December 2024 arXiv papers — page 59

Alexander Katsevich

Local reconstruction analysis (LRA) is a powerful and flexible technique to study images reconstructed from discrete generalized Radon transform (GRT) data, $g=\mathcal R f$. The main idea of LRA is to obtain a simple formula to accurately approximate an image, $f_\epsilon(x)$, reconstructed from discrete data $g(y_j)$ in an $\epsilon$-neighborhood of a poin

Akshit Singh, Karan Bhakuni, Rajendra Nagar

Neural distance fields (NDF) have emerged as a powerful tool for addressing challenges in 3D computer vision and graphics downstream problems. While significant progress has been made to learn NDF from various kind of sensor data, a crucial aspect that demands attention is the supervision of neural fields during training as the ground-truth NDFs are not avai

He Jiang, Muhan Lin, Jiaoyang Li

Multi-Agent Path Finding (MAPF) focuses on planning collision-free paths for multiple agents. However, during the execution of a MAPF plan, agents may encounter unexpected delays, which can lead to inefficiencies, deadlocks, or even collisions. To address these issues, the Switchable Temporal Plan Graph provides a framework for finding an acyclic Temporal Pl

Yosuke Yamagishi, Yuta Nakamura, Tomohiro Kikuchi, Yuki Sonoda

Background: Recent advances in large language models highlight the need for high-quality multilingual medical datasets. While Japan leads globally in CT scanner deployment and utilization, the lack of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Objective: To develop a compr

Filippo de Feo, Salvatore Federico, Fausto Gozzi, Nizar Touzi

We examine the sensitivity at the origin of the distributional robust optimization problem in the context of a model generated by a mean field stochastic differential equation. We adapt the finite dimensional argument developed by Bartl, Drapeau, Obloj \& Wiesel to our framework involving the infinite dimensional gradient of the solution of the mean field SD

Syafiq Al Atiiq, Christian Gehrmann, Kevin Dahlén

Vulnerability detection is crucial for maintaining software security, and recent research has explored the use of Language Models (LMs) for this task. While LMs have shown promising results, their performance has been inconsistent across datasets, particularly when generalizing to unseen code. Moreover, most studies have focused on the C/C++ programming lang

Yiran Ma, Zui Chen, Tianqiao Liu, Mi Tian

Step-level reward models (SRMs) can significantly enhance mathematical reasoning performance through process supervision or step-level preference alignment based on reinforcement learning. The performance of SRMs is pivotal, as they serve as critical guidelines, ensuring that each step in the reasoning process is aligned with desired outcomes. Recently, Alph

Soroush Arabi, Qili Li, Ritika Dhundhwal, Dirk Fuchs

In the fabrication of superconducting devices, both in situ and ex situ processes are utilized, making the removal of unwanted oxide layers and impurities under vacuum conditions crucial. Oxygen descumming and argon milling are standard in situ cleaning methods employed for device preparation. We investigated the impact of these techniques on tantalum superc

Lorenz Wendlinger, Christian Braun, Abdullah Al Zubaer, Simon Alexander Nonn

We show that current open-source foundational LLMs possess instruction capability and German legal background knowledge that is sufficient for some legal analysis in an educational context. However, model capability breaks down in very specific tasks, such as the classification of "Gutachtenstil" appraisal style components, or with complex contexts, such as

Tommaso Di Francesco, Daniel Torren Peraire

This paper investigates the interplay between information diffusion in social networks and its impact on financial markets with an Agent-Based Model (ABM). Agents receive and exchange information about an observable stochastic component of the dividend process of a risky asset \`a la Grossman and Stiglitz. A small proportion of the network has access to a pr

Diego M Fieguth

In this work we show how friction enables a non-linear energy transfer in a slow-fast Hamiltonian system. We first introduce a paradigmatic system consisting of a weakly coupled fast and slow oscillator that gives rise to a non-linear resonance. We state known Assertions about this system and the conservation of energy in the slow variables. We reexamine the

Chang Weng, Scott Rood, Mehdi Ali Ramezani, Amir Aslani

Natural Language Processing (NLP) is widely used in fields like machine translation and sentiment analysis. However, traditional NLP models struggle with accuracy and efficiency. This paper introduces Deep Convolutional Neural Networks (DCNN) into NLP to address these issues. By integrating DCNN, machine learning (ML) algorithms, and generative adversarial n

