December 2024 arXiv papers — page 115

Manuel Haag, Florian Kurpicz, Peter Sanders, Matthias Schimek

We present first algorithmic ideas for a practical and lightweight adaption of the DCX suffix array construction algorithm [Sanders et al., 2003] to the distributed-memory setting. Our approach relies on a bucketing technique which enables a lightweight implementation which uses less than half of the memory required by the currently fastest distributed-memor

Jiahao Lyu, Wei Wang, Dongbao Yang, Jinwen Zhong

Scene text spotting has attracted the enthusiasm of relative researchers in recent years. Most existing scene text spotters follow the detection-then-recognition paradigm, where the vanilla detection module hardly determines the reading order and leads to failure recognition. After rethinking the auto-regressive scene text recognition method, we find that a

Qingshun Hu, Songmei Qin, Yangping Luo, Yuting Li

Context. The study of open cluster morphology is pivotal for exploring their formation and evolutionary processes. Aims. We manage to assess the morphological stability of 105 nearby open clusters within tidal radii on the X-Y, X-Z, and Y-Z planes of the heliocentric Cartesian coordinate system, utilizing member catalogs from the literature. Meanwhile, we al

Shuo Xie, Fangzhi Zhu, Jiahui Wang, Lulu Wen

Aligning Large Language Models (LLMs) with human feedback is crucial for their development. Existing preference optimization methods such as DPO and KTO, while improved based on Reinforcement Learning from Human Feedback (RLHF), are inherently derived from PPO, requiring a reference model that adds GPU memory resources and relies heavily on abundant preferen

Isam Ben Soltane, Nicolas Bonod

Resonances are common in wave physics and their full and rigorous characterization is crucial to correctly tailor the response of a system in both time and frequency domains. However, they have been conventionally described by the quality factor, a real-valued number quantifying the sharpness of a single peak in the amplitude spectrum, and associated with a

Nicolas Brantut

We analyse high resolution slip rate data obtained during dynamic shear rupture experiments by Berman et al. (2020). We use an inverse method to extract the details of strength evolution within the cohesive zone. The overall behaviour is slip-weakening at high rupture speeds ($>0.76C_\mathrm{R}$, where $C_\mathrm{R}$ is the Rayleigh wavespeed), but non-monot

Gemma Galdon Clavell, Rubén González-Sendino

Since July 5, 2023, New York City's Local Law 144 requires employers to conduct independent bias audits for any automated employment decision tools (AEDTs) used in hiring processes. The law outlines a minimum set of bias tests that AI developers and implementers must perform to ensure compliance. Over the past few months, we have collected and analyzed audit

Umar Khan, Saifullah, Stefan Agne, Andreas Dengel

Document classification is considered a critical element in automated document processing systems. In recent years multi-modal approaches have become increasingly popular for document classification. Despite their improvements, these approaches are underutilized in the industry due to their requirement for a tremendous volume of training data and extensive c

Emma Reznick, T. Kevin Best, Robert Gregg

Configuring a prosthetic leg is an integral part of the fitting process, but the personalization of a multi-modal powered knee-ankle prosthesis is often too complex to realize in a clinical environment. This paper develops both the technical means to individualize a hybrid kinematic-impedance controller for variable-incline walking and sit-stand transitions,

Weixiang Zhang, Shuzhao Xie, Chengwei Ren, Siyi Xie

We propose EVOlutionary Selector (EVOS), an efficient training paradigm for accelerating Implicit Neural Representation (INR). Unlike conventional INR training that feeds all samples through the neural network in each iteration, our approach restricts training to strategically selected points, reducing computational overhead by eliminating redundant forward

Francesco Chiariello, Valeria Fionda, Antonio Ielo, Francesco Ricca

Answer Set Programming (ASP), a well-known declarative logic programming paradigm, has recently found practical application in Process Mining. In particular, ASP has been used to model tasks involving declarative specifications of business processes. In this area, Declare stands out as the most widely adopted declarative process modeling language, offering a

Hyeonseok Lim, Dongjae Shin, Seohyun Song, Inho Won

We propose the VLR-Bench, a visual question answering (VQA) benchmark for evaluating vision language models (VLMs) based on retrieval augmented generation (RAG). Unlike existing evaluation datasets for external knowledge-based VQA, the proposed VLR-Bench includes five input passages. This allows testing of the ability to determine which passage is useful for

I. V. Voronchikhin, D. V. Kirpichnikov

The link between Standard Model (SM) particles and dark matter (DM) can be introduced via spin-2 massive mediator, G, that couples to photon and charged leptons. Moreover, in a mediator mass range from sub-MeV to sub-GeV, fixed-target facilities such as NA64e, LDMX, NA64$\mu$, M$^3$, and E137, can potentially probe such particle of the hidden sector via the

