finite element method for the second-order elliptic problem. Although the theory for adaptive finite element methods based on residual-type a posteriori error estimator and bisection refinement has been well established, in practical computations, the use of non-asymptotic exact of error estimator and the exce

Nicolo' Valle

The upgraded Inner Tracking System (ITS2) of the ALICE experiment at the CERN Large Hadron Collider is based on Monolithic Active Pixel Sensors (MAPS). With a sensitive area of about 10 $m^2$ and 12.5 billion pixels, ITS2 represents the largest pixel detector in high-energy physics. The detector consists of seven concentric layers equipped with ALPIDE pixel

Clément Cosco, Shuta Nakajima

We consider two-dimensional directed polymers in random environment in the sub-critical regime and in the quasi-critical regime introduced recently by Caravenna, Cottini and Rossi, arXiv:2307.02453v1. For $q\leq q_N$ with $q_N\to\infty$ diverging at a suitable rate with the size of the system, we obtain upper bound estimates on the $q$-moment of the partitio

Li Zhang, Chaochao Chen, Zhongxuan Han, Qiyong Zhong

Federated learning (FL) has garnered considerable interest for its capability to learn from decentralized data sources. Given the increasing application of FL in decision-making scenarios, addressing fairness issues across different sensitive groups (e.g., female, male) in FL is crucial. Current research often focuses on facilitating fairness at each client'

Albert Sawczyn, Jakub Binkowski, Denis Janiak, Bogdan Gabrys

Large Language Models (LLMs) frequently generate hallucinated content, posing significant challenges for applications where factuality is crucial. While existing hallucination detection methods typically operate at the sentence level or passage level, we propose FactSelfCheck, a novel zero-resource black-box sampling-based method that enables fine-grained fa

Ziwei Hong, Zhongqiu Fang

We investigate the mean value of the first moment of primitive cubic $L$-functions over $\mathbb{F}_q(T)$ in the non-Kummer setting. Specifically, we study the sum \begin{equation*} \sum_{\substack{\chi\ primitive\ cubic\\ genus(\chi)=g}}L_q(\frac{1}{2}, \chi), \end{equation*} where $L_q(s,\chi)$ denotes the $L$-function associated with primitive cubic chara

Qinghua Guan, Hung Hon Cheng, Benhui Dai, Josie Hughes

To exploit the compliant capabilities of soft robot arms we require controller which can exploit their physical capabilities. Teleoperation, leveraging a human in the loop, is a key step towards achieving more complex control strategies. Whilst teleoperation is widely used for rigid robots, for soft robots we require teleoperation methods where the configura

Aryan Yazdan Parast, Basim Azam, Naveed Akhtar

Deep neural networks trained with Empirical Risk Minimization (ERM) perform well when both training and test data come from the same domain, but they often fail to generalize to out-of-distribution samples. In image classification, these models may rely on spurious correlations that often exist between labels and irrelevant features of images, making predict

N. S. Gonchar, O. P. Dovzhyk, A. S. Zhokhin, W. H. Kozyrski

The paper analyses trade between the most developed economies of the world. The analysis is based on the previously proposed model of international trade. This model of international trade is based on the theory of general economic equilibrium. The demand for goods in this model is built on the import of goods by each of the countries participating in the tr

Giacomo Savazzi, Eugenio Lomurno, Cristian Sbrolli, Agnese Chiatti

As machine learning models increase in scale and complexity, obtaining sufficient training data has become a critical bottleneck due to acquisition costs, privacy constraints, and data scarcity in specialised domains. While synthetic data generation has emerged as a promising alternative, a notable performance gap remains compared to models trained on real d

Xunwu Hu, Wenyuan Qiu, Cun-Qun Chen, Zhihui Luo

The recent discovery of superconductivity with a transition temperature $T_c$ exceeding 40 K in La$_3$Ni$_2$O$_7$ and (La,Pr)$_{3}$Ni$_2$O$_7$ thin films at ambient pressure marks a significant breakthrough in the field of nickelate superconductors. Using density functional theory (DFT), we propose a double-stacked two-orbital effective model for La$_3$Ni$_2

Sonish Sivarajkumar, Kimia Ameri, Chuqin Li, Yanshan Wang

Cardiovascular events, such as heart attacks and strokes, remain a leading cause of mortality globally, necessitating meticulous monitoring and adjudication in clinical trials. This process, traditionally performed manually by clinical experts, is time-consuming, resource-intensive, and prone to inter-reviewer variability, potentially introducing bias and hi

