March 2025 arXiv papers — page 204

Shiliang Zhang, Sabita Maharjan, Lee Andrew Bygrave, Shui Yu

Decarbonization, decentralization and digitalization are the three key elements driving the twin energy transition. The energy system is evolving to a more data driven ecosystem, leading to the need of communication and storage of large amount of data of different resolution from the prosumers and other stakeholders in the energy ecosystem. While the energy

Hariharan Umashankar, Daniel Scheiber, Vsevolod I. Razumovskiy, Matthias Militzer

Solute segregation in alloys is a key phenomenon which affects various material characteristics such as embrittlement, grain growth and precipitation kinetics. In this work, the segregation energies of Y, Zr, and Nb to a \textgreek{S}5 grain boundary in a bcc Ti-25 at \% Mo alloy were determined using density functional theory (DFT) calculations. A systemati

Fabian Stolp, Charlotte Brandebusemeyer, Franziska Hradilak, Lara Kursawe

Integrated development environments (IDE) support developers in a variety of tasks. Unobtrusively capturing developers' cognitive load while working on different programming tasks could help optimize developers' work experience, increase their productivity, and positively impact code quality. In this paper, we propose a study in which the IntelliJ-based IDE

Po-Chien Luan, Yang Gao, Celine Demonsant, Alexandre Alahi

Conventional human trajectory prediction models rely on clean curated data, requiring specialized equipment or manual labeling, which is often impractical for robotic applications. The existing predictors tend to overfit to clean observation affecting their robustness when used with noisy inputs. In this work, we propose MonoTransmotion (MT), a Transformer-b

Milin Patel, Rolf Jung

The development of Automated Driving Systems (ADS) has the potential to revolutionise the transportation industry, but it also presents significant safety challenges. One of the key challenges is ensuring that the ADS is safe in the event of Foreseeable Misuse (FM) by the human driver. To address this challenge, a case study on simulation-based testing to mi

Samuel Bilson

Motivated by the holographic principle, within the context of the AdS/CFT Correspondence in the large t'Hooft limit, we investigate how the geometry of certain highly symmetric bulk spacetimes can be recovered given information of physical quantities in the dual boundary CFT. In particular, we use the existence of bulk-cone singularities (relating the locati

Mashrur Rashik, Shilpa Sweth, Nishtha Agrawal, Saiyyam Kochar

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential data for long-term care. While valuable, traditional journaling methods often rely on static, self-directed entries, lacking interactive feedback and real-time guidance. This gap can result in incomplete or impre

Liming Lu, Xiang Gu, Shuchao Pang, Siyuan Liang

Research endeavors have been made in learning robust Multimodal Large Language Models (MLLMs) against jailbreak attacks. However, existing methods for improving MLLMs' robustness still face critical challenges: \ding{172} how to efficiently tune massive weight parameters and \ding{173} how to ensure robustness against attacks across both visual and textual m

Cong Wang

This paper investigates the gradient flow structure, well-posedness, and asymptotic behavior of the Fokker-Planck equation defined on locally uniformly finite graphs, which is highly non-trivial compared with the finite case. We first construct a 2-Wasserstein-type metric and gradient flow equation in the probability density space associated with the underly

Cyrill Scheidegger, Zijian Guo, Peter Bühlmann

We introduce a new instrumental variable (IV) estimator for heterogeneous treatment effects in the presence of endogeneity. Our estimator is based on double/debiased machine learning (DML) and uses efficient machine learning instruments (MLIV) and kernel smoothing. We prove consistency and asymptotic normality of our estimator and also construct confidence s

David S. Johnson

Human-AI collaboration is often proposed to improve high-stakes decision-making, yet the influence of increased stakes and imperfect AI on decision-making strategies is not fully understood. Studying such behavior in realistic settings is challenging, as application-grounded evaluations are costly, rely on experts, or lack meaningful consequences for decisio

Qiqi Guo, Zhuowen Zheng, Guanghua Yang, Zhiquan Liu

In recent years, the emergence of deep convolutional neural networks has positioned face recognition as a prominent research focus in computer vision. Traditional loss functions, such as margin-based, hard-sample mining-based, and hybrid approaches, have achieved notable performance improvements, with some leveraging curriculum learning to optimize training.

