February 2025 arXiv papers — page 5

Tuowei Chen, Zhifang Du

In the finite volume framework, a Lax-Wendrof type second-order flux solver for the compressible Navier-Stokes equations is proposed by utilizing a hyperbolic relaxation model. The flux solver is developed by applying the generalized Riemann problem (GRP) method to the relaxation model that approximates the compressible Navier-Stokes equations. The GRP-based

Christopher Wewer, Bart Pogodzinski, Bernt Schiele, Jan Eric Lenssen

We introduce Spatial Reasoning Models (SRMs), a framework to perform reasoning over sets of continuous variables via denoising generative models. SRMs infer continuous representations on a set of unobserved variables, given observations on observed variables. Current generative models on spatial domains, such as diffusion and flow matching models, often coll

Zhenyi Shen, Hanqi Yan, Linhai Zhang, Zhanghao Hu

Chain-of-Thought (CoT) reasoning enhances Large Language Models (LLMs) by encouraging step-by-step reasoning in natural language. However, leveraging a latent continuous space for reasoning may offer benefits in terms of both efficiency and robustness. Prior implicit CoT methods attempt to bypass language completely by reasoning in continuous space but have

Dirk van Bree, Weisheng Wang

Let $Y$ be an Enriques surface and let $\mathcal{A}$ be an Azumaya algebra corresponding to the non-trivial Brauer class. Let $M$ be the moduli space of stable twisted sheaves on Enriques surfaces with twisted Chern character $M^H_{\mathcal{A}/Y}(2,c_1,\operatorname{ch}_2)$ with virtual dimension $N$. We show that the virtual Euler characteristic $e^\mathrm{

Edward D. Lee, Ernesto Ortega-Díaz

Innovation and its complement exnovation describe the progression of realized possibilities from the past to the future, and the process depends on the structure of the underlying graph. For example, the phylogenetic tree represents the unique path of mutations to a single species. To a technology, paths are manifold, like a "truss." We solve for the phase d

Debraj Chakrabarti, Zhenghui Huo

We obtain (weighted) restricted type estimates for the Bergman projection operator on monomial polyhedra, a class of domains generalizing the Hartogs triangle. From these estimates, we recapture $L^p$ boundedness results of the Bergman projection on these domains. On some monomial polyhedra, we also discover that the Bergman projection could fail to be of we

Julian Wojciech Myrcha

A Large Ion Collider Experiment (ALICE) is one of the four big CERN experiments at the LHC. The area of interest is the study of the Quark-Gluon Plasma which is produced in heavy-ion collisions. The trajectories of particles created in collisions are reconstructed online and are visualized together with the detector geometry to provide proper augmentation of

Wen Teng

Loday introduced di-associative algebras and tri-associative algebras motivated by periodicity phenomena in algebraic $K$-theory. The purpose of this paper is to study the splittings of operations of di-associative algebras and tri-associative algebras. First, we introduce the notion of a quadri-dendriform algebra, which is a splitting of a di-associative al

Yong Xia, Jingbo Wang, Sachiko Kuroyanagi, Wenming Yan

Pulsar timing arrays (PTAs) are designed to detect nanohertz-frequency gravitational waves (GWs). Since GWs are anticipated from cosmic strings, PTAs offer a viable approach to testing their existence. We present the results of the first Bayesian search for gravitational-wave bursts from cosmic string cusps (GWCS) using the third PPTA data release for 30 mil

Kristian Kolthoff, Felix Kretzer, Christian Bartelt, Alexander Maedche

GUI prototyping serves as one of the most valuable techniques for enhancing the elicitation of requirements and facilitating the visualization and refinement of customer needs. While GUI prototyping has a positive impact on the software development process, it simultaneously demands significant effort and resources. The emergence of Large Language Models (LL

Chahine-Nicolas Zede, Laurent Carrafa, Valérie Gouet-Brunet

Retrieval in 3D point clouds is a challenging task that consists in retrieving the most similar point clouds to a given query within a reference of 3D points. Current methods focus on comparing descriptors of point clouds in order to identify similar ones. Due to the complexity of this latter step, here we focus on the acceleration of the retrieval by adapti

Nagamalleswara Rao Alluri, Longfei Song, Stephanie Girod, Barnik Mandal

K$_{0.5}$Na$_{0.5}$NbO$_3$ is among the most promising lead-free piezoelectrics. While its sputtered films match the performance of the champion piezoelectric Pb(Zr,Ti)O$_3$, reproducible processing of high-quality and time-stable solution-processed K$_{0.5}$Na$_{0.5}$NbO$_3$ films remains challenging. Here, we report 1 $\mu$m-thick Mn-doped K$_{0.5}$Na$_{0.