Fanghua Yu, Jinjin Gu, Jinfan Hu, Zheyuan Li

We introduce UniCon, a novel architecture designed to enhance control and efficiency in training adapters for large-scale diffusion models. Unlike existing methods that rely on bidirectional interaction between the diffusion model and control adapter, UniCon implements a unidirectional flow from the diffusion network to the adapter, allowing the adapter alon

Nuno Saavedra, João F. Ferreira, Alexandra Mendes

Infrastructure as Code (IaC) enables scalable and automated IT infrastructure management but is prone to errors that can lead to security vulnerabilities, outages, and data loss. While prior research has focused on detecting IaC issues, Automated Program Repair (APR) remains underexplored, largely due to the lack of suitable specifications. In this work, we

Ronan Mouchoux, François Moerman

This paper introduces a novel mathematical framework for analyzing cyber threat campaigns through fractal geometry. By conceptualizing hierarchical taxonomies (MITRE ATT&CK, DISARM) as snowflake-like structures with tactics, techniques, and sub-techniques forming concentric layers, we establish a rigorous method for campaign comparison using Hutchinson's The

Zs. Regaly, V. Frohlich, Cs. Kiss

Context: Rings around giant planets are a common feature of the solar system. Even though solar radiation pressure is known to destabilize rings by exciting the orbital eccentricity of its particles, the Centaur Chariklo (and possibly Chiron), the dwarf planet Haumea, and trans-Neptunian object Quaoar also host rings of solid material. Aims: We explore the d

Alice Marché, Alberto Nardin, Hosho Katsura, Leonardo Mazza

We study an $\mathrm{SU}(3)$ invariant Fermi-Hubbard gas undergoing on-site three-body losses. The model presents eight independent strong symmetries preventing the complete depletion of the gas. By making use of a basis of semi-standard Young tableaux states, we reveal the presence of a rich phenomenology of stationary states. We classify the latter accordi

C. Maraventano, G. Ghirlanda, L. Nava, T. Di Salvo

Positive lags between the arrival time of different photon energies are commonly observed in Gamma-Ray Bursts (GRBs), where soft photons lag behind harder ones. However, some GRBs exhibit the opposite behavior. In particular, Fermi LAT observations have revealed that high-energy photons often have a delayed onset. We explore spectral lags as a tool to identi

Daniel Gil Gaviria, Benedikt Geiger, Charlotte Muth, Laurent Schmalen

Integrated sensing and communications (ISAC) is expected to play a major role in numerous future applications, e.g., smart cities. Leveraging native radar signals like the frequency modulated continuous wave (FMCW) waveform additionally for data transmission offers a highly efficient use of valuable physical radio frequency (RF) resources allocated for autom

Vincent Bezold, Lukas Baur, Alexander Sauer

The increasing integration of renewable energy sources has led to greater volatility and unpredictability in electricity generation, posing challenges to grid stability. Ancillary service markets, such as the German control reserve market, allow industrial consumers and producers to offer flexibility in their power consumption or generation, contributing to

Ting Sun, Cheng Cui, Yuning Du, Yi Liu

Document layout analysis is a critical preprocessing step in document intelligence, enabling the detection and localization of structural elements such as titles, text blocks, tables, and formulas. Despite its importance, existing layout detection models face significant challenges in generalizing across diverse document types, handling complex layouts, and

Matthew Kenely, Dylan Seychell, Carl James Debono, Chris Porter

News outlets' competition for attention in news interfaces has highlighted the need for demographically-aware saliency prediction models. Despite recent advancements in saliency detection applied to user interfaces (UI), existing datasets are limited in size and demographic representation. We present a deep learning framework that enhances the SaRa (Saliency

Zilin Dai, Lehong Wang, Fangzhou Lin, Yidong Wang

Real-world machine learning applications often struggle with two major challenges: distribution shift and label noise. Models tend to overfit by focusing on redundant and uninformative features in the training data, which makes it hard for them to generalize to the target domain. Noisy data worsens this problem by causing further overfitting to the noise, me

Franz Bamer, Zhao Wu, Somar Shekh Alshabab

Oxide glasses have the structure of disordered covalent networks that are accountable for their mechanical response. Identifying the topological phenomena of the elastic structural response, we statistically backpropagate local regions that have the highest susceptibility of rearrangement. Shear transformation zones in network glasses highly correlate with r