Chia-Yi Ju, Adam Miranowicz, Jacob Barnett, Guang-Yin Chen

The equivalence between the Schrödinger and Heisenberg representations is a cornerstone of quantum mechanics. However, this relationship remains unclear in the non-Hermitian regime, particularly when the Hamiltonian is time-dependent. In this study, we address this gap by establishing the connection between the two representations, incorporating the metric o

Christian Varner, Vivak Patel

Arising in semi-parametric statistics, control applications, and as sub-problems in global optimization methods, certain optimization problems can have objective functions requiring numerical integration to evaluate, yet gradient function evaluations that are relatively cheap. For such problems, typical optimization methods that require multiple evaluations

Yixin Su, Wei Jiang, Fangquan Lin, Cheng Yang

In recommender systems, the patterns of user behaviors (e.g., purchase, click) may vary greatly in different contexts (e.g., time and location). This is because user behavior is jointly determined by two types of factors: intrinsic factors, which reflect consistent user preference, and extrinsic factors, which reflect external incentives that may vary in dif

Diego Pazó, Rok Cestnik

We report on an exact theory for ensembles of globally coupled, heterogeneous complex Riccati equations. A drastic dimensionality reduction to a few ordinary differential equations is achieved for Lorentzian heterogeneity. By applying this technique, we obtain low-dimensional firing-rate equations for populations of spiking neurons with a clustered substruct

Jinwen Hu, Huan Hu

The possibility of the breaking of Lorentz symmetry has been discussed in many models of quantum gravity. In this paper we follow the Lorentz violation model in Ref. [1] (i.e., our previous work) to discuss the Doppler frequency shift of photons and the Compton scattering process between photons and electrons, pointing out that following the idea in Ref. [1]

Milin Patel, Rolf Jung, Yasin Cakir

With Highly Automated Driving (HAD), the driver can engage in non-driving-related tasks. In the event of a system failure, the driver is expected to reasonably regain control of the Automated Vehicle (AV). Incorrect system understanding may provoke misuse by the driver and can lead to vehicle-level hazards. ISO 21448, referred to as the standard for Safety o

Sándor Rácz, Norbert Reider

In this paper we propose and quantitatively evaluate three performance optimization methods that exploit the concept of communication-compute-control co-design by introducing awareness of communication and compute characteristics into the application logic in different ways to improve overall system performance. We have implemented a closed-loop control of a

Elżbieta Adamus

In our previous paper an effective algorithm for inverting polynomial automorphisms was proposed. We extend its application to the case of formal power series over a field of arbitrary characteristic and illustrate the proposed approach with some examples.

Shunxin Wang, Raymond Veldhuis, Nicola Strisciuglio

Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image datasets often exploit such shortcuts, potentially impairing their generalization performance. However, existing methods for identifying frequency shortcuts require expensive computati

Anna Joliot, M. Yassine Naghmouchi, Wesley Coelho

This paper presents key enhancements to our previous work~\cite{naghmouchi2024mixed} on a hybrid Benders decomposition (HBD) framework for solving mixed integer linear programs (MILPs). In our approach, the master problem is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) model and solved on a neutral-atom quantum processor using automat

Ayush Suhane, Daniel Scheiber, Vsevolod I. Razumovskiy, Matthias Militzer

Atomistically-informed phase field simulations have been performed to investigate the effect of five common alloying elements (Nb, Ti, Mo, V, Mn) on austenite grain growth. The anisotropic simulations based on the segregation energy profiles of the solutes to four different grain boundary (GB) types from density functional theory calculations suggest a secon

Jan Slovák, Radek Suchánek

These notes present elementary introduction to tractors based on classical examples, together with glimpses towards modern invariant differential calculus related to vast class of Cartan geometries, the so called parabolic geometries.

Anna Kuchko

We consider the Inverse Optimal Stopping (IOS) problem where, based on stopped expert trajectories, one aims to recover the optimal stopping region through the continuation and stopping gain functions approximation. The uniqueness of the stopping region allows the use of IOS in real-world applications with safety concerns. Although current state-of-the-art i

Shamba Ghosh, Arpan Roy, Bishnu P. Pal, Somnath Ghosh

We report an all-lossy index-guided dual-core photonic crystal fiber (PCF) that hosts a second-order exceptional point (EP) in the systems parameter space. By appropriately selecting a parametric encirclement scheme around the EP, the interaction between the coupled modes has been studied, and the mode conversion is subsequently observed.