Jingkun Shen, Lucy E. Walker, Kevin Ma, James D. Green

Emissive organic radicals are currently of great interest for their potential use in the next generation of highly efficient organic light emitting diode (OLED) devices and as molecular qubits. However, simulating their optoelectronic properties is challenging, largely due to spin-contamination and the multireference character of their excited states. Here w

Shiki Ogata, Shunsaku Kitagawa, Katsuki Kinjo, Kenji Ishida

CeRh$_2$As$_2$ shows the superconducting (SC) multiphase under the $c$-axis magnetic field, which is considered to originate from local inversion symmetry breaking at the Ce site. We reported that the antiferromagnetic (AFM) order is inside the SC phase and that the AFM state disappears at the transition field to the high-field SC phase. However, the magneti

Yuta Sekino, Yuya Ominato, Hiroyuki Tajima, Shun Uchino

We study magnon-driven spin and heat transport in a magnetic linear junction (MLJ) formed by two ferromagnets in optical lattices linked via linearly aligned bonds. Using the Schwinger-Keldysh formalism, we uncover that under weak effective Zeeman fields, where Bose-Einstein statistics of magnons dominate, magnonic criticality dramatically enhances spin and

Nikita Gabdullin

This paper studies generalization capabilities of neural networks (NNs) using new and improved PyTorch library Loss Landscape Analysis (LLA). LLA facilitates visualization and analysis of loss landscapes along with the properties of NN Hessian. Different approaches to NN loss landscape plotting are discussed with particular focus on normalization techniques

Ryan Martin, Jonathan P. Williams

The inferential model (IM) framework offers an alternative to the classical probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. A key distinction is that classical uncertainty quantification takes the form of precise probabilities and offers only limited large-sample validity guarantees, whereas the IM's uncertain

Wladislaw Krinitsin, Niklas Tausendpfund, Matteo Rizzi, Markus Heyl

The properties of interfaces are key to understand the physics of matter. However, the study of quantum interface dynamics has remained an outstanding challenge. Here, we use large-scale Tree Tensor Network simulations to identify the dynamical signature of an interface roughening transition within the ferromagnetic phase of the 2D quantum Ising model. For i

Albert Chau, Ben Weinkove

The $p$-Laplacian evolution equation and the $\alpha$-Gauss curvature flow with a flat side are degenerate parabolic equations with evolving free boundaries. We give proofs of smooth short-time existence, up to the free boundaries, using a result of the authors on linear degenerate equations on a fixed domain.

Louis-Simon Guité, Antoine Strugarek, Paul Charbonneau

Sympathetic solar flares are eruptions that occur nearby in space and time, driven by an apparent interaction between the active regions in which they are triggered. Their statistical existence on the Sun has yet to be firmly established. The main goal of this paper is to identify a statistical signature of sympathetic flares, characterize their properties a

Dirk Hennig

We study the $d$-dimensional discrete nonlinear Schr\"odinger equation with general power nonlinearity and a delta potential. Our interest lies in the interplay between two localization mechanisms. On the one hand, the attractive (repulsive) delta potential acting as a point defect breaks the translational invariance of the lattice so that a linear staggerin

Xiaochun Fang, Haotian Tian

In this paper, let $A$ be a unital separable simple infinite dimensional C*-algebra which has uniform property $\Gamma$. Let $\alpha\colon G\to \mathrm{Aut}(A)$ be an action of a finite group which has the weak tracial Rokhlin property. Then we prove that the crossed product $A\rtimes_\alpha G$ and fixed point algebra $A^\alpha$ have uniform property $\Gamma

Giulia Ripellino, Magdalena Vande Voorde, Axel Gallén, Rebeca Gonzalez Suarez

This paper investigates the search for long-lived dark scalars from exotic Higgs boson decays at the Future Circular Collider in its $e^+e^-$ stage, FCC-ee, considering an integrated luminosity of 10.8 $\text{ab}^{-1}$ collected during the ZH run at a center-of-mass energy $\sqrt{s}=240$ GeV. The work considers $Zh$ events where the $Z$ boson decays leptonic

Kolja Joeris, Laurent Schönau, Matthias Keulen, Jonathan E. Kollmer

We examine ejecta generated by ultra low velocity impacts under asteroid conditions. In an environment of precisely controlled milligravity and under vacuum, impacts with velocities in the range of centimeters/second are performed with irregularly shaped impactors onto granular beds. The resulting ejecta velocities are compared to existing literature values