M. Garg, O. Maillet, N. L. Samuelson, T. Wang

Shot noise measurements in quantum point contacts are a powerful tool to investigate charge transport in the integer and fractional quantum Hall regime, in particular to unveil the charge, quantum statistics and tunneling dynamics of edge excitations. In this letter, we describe shot noise measurements in a graphene quantum point contact in the quantum Hall

Henok Tenaw Moges

The primary focus of this thesis is the numerical investigation of chaos in Hamiltonian models describing charged particle orbits in plasma, star motions in barred galaxies, and orbits' diffusion in multidimensional maps. We systematically explore the interplay between magnetic and kinetic chaos in toroidal fusion plasmas, where non-axisymmetric perturbation

Sinan Altinisik, Eric Lutz

We derive a Markovian master equation for a linearly driven dissipative quantum harmonic oscillator, valid for generic driving beyond the adiabatic limit. We solve this quantum master equation for arbitrary Gaussian initial states and investigate its departure from the adiabatic master equation in the regime of fast driving. We concretely examine the behavio

Haihao Shi, Junda Zhou, Zhenyang Huang, Guoliang Lü

Dark matter heating in planets has been proposed as a potential probe for dark matter detection. Assuming near-equilibrium conditions, we find that the energy input from dark matter raises planetary temperatures and accelerates rotation. The distribution of energy between heating and rotational acceleration depends on both planetary properties and external i

Chandan Kumar Sheemar, Wali Ullah Khan, George C. Alexandropoulos, Manzoor Ahmed

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substanti

Zhenzhi Liu, Ke Li, Yanpeng Zhang, Fu Liu

Systems with non-Hermitian potential or Floquet modulation often result in phase transition related phenomena. In this paper, we study the dual phase transitions in a one-dimensional lattice by introducing a defect containing both Floquet modulation and PT-symmetric potential. In such a configuration, we demonstrate how the gain-loss from PT-symmetry and the

Renata Ferrero, Vincenzo Naso, Roberto Percacci

It has been shown that if one solves self-consistently the semiclassical Einstein equations in the presence of a quantum scalar field, with a cutoff on the number of modes, spacetime become flatter when the cutoff increases. Here we extend the result to include the effect of fields with spin 0, 1/2, 1 and 2. With minor adjustments, the main result persists.

Alex Vera-Casanova., Nicolas Monsalves Gonzalez, Facundo A. Gómez, Marcelo Jaque Arancibia.

Context. Galactic halos host faint substructures, such as stellar streams and shells, which provide insights into the hierarchical assembly history of galaxies. To date, such features have been identified in external galaxies by visual inspection. However, with the advent of larger and deeper surveys and the associated increase in data volume, this methodolo

Raoul Kalisvaart, Masoud Mansoury, Alan Hanjalic, Elvin Isufi

The commodity and widespread use of online shopping are having an unprecedented impact on climate, with emission figures from key actors that are easily comparable to those of a large-scale metropolis. Despite online shopping being fueled by recommender systems (RecSys) algorithms, the role and potential of the latter in promoting more sustainable choices is

Zhenyu Zhang, Yehui Hou, Minyong Guo, Yosuke Mizuno

The strong gravitational field of a black hole bends light, forming multi-level images, yet extracting precise spacetime information from them remains challenging. In this study, we investigate how gravitational lensing leaves unique and detectable signatures in black hole movies using autocorrelation analysis. By examining the two-dimensional autocorrelatio

Owais Ahmad, Jasifa Fayaz

The Continuous Boostlet Transform (CBT) is introduced as a powerful tool for analyzing spatiotemporal signals, particularly acoustic wavefields. Overcoming the limitations of classical wavelets, the CBT leverages the Poincar\'e group and isotropic dilations to capture sparse features of natural acoustic fields. This paper presents the mathematical framework

Yurii Parzhyn

The paper explores an approach to constructing energy landscapes of a formal neuron and multilayer artificial neural networks (ANNs). Their analysis makes it possible to determine the conceptual limitations of both classification ANNs (e.g., MLP or CNN) and generative ANN models. The study of informational and thermodynamic entropy in formal neuron and ANN m

Pieter Braam, Jan ten Thije Boonkkamp, Martijn Anthonissen, Koondanibha Mitra

We present an inverse method for transforming a given parallel light emittance to two light distributions at different parallel target planes using two freeform reflectors. The reflectors control both the spatial and directional target coordinates of light rays. To determine the shape and position of the reflectors, we derive generating functions and use Jac

Yongli Xiang, Ziming Hong, Lina Yao, Dadong Wang

Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains. Recently, well-designed attack, which restores the unauthorized-domain performance by fine-tuning NTL models on few authorized samples, highlights the securi

Xingchao Yang, Takafumi Taketomi, Yuki Endo, Yoshihiro Kanamori

Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under constraints of limited data and complex facial details such as makeup, wrinkles, and occlusions. In this paper, we introduce FreeUV, a novel ground-truth-free UV texture recovery framework that eliminates the need for annotated or synthetic UV data.