Zhong Jing Yap, Dharini Pathmanathan, Sophie Dabo-Niang

We propose a novel extension of the Hyndman-Ullah (HU) model to forecast mortality rates by integrating randomized signatures, referred to as the HU model with randomized signatures (HUrs). Unlike truncated signatures, which grow exponentially with order, randomized signatures, based on the Johnson-Lindenstrauss lemma, are able to approximate higher-order in

Ali Erkan, Tunga Güngör

Aspect-based sentiment analysis has gained significant attention in recent years due to its ability to provide fine-grained insights for sentiment expressions related to specific features of entities. An important component of aspect-based sentiment analysis is aspect extraction, which involves identifying and extracting aspect terms from text. Effective asp

Qingyu Fan, Yinghao Cai, Chao Li, Wenzhe He

Robotic grasping in scenes with transparent and specular objects presents great challenges for methods relying on accurate depth information. In this paper, we introduce NeuGrasp, a neural surface reconstruction method that leverages background priors for material-agnostic grasp detection. NeuGrasp integrates transformers and global prior volumes to aggregat

Yuri Kozitsky

According to the Lieb-Sokal theorem, the partition function, $Z$, of a ferromagnetic spin model has the Lee-Yang property if the single-spin partition function has it. In this note, it is shown that for some spin models a ferromagnetic interaction can induce the Lee-Yang property of $Z$ even if the single-spin partition function fails to have it. In particul

Valentin N. Hartmann, Tirza Heinle, Yijiang Huang, Stelian Coros

In many robotics applications, multiple robots are working in a shared workspace to complete a set of tasks as fast as possible. Such settings can be treated as multi-modal multi-robot multi-goal path planning problems, where each robot has to reach a set of goals. Existing approaches to this type of problem solve this using prioritization or assume synchron

Haomin Sun, Jian Chen, Alexander Khrabrov, Igor D. Kaganovich

Two-dimensional, electromagnetic particle-in-cell simulations are employed to study particle kinetics and power deposition in the skin layer when a Radio Frequency (RF) electromagnetic field penetrates into a background plasma. We identify a new regime at low frequency ($\sim\mathrm{MHz}$) and low pressure, where the motion of electrons can be highly nonline

Samuel Repka, Bořek Reich, Fedor Zolotarev, Tuomas Eerola

We propose a novel Graph Neural Network-based method for segmentation based on data fusion of multimodal Scanning Electron Microscope (SEM) images. In most cases, Backscattered Electron (BSE) images obtained using SEM do not contain sufficient information for mineral segmentation. Therefore, imaging is often complemented with point-wise Energy-Dispersive X-r

Vibeke Binz Vallevik, Serena Elizabeth Marshall, Aleksandar Babic, Jan Franz Nygaard

Synthetic data is emerging as a cost-effective solution necessary to meet the increasing data demands of AI development, created either from existing knowledge or derived from real data. The traditional classification of synthetic data types into hybrid, partial or fully synthetic datasets has limited value and does not reflect the ever-increasing methods to

Yaoru Li, Shunyu Liu, Tongya Zheng, Li Sun

Recent advancements in Large Language Model~(LLM)-based Multi-Agent Systems (MAS) have demonstrated remarkable potential for tackling complex decision-making tasks. However, existing frameworks inevitably rely on serialized execution paradigms, where agents must complete sequential LLM planning before taking action. This fundamental constraint severely limit

Yandong Bai, Binlong Li, Yufeng Pan, Shenggui Zhang

Burr and Erd\H{o}s conjectured in 1976 that for every two integers $k>\ell\geqslant 0$ satisfying that $k\mathbb{Z}+\ell$ contains an even integer, an $n$-vertex graph containing no cycles of length $\ell$ modulo $k$ can contain at most a linear number of edges on $n$. Bollob\'{a}s confirmed this conjecture in 1977 and then Erd\H{o}s proposed the problem of

Dian Xu, Shanshan Wang, Wei Li, Weibing Deng

Modern machine learning, grounded in the Universal Approximation Theorem, has achieved significant success in the study of phase transitions in both equilibrium and non-equilibrium systems. However, identifying the critical points of percolation models using raw configurations remains a challenging and intriguing problem. This paper proposes the use of the K

Xianan Hu, Fu Li, Kairui Niu, Peihan Qi

Our study proposes a novel embedding method, Wide-Value-Embeddings (WVEmbs), for processing Pulse Descriptor Words (PDWs) as normalized inputs to neural networks. This method adapts to the distribution of interleaved radar signals, ranking original signal features from trivial to useful and stabilizing the learning process. To address the imbalance in radar

Jiajun Yu, Yizhen Zheng, Huan Yee Koh, Shirui Pan

Molecular optimization is a crucial yet complex and time-intensive process that often acts as a bottleneck for drug development. Traditional methods rely heavily on trial and error, making multi-objective optimization both time-consuming and resource-intensive. Current AI-based methods have shown limited success in handling multi-objective optimization tasks