Max van Haren, Masahiro Mae, Lennart Blanken, Tom Oomen

Frequency-domain representations are crucial for the design and performance evaluation of controllers in multirate systems, specifically to address intersample performance. The aim of this paper is to develop an effective frequency-domain system identification technique for closed-loop multirate systems using solely slow-rate output measurements. By indirect

Daniele Caliari, Henrik Petri

We characterize regularity (Block & Marschak, 1960) within a novel stochastic model: the General Threshold Luce model [GTLM]. We apply our results to study choice overload, identified by regularity violations that impose a welfare cost on the decision-maker. Generalizing our characterization results, we identify necessary and sufficient conditions for choice

Daniel Matthes, Eva-Maria Rott

We propose and analyse a spatial discretization of the non-local Quantum Drift Diffusion (nlQDD) model by Degond, M\`{e}hats and Ringhofer in one space dimension. With our approach, that uses consistently matrices on ${\mathbb C}^N$ instead of operators on $L^2$, we circumvent a variety of analytical subtleties in the analysis of the original nlQDD equation,

J. A. S. Lima, M. H. Benetti

In 1988, Constantino Tsallis proposed an extension of the Boltzmann statistical mechanics by postulating a new entropy formula, $S_q = k_B\ln_q W$, where $W$ is the number of microstates accessible to the system, and $\ln_q$ defines a deformation of the logarithmic function. This ``top-down" , approach recovers the celebrated Boltzmann entropy in the limit $

Yali Wei, Alan J. X. Guo, Zihui Yan, Yufan Dai

In recent years, widespread attention has been drawn to the challenge of correcting insertion, deletion, and substitution (IDS) errors in DNA-based data storage. Among various IDS-correcting codes, Varshamov-Tenengolts (VT) codes, originally designed for single-error correction, have been established as a central research focus. While existing decoding metho

Ziyi Zhang, Zhen Sun, Zongmin Zhang, Jihui Guo

Multimodal Large Language Models (MLLMs) have become powerful and widely adopted in some practical applications. However, recent research has revealed their vulnerability to multimodal jailbreak attacks, whereby the model can be induced to generate harmful content, leading to safety risks. Although most MLLMs have undergone safety alignment, recent research

Vitor O. Ferreira, Érica Z. Fornaroli, Javier Sánchez

We consider skew free extensions of rings, also known as free multivariate skew polynomial rings, and explore some of the algebraic aspects of this construction. We give different characterizations of such rings and present conditions for such a ring to be a domain, to be embeddable in a series ring and to be prime.

Taeho Lee, Donghwan Lee

This paper presents a robust reinforcement learning algorithm called robust deterministic policy gradient (RDPG), which reformulates the H-infinity control problem as a two-player zero-sum dynamic game between a user and an adversary. The method combines deterministic policy gradients with deep reinforcement learning to train a robust policy that attenuates

Adrian Vecina Tercero, Praminda Caleb-Solly

This paper explores the use of a body-worn vibrotactile vest to convey real-time information from robot to operator. Vibrotactile communication could be useful in providing information without compropmising or loading a person's visual or auditory perception. This paper considers applications in Urban Search and Rescue (USAR) scenarios where a human working

Emanuele Vivoli, Lorenzo Capineri, Marco Bertini

The detection and removal of landmines is a complex and risky task that requires advanced remote sensing techniques to reduce the risk for the professionals involved in this task. In this paper, we propose a novel synthetic dataset for buried landmine detection to provide researchers with a valuable resource to observe, measure, locate, and address issues in

Yukihiro Oda

Partial incorrectness logic (partial reverse Hoare logic) has recently been introduced as a new Hoare-style logic that over-approximates the weakest pre-conditions of a program and a post-condition. It is expected to verify systems where the final state must guarantee its initial state, such as authentication, secure communication tools and digital signature