Julio Melio, Martin van Hecke, Silke E. Henkes, Daniela J. Kraft

Biological machines harness targeted deformations that can be actuated by Brownian fluctuations. However, while synthetic micromachines can similarly leverage targeted deformations they are too stiff to be driven by thermal fluctuations and thus require strong forcing. Furthermore, systems that are able to change their conformation by thermal fluctuations do

Sheng Wang, Pengan Chen, Jingqi Zhou, Qintong Li

Model customization necessitates high-quality and diverse datasets, but acquiring such data remains time-consuming and labor-intensive. Despite the great potential of large language models (LLMs) for data synthesis, current approaches are constrained by limited seed data, model biases, and low-variation prompts, resulting in limited diversity and biased dist

Abhijeet Pendyala, Tobias Glasmachers

In this work, we augment reinforcement learning with an inference-time collision model to ensure safe and efficient container management in a waste-sorting facility with limited processing capacity. Each container has two optimal emptying volumes that trade off higher throughput against overflow risk. Conventional reinforcement learning (RL) approaches strug

Xiaojin Lu, Taoran yue, Jiaxi cai, Yuanping Chen

In complex environments, detecting tiny infrared targets has always been challenging because of the low contrast and high noise levels inherent in infrared images. These factors often lead to the loss of crucial details during feature extraction. Moreover, existing detection methods have limitations in adequately integrating global and local information, whi

Teoman Toprak, Michael Loibl, Guilherme H. Teixeira, Irina Shiskina

In the field of scientific computing, one often finds several alternative software packages (with open or closed source code) for solving a specific problem. These packages sometimes even use alternative methodological approaches, e.g., different numerical discretizations. If one decides to use one of these packages, it is often not clear which one is the be

Chris Purdy, Stefania Damato

Containers are used to carve out a class of strictly positive data types in terms of shapes and positions. They can be interpreted via a fully-faithful functor into endofunctors on Set. Monadic containers are those containers whose interpretation as a Set functor carries a monad structure. The category of containers is closed under container composition and

Sören Bartels, Andrea Bonito, Peter Hornung, Michael Neunteufel

The Babu\v{s}ka or plate paradox concerns the failure of convergence when a domain with curved boundary is approximated by polygonal domains in linear bending problems with simply supported boundary conditions. It can be explained via a boundary integral representation of the total Gaussian curvature that is part of the Kirchhoff--Love bending energy. It is

Timothy Nunn, Kamran Pentland, Vignesh Gopakumar, James Buchanan

The tokamak is a world-leading concept for producing sustainable energy via magnetically-confined nuclear fusion. Identifying where to position the magnets within a tokamak, specifically the poloidal field (PF) coils, is a design problem which requires balancing a number of competing economic, physical, and engineering objectives and constraints. In this pap

Annika Stier, Alberto Bottino, David Coster, Thomas Hayward-Schneider

The gyrokinetic particle-in-cell code PICLS is a full-f finite element tool to simulate turbulence in the tokamak scrape-off layer. During the previous year, the capability of PICLS was extended to encompass electromagnetic effects. Successful tests using the method of manufactured solutions were conducted on the freshly added Amp\`ere's-law-solver, and shea

Feihu Liu, Ying Wang, Yingrui Zhang, Zihao Zhang

Cigler considered certain shifted Hankel determinants of convolution powers of Catalan numbers and conjectured identities for these determinants. Recently, Fulmek gave a bijective proof of Cigler's conjecture. Cigler then provided a computational proof. We extend Cigler's determinant identities to the convolution of general power series $F(x)$, where $F(x)$

ATLAS Collaboration

This paper presents searches for the direct pair production of charged light-flavour sleptons, each decaying into a stable neutralino and an associated Standard Model lepton. The analyses focus on the challenging ``corridor'' region, where the mass difference, $\Delta m$, between the slepton ($\tilde{e}$ or $\tilde{\mu}$) and the lightest neutralino ($\tilde