Canaan Yung, Hanxun Huang, Christopher Leckie, Sarah Erfani

Adversarial prompts are capable of jailbreaking frontier large language models (LLMs) and inducing undesirable behaviours, posing a significant obstacle to their safe deployment. Current mitigation strategies primarily rely on activating built-in defence mechanisms or fine-tuning LLMs, both of which are computationally expensive and can sacrifice model utili

Gavriel Habib, Noa Barzilay, Or Shimshi, Rami Ben-Ari

Gait recognition is a computer vision task that identifies individuals based on their walking patterns. Gait recognition performance is commonly evaluated by ranking a gallery of candidates and measuring the accuracy at the top Rank-$K$. Existing models are typically single-staged, i.e. searching for the probe's nearest neighbors in a gallery using a single

Arvindh Arun, Karuna K Chandra, Akshit Sinha, Balakumar Velayutham

The detection of controversial content in political discussions on the Internet is a critical challenge in maintaining healthy digital discourse. Unlike much of the existing literature that relies on synthetically balanced data, our work preserves the natural distribution of controversial and non-controversial posts. This real-world imbalance highlights a co

Wonjun Kang, Kevin Galim, Yuchen Zeng, Minjae Lee

State Space Models (SSMs) have emerged as efficient alternatives to Transformers, mitigating their quadratic computational cost. However, the application of Parameter-Efficient Fine-Tuning (PEFT) methods to SSMs remains largely unexplored. In particular, prompt-based methods like Prompt Tuning and Prefix-Tuning, which are widely used in Transformers, do not

Javier Gutiérrez García, Ulrich Höhle

In this paper, we provide a comprehensive analysis of involutive quantales, with a particular focus on quantic frames. We extend the axiomatic foundations of quantale-enriched topological spaces to include closure under the anti-homomorphic involution, facilitating a balanced topologization of the spectrum of unital $C^*$-algebras that encompasses both close

Xiaoyu Chen, Jingmin Huang, Yibo Lian

A platform commits to a search algorithm that maps prices to search order. Given this algorithm, sellers set prices, and consumers engage in sequential search. This framework generalizes the ordered search literature. We introduce a special class of search algorithms, termed ''contracts,'' show that they implement all possible equilibrium prices and then cha

K. Kouroumpatzakis, J. Svoboda

The origin of He II emission in galaxies remains a debated topic, requiring ionizing photons with energies exceeding 54 eV. While massive stars, such as Wolf-Rayet stars, have been considered potential sources, their UV flux often fails to fully explain the observed He II emission. Recent studies suggest that X-ray binaries (XRBs) might contribute significan

Athanassios Raftopoulos, Vincent C. Müller

In this paper we address the issue of grounding for experiential concepts. Given that perceptual demonstratives are a basic form of such concepts, we examine ways of fixing the referents of such demonstratives. To avoid 'encodingism', that is, relating representations to representations, we postulate that the process of reference fixing must be bottom-up and

Mihael Liskij, Xuhua Ding, Gene Tsudik, David Basin

A computing device typically identifies itself by exhibiting unique measurable behavior or by proving its knowledge of a secret. In both cases, the identifying device must reveal information to a verifier. Considerable research has focused on protecting identifying entities (provers) and reducing the amount of leaked data. However, little has been done to co

Davide Belfiori, Rosita Paladino, Annie Hughes, Jean-Philippe Bernard

Magnetic fields have an impact on galaxy evolution at multiple scales. They are particularly important for starburst galaxies, where they play a crucial role in shaping the interstellar medium (ISM), influencing star formation processes and interacting with galactic outflows. The primary aim of this study is to obtain a parsec scale map of dust polarisation

Suhwan Cho, Seunghoon Lee, Minhyeok Lee, Jungho Lee

Referring video object segmentation aims to segment and track a target object in a video using a natural language prompt. Existing methods typically fuse visual and textual features in a highly entangled manner, processing multi-modal information together to generate per-frame masks. However, this approach often struggles with ambiguous target identification

Heiko Gimperlein, Michael Grinfeld, Robin J. Knops, Marshall Slemrod

We formulate new admissibility criteria for initial value problems motivated by the least action principle. These are applied to a two-dimensional Riemann initial value problem for the isentropic compressible Euler fluid flow. It is shown that the criterion prefers the 2-shock solution to solutions obtained by convex integration by Chiodaroli and Kreml or to

Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang

In this work, we refine recent results on the explicit construction of polynomial algebras associated with commutants of subalgebras in enveloping algebras of Lie algebras by considering an additional grading with respect to the subalgebra. It is shown that such an approach simplifies and systematizes the explicit derivation of the Lie--Poisson brackets of e

Gaurang Sharma, Elaheh Moradi, Juha Pajula, Mika Hilvo

Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often serving as its precursor. The prediction of MCI to dementia conversion has been well studied, but previous studies have almost always focused on traditional Machine Learning (ML) based methods that require sharin

Anouk Duyster, Tomasz Kociumaka

Internal Pattern Matching (IPM) queries on a text $T$, given two fragments $X$ and $Y$ of $T$ such that $|Y|<2|X|$, ask to compute all exact occurrences of $X$ within $Y$. IPM queries have been introduced by Kociumaka, Radoszewski, Rytter, and Wale\'n [SODA'15 & SICOMP'24], who showed that they can be answered in $O(1)$ time using a data structure of size $O

Dandan Liu, Shoujun Xu

Given a graph H and a positive integer n, the planar Turan number of H, denoted by exp(n, H), is the maximum number of edges in an n-vertex H-free planar graph.D.Ghosh, et al.initiated the topic of double stars S_(k,l). Recently Xu et al.[AIMS Mathematics, 2025, 10(1): 1628-1644.] mentioned that exp(n, S_(3,5)) is still unknown.In this paper, we first establ

Maresa Schröder, Valentyn Melnychuk, Stefan Feuerriegel

Patient data is widely used to estimate heterogeneous treatment effects and thus understand the effectiveness and safety of drugs. Yet, patient data includes highly sensitive information that must be kept private. In this work, we aim to estimate the conditional average treatment effect (CATE) from observational data under differential privacy. Specifically,

Alexis Chevalier, Soumya Ghosh, Urvi Awasthi, James Watkins

Understanding the biological mechanisms of disease is crucial for medicine, and in particular, for drug discovery. AI-powered analysis of genome-scale biological data holds great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existin

Maximilian M. Mandl, Frank Weber, Tobias Wöhrle, Anne-Laure Boulesteix

The term "researcher degrees of freedom" (RDF), which was introduced in metascientific literature in the context of the replication crisis in science, refers to the extent of flexibility a scientist has in making decisions related to data analysis. These choices occur at all stages of the data analysis process. In combination with selective reporting, RDF ma

Sushma Kurapati, D. J. Pisano, W. J. G. de Blok, Peter Kamphuis

We use the neutral atomic hydrogen (HI) observations of the edge-on galaxy UGCA 250, taken as part of the MeerKAT HI Observations of Nearby Galactic Objects - Observing Southern Emitters (MHONGOOSE) survey to investigate the amount, morphology, and kinematics of extraplanar gas. The combination of high column density sensitivity and high spatial resolution o

Jia Li

We establish the Bonnet-Myers theorem and the Bishop-Gromov volume comparison theorem in the spectral sense for manifolds with weakly convex boundary. For $n\geq 3$, let $(M^n,g)$ be a simply connected compact smooth $n$-manifold with weakly convex boundary $\partial M$. If there exists a positive function $w\in C^{\infty}(M)$ that satisfies: \begin{equation

Borong Zhang, Yuhao Zhang, Jiaming Ji, Yingshan Lei

Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robot itself, and humans. How can safety constraints be explicitly integrated into VLAs? We address this by exploring an integrated safety appro

Tao Wang, Yinghui Wang, Yanxing Liang, Liangyi Huang

The issue concerning the significant decline in the stability of feature extraction for images subjected to large-angle affine transformations, where the angle exceeds 50 degrees, still awaits a satisfactory solution. Even ASIFT, which is built upon SIFT and entails a considerable number of image comparisons simulated by affine transformations, inevitably ex

K. Knakkergaard Nielsen, M. Zwierlein, G. M. Bruun

Quantum gas microscopy with atoms in optical lattices provides remarkable insights into the real space properties of many-body systems, but does not directly reveal the nature of their fundamental excitation spectrum. Here, we demonstrate that radio-frequency spectroscopy can reveal the quasi-particle nature of doped quantum many-body systems, crucial for ou

Jiaxin Tu, Xiaoyi Wei, Yueqi Zhang, Taixian Hou

Learning diverse skills for quadruped robots presents significant challenges, such as mastering complex transitions between different skills and handling tasks of varying difficulty. Existing imitation learning methods, while successful, rely on expensive datasets to reproduce expert behaviors. Inspired by introspective learning, we propose Progressive Adver