Chiara Micoli, Alessio Crippa, Jason T. Connor, I-SPY COVID Consortium

Bayesian predictive probabilities of success (PPoS) use interim trial data to calculate the probability of trial success. These quantities can be used to optimize trial size or to stop for futility. In this paper, we describe a simulation-based approach to compute the PPoS for clinical trials with competing event data, for which no specific methodology is cu

N. P. Giha, S. Marin, I. A. Tolstukhin, M. B. Oberling

We measure the average spin of $^{144}$Ba, a common fragment produced in $^{252}$Cf(sf), as a function of the total kinetic energy (TKE). We combined for the first time a twin Frisch-gridded ionization chamber with a world-class $\gamma$-ray spectrometer that was designed to measure high-multiplicity $\gamma$-ray events, Gammasphere. The chamber, loaded with

Alexander von Bank, Eike-Manuel Edelmann, Jonathan Mandelbaum, Laurent Schmalen

Spiking neural networks (SNNs) promise energy-efficient data processing by imitating the event-based behavior of biological neurons. In previous work, we introduced the enlarge-likelihood-each-notable-amplitude spiking-neural-network (ELENA-SNN) decoder, a novel decoding algorithm for low-density parity-check (LDPC) codes. The decoder integrates SNNs into be

Manuel Pratelli, John Bianchi, Fabio Pinelli, Marinella Petrocchi

In this study, we investigate the use of a large language model to assist in the evaluation of the reliability of the vast number of existing online news publishers, addressing the impracticality of relying solely on human expert annotators for this task. In the context of the Italian news media market, we first task the model with evaluating expert-designed

Tom Hutchcroft, Minghao Pan

Consider percolation on $T\times \mathbb{Z}^d$, the product of a regular tree of degree $k\geq 3$ with the hypercubic lattice $\mathbb{Z}^d$. It is known that this graph has $0<p_c<p_u<1$, so that there are non-trivial regimes in which percolation has $0$, $\infty$, and $1$ infinite clusters a.s., and it was proven by Schonmann (1999) that there are infinite

Paraskevi Chasani, Aristidis Likas

Unimodality constitutes a key property indicating grouping behavior of the data around a single mode of its density. We propose a method that partitions univariate data into unimodal subsets through recursive splitting around valley points of the data density. For valley point detection, we introduce properties of critical points on the convex hull of the em

Kimeel Sooknunan, Emma Chapman, Luke Conaboy, Daniel Mortlock

Machine learning (ML) methods have become popular for parameter inference in cosmology, although their reliance on specific training data can cause difficulties when applied across different data sets. By reproducing and testing networks previously used in the field, and applied to 21cmFast and Simfast21 simulations, we show that convolutional neural network

Artur Gesla, Patrick Le Quéré, Yohann Duguet, Laurent Martin Witkowski

Spatio-temporally complex flows are found at the onset of unsteadiness in (axisymmetric) rotor-stator turbulence in the shape of concentric rolls. The emergence of these rolls is rationalised using a homotopy approach, where the original flow configuration is continuously deformed into a simpler, better understood configuration. We deform here rotor-stator f

Camille Barboule, Viet-Phi Huynh, Adrien Bufort, Yoan Chabot

Despite outstanding processes in many tasks, Large Language Models (LLMs) still lack accuracy when dealing with highly technical domains. Especially, telecommunications (telco) is a particularly challenging domain due the large amount of lexical, semantic and conceptual peculiarities. Yet, this domain holds many valuable use cases, directly linked to industr

Yue Guo, Haoxiang Liao, Haibin Ling, Bingyao Huang

Underwater image restoration aims to remove geometric and color distortions due to water refraction, absorption and scattering. Previous studies focus on restoring either color or the geometry, but to our best knowledge, not both. However, in practice it may be cumbersome to address the two rectifications one-by-one. In this paper, we propose NeuroPump, a se

Felix Fischer, Daniel Burgarth, Davide Lonigro

When numerically simulating the unitary time evolution of an infinite-dimensional quantum system, one is usually led to treat the Hamiltonian $H$ as an "infinite-dimensional matrix" by expressing it in some orthonormal basis of the Hilbert space, and then truncate it to some finite dimensions. However, the solutions of the Schr\"odinger equations generated b