Taro V. Brown, Johannes M. Henn, Elia Mazzucchelli, Jaroslav Trnka

We study correlators of null, $n$-sided polygonal Wilson loops with a Lagrangian insertion in the planar limit of the ${\cal N}=4$ supersymmetric Yang-Mills theory. This finite observable is closely related to loop integrands of maximally-helicity-violating amplitudes in the same theory, and, conjecturally, to all-plus helicity amplitudes in pure Yang-Mills

Xueqi Qiu, Xingyu Miao, Fan Wan, Haoran Duan

Deepfake detection is crucial for curbing the harm it causes to society. However, current Deepfake detection methods fail to thoroughly explore artifact information across different domains due to insufficient intrinsic interactions. These interactions refer to the fusion and coordination after feature extraction processes across different domains, which are

Anna Sajina, Alexandra Pope, Henrik Spoon, Lee Armus

This paper presents the first combined detections of CO$_2$, CO, XCN and water ices beyond the local Universe. We find gas-phase CO in addition to the solid phase CO. Our source, SSTXFLS J172458.3+591545, is a $z=0.494$ star-forming galaxy which also hosts a deeply obscured AGN. The profiles of its ice features are consistent with those of other Galactic and

M. Orte-García, C. Esteban, J. E. Méndez-Delgado, J. García-Rojas

Aims. We study the behaviour of Cl abundance and its ratios with respect to O, S and Ar abundances in a sample of more than 200 spectra of Galactic and extragalactic H ii regions and star-forming galaxies (SFGs) of the local Universe. Methods. We use the DEep Spectra of Ionised REgions Database (DESIRED) Extended project (DESIRED-E) that comprises more than

Show-Li Jan, Gwowen Shieh

The Friedman test has been extensively applied as a nonparametric alternative to the conventional F procedure for comparing treatment effects in randomized complete block designs. A chi-square distribution provides a convenient approximation to determining the critical values for the Friedman procedure in hypothesis testing. However, the chi-square approxima

Carlos Vargas

We study, count and locate the exceptional points where eigenvalues collide for certain families of matrices $$R(s,t) = \cos(s \pi / 2)C + \sin(s \pi / 2)U(t), \quad s,t \in [0,1]$$ where $C$ is a realization of a Ginibre random matrix, or a closely related matrix, and $U(t)$ is a $t$-periodic diagonal matrix whose eigenvalues move along the unit circle or o

Martyn Gwynne, Simon Cox

Least perimeter solutions for a region with fixed mass are sought in ${\mathbb{R}^d}$ on which a density function $\rho(r) = r^p+a$, with $p>0, a>0$, weights both perimeter and mass. On the real line ($d=1$) this is a single interval that includes the origin. For $p \le 1$ the isoperimetric interval has one end at the origin; for larger $p$ there is a critic

Jiangdong Ai, Fankang He, Seonghyuk Im, Hyunwoo Lee

We proved that for every sufficiently large $n$, the complete graph $K_{2n}$ with an arbitrary edge signing $\sigma: E(K_{2n}) \to \{-1, +1\}$ admits a high discrepancy $1$-factor decomposition. That is, there exists a universal constant $c > 0$ such that every edge-signed $K_{2n}$ has a perfect matching decomposition $\{\psi_1, \ldots, \psi_{2n-1}\}$, where

Duanrui Yu, Jing You, Xin Pei, Anqi Qu

Collaborative perception allows real-time inter-agent information exchange and thus offers invaluable opportunities to enhance the perception capabilities of individual agents. However, limited communication bandwidth in practical scenarios restricts the inter-agent data transmission volume, consequently resulting in performance declines in collaborative per

Mingliang Li, Yanrong Li, Lai Wei, Wei Jiang

Recently, autonomous driving system (ADS) has been widely adopted due to its potential to enhance travel convenience and alleviate traffic congestion, thereby improving the driving experience for consumers and creating lucrative opportunities for manufacturers. With the advancement of data sensing and control technologies, the reliability of ADS and the purc

Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver, Vijay Ganesh

The ability of machine learning (ML) classification models to resist small, targeted input perturbations -- known as adversarial attacks -- is a key measure of their safety and reliability. We show that floating-point non-associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any pert

Gaojie Jin, Tianjin Huang, Ronghui Mu, Xiaowei Huang

Recent studies have identified a critical challenge in deep neural networks (DNNs) known as ``robust fairness", where models exhibit significant disparities in robust accuracy across different classes. While prior work has attempted to address this issue in adversarial robustness, the study of worst-class certified robustness for smoothed classifiers remains