Baoqiang Xia

We study integrable boundary conditions associated with the whole hierarchy of nonlinear Schr\"{o}dinger (NLS) equations defined on the half-line. We find that the even order NLS equations and the odd order NLS equations admit rather different integrable boundary conditions. In particular, the odd order NLS equations permit a new class of integrable boundary

Valentin Guien, Violaine Antoine, Romain Lardy, Isabelle Veissier

In Precision Livestock Farming, detecting deviations from optimal or baseline values - i.e. anomalies in time series - is essential to allow undertaking corrective actions rapidly. Here we aim at detecting anomalies in 24h time series of cow activity, with a view to detect cases of disease or oestrus. Deviations must be distinguished from noise which can be

José Medeiros da Costa Netto, Heydson Henrique Brito da Silva

Thermodynamic analyses of dark energy as a relativistic fluid indicates that this intriguing component of the universe mimics a bulk viscous pressure when the parameter of its barotropic equation of state varies with time. Since in cosmology bulk viscosity and creation or destruction of matter are closely linked processes, we propose in this work a brief the

Ying Wang, Yingrui Zhang

We evaluate the Hankel determinants of the convolution powers of Motzkin numbers for $r\leq 27$ by finding shifted periodic continued fractions, which arose in application of Sulanke and Xin's continued fraction method. We also conjecture some polynomial characterization of these determinants.

Jingru Fu, Yuqi Zheng, Neel Dey, Daniel Ferreira

Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or disease progression while also considering the individual brain's current status and unique characteristics. While current deep generative models can produce high-resolution anatomic

Chanhui Lee, Yeonghwan Song, Jeany Son

Data-free Universal Adversarial Perturbation (UAP) is an image-agnostic adversarial attack that deceives deep neural networks using a single perturbation generated solely from random noise without relying on data priors. However, traditional data-free UAP methods often suffer from limited transferability due to the absence of semantic content in random noise

Jan Rais, Hendrik van Hees, Carsten Greiner

The Lindblad equation, as one approach to open quantum systems, describes the density matrix of a particle or a chain of interacting particles, which are in contact with a thermal bath. Still, it is not fully understood yet, how arbitrary systems evolve towards a stationary distribution, which guarantees thermalization in a thermodynamical context, and how t

Jonathan Will, Lauritz Thamsen, Jonathan Bader, Odej Kao

Distributed dataflow systems like Spark and Flink enable data-parallel processing of large datasets on clusters of cloud resources. Yet, selecting appropriate computational resources for dataflow jobs is often challenging. For efficient execution, individual resource allocations, such as memory and CPU cores, must meet the specific resource demands of the jo

Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr

Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial regions. In this work, we introduce FieldMACE, an extension of the message-passing atomic cluster expansion (MACE) architecture that integrates the multipole expansion to model long

Evan T. Hockings, Andrew C. Doherty, Robin Harper

We demonstrate that the performance of quantum error correction can be improved with noise-aware decoders that are calibrated to the likelihood of physical error configurations in a device. We show that noise-aware decoding increases the error suppression factor of the surface code, yielding reductions in the logical error rate that increase exponentially wi

Maksym Mohorian, Devika Kamath, Meghna Menon, Anish M. Amarsi

The mechanisms responsible for chemical depletion across diverse astrophysical environments are not yet fully understood. In this paper, we investigate chemical depletion in post-AGB/post-RGB binary stars hosting second-generation transition discs using high-resolution optical spectra from HERMES/Mercator and UVES/VLT. We performed a detailed chemical abunda

Anargh Viswanath, Lokesh Veeramacheneni, Hendrik Buschmeier

Artificial Intelligence (AI) has significantly advanced in recent years, driving innovation across various fields, especially in robotics. Even though robots can perform complex tasks with increasing autonomy, challenges remain in ensuring explainability and user-centered design for effective interaction. A key issue in Human-Robot Interaction (HRI) is enabl

Arthur Vereijken

The $\psi(3686)$ is identified as the radial excitation of the $J/\psi$. Based on perturbative QCD, the branching ratio of the $\psi(3686)$ into some final hadron state should be approximately 13% of the branching ratio of the $J/\psi$ to that same hadron final state. This is called the "13\% rule". However, certain decay channels such as the $\rho\pi$ sever

Xuyang Zhong, Yixiao Huang, Chen Liu

This paper studies fast adversarial training against sparse adversarial perturbations bounded by $l_0$ norm. We demonstrate the challenges of employing $1$-step attacks on $l_0$ bounded perturbations for fast adversarial training, including degraded performance and the occurrence of catastrophic overfitting (CO). We highlight that CO in $l_0$ adversarial tra