Yang Qin, Chao Chen, Zhihang Fu, Ze Chen

Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-context learning of closed-source LLMs (e.g., GPT-4), which limits their applicability in open scenarios. To address this challenge, we propose a novel RObust mUltitask Tuning and colla

Kehan Chen, Dong An, Yan Huang, Rongtao Xu

We address the task of Vision-Language Navigation in Continuous Environments (VLN-CE) under the zero-shot setting. Zero-shot VLN-CE is particularly challenging due to the absence of expert demonstrations for training and minimal environment structural prior to guide navigation. To confront these challenges, we propose a Constraint-Aware Navigator (CA-Nav), w

Zehong Wang, Sidney Liu, Zheyuan Zhang, Tianyi Ma

Graphs are ubiquitous structures found in numerous real-world applications, such as drug discovery, recommender systems, and social network analysis. To model graph-structured data, graph neural networks (GNNs) have become a popular tool. However, existing GNN architectures encounter challenges in cross-graph learning where multiple graphs have different fea

Junyan Hu, Xue Xiao, Mengqi Zhang, Yao Chen

As large language models (LLMs) grow in size, traditional full fine-tuning becomes increasingly impractical due to its high computational and storage costs. Although popular parameter-efficient fine-tuning methods, such as LoRA, have significantly reduced the number of tunable parameters, there is still room for further optimization. In this work, we propose

Islem Bouzenia, Michael Pradel

The ability to execute the test suite of a project is essential in many scenarios, e.g., to assess code quality and code coverage, to validate code changes made by developers or automated tools, and to ensure compatibility with dependencies. Despite its importance, executing the test suite of a project can be challenging in practice because different project

Robbie G. Hunt, Dmitrii Moldarev, Matías P. Grassi, Daniel Primetzhofer

The tuning of magnetic properties through electrochemical loading of hydrogen has recently attracted significant interest as a way to manipulate magnetic devices with electric fields. In this paper we investigate quantitatively the magneto-ionic effect of hydrogen uptake on the magnetic properties of rare-earth transition metal alloy Tb$_x$Co$_{(100-x)}$ in

Benjamin Ricketts, Daniela Huppenkothen, Matteo Lucchini, Adam Ingram

Bayesian analysis has begun to be more widely adopted in X-ray spectroscopy, but it has largely been constrained to relatively simple physical models due to limitations in X-ray modelling software and computation time. As a result, Bayesian analysis of numerical models with high physics complexity have remained out of reach. This is a challenge, for example

Rasmus Pagh, Lukas Retschmeier, Hao Wu, Hanwen Zhang

We study the problem of privately releasing an approximate minimum spanning tree (MST). Given a graph $G = (V, E, \vec{W})$ where $V$ is a set of $n$ vertices, $E$ is a set of $m$ undirected edges, and $ \vec{W} \in \mathbb{R}^{|E|} $ is an edge-weight vector, our goal is to publish an approximate MST under edge-weight differential privacy, as introduced by

Ander Biguri, Tomoyuki Sadakane, Reuben Lindroos, Yi Liu

Computed Tomography (CT) has been widely adopted in medicine and it is increasingly being used in scientific and industrial applications. Parallelly, research in different mathematical areas concerning discrete inverse problems has led to the development of new sophisticated numerical solvers that can be applied in the context of CT. The Tomographic Iterativ

Rittwika Kansabanik, Adrian Barbu

Feature selection is crucial for pinpointing relevant features in high-dimensional datasets, mitigating the 'curse of dimensionality,' and enhancing machine learning performance. Traditional feature selection methods for classification use data from all classes to select features for each class. This paper explores feature selection methods that select featu

Hari Mohan Pandey

Large Language Models (LLMs) are transforming mental health care by enhancing accessibility, personalization, and efficiency in therapeutic interventions. These AI-driven tools empower mental health professionals with real-time support, improved data integration, and the ability to encourage care-seeking behaviors, particularly in underserved communities. By

Joachim Falck Brodin, Kevin Pierce, Paula Reis, Per Arne Rikvold

We present an experimental study of immiscible, two-phase fluid flow through a three-dimensional porous medium consisting of randomly-packed, monodisperse glass spheres. Our experiments combine refractive-index matching and laser-induced fluorescence imaging to resolve the morphology and stability of the moving interface resulting from the injection of one f

K. Matsuura, S. Yasui, R. Kaji, H. Sasakura

The dependence of the magnetic fluctuations and the spin coherence time $T_2^{\rm hyp}$ of the lowest Stark states $^4I_{15/2}\ (Z_1)$ in $^{167}$Er$^{3+}$:Y$_2$SiO$_5$ under zero magnetic field on Er concentration is numerically investigated in the range of 10 to 100 parts per million (ppm). We investigate two primary sources of magnetic fluctuation limitin