Orkun Furat, Sabrina Weber, Johannes Schubert, René Rekers

This paper presents a computational method for generating virtual 3D morphologies of functional materials using low-parametric stochastic geometry models, i.e., digital twins, calibrated with 2D microscopy images. These digital twins allow systematic parameter variations to simulate various morphologies, that can be deployed for virtual materials testing by

Tom-Niklas Kreutzer, Muhammad Zubair Ghori, Md Redwanul Islam, Fabian Lofink

This paper presents progress towards the large-scale manufacturability of piezo- and ferroelectric Al$_{1-x}$Sc$_x$N thin films with very high Sc content. Al$_{0.6}$Sc$_{0.4}$N layers were deposited by reactive sputtering from a 300 mm diameter Al$_{0.6}$Sc$_{0.4}$N target on standard 200 mm Si wafers with Pt bottom- and Mo top-electrodes. The deposited film

Stefan Karatodorov, Marios Kounalakis, Gabriel Flores Alfaro, Yingjie Zhao

An upgraded high-energy nanosecond pulsed laser system tailored for optical particle diagnostics and manipulation capable of pulse energies beyond Joule-level is presented. In addition to the notable output energy increase, the laser system maintains its capability to generate laser pulses with customizable temporal profiles and variable durations ($1$ ns to

Alexandra Arzberger, Ramin Tavakoli Kolagari

Light Detection and Ranging (LiDAR) is an essential sensor technology for autonomous driving as it can capture high-resolution 3D data. As 3D object detection systems (OD) can interpret such point cloud data, they play a key role in the driving decisions of autonomous vehicles. Consequently, such 3D OD must be robust against all types of perturbations and mu

Huy Truong, Andrés Tello, Alexander Lazovik, Victoria Degeler

Privacy restrictions hinder the sharing of real-world Water Distribution Network (WDN) models, limiting the application of emerging data-driven machine learning, which typically requires extensive observations. To address this challenge, we propose the dataset DiTEC-WDN that comprises 36,000 unique scenarios simulated over either short-term (24 hours) or lon

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

The non-leptonic two-body weak decays $\Sigma^{+} \to p \pi^{0}$ and $\bar{\Sigma}^{-} \to \bar{p} \pi^{0}$ are investigated, utilizing $(1.0087\pm0.0044)\times10^{10}$ $J/\psi$ events and $(2.7124\pm0.0143)\times10^{9}$ $\psi(3686)$ events collected by BESIII experiment. The precision of the weak-decay parameters for the decays $\Sigma^{+} \to p \pi^{0}$ ($

Xuan Wang, Wenbin Lin, Ghulam Mustafa, Guansheng He

The gravitational deflection effect of relativistic massive and massless particles up to the first post-Minkowskian order caused by a moving Schwarzschild black hole with a two-dimensional equatorial velocity, which contains the radial and transversal components, is studied analytically, and a new unified formula for the deflection angle is achieved. The exp

Marius A. Oancea, Thomas B. Mieling, Giandomenico Palumbo

The geometric properties of quantum states are crucial for understanding many physical phenomena in quantum mechanics, condensed matter physics, and optics. The central object describing these properties is the quantum geometric tensor, which unifies the Berry curvature and the quantum metric. In this work, we use the differential-geometric framework of vect

Tonmoy Hossain, Miaomiao Zhang

Deformable shape representations, parameterized by deformations relative to a given template, have proven effective for improved image analysis tasks. However, their broader applicability is hindered by two major challenges. First, existing methods mainly rely on a known template during testing, which is impractical and limits flexibility. Second, they often

Raphael Rehms, Nicole Ellenbach, Veronika Deffner, Sabine Hoffmann

Exposure assessment in occupational epidemiology may involve multiple unknown quantities that are measured or reconstructed simultaneously for groups of workers and over several years. Additionally, exposures may be collected using different assessment strategies, depending on the period of exposure. As a consequence, researchers who are analyzing occupation

Yan-Ting Xie, Shou-Jun Xu

The simplex graph $S(G)$ of a graph $G$ is defined as the graph whose vertices are the cliques of $G$ (including the empty set), with two vertices being adjacent if, as cliques of $G$, they differ in exactly one vertex. Simplex graphs form a subclass of median graphs and include many well-known families of graphs, such as gear graphs, Fibonacci cubes and Luc