Zhenxing Cui, Lu Chen, Yunhai Wang, Daniel Haehn

This paper presents a systematic study of the generalization of convolutional neural networks (CNNs) and humans on relational reasoning tasks with bar charts. We first revisit previous experiments on graphical perception and update the benchmark performance of CNNs. We then test the generalization performance of CNNs on a classic relational reasoning task: e

G. Di Russo, M. Bianchi, D. Bini

We analyze scalar wave emission from unbound orbits in a Topological Star spacetime. Our study uses a self-force approach and leads to a Post-Newtonian reconstruction of the field along the orbit, both in the time domain and in the frequency domain. We also compute leading-order radiation losses, namely energy and angular momentum.

Hassan Laghbi, Nigel Thomas

In this paper, we propose procedures to address platoon follower dynamics within adaptive beaconing. We implement them in a known adaptive beaconing scheme which is Jerk Beaconing (JB) to improve its safety. We evaluate our proposed approach in terms of safety, string stability and the channel busy ratio (CBR) overhead. The results reveal that our proposal s

Sunghyeon Woo, Sol Namkung, Sunwoo Lee, Inho Jeong

Prior parameter-efficient fine-tuning (PEFT) algorithms reduce memory usage and computational costs of fine-tuning large neural network models by training only a few additional adapter parameters, rather than the entire model. However, the reduction in computational costs due to PEFT does not necessarily translate to a reduction in training time; although th

Yannick Metz, András Geiszl, Raphaël Baur, Mennatallah El-Assady

Learning rewards from preference feedback has become an important tool in the alignment of agentic models. Preference-based feedback, often implemented as a binary comparison between multiple completions, is an established method to acquire large-scale human feedback. However, human feedback in other contexts is often much more diverse. Such diverse feedback

Eric Hitz, Mingmin Feng, Radu Tanase, René Algesheimer

Recent advances in artificial intelligence have led to the proliferation of artificial agents in social contexts, ranging from education to online social media and financial markets, among many others. The increasing rate at which artificial and human agents interact makes it urgent to understand the consequences of human-machine interactions for the propaga

Qi Dai, Beixiong Zheng, Qiyao Wang, Xue Xiong

Rotatable antenna (RA) represents a novel antenna architecture that enhances wireless communication system performance by independently or collectively adjusting each antenna's boresight/orientation. In this demonstration, we develop a prototype of radar sensing-aided rotatable antenna that integrates radar sensing with dynamic antenna orientation to enhance

Melanie Schaller, Bodo Rosenhahn

Predicting energy consumption in smart buildings is challenging due to dependencies in sensor data and the variability of environmental conditions. We introduce S4ConvD, a novel convolutional variant of Deep State Space Models (Deep-SSMs), that minimizes reliance on extensive preprocessing steps. S4ConvD is designed to optimize runtime in resource-constraine

Youran Zhou, Jianzhong Qi

As E-commerce platforms face surging transactions during major shopping events like Black Friday, stress testing with synthesized data is crucial for resource planning. Most recent studies use Generative Adversarial Networks (GANs) to generate tabular data while ensuring privacy and machine learning utility. However, these methods overlook the computational

Muhammad Awais, Abu Safyan Ali, Giacomo Dimarco, Federica Ferrarese

In this work, we integrate the predictive capabilities of compartmental disease dynamics models with machine learning ability to analyze complex, high-dimensional data and uncover patterns that conventional models may overlook. Specifically, we present a proof of concept demonstrating the application of data-driven methods and deep neural networks to a recen

Jaekwang Kim, Hyo-Jin Kim, Hyung-Jun Park

We compare two widely used Lagrangian approaches for modeling granular materials: the Discrete Element Method (DEM) and Smoothed Particle Hydrodynamics (SPH). DEM models individual particle interactions, while SPH treats granular materials as a continuum using constitutive rheological models. In particular, we employ the Drucker Prager viscoplastic model for

Yuhan He

This study examines the influence of employee education and health on firm-level Total Factor Productivity (TFP) in China, using panel data from A-share listed companies spanning from 2007 to 2022. The analysis shows that life expectancy and higher education have a significant impact on TFP. More optimal health conditions can result in increased productivity