Melanie Qiu, Caoyueshan Fan, Gulafshan, Salar Shakibhamedan

To overcome the performance limitations in modern computing, such as the power wall, emerging computing paradigms are gaining increasing importance. Approximate computing offers a promising solution by substantially enhancing energy efficiency and reducing latency, albeit with a trade-off in accuracy. Another emerging method is memristor-based In-Memory Comp

David Gontier, Clément Tauber

We study and classify the emergence of protected edge modes at the junction of one-dimensional materials. Using symmetries of Lagrangian planes in boundary symplectic spaces, we present a novel proof of the periodic table of topological insulators in one dimension. We show that edge modes necessarily arise at the junction of two materials having different to

Qing Xia

We report the limits on the diffuse supernova neutrino background (DSNB) flux and the fundamental DSNB parameters measured from the first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon detector located at the Sanford Underground Research Facility in Lead, South Dakota, USA. This is the first time the DSNB limit is measured through the proc

Martin Jourdan, Jonathan Bläßer, Guzmán Orero Gámez, Sonka Reimers

Antiferromagnets are promising candidates for ultrafast spintronic applications, leveraging current-induced spin-orbit torques. However, experimentally distinguishing between different switching mechanisms of the staggered magnetization (N\'eel vector) driven by current pulses remains a challenge. In an exemplary study of the collinear antiferromagnetic comp

Jean Spièce, Valentin Fonck, Charalambos Evangeli, Phil S. Dobson

The electrocaloric effect refers to the temperature change in a material when an electric field is applied or removed. Significant breakthroughs revealed its potential for solid-state cooling technologies in past decades. These devices offer a sustainable alternative to traditional vapor compression refrigeration, with advantages such as compactness, silent

Christina Lienstromberg, Katerina Nik

For the doubly-degenerate parabolic non-Newtonian thin-film equation $$ u_t + \text{div}\bigl(u^n |\nabla \Delta u|^{p-2} \nabla \Delta u\bigr) = 0, $$ we derive (local versions) of Bernis estimates of the form $$ \int_{\Omega} u^{n-2p} |\nabla u|^{3p}\, dx + \int_{\Omega} u^{n-\frac{p}{2}} |\Delta u|^{\frac{3p}{2}}\, dx \leq c(n,p,d) \int_{\Omega} u^n|\nabl

S. Mehboodi, V. Ukleev, C. Luo, R. Abrudan

We report on various magnetic configurations including spirals and skyrmions at the surface of the magnetic insulator Cu$_2$OSeO$_3$ at low temperatures with a magnetic field applied along <100> using resonant elastic X-ray scattering (REXS). We observe a well-ordered surface state referred to as a distorted tilted conical spiral (TC) phase over a wide range

Yan Cao, Cheng Yang, Jiteng Sheng, Haibin Wu

Optomechanical cooling of multiple degenerate mechanical modes is prevented by the mechanical dark mode due to destructive interference. Here we report the first experimental demonstration of simultaneous cooling of two near-degenerate mechanical modes by breaking the mechanical dark mode in a two-membrane cavity optomechanical system. The dark mode is gener

Xiang Li, Yong Luo, Jun Sun

In this paper, we will prove some rigidity theorems for blow up limits to Type II singularities of Lagrangian mean curvature flow with zero Maslov class or almost calibrated Lagrangian mean curvature flows, especially for Lagrangian translating solitons in any dimension. These theorems generalized previous corresponding results from two dimensional case to a

Marienza Caldarola, Gonzalo Morrás, Santiago Jaraba, Sachiko Kuroyanagi

Astrometric measurements provide a unique avenue for constraining the stochastic gravitational wave background (SGWB). In this work, we investigate the application of two neural network architectures, a fully connected network and a graph neural network, for analyzing astrometric data to detect the SGWB. Specifically, we generate mock Gaia astrometric measur

Haya Nachimovsky, Moshe Tennenholtz

Search and recommendation ecosystems exhibit competition among content creators. This competition has been tackled in a variety of game-theoretic frameworks. Content creators generate documents with the aim of being recommended by a content ranker for various information needs. In order for the ecosystem, modeled as a content ranking game, to be effective an

Hiroki Ishibashi, Kenshi Abe, Atsushi Iwasaki

This paper introduces state abstraction for two-player zero-sum Markov games (TZMGs), where the payoffs for the two players are determined by the state representing the environment and their respective actions, with state transitions following Markov decision processes. For example, in games like soccer, the value of actions changes according to the state of

Katja Bühler, Thomas Höllt, Thomas Schulz, Pere-Pau Vázquez

AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics workflows? What is the potential of AI to