Dimitrios Gousopoulos

Generative Artificial Intelligence (GenAI) has emerged as a valuable assistant in many fields such as marketing, finance, project management, and education. In education, many GenAI tools have been developed to aid teachers in preparing proper educational material and offering personalized learning to their students, tailored to their educational needs. In t

Igor Kolokolov, Victor S. L'vov, Anna Pomyalov

Recent developments in spintronics have drawn renewed attention to the spin dynamics of cubic ferromagnetic crystals EuO and EuS. These ferromagnets have the simplest possible magnetic structure, making them the most suitable systems for testing various theoretical models of magnetic materials. A commonly used Weiss mean-field approximation (MFA) provides on

Anael Ben-Asher, Thomas Schnappinger, Markus Kowalewski, Johannes Feist

The ability to control chemical reactions by coupling organic molecules to confined light in a cavity has recently attracted much attention. While most previous studies have focused on single-mode photonic or plasmonic cavities, here we investigate the effect of hybrid metallodielectric cavities on photoisomerization reactions. Hybrid cavities, which support

Alex Gomez-Villa, Kai Wang, Alejandro C. Parraga, Bartlomiej Twardowski

Visual illusions in humans arise when interpreting out-of-distribution stimuli: if the observer is adapted to certain statistics, perception of outliers deviates from reality. Recent studies have shown that artificial neural networks (ANNs) can also be deceived by visual illusions. This revelation raises profound questions about the nature of visual informat

Jonas Golde, Patrick Haller, Max Ploner, Fabio Barth

Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as 'Person' or 'Medicine') without any training examples. Current research increasingly relies on large synthetic datasets, automatically generated to cover tens of thousands of distinct entity types, to train zero-shot NER models. However, in this paper,

Netzer Moriya

Efficient algorithms for solving the Smallest Enclosing Sphere (SES) problem, such as Welzl's algorithm, often fail to handle degenerate subsets of points in 3D space. Degeneracies and ill-posed configurations present significant challenges, leading to failures in convergence, inaccuracies or increased computational cost in such cases. Existing improvements

Louis Chislett, Catalina A. Vallejos, Timothy I. Cannings, James Liley

Prediction models frequently face the challenge of concept drift, in which the underlying data distribution changes over time, weakening performance. Examples can include models which predict loan default, or those used in healthcare contexts. Typical management strategies involve regular model updates or updates triggered by concept drift detection. However

Anton I. Gorokhov, Evgeny A. Perevezentsev, Mikhail R. Volkov, Ivan B. Mukhin

A comprehensive diagnostics of new-generation dispersive elements based on Bragg structures was completed. The influence of inscription parameters on the properties of samples was studied and a threshold inscription intensity of ~ 6.38 TW/cm2 at which the linear absorption coefficient as well as the phase and polarization stresses sharply increase was found.

Zhihao Du, Yuxuan Wang, Qian Chen, Xian Shi

In our previous work, we introduced CosyVoice, a multilingual speech synthesis model based on supervised discrete speech tokens. By employing progressive semantic decoding with two popular generative models, language models (LMs) and Flow Matching, CosyVoice demonstrated high prosody naturalness, content consistency, and speaker similarity in speech in-conte

Zican Shi, Jing Hu, Jie Ren, Hengkang Ye

The introduction of Feature Pyramid Network (FPN) has significantly improved object detection performance. However, substantial challenges remain in detecting tiny objects, as their features occupy only a very small proportion of the feature maps. Although FPN integrates multi-scale features, it does not directly enhance or enrich the features of tiny object

Zining Chen, Xingshuang Luo, Weiqiu Wang, Zhicheng Zhao

Recent Anomaly Detection (AD) methods have achieved great success with In-Distribution (ID) data. However, real-world data often exhibits distribution shift, causing huge performance decay on traditional AD methods. From this perspective, few previous work has explored AD with distribution shift, and the distribution-invariant normality learning has been pro

Yunus Waheed, Sumitra Shit, Jithin T Surendran, Indrajeet D Prasad

Transition metal dichalcogenides and related layered materials in their monolayer and a few layers thicknesses regime provide a promising optoelectronic platform for exploring the excitonic- and many-body physics. Strain engineering has emerged as a potent technique for tuning the excitonic properties favorable for exciton-based devices. We have investigated