Giovanna Takano Natti, Érica Regina Takano Natti, Paulo Laerte Natti

We present a review of the current and future industrial applications of neutrinos. We address the industrial applications of neutrinos in geological and geochemical studies of the Earth's interior, in monitoring earthquakes, in terrestrial communications, in applications for submarines, in monitoring nuclear power plants and fusion reactors, in the manageme

Linyu Fan, Che Wang, Ming Ye, Qizhi Yang

Data-centric artificial intelligence (AI) has remarkably advanced medical imaging, with emerging methods using synthetic data to address data scarcity while introducing synthetic-to-real gaps. Unsupervised domain adaptation (UDA) shows promise in ground truth-scarce tasks, but its application in reconstruction remains underexplored. Although multiple overlap

Varsha Suresh, M. Hamza Mughal, Christian Theobalt, Vera Demberg

Research in linguistics shows that non-verbal cues, such as gestures, play a crucial role in spoken discourse. For example, speakers perform hand gestures to indicate topic shifts, helping listeners identify transitions in discourse. In this work, we investigate whether the joint modeling of gestures using human motion sequences and language can improve spok

Peng Li, Yuzhe Wang, Yabin Liu, Jianghao Yao

Electron-boson coupling in unconventional superconductors is one of the key parameters in understanding the superconducting pairing symmetry. Here, we report definitive photoemission evidence of electron-spin exciton coupling in the iron-based superconductor CaKFe4As4, obtained via high-resolution ARPES. Our study identifies a distinct kink structure on the

Abbas Ali Saberi, Sina Saber, Roderich Moessner

We introduce a family of random matrices where correlations between matrix elements are induced via interaction-derived Boltzmann factors. Varying these yields access to different ensembles. We find a universal scaling behavior of the finite-size statistics characterized by a heavy-tailed eigenvalue distribution whose extremes are governed by the Fr\'echet e

Akif Ince, Marlon Moresco, Ilaria Peri, Silvana M. Pesenti

We provide a constructive way of defining new elicitable risk measures that are characterised by a multiplicative scoring function. We show that depending on the choice of the scoring function's components, the resulting risk measure possesses properties such as monotonicity, translation invariance, convexity, and positive homogeneity. Our framework encompas

Zhi-Han Zhang, Han-Chuan Kou, Peng Li

In this work, we focus on the critical dynamics of the one-dimensional quasiperiodic p-wave-paired Aubry-Andr\'{e}-Harper model, which exhibits a transition point between the gapped critical and the gapless localized phases. First, we disclose that the dynamical exponent features two distinct plateaus in the gapless localized phase. Besides the plateau with

Barbora Batíková, Tomáš J. Kepka, Petr C. Němec

Inspired by the proof of the Bertrand postulate given by P. Erd\H{o}S, we carefully examine and solve one less usual inequality in positive integers which could help to find an arithmetically pure proof that for every positive integer $n\ge2$ there is a prime $p$ such that $n<p<2n$.

Rumin Dong, Lin Zhu, Qin Sheng, Bingxin Zhao

This paper investigates quenching solutions of an one-dimensional, two-sided Riemann-Liouville fractional order convection-diffusion problem. Fractional order spatial derivatives are discretized using weighted averaging approximations in conjunction with standard and shifted Gr\"{u}nwald formulas. The advective term is handled utilizing a straightforward Eul

Akshay Chandran, Fabien Evrard, Berend van Wachem

Euler-Lagrange (EL) point-particle simulations rely on hydrodynamic force closure models to accurately predict particle dynamics in flows. The closure models currently employed for dilute particle-laden flows require the undisturbed fluid velocity estimated at the particle center. Recovering this undisturbed velocity necessitates modeling the particle-induce

Idan Kizel, Omri Meron, Dror Hershkovitz, Maayan Vizner Stern

The rhombohedral (r) polytypes of transition metal dichalcogenides (TMDs) constitute a novel class of two-dimensional ferroelectric materials, where lateral shifts between parallel layers induce reversible out-of-plane polarization. This emerging field, known as SlideTronics, holds significant potential for next-generation electronic and optoelectronic appli

ChenTong Wang, Jincheng Gao, Fei Zhu, Abderrahim Halimi

Transformers have shown significant success in hyperspectral unmixing (HU). However, challenges remain. While multi-scale and long-range spatial correlations are essential in unmixing tasks, current Transformer-based unmixing networks, built on Vision Transformer (ViT) or Swin-Transformer, struggle to capture them effectively. Additionally, current Transform