Keren Censor-Hillel, Tomer Even, Maxime Flin, Magnús M. Halldórsson

Two of the most fundamental distributed symmetry-breaking problems are that of finding a maximal independent set (MIS) and a maximal matching (MM) in a graph. It is a major open question whether these problems can be solved in constant rounds of the all-to-all communication model of \textsf{Congested\ Clique}, with $O(\log\log \Delta)$ being the best upper b

José I. Orlicki

Recent advances in large language models (LLMs) have popularized the chain-of-thought (CoT) paradigm, in which models produce explicit reasoning steps in natural language. Although this approach improves interpretability and facilitates external auditing, it may not represent the most computationally efficient method for internal reasoning. In contrast, huma

Simone Arreghini, Nicholas Carlotti, Mirko Nava, Antonio Paolillo

Reliable localization of people is fundamental for service and social robots that must operate in close interaction with humans. State-of-the-art human detectors often rely on RGB-D cameras or costly 3D LiDARs. However, most commercial robots are equipped with cameras with a narrow field of view, leaving them unaware of users approaching from other direction

Edoardo Sebastiano De Duro, Giuseppe Alessandro Veltri, Hudson Golino, Massimo Stella

Large Language Models (LLMs) can engage in human-looking conversational exchanges. Although conversations can elicit trust between users and LLMs, scarce empirical research has examined trust formation in human-LLM contexts, beyond LLMs' trustworthiness or human trust in AI in general. Here, we introduce the Trust-In-LLMs Index (TILLMI) as a new framework to

Marc Solé i Bonet, Jannis Wolf, Leonidas Kosmidis

In the context of the Horizon Europe project, METASAT, a hardware platform was developed as a prototype of future space systems. The platform is based on a multiprocessor NOEL-V, an established space-grade processor, which is integrated with the SPARROW AI accelerator and connected to a GPU, Vortex. Both processing systems follow the RISC-V specification. Th

Yuchen Ji, Ting Dai, Zhichao Zhou, Yutian Tang

Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP's dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF v

Marco Roth, David A. Kreplin, Daniel Basilewitsch, João F. Bravo

Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum comput

Abdelrahman Abdallah, Bhawna Piryani, Jonas Wallat, Avishek Anand

Temporal awareness is crucial in many information retrieval tasks, particularly in scenarios where the relevance of documents depends on their alignment with the query's temporal context. Traditional approaches such as BM25 and Dense Passage Retrieval (DPR) focus on lexical or semantic similarity but tend to neglect the temporal alignment between queries and

Matteo Bonforte, Carlos Fuertes-Moran

This paper provides sharp quantitative and constructive estimates of nonnegative solutions $u(t,x)\geq 0$ to the nonlinear fractional diffusion equation, $$\partial_t u +{\mathcal L} F(u)=0,$$ also known as filtration equation, posed in a smooth bounded domain $x\in \Omega \subset {\mathbb R}^N$ with suitable homogeneous Dirichlet boundary conditions. Both t

Nesryne Mejri, Enjie Ghorbel, Anis Kacem, Pavel Chernakov

This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significantly in the presence of a domain shift, difficult to avoid in a real-world setting. While UDA has contributed to solving this issue in binary and multi-class classification, such a

Seungki Kim, Phong Q. Nguyen

We use state-of-art lattice algorithms to improve the upper bound on the lowest counterexample to the Mertens conjecture to $\approx \exp(1.96 \times 10^{19})$, which is significantly below the conjectured value of $\approx \exp(5.15 \times 10^{23})$ by Kotnik and van de Lune [KvdL04].

Alfred Geroldinger, Hwankoo Kim, K. Alan Loper

In this survey article we discuss key open problems which could serve as a guidance for further research directions of multiplicative ideal theory and factorization theory.