Antonino Ficarra, Somayeh Moradi

Let $\Gamma$ be a $d$-flag sortable simplicial complex. We consider the toric ring $R_{\Gamma}=K[{\bf x}_Ft:F\in \Gamma]$ and the Rees algebra of the facet ideals $I(\Gamma^{[i]})$ of pure skeletons of $\Gamma$. We show that these algebras are Koszul, normal Cohen-Macaulay domains. Moreover, we study the Gorenstein property, the canonical module, and the $a$

Chethan N. Gowdigere, Sachin Kala, Jagannath Santara

We study one-character CFTs obtained as one-character extensions of the tensor products of a single CFT $\mathcal{C}$. The motivation comes from the fact that $28$ of the $71$ CFTs in the Schelleken's list of $c = 24$ CFTs are such CFTs. We study for $\mathcal{C}$ : (i) any two-character WZW CFT with vanishing Wronskian index, (ii) the Ising CFT, (iii) the i

M. Asorey, F. Ezquerro, M. Pardina

We introduce a large family of homogeneous and isotropic cosmological solutions in quadratic gravity which are singularity-free at early and late times. This kind of smooth solutions only emerges beyond the unstable de Sitter branch $3\alpha < \beta$, $\alpha$ being the coupling of the $R^2$ term and $-\beta$ the coupling of the $R_{\mu\nu}^2$ term. We have

Guanghua Hou, Shuhui Cao, Deqiang Ouyang, Ning Wang

As an algorithmic framework for learning to learn, meta-learning provides a promising solution for few-shot text classification. However, most existing research fail to give enough attention to class labels. Traditional basic framework building meta-learner based on prototype networks heavily relies on inter-class variance, and it is easily influenced by noi

I. Afanasieva, V. Murzin, V. Ardilanov, N. Ivaschenko

The pCam6060 photodetecting system was developed at SAO RAS and is based on the GSENSE6060BSI photodetector manufactured by GPixel (China) with a frame format of 6144x6144 active pixels and a pixel size of 10 mkm. The readout speed reached 11 fps. The back-illuminated detector has a wide spectral range of 200-1040 nm with a minimum quantum efficiency (QE) of

Tomáš Faikl

Negative-index metamaterials possess a negative refractive index and thus present an interesting substance for designing uncommon optical effects such as invisibility cloaking. This paper deals with operators encountered in an operator-theoretic description of metamaterials. First, we introduce an indefinite Laplacian and consider it on a compact tubular nei

Asmaa Abdallah, Abdullatif Albaseer, Abdulkadir Celik, Mohamed Abdallah

The transition to 6G networks promises unprecedented advancements in wireless communication, with increased data rates, ultra-low latency, and enhanced capacity. However, the complexity of managing and optimizing these next-generation networks presents significant challenges. The advent of large language models (LLMs) has revolutionized various domains by le

Ayush Deshmukh

The global outbreak of the Mpox virus, classified as a Public Health Emergency of International Concern (PHEIC) by the World Health Organization, presents significant diagnostic challenges due to its visual similarity to other skin lesion diseases. Traditional diagnostic methods for Mpox, which rely on clinical symptoms and laboratory tests, are slow and lab

Sagi Shaier, George Arthur Baker, Chiranthan Sridhar, Lawrence E Hunter

Language models (LMs) have excelled in various broad domains. However, to ensure their safe and effective integration into real-world educational settings, they must demonstrate proficiency in specific, granular areas of knowledge. Existing cloze-style benchmarks, commonly used to evaluate LMs' knowledge, have three major limitations. They: 1) do not cover t

Zhensheng Wang, Wenmian Yang, Kun Zhou, Yiquan Zhang

The real estate market relies heavily on structured data, such as property details, market trends, and price fluctuations. However, the lack of specialized Tabular Question Answering datasets in this domain limits the development of automated question-answering systems. To fill this gap, we introduce RETQA, the first large-scale open-domain Chinese Tabular Q

Xiyuan Gao, Shubhi Bansal, Kushaan Gowda, Zhu Li

Detecting sarcasm effectively requires a nuanced understanding of context, including vocal tones and facial expressions. The progression towards multimodal computational methods in sarcasm detection, however, faces challenges due to the scarcity of data. To address this, we present AMuSeD (Attentive deep neural network for MUltimodal Sarcasm dEtection incorp

Tom Kaufmann, Johann Reger

The update law in the indirect adaptive control scheme can be extended to include feedthrough of an error term. This reduces undesired oscillations of the calculated weights. When the ${\sigma}$-modification is used for achieving robustness against unstructured uncertainties, the gain of the feedthrough in the update law cannot be chosen arbitrarily. Compare