Kun Zhang, Peng Yun, Jun Cen, Junhao Cai

This survey provides a comprehensive review on recent advancements of generative learning models in robotic manipulation, addressing key challenges in the field. Robotic manipulation faces critical bottlenecks, including significant challenges in insufficient data and inefficient data acquisition, long-horizon and complex task planning, and the multi-modalit

Xavier Rivas, Narciso Román-Roy, Bartosz M. Zawora

A geometric framework, called multicontact geometry, has recently been developed to study action-dependent field theories. In this work, we use this framework to analyze symmetries in action-dependent Lagrangian and Hamiltonian field theories, as well as their associated dissipation laws. Specifically, we establish the definitions of conserved and dissipated

Ahmed Njifenjou, Virgile Sucal, Bassam Jabaian, Fabrice Lefèvre

The prevailing paradigm in the domain of Open-Domain Dialogue agents predominantly focuses on the English language, encompassing both models and datasets. Furthermore, the financial and temporal investments required for crowdsourcing such datasets for finetuning are substantial, particularly when multiple languages are involved. Fortunately, advancements in

George Peterzil, Guy Sapire

It is a classical result of Dal'Bo that the length spectrum of a non-elementary Fuchsian group is non-arithmetic, namely, it generates a dense additive subgroup of $\mathbb{R}$. In this note we provide an elementary proof of an extension of this theorem: a non-elementary Fuchsian group contains two elements whose lengths are linearly independent over $\mathb

Alessio Galatolo, Zhenbang Dai, Katie Winkle, Meriem Beloucif

Fine-tuning Large Language Models (LLMs) with first-order methods like back-propagation is computationally intensive. Zeroth-Order (ZO) optimisation uses function evaluations instead of gradients, reducing memory usage, but suffers from slow convergence in high-dimensional models. As a result, ZO research in LLMs has mostly focused on classification, overloo

Pengbo Hu, Xiang Ying

Large language models (LLMs) have recently demonstrated remarkable capabilities across domains, tasks, and languages (e.g., ChatGPT and GPT-4), reviving the research of general autonomous agents with human-like cognitive abilities. Such human-level agents require semantic comprehension and instruction-following capabilities, which exactly fall into the stren

Ryotaku Suzuki, Shinya Tomizawa

The topological censorship theorem suggests that higher dimensional black holes can possess the domain of outer communication (DOC) of nontrivial topology. In this paper, we seek for a black hole coexisting with two bubbles adjacent to the horizon in five-dimensional minimal supergravity, under the assumptions of stationarity and bi-axisymmetry. For simplici

Tristan Humbert

For a minimal Anosov $\mathbb R^{\kappa}$-action on a closed manifold, we study the measure of maximal entropy constructed by Carrasco and Rodriguez-Hertz in \cite{CarHer} and show that it fits into the theory of Ruelle-Taylor resonances introduced by Guedes Bonthonneau, Guillarmou, Hilgert, and Weich in \cite{GBGHW}. More precisely, we show that the topolog

Andrii Dmytryshyn, Massimiliano Fasi, Nicholas J. Higham, Xiaobo Liu

We consider the solution of the Sylvester equation $AX+XB=C$ in mixed precision. We derive a new iterative refinement scheme to solve perturbed quasi-triangular Sylvester equations; our rounding error analysis provides sufficient conditions for convergence and a bound on the attainable relative residual. We leverage this iterative scheme to solve the general

Keerthiga Rajenthiram, Milad Abdullah, Ilias Gerostathopoulos, Petr Hnetynka

Despite advancements in MLOps and AutoML, ML development still remains challenging for data scientists. First, there is poor support for and limited control over optimizing and evolving ML models. Second, there is lack of efficient mechanisms for continuous evolution of ML models which would leverage the knowledge gained in previous optimizations of the same

Ting-Wei Liao, Chih-Hsun Lin, Yu-Lin Tsai, Takao Murakami

Local Differential Privacy (LDP) has been widely adopted to protect user privacy in decentralized data collection. However, recent studies have revealed that LDP protocols are vulnerable to data poisoning attacks, where malicious users manipulate their reported data to distort aggregated results. In this work, we present the first study on data poisoning att

Patryk Rygiel, Julian Suk, Kak Khee Yeung, Christoph Brune

Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters can be accurately computed using computational fluid dynamics (CFD), but CFD is computationally intensive. Hence, deep learning methods have been adopted as a surrogate to rapidly es