Martin Cooney, Sivadinesh Ponrajan, Fernando Alonso-Fernandez

Technologies such as robotics, Artificial Intelligence (AI), and Computer Vision (CV) can be applied to crime scene analysis (CSA) to help protect lives, facilitate justice, and deter crime, but an overview of the tasks that can be automated has been lacking. Here we follow a speculative prototyping approach: First, the STAIR tool is used to rapidly review t

Laurent H. A. Simons, Ralf Klemt, Tilman Pfau, Michiel Wouters

The ground-state properties of the supersolid polaron consisting of a neutral impurity immersed in a dipolar supersolid have recently been studied. Here, the optical response of an impurity in a dipolar supersolid is calculated and interpreted in terms of the contributions of the different excitation modes of the supersolid. The optical absorption spectrum r

Fangxu Yu, Lai Jiang, Shenyi Huang, Zhen Wu

The ability to understand and predict the mental states of oneself and others, known as the Theory of Mind (ToM), is crucial for effective social scenarios. Although recent studies have evaluated ToM in Large Language Models (LLMs), existing benchmarks focus on simplified settings (e.g., Sally-Anne-style tasks) and overlook the complexity of real-world socia

Yucong Yin, Jun Zhan, Boyang Liu, Xinloong Han

The recently discovered bilayer Ruddlesden-Popper nickelate La$_3$Ni$_2$O$_7$ exhibits superconductivity with a remarkable transition temperature $T_c\approx 80 $ K under applied pressures above 14.0 GPa. This discovery of new family of high-temperature superconductors has garnered significant attention in the condensed matter physics community. In this work

Nilanjan Das, Soma Das, Jaydeb Sarkar

We consider the restriction of Brownian shifts to their invariant subspaces and classify when they are unitarily equivalent. Additionally, we prove an asymptotic property stating that normalized Brownian shifts belong to the classical $C_{00}$-class.

Junqi Liu, Jujian Zhang, Lihong Zhi

We formalize a proof of the irrationality of $\zeta(3)$ in Lean 4, using Beukers' method. To support this, we extend the Lean mathematical library (Mathlib) by formalizing shifted Legendre polynomials and important results in analytic number theory that were previously missing. As part of the Lean 4 PrimeNumberTheoremAnd project, we also formalize the asympt

Siting Liang, Daniel Sonntag

Verification of biomedical claims is critical for healthcare decision-making, public health policy and scientific research. We present an interactive biomedical claim verification system by integrating LLMs, transparent model explanations, and user-guided justification. In the system, users first retrieve relevant scientific studies from a persistent medical

Herbert Egger, Andreas Schafelner

A class of abstract nonlinear time-periodic evolution problems is considered which arise in electrical engineering and other scientific disciplines. An efficient solver is proposed for the systems arising after discretization in time based on a fixed-point iteration. Every step of this iteration amounts to the solution of a discretized time-periodic and time

Silin Chen, Andy Liu, Kangjian Di, Yichu Xu

Unsupervised image anomaly detection (UAD) has become a critical process in industrial and medical applications, but it faces growing challenges due to increasing concerns over data privacy. The limited class diversity inherent to one-class classification tasks, combined with distribution biases caused by variations in products across and within clients, pos

Junchao Zhu, Ruining Deng, Tianyuan Yao, Juming Xiong

The rapid development of spatial transcriptomics (ST) offers new opportunities to explore the gene expression patterns within the spatial microenvironment. Current research integrates pathological images to infer gene expression, addressing the high costs and time-consuming processes to generate spatial transcriptomics data. However, as spatial transcriptomi

Jianming Zhou, Xiaoli Hu, Honglian Zhang, Naihuan Jing

Quantum discord, a key indicator of non-classical correlations in bipartite systems, has been recently extended to multipartite scenarios [Phys. Rev. Lett. 2020, 124:110401]. We present exact analytic formulas for the quantum discord of special families of N-qubit states, including generalized class of GHZ states. Our formulations span $2$, $3$, $4n$, $4n+1$

Yoonsoo Nam, Seok Hyeong Lee, Clementine C J Domine, Yeachan Park

In physics, complex systems are often simplified into minimal, solvable models that retain only the core principles. In machine learning, layerwise linear models (e.g., linear neural networks) act as simplified representations of neural network dynamics. These models follow the dynamical feedback principle, which describes how layers mutually govern and ampl

Silvia Falletta, Stefan Sauter

In this paper we consider high-frequency acoustic transmission problems with jumping coefficients modelled by Helmholtz equations. The solution then is highly oscillatory and, in addition, may be localized in a very small vicinity of interfaces (whispering gallery modes). For the reliable numerical approximation a) the PDE is tranformed in a classical single