Ziyue Wang, Junde Wu, Linghan Cai, Chang Han Low

In modern medicine, clinical diagnosis relies on the comprehensive analysis of primarily textual and visual data, drawing on medical expertise to ensure systematic and rigorous reasoning. Recent advances in large Vision-Language Models (VLMs) and agent-based methods hold great potential for medical diagnosis, thanks to the ability to effectively integrate mu

Jonte Deakin, Ian Shillito

Since the discovery of critical mistakes in Rauszer's work on bi-intuitionistic logics, solid foundations for these have progressively been rebuilt. However, the algebraic treatment of these logics has not yet been tended to. We fill this gap by algebraically analysing the bi-intuitionistic logics wBIL and sBIL. Given that these logics are only distinguished

Jieqiong Zhang, Jizu Huang, Zihao Yang

The embedded atom method (EAM) is one of the most widely used many-body, short-range potentials in molecular dynamics simulations, particularly for metallic systems. To enhance the efficiency of calculating these short-range interactions, we extend the random batch list (RBL) concept to the EAM potential, resulting in the RBL-EAM algorithm. The newly present

Sora Kang, Andreea-Elena Potinteu, Nadia Said

This study investigates cross-cultural differences in the perception of AI-driven chatbots between Germany and South Korea, focusing on topic dependency and explainability. Using a custom AI chat interface, ExplainitAI, we systematically examined these factors with quota-based samples from both countries (N = 297). Our findings revealed significant cultural

Igor V. Ovchinnikov

A close relation has recently emerged between two of the most fundamental concepts in physics and mathematics: chaos and supersymmetry. In striking contrast to the semantics of the word 'chaos,' the true physical essence of this phenomenon now appears to be a spontaneous order associated with the breakdown of the topological supersymmetry (TS) hidden in all

Théo Delemazure, Rupert Freeman, Jérôme Lang, Jean-François Laslier

In many proportional parliamentary elections, electoral thresholds (typically 3-5%) are used to promote stability and governability by preventing the election of parties with very small representation. However, these thresholds often result in a significant number of "wasted votes" cast for parties that fail to meet the threshold, which reduces representativ

Panpan Wang, Liqiang Niu, Fandong Meng, Jinan Xu

In the domain of image generation, latent-based generative models occupy a dominant status; however, these models rely heavily on image tokenizer. To meet modeling requirements, autoregressive models possessing the characteristics of scalability and flexibility embrace a discrete-valued tokenizer, but face the challenge of poor image generation quality. In c

Michael L. Wagman

Acceptance talk for the 2024 Kenneth G. Wilson Award for Excellence in Lattice Field Theory: For key contributions to lattice QCD studies of noise reduction in nuclear systems, the structure of nuclei, and transverse-momentum dependent hadronic structure functions.

Fouad Makiyeh, Huy-Dung Nguyen, Patrick Chareyre, Ramin Hasani

Steering estimation is a critical task in autonomous driving, traditionally relying on 2D image-based models. In this work, we explore the advantages of incorporating 3D spatial information through hybrid architectures that combine 3D neural network models with recurrent neural networks (RNNs) for temporal modeling, using LiDAR-based point clouds as input. W

Lorenzo Bertini, Joel L. Lebowitz, Mario Pulvirenti

Errico Presutti was a leading figure in mathematical physics and an important contributor to rigorous results in statistical mechanics. Due to his strong scientific personality and human qualities, there are many who remember Errico Presutti as colleague, mentor, and friend.

Gabriela Araujo-Pardo, Lydia Mirabel Mendoza-Cadena

A mixed regular graph is a graph where every vertex has $z$ incoming arcs, $z$ outgoing arcs, and $r$ edges; furthermore, if it has girth $g$, we say that the graph is a \emph{$[z,r;g]$-mixed graph}. A \emph{$[z,r;g]$-mixed cage} is a $[z,r;g]$-mixed graph with the smallest possible order. In this note, we give a family of $[z,q;5]$-mixed graphs for $q\geq 7

Mahmoud Sesa, Hagen Holthusen, Christian Böhm, Stefan Jockenhövel

Developing clinically viable tissue-engineered cardiovascular implants remains a formidable challenge. Achieving reliable and durable outcomes requires a deeper understanding of the fundamental mechanisms driving tissue evolution during in vitro maturation. Although considerable progress has been made in modeling soft tissue growth and remodeling, studies fo