Longfei Hao, Zhixuan Li, Faxin Shen, Yonghua Xu

In this paper, we present the linear decomposition method (LDM), which we developed to detect and analyze pulsar profile variations and mode changing behaviour. We developed LDM utilizing the likelihood function approach assuming the Gaussian noise. The LDM projects pulse profiles onto significance-ordered orthonormal vector bases. We show that the method is

Maria Tantalo, Giuseppe Bono, Maurizio Salaris, Adriano Pietrinferni

This paper presents a quantitative analysis of the stellar content in the Local Group dwarf irregular galaxy NGC 6822 by comparing stellar evolution models and observations in color-magnitude diagrams (CMDs) and color-color diagrams (CC-Ds). Our analysis is based on optical ground-based g,r,i photometry, and deep archive HST photometry of two fields in the g

Julia Gierke, Pascal Peter

The medial axis transform is a well-known tool for shape recognition. Instead of the object contour, it equivalently describes a binary object in terms of a skeleton containing all centres of maximal inscribed discs. While this shape descriptor is useful for many applications, it is also sensitive to noise: Small boundary perturbations can result in large un

Giammarco Caroleo, Alessandro Albini, Daniele De Martini, Timothy D. Barfoot

For several tasks, ranging from manipulation to inspection, it is beneficial for robots to localize a target object in their surroundings. In this paper, we propose an approach that utilizes coarse point clouds obtained from miniaturized VL53L5CX Time-of-Flight (ToF) sensors (tiny LiDARs) to localize a target object in the robot's workspace. We first conduct

Futaba Sato

In this paper, we investigate heat semigroups on a quantum automorphism group ${\rm Aut}^+(B)$ of a finite dimensional C*-algebra $B$ and its Plancherel trace. We show ultracontractivity, hypercontractivity, and the spectral gap inequality of the heat semigroups on ${\rm Aut}^+(B)$. Furthermore, we obtain the sharpness of the Sobolev embedding property and t

Szymon Królak, Michał J. Winiarski, Duygu Yazici, Soohyeon Shin

We have synthesized and characterized the physical properties of a layered, mixed valent oxypnictide $\mathrm{La_{3}Cu_{4}P_{4}O_{2}}$ via magnetization, electrical resistivity, and specific heat measurements. Although $\mathrm{La_{3}Cu_{4}P_{4}O_{2}}$ does not exhibit superconductivity down to T = 0.5 K, it demonstrates an intriguing resistivity minimum obs

Iris Dominguez-Catena, Daniel Paternain, Mikel Galar, MaryBeth Defrance

In recent years, the rapid development of artificial intelligence (AI) systems has raised concerns about our ability to ensure their fairness, that is, how to avoid discrimination based on protected characteristics such as gender, race, or age. While algorithmic fairness is well-studied in simple binary classification tasks on tabular data, its application t

Tony Zorman

In this note, we define an analogue of R-matrices for bialgebras in the setting of a monad that is opmonoidal over two tensor products. Analogous to the classical case, such structures bijectively correspond to duoidal structures on the Eilenberg--Moore category of the monad. Further, we investigate how a cocommutative version of this lifts the linearly dist

Eunkyung Choi, Youngjin Suh, Siun Lee, Hongseok Oh

How capable are large language models (LLMs) in the domain of taxation? Although numerous studies have explored the legal domain, research dedicated to taxation remains scarce. Moreover, the datasets used in these studies are either simplified, failing to reflect the real-world complexities, or not released as open-source. To address this gap, we introduce P

Isaac Roberts, Alexander Schulz, Sarah Schroeder, Fabian Hinder

Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncertainty, especially in high-dimensional settings, remain an open challenge. Existing work focuses on feature attribution approaches which are restricted to local explanations. Underst

Andrei Sipos

In 1983, Z\u{a}linescu showed that the squared norm of a uniformly convex normed space is uniformly convex on bounded subsets. We extend this result to the metric setting of uniformly convex hyperbolic spaces. We derive applications to the convergence of shadow sequences and to proximal minimization.

Yu He, Zifeng Wang, Jiaxu Li, Fenglin Zhong

Nonreciprocal transport in superconducting systems serves as a powerful probe of symmetry-breaking mechanisms, with the superconducting diode effect emerging as a key manifestation enabling cryogenic rectification. While theoretical models have extensively explored superconducting nonreciprocity, experimental verification remains challenging, as conventional