Stéphane Guillet, Alexis Poncet, Marine Le Blay, William. T. M Irvine

When soft matter is driven out of equilibrium its constituents interact via effective interactions that escape Newton's action-reaction principle. Prominent examples include the hydrodynamic interactions between colloidal particles driven in viscous fluids, phoretic interactions between chemically active colloids, and quorum sensing interactions in bacterial

Lei Han

Recent field campaigns have consistently documented bottom-intensified mixing near the seafloor, suggesting diabatic downwelling in the abyssal ocean. This phenomenon appears to contradict with the mass balance of the abyssal ocean, where dense bottom water plunges into the region from the Antarctic side. Previous studies have sought to resolve this apparent

Erik Høg

The aim is to document in some detail the last 35 years of meridian circles, a type of instrument with a fundamental role in astronomy for a very long time, and to do so while witnesses are still alive and can contribute. This is about finding facts. Meridian circles provided fundamental star positions for centuries. These positions were tied to a well-defin

Mateusz Mrukiewicz, Martin Cigl, Paweł Perkowski, Jakub Karcz

The oblique helicoidal structure is formed in right-angle cholesterics under the applied electric field. The electric field changes the pitch and cone angle but preserves the single-harmonic modulation of the refractive index. As a result, in such a supramolecular system, we can tune the selective reflection of light in a broad range. Here, we report that st

Ying-Ming Xie, Naoto Nagaosa

The bulk photovoltaic effect (BPVE) generates a direct current photocurrent under uniform irradiation and is a nonlinear optical effect traditionally studied in non-centrosymmetric materials. The two main origins of BPVE are the shift and injection currents, arising from transitions in electron position and electron velocity during optical excitation, respec

Woo Kyoung Han, Byeonghun Lee, Hyunmin Cho, Sunghoon Im

We quantify the upper bound on the size of the implicit neural representation (INR) model from a digital perspective. The upper bound of the model size increases exponentially as the required bit-precision increases. To this end, we present a bit-plane decomposition method that makes INR predict bit-planes, producing the same effect as reducing the upper bou

Xinglei Dou, Lei Liu, Zhuohao Wang, Pengyu Li

Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut

Aicha Balhag, Zakaria Mazgouri, Hassan Riahi, Michel Théra

In this article, we aim to approximate a solution to the bilevel equilibrium problem $\mathbf{(BEP})$ for short: find $\bar{x} \in \mathbf{S}_f$ such that $ g(\bar{x}, y) \geq 0, \,\, \forall y \in \mathbf{S}_f, $ where $ \mathbf{S}_f = \{ u \in \mathbf{K} : f(u, z) \geq 0, \forall z \in \mathbf{K} \}. $ Here, $\mathbf{K}$ is a closed convex subset of a real

Christian Gapp, Elias Tappeiner, Martin Welk, Karl Fritscher

Purpose High dimensional, multimodal data can nowadays be analyzed by huge deep neural networks with little effort. Several fusion methods for bringing together different modalities have been developed. Given the prevalence of high-dimensional, multimodal patient data in medicine, the development of multimodal models marks a significant advancement. However,

Shouhei Honda, Song Sun

We provide various characterizations for a given almost smooth space to be an RCD space, in terms of a local volume doubling and a local Poincar\'e inequality. Applications include a characterization of Einstein $4$-orbifolds.

Felipe Sánchez González, Javier Pastor-Galindo, José A. Ruipérez-Valiente

A key countermeasure in cybersecurity has been the development of standardized computational protocols for modeling and sharing cyber threat intelligence (CTI) between organizations, enabling a shared understanding of threats and coordinated global responses. However, while the cybersecurity domain benefits from mature threat exchange frameworks, there has b

Kamil Szczech, Maksymilian Wojnar, Katarzyna Kosek-Szott, Krzysztof Rusek

Future wireless networks will need to support diverse applications (such as extended reality), scenarios (such as fully automated industries), and technological advances (such as terahertz communications). Current wireless networks are designed to perform adequately across multiple scenarios so they lack the adaptability needed for specific use cases. Theref

Chanwoo Choi, Jinsoo Kim, Sukmin Cho, Soyeong Jeong

With the growing adoption of retrieval-augmented generation (RAG) systems, various attack methods have been proposed to degrade their performance. However, most existing approaches rely on unrealistic assumptions in which external attackers have access to internal components such as the retriever. To address this issue, we introduce a realistic black-box att