So-Myoung Park, Jihye Shin, Sang-Hyun Chun, Simon P. Goodwin

We investigate the evolution of initial fractal clusters at 3 kpc from the Galactic Center (GC) of the Milky Way and show how red supergiant clusters (RSGCs)-like objects, which are considered to be the result of active star formation in the Scutum complex, can form by 16 Myr. We find that initial tidal filling and tidal over-filling fractals are shredded by

Luca Benfenati, Sofia Belloni, Alessio Burrello, Panagiotis Kasnesis

Heart rate (HR) estimation from photoplethysmography (PPG) signals is a key feature of modern wearable devices for health and wellness monitoring. While deep learning models show promise, their performance relies on the availability of large datasets. We present EnhancePPG, a method that enhances state-of-the-art models by integrating self-supervised learnin

D. G. Sangiovanni, A. Kjellén, F. Trybel, L. J. S. Johnson

From nanoscale devices including sensors, electronics, or biocompatible coatings to macroscale structural, automotive or aerospace components, fundamental understanding of plasticity and fracture can guide the realization of materials that ensure safe and durable performance. Identifying the role of atomic-scale plasticity is crucial, especially for applicat

ALICE Collaboration

Correlations between event-by-event fluctuations of anisotropic flow harmonics are measured in Pb$-$Pb collisions at a center-of-mass energy per nucleon pair of 5.02 TeV, as recorded by the ALICE detector at the LHC. This study presents correlations up to the hexagonal flow harmonic, $v_6$, which was measured for the first time. The magnitudes of these highe

Simon Hands, Seyong Kim, Dale Lawlor, Andrew Lee-Mitchell

We present recent updates and results from QC$_2$D (Two Colour QCD) simulations at non-zero baryon density, including progress toward determining the speed of sound.

Aaron Conrardy, Alfredo Capozucca, Jordi Cabot

In software applications, user models can be used to specify the profile of the typical users of the application, including personality traits, preferences, skills, etc. In theory, this would enable an adaptive application behavior that could lead to a better user experience. Nevertheless, user models do not seem to be part of standard modeling languages nor

Fan Lei

Fluid flow is a widely applied physical problem, crucial in various fields. Due to the highly nonlinear and chaotic nature of fluids, analyzing fluid-related problems is exceptionally challenging. Computational fluid dynamics (CFD) is the best tool for this analysis but involves significant computational resources, especially for 3D simulations, which are sl

Anwesha Chakraborty, Lucas Hackl, Magdalena Zych

We investigate the phenomenon of entanglement harvesting for a spacetime in quantum superposition, using two Unruh-DeWitt detectors interacting with a quantum scalar field where the spacetime background is modeled as a superposition of two quotient Minkowski spaces which are not related by diffeomorphisms. Our results demonstrate that the superposed nature o

D. M. J. van de Sande, A. T. Gudmundson, S. Murali-Manohar, C. W. Davies-Jenkins

Simulated data is increasingly valued by researchers for validating MRS processing and analysis algorithms. However, there is no consensus on the optimal approaches for simulation models and parameters. This study introduces a novel MRS digital brain phantom framework, providing a comprehensive and modular foundation for MRS data simulation. The framework ge

Xin Fu, Tseleung So, Jongbaek Song

Given a compact toric surface, the multiplication of its rational cohomology can be described in terms of the intersection products of Weil divisors, or in terms of the cup products of cohomology classes representing specific cells. In this paper, we aim to compare these two descriptions. More precisely, we define two different cohomology bases, the \emph{Po

Chun Gu, Xiaofei Wei, Zixuan Zeng, Yuxuan Yao

In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate incident light, resulting in inaccurate ma

Diana Estévez Schwarz, René Lamour, Roswitha März

We analyze different approaches to differential-algebraic equations with attention to the implemented rank conditions of various matrix functions. These conditions are apparently very different and certain rank drops in some matrix functions actually indicate a critical solution behavior. We look for common ground by considering various index and regularity

Julio Taveira, Fernando Buarque de Lima Neto, Ronaldo Menezes

Understanding how transportation networks work is important for improving connectivity, efficiency, and safety. In Brazil, where road transport is a significant portion of freight and passenger movement, network science can provide valuable insights into the structural properties of the infrastructure, thus helping decision makers responsible for proposing i