Enrico Bertuzzo, Michele Frigerio

Massive sterile neutrinos, also known as heavy neutral leptons, can have a mixing with active neutrinos, $\theta$, as well as a dipole coupling to the photon, $d$. We study the interplay between these two portals, considering the production from meson decays of sterile neutrinos with mass $0.1$ GeV $\lesssim M_N \lesssim 10$ GeV, at beam-dump facilities such

P. Jucha, K. Mazurek, M. Klusek-Gawenda, M. Ciemala

In ultraperipheral heavy-ion collisions (UPCs) at the Large Hadron Collider (LHC) Pb nuclei are excited through interactions induced by strong electromagnetic fields. The expected excitation energy could reach hundreds MeV, which leads to the subsequent emission of various particles, including neutrons, protons, and alpha particles. To accurately describe de

Berend Markhorst, Markus Leitner, Joost Berkhout, Alessandro Zocca

Two-stage stochastic programs become computationally challenging when the number of scenarios representing parameter uncertainties grows. Motivated by this, we propose the TULIP-algorithm ("Two-step warm start method Used for solving Large-scale stochastic mixed-Integer Problems"), a two-step approach for solving two-stage stochastic (mixed) integer linear p

Anji Dong, Katerina Saettone, Kendra Song, Alexandru Zaharescu

We provide an asymptotic formula for the average value of the sequence A351830: $a_{n} = |P_{n} - y^{2}_{n}|$ for $1 \leq n \leq x$, where $P_{n}$ is the $n$-th square pyramidal number and $y^{2}_{n}$ is the closest square to $P_{n}$. Moreover, we supply asymptotic formulas for the $k$-th moment of the same sequence, for any fixed natural number $k$.

Mattijs Baert, Sam Leroux, Pieter Simoens

Learning from Demonstrations (LfD) and Reinforcement Learning (RL) have enabled robot agents to accomplish complex tasks. Reward Machines (RMs) enhance RL's capability to train policies over extended time horizons by structuring high-level task information. In this work, we introduce a novel LfD approach for learning RMs directly from visual demonstrations o

Jaione Bengoetxea, Mikel Zubillaga, Ekhi Azurmendi, Maite Heredia

In this paper we present our submission for the NorSID Shared Task as part of the 2025 VarDial Workshop (Scherrer et al., 2025), consisting of three tasks: Intent Detection, Slot Filling and Dialect Identification, evaluated using data in different dialects of the Norwegian language. For Intent Detection and Slot Filling, we have fine-tuned a multitask model

John Hertz, Joanna Tyrcha

We examine learning dynamics in deep recurrent networks, focusing on the behavior near the boundary in the depth-width plane separating under- from over-parametrized networks, known as the interpolation transition. The training data are Bach chorales in 4-part harmony, and the learning is by stochastic gradient descent with a cross-entropy loss function. We

N. Sahakyan

Artificial intelligence (AI) is revolutionizing research by enabling the efficient analysis of large datasets and the discovery of hidden patterns. In astrophysics, AI has become essential, transforming the classification of celestial sources, data modeling, and the interpretation of observations. In this review, I highlight examples of AI applications in as

Jeffrey Sardina, John D. Kelleher, Declan O'Sullivan

Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typically applied to KGs to solve the link prediction task; i.e. to predict new facts in the domain of a KG based on existing, observed facts. While

Zi Yang, Haojin Yang, Soumajit Majumder, Jorge Cardoso

Previous studies have demonstrated that not each sample in a dataset is of equal importance during training. Data pruning aims to remove less important or informative samples while still achieving comparable results as training on the original (untruncated) dataset, thereby reducing storage and training costs. However, the majority of data pruning methods ar

Koji Matsushita, Akiyoshi Tsuchiya

The codegree ${\rm codeg}(\mathcal{P})$ of a lattice polytope $\mathcal{P}$ is a fundamental invariant in discrete geometry. In the present paper, we investigate the codegree of the stable set polytope $\mathcal{P}_G$ associated with a simple graph $G$. Specifically, we establish the inequalities \[ \omega(G) + 1 \leq {\rm codeg}(\mathcal{P}_G) \leq \chi(G)

Meng Cao, Songcan Chen

Domain generalization addresses domain shift in real-world applications. Most approaches adopt a domain angle, seeking invariant representation across domains by aligning their marginal distributions, irrespective of individual classes, naturally leading to insufficient exploration of discriminative information. Switching to a class angle, we find that multi

Zilong Gong, Junyu Mao, Adrià Junyent-Ferré, Giordano Scarciotti

This paper proposes a data-driven algorithm for model order reduction (MOR) of large-scale wind farms and studies the effects that the obtained reduced-order model (ROM) has when this is integrated into the power grid. With respect to standard MOR methods, the proposed algorithm has the advantages of having low computational complexity and not requiring any

Xuekai Qiu, Pengming Zhu, Yiming Hu, Zhiwen Zeng

This paper presents a consensus-based payload algorithm (CBPA) to deal with the condition of robots' capability decrease for multi-robot task allocation. During the execution of complex tasks, robots' capabilities could decrease with the consumption of payloads, which causes a problem that the robot coalition would not meet the tasks' requirements in real ti

Simone Franchini

In this thesis we study in detail the self-intersection properties of Random Walks. Although notoriously hard to tackle, these properties are crucially related to the excluded-volume effect and other central features of real polymers. Our main purpose will be to study ideal chains (Random Walks) on lattice where the ratio between the number of self-intersect

Luciana F. Martins, Samuel P. dos Santos

We investigate helicoidal surfaces in three-dimensional Euclidean space whose profile curves are frontals. Using the framework of Legendre curves and framed surfaces, we establish conditions under which helicoidal surfaces generated by frontals are themselves frontals or fronts. We then derive curvature expressions in terms of the invariants of the generatin

Dennis Christian Kirsch, Anastasia Bednyakova, Maria Chernysheva

Vector solitons present unique optical pulses characterised by coupled orthogonal polarisation components, offering rich, multidimensional dynamics. Their enhanced degrees of freedom make them highly versatile for exploring nonlinear wave phenomena and enabling advanced applications in optical communications and ultrafast technologies. Despite significant re

Briac Toussaint, Diego Thomas, Jean-Sébastien Franco

SDF-based differential rendering frameworks have achieved state-of-the-art multiview 3D shape reconstruction. In this work, we re-examine this family of approaches by minimally reformulating its core appearance model in a way that simultaneously yields faster computation and increased performance. To this goal, we exhibit a physically-inspired minimal radian

Dolev Mutzari, Yonatan Aumann, Sarit Kraus

Multi-Robot Coverage problems have been extensively studied in robotics, planning and multi-agent systems. In this work, we consider the coverage problem when there are constraints on the proximity (e.g., maximum distance between the agents, or a blue agent must be adjacent to a red agent) and the movement (e.g., terrain traversability and material load capa

Sina Ghasemi Nezhad, Maryam Moghaddas, Fahad Panolan

In this paper, we investigate the \textsc{Grundy Coloring} problem for graphs with a cluster modulator, a structure commonly found in dense graphs. The Grundy chromatic number, representing the maximum number of colors needed for the first-fit coloring of a graph in the worst-case vertex ordering, is known to be $W[1]$-hard when parameterized by the number o

Kenneth M. Lanzetta, Stefan Gromoll, Michael M. Shara, Oleksii Sololiuk

Most of the baryonic matter of the Universe resides in a highly-ionized gaseous intergalactic medium. This gas flows along dark-matter filaments toward galaxy superclusters, clusters, and groups until it pools around the galaxies into a circumgalactic medium. Eventually, the gas settles into the interstellar medium of the galaxies, where it fuels the success

Oleg M. Smirnov, Sphesihle Makhathini, Jonathan S. Kenyon, Hertzog L. Bester

Stimela2 is a new-generation framework for developing data reduction workflows. It is designed for radio astronomy data but can be adapted for other data processing applications. Stimela2 aims at the middle ground between ease of development, human readability, and enabling robust, scalable and reproducible workflows. It represents workflows by linear, conci

George Arthur Baker, Ankush Raut, Sagi Shaier, Lawrence E Hunter

Previous work finds that recent long-context language models fail to make equal use of information in the middle of their inputs, preferring pieces of information located at the tail ends which creates an undue bias in situations where we would like models to be equally capable of using different parts of the input. Thus far, the problem has mainly only been

Robert Wasenmüller, Kevin Hilbert, Christoph Benzmüller

Large Language Model (LLM)-Powered Conversational Agents have the potential to provide users with scaled behavioral healthcare support, and potentially even deliver full-scale "AI therapy'" in the future. While such agents can already conduct fluent and proactive emotional support conversations, they inherently lack the ability to (a) consistently and reliab

Xiaohan Zhang, Zhenyu Sun, Yukui Qiu, Junyan Su

Currently, 3D rendering for large-scale free camera trajectories, namely, arbitrary input camera trajectories, poses significant challenges: 1) The distribution and observation angles of the cameras are irregular, and various types of scenes are included in the free trajectories; 2) Processing the entire point cloud and all images at once for large-scale sce

Andreas Herklotz, Robert Roth, Zhi Xiang Chong, Liang Luo

Domain engineering in ferroelectric thin films is crucial for next-generation microelectronic and photonic technologies. Here, a method is demonstrated to precisely control domain configurations in BaTiO$_3$ thin films through low-energy He ion implantation. The approach transforms a mixed ferroelectric domain state with significant in-plane polarization int

Y. Fang, J. Kuttruff, P. Baum

Angular momentum and torque are important principles for basic and applied physics on any spatial scales, for example, in elementary particles, cold gases, optical tweezers, quantum information technology, metamaterials, gyroscopes or astrophysical entities. Investigating or controlling angular momentum in atoms or sub-atomic structures requires torque on fe

Amadeusz Dydniański, Aleksandra Łopion, Mateusz Raczyński, Tomasz Kazimierczuk

In this work, we study the impact of carrier localization on three quantities sensitive to carrier gas density at the micrometer scale: charged exciton (X+) oscillator strength, local free carrier conductivity, and the Knight shift. The last two are observed in a micrometer-scale, spatially resolved optically detected magnetic resonance experiment (ODMR). On

Zhen Wang, Bao-Zhi Sun, Shao-Ming Fei, Zhi-Xi Wang

The Schmidt number characterizes the quantum entanglement of a bipartite mixed state and plays a significant role in certifying entanglement of quantum states. We derive a Schmidt number criterion based on the trace norm of the correlation matrix obtained from the general symmetric informationally complete measurements. The criterion gives an effective way t

Hertzog L. Bester, Jonathan S. Kenyon, Audrey Repetti, Simon J. Perkins

The popularity of the CLEAN algorithm in radio interferometric imaging stems from its maturity, speed, and robustness. While many alternatives have been proposed in the literature, none have achieved mainstream adoption by astronomers working with data from interferometric arrays operating in the big data regime. This lack of adoption is largely due to incre

Jonathan S. Kenyon, Simon J. Perkins, Hertzog L. Bester, Oleg M. Smirnov

Calibration of radio interferometer data ought to be a solved problem; it has been an integral part of data reduction for some time. However, as larger, more sensitive radio interferometers are conceived and built, the calibration problem grows in both size and difficulty. The increasing size can be attributed to the fact that the data volume scales quadrati

Raju Bhakta, Nuria Torrado, Sangita Das, Suchandan Kayal

In this article, we introduce finite mixture models (FMMs) renowned for capturing population heterogeneity. Our focus lies in establishing stochastic comparisons between two arithmetic (finite) mixture models, employing the vector majorization concept in the context of various univariate orders of magnitude, transform, and variability. These comparisons are

Matteo Ripoli, Eduardo Ahedo, Mario Merino

Magnetic nozzles are a key component of electrodeless plasma thrusters, acting as their main acceleration stage. Non-stationary phenomena common to the entire range of $E \times B$ devices, such as oscillations and instabilities, are likely to exist in the magnetic nozzle, according to the mounting experimental evidence. These mechanisms could lead to anomal

Chenyu Dong, Gabriele Messori, Davide Faranda, Adriano Gualandi

Complex systems span multiple spatial and temporal scales, making their dynamics challenging to understand and predict. This challenge is especially daunting when one wants to study localized and/or rare events. Advances in dynamical systems theory, including the development of state-dependent dynamical indices, namely local dimension and persistence, have p

Pedro Cardoso, Patrícia Gonçalves

The purpose of this article is to derive the crossover from the Ornstein-Uhlenbeck process to energy solutions of the stochastic Burgers equation with characteristic operators given in terms of fractional operators, such as the regional fractional Laplacian. The approach is to consider a boundary driven exclusion process with long jumps and asymmetric jump r

Antonio Azzolini, Alessio Pomponio, Simone Secchi

In this paper we study the embedding properties for the weighted Sobolev space $H^1_V(\mathbb{R}^N)$ into the Lebesgue weighted space $L^\tau_W(\mathbb{R}^N)$. Here $V$ and $W$ are diverging weight functions. The different behaviour of $V$ with respect to $W$ at infinity plays a crucial role. Particular attention is paid to the case $V=W$. This situation is

Hendrik Leidinger, Christoph Weidenbach

Congruence closure on ground equations is a well-established and efficient algorithm for deciding ground equalities. It constructs an explicit representation of ground equivalence classes based on a given set of input equations, allowing ground equalities to be decided by membership. In many applications, these ground equations originate from grounding non-g

Vinay Kumar, Pallavi Bhat

Magnetic reconnection, a fundamental plasma process, is pivotal in understanding energy conversion and particle acceleration in astrophysical systems. While extensively studied in two-dimensional (2D) configurations, the dynamics of reconnection in three-dimensional (3D) systems remain under-explored. In this work, we extend the classical tearing mode instab