Benjamin Dequêne, Michaël Schoonheere

This paper is the first part of a series that intends to study the resolving subcategories for gentle algebras over an algebraically closed field $\mathbb{K}$. In a general setting, we improve the precision of an algorithm from Takahashi for resolving closure calculations in well-behaved abelian categories. Then, we modify the geometric model of Baur--Coelho

Xiusheng Huang, Jiaxiang Liu, Yequan Wang, Jun Zhao

Large scale language models have achieved superior performance in tasks related to natural language processing, however, it is still unclear how model parameters affect performance improvement. Previous studies assumed that individual knowledge is stored in local parameters, and the storage form of individual knowledge is dispersed parameters, parameter laye

Dieter Spreen

A generalization of Scott's information systems~\cite{sco82} is presented that captures exactly all continuous domains. The global consistency predicate in Scott's definition is relativized. Now, for every atomic statement, there is a consistency predicate that states which finite sets of statements express information that is consistent with the given state

Shuyu Liu, Ruoxi Wang, Ling Zhang, Xuequan Zhu

The advent of Large Language Models (LLMs) offers potential solutions to address problems such as shortage of medical resources and low diagnostic consistency in psychiatric clinical practice. Despite this potential, a robust and comprehensive benchmarking framework to assess the efficacy of LLMs in authentic psychiatric clinical environments is absent. This

Rose Connolly, Lauren Buck, Victor Zordan, Rachel McDonnell

As social VR grows in popularity, understanding how to optimise interactions becomes increasingly important. Interpersonal distance (the physical space people maintain between each other) is a key aspect of user experience. Previous work in psychology has shown that breaches of personal space cause stress and discomfort. Thus, effectively managing this dista

Burak Alakent, Erkan Isikli, Cigdem Kadaifci, Tonguc S. Taspinar

Natural gas (NG) is relatively a clean source of energy, particularly compared to fossil fuels, and worldwide consumption of NG has been increasing almost linearly in the last two decades. A similar trend can also be seen in Turkey, while another similarity is the high dependence on imports for the continuous NG supply. It is crucial to accurately forecast f

Qiyuan Li, Haijiang Liu, Caicai Guo, Chao Gao

The large-scale development of large language models (LLMs) in medical contexts, such as diagnostic assistance and treatment recommendations, necessitates that these models possess accurate medical knowledge and deliver traceable decision-making processes. Clinical knowledge, encompassing the insights gained from research on the causes, prognosis, diagnosis,

Junho Choe, Jaewoo Jung

We generalize the celebrated Fr\"{o}berg's theorem to embedded joins of copies of a simplicial complex, namely higher secant complexes to the simplicial complex, in terms of property $N_{q+1,p}$ due to Green and Lazarsfeld. Furthermore, we investigate combinatorial phenomena parallel to geometric ones observed for higher secant varieties of minimal degree.

Jingwei Hu, Dave Zachariah, Petre Stoica

We consider the problem of tracking moving targets using mobile wireless sensors (of possibly different types). This is a joint estimation and control problem in which a tracking system must take into account both target and sensor dynamics. We make minimal assumptions about the target dynamics, namely only that their accelerations are bounded. We develop a

Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal, Balint Kovacs

In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capable of 4D tracking with dense spatial prompts. Our model leverages an extensive dataset of 23,262 annotated medical scans, as well as synthesized longitudinal data across diverse le

Thanet Markchom, Tong Wu, Liting Huang, Huizhi Liang

SemEval-2025 Task 1 focuses on ranking images based on their alignment with a given nominal compound that may carry idiomatic meaning in both English and Brazilian Portuguese. To address this challenge, this work uses generative large language models (LLMs) and multilingual CLIP models to enhance idiomatic compound representations. LLMs generate idiomatic me

Gursharanjit Kaur, Maciej Bilicki, Wojciech Hellwing, the WAVES team

The forthcoming Wide Area Vista Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) has a key science goal of probing the halo mass function to lower limits than possible with previous surveys. For that purpose, in its Wide component, galaxies targetted by WAVES will be flux-limited to $Z<21.1$ mag and will cover the reds

Koki Inami, Sho Sakaino, Toshiaki Tsuji

Recent research has demonstrated the usefulness of imitation learning in autonomous robot operation. In particular, teaching using four-channel bilateral control, which can obtain position and force information, has been proven effective. However, control performance that can easily execute high-speed, complex tasks in one go has not yet been achieved. We pr