Ming Liu, Tong-Yu He, Bohai Chen, Zhan-Wen Han

We explore a square-law k-inflation using the Hamilton-Jacobi approach. Focusing on scenarios where the Hubble parameter exhibits a power-law dependence on the k-field, our analysis encompasses the computations of crucial observables, such as the scalar power spectrum, the tensor-to-scalar ratio, and the scalar spectral index. We further constrain the model'

Tomasz P. Dutka, Tae Hyun Jung, Chang Sub Shin

We explore the nature of a certain type of supercooled phase transition, where the supercooling is guaranteed to end due to the curvature of the finite-temperature effective potential at the origin experiencing a sign flip at some temperature. In such models the potential barrier trapping the scalar field at the meta-stable origin is quickly vanishing at the

Vu Viet Hoang, Quoc Anh Hoang Nguyen, Hung Tran The

Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query variables without additional constraints. However, in many real-world scenarios, controlling certain query variables may incur costs. Therefore, the learner needs to balance the s

Elifnur Sunger, Yunus Bicer, Deniz Erdogmus, Tales Imbiriba

Brain-Computer Interfaces (BCIs) help people with severe speech and motor disabilities communicate and interact with their environment using neural activity. This work focuses on the Rapid Serial Visual Presentation (RSVP) paradigm of BCIs using noninvasive electroencephalography (EEG). The RSVP typing task is a recursive task with multiple sequences, where

Anish Chakrabarty, Arkaprabha Basu, Swagatam Das

The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure from isometry, it has found vast usage in domain translation and network analysis. It has long been shown to be vulnerable to contamination in the underlying measures. All efforts t

V. Lebouteiller, C. T. Richardson, M. S. Polimera, D. S. Carr

Deriving physical parameters from integrated galaxy spectra is paramount to interpret the cosmic evolution of star formation, chemical enrichment, and energetic sources. We develop modeling techniques to characterize the ionized gas properties in the subset of 2052 star-forming galaxies from the volume-limited, dwarf-dominated, z~0 ECO catalog. The MULTIGRIS

Brady Planden, Nicola E. Courtier, Martin Robinson, Agriya Khetarpal

The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, including the electrochemical and equivalent ci

Abhimanyu Susobhanan

We present Vela, an efficient, modular, easy-to-use Bayesian pulsar timing and noise analysis package written in Julia. Vela provides an independent, efficient, and parallelized implementation of the full non-linear pulsar timing and noise model along with a Python binding named pyvela. One-time operations such as data file input, clock corrections, and sola

T. H. Hansson, Rodrigo Arouca, Thomas Klein Kvorning

We revisit an argument, originally given by Kivelson and Ro\v{c}ek, for why the existence of fractional charge necessarily implies fractional statistics. In doing so, we resolve a contradiction in the original argument, and in the case of a $\nu = 1/m$ Laughlin holes, we also show that the standard relation between fractional charge and statistics is necessa

Zi-Qing Huang, Shu-Kun Ye, Yong-Qiang Xu, Tian-Yi Jiang

Material disorders are one of the major sources of noise and loss in solid-state quantum devices, whose behaviors are often modeled as two-level systems (TLSs) formed by charge tunneling between neighboring sites. However, the role of their spins in tunneling and its impact on device performance remain highly unexplored. In this work, employing ultra-thin Ti

Vincent Rompel, Sabrina Franke, Florian Kübelbäck, Lothar Oberauer

Future neutrino experiments at low energies such as JUNO or THEIA will use large volume homogeneous liquid scintillator detectors. The optical attenuation length of the liquid is of uttermost importance for the successful realization of these experiments. At TU Munich a new optical spectrometer (Precision Attenuation Length Measurement (PALM)) has been set u

G. Akemann, M. Duits, L. D. Molag

The variance of the number of particles in a set is an important quantity in understanding the statistics of non-interacting fermionic systems in low dimensions. An exact map of their ground state in a harmonic trap in one and two dimensions to the classical Gaussian unitary and complex Ginibre ensemble, respectively, allows to determine the counting statist

Kai Brandenbusch

The generation of images of realistic looking, readable handwritten text is a challenging task which is referred to as handwritten text generation (HTG). Given a string and examples from a writer, the goal is to synthesize an image depicting the correctly spelled word in handwriting with the calligraphic style of the desired writer. An important application

F. Buckland-Willis, M. A. Miville-Deschenes, A. Marchal, J. R. Dawson