A. M. T. Pollock, P. A. Crowther, J. M. Bestenlehner, Patrick S. Broos

Individually identified binary systems of very massive stars define fixed points on possible evolutionary pathways that begin with extreme star formation and end in either coalescence of compact remnants or complete disruption as pair-production supernovae. The LMC star Melnick 39 in the Tarantula Nebula is revealed to be an eccentric ($e = 0.618\pm0.014$) b

Guchuan Li, Sarah Petersen, Elizabeth Tatum

In the 1980's, Mahowald and Kane used integral Brown--Gitler spectra to decompose $ku \wedge ku$ as a sum of finitely generated $ku$-module spectra. This splitting, along with an analogous decomposition of $ko \wedge ko,$ led to a great deal of progress in stable homotopy computations and understanding of $v_1$-periodicity in the stable homotopy groups of sp

D. Lange, Y. Ren, I. Grevemeyer

The Blanco transform fault system (BTFS) is highly segmented and represents an evolving transform plate boundary in the Northeast Pacific Ocean. Its seismic behavior was captured with a dense network of 54 ocean-bottom-seismometers operated for one year. We created a high-resolution earthquake catalog based on different machine learning onset pickers, result

Regina Finsterhoelzl, Guido Burkard

We consider entangling operations in a single nitrogen-vacancy (NV) center in diamond where the hyperfine-coupled nuclear spin qubits are addressed with radio-frequency (rf) pulses conditioned on the state of the central electron spin. Limiting factors for the gate fidelity are coherent errors due to off-resonant driving of neighboring transitions in the den

Mariia Sidorova, Timon Schapeler, Alexej D. Semenov, Fabian Schlue

By analyzing the physics of multi-photon absorption in superconducting nanowire single-photon detectors (SNSPDs), we identify physical components of jitter. From this, we formulate a quantitative physical model of the multi-photon detector response which combines local detection mechanism and local fluctuations (hotspot formation and intrinsic jitter) with t

Stefan Frei, Tobias Knoke, Marc C. Steinbach, Anne-Kathrin Wenske

In this work, we develop a cut-based unfitted finite element formulation for solving nonlinear, nonstationary fluid-structure interaction with contact in Eulerian coordinates. In the Eulerian description fluid flow modeled by the incompressible Navier-Stokes equations remains in Eulerian coordinates, while elastic solids are transformed from Lagrangian coord

José Luis Montiel Olea, Mikkel Plagborg-Møller, Eric Qian, Christian K. Wolf

What should applied macroeconomists know about local projection (LP) and vector autoregression (VAR) impulse response estimators? The two methods share the same estimand, but in finite samples lie on opposite ends of a bias-variance trade-off. While the low bias of LPs comes at a quite steep variance cost, this cost must be paid to achieve robust uncertainty

P. S. Koliogiannis, E. Yuksel, T. Ghosh, N. Paar

Nuclear ground state and collective excitation properties provide a means to probe the nuclear matter equation of state and establish connections between observables in finite nuclei and neutron stars. Specifically, the electric dipole polarizability, measured with high precision in various neutron-rich nuclei, serves as a robust constraint on the density de

Davide Berasi, Matteo Farina, Massimiliano Mancini, Elisa Ricci

Vision-Language Models (VLMs) learn a shared feature space for text and images, enabling the comparison of inputs of different modalities. While prior works demonstrated that VLMs organize natural language representations into regular structures encoding composite meanings, it remains unclear if compositional patterns also emerge in the visual embedding spac

Ekaterina Dmitrieva, Maksim Kaledin

Speech Enhancement techniques have become core technologies in mobile devices and voice software. Still, modern deep learning solutions often require high amount of computational resources what makes their usage on low-resource devices challenging. We present HiFi-Stream, an optimized version of recently published HiFi++ model. Our experiments demonstrate th

Semra Gurtas Dogan, Abdullah Guvendi, Omar Mustafa

We investigate the geometric and wave optical properties of a $(2+1)$-dimensional ultra-static spacetime conformally related to the static BTZ black hole, characterized by constant negative Gaussian curvature. The associated optical metric defines a hyperbolic wormhole geometry, wherein null geodesics experience a P\"oschl--Teller-type repulsive effective po

Prateek Benhal, Muhammad Garba, Jamel Ali, Theo Siegrist

Magnetic separation has emerged as an eco-friendly and sustainable technique with applications in water purification, chemical separation, biochemical, medical, and mining. In this study, we present, a combined experimental and theoretical investigation of the transport of transition metal ions using high-gradient magnetic fields. Experiments were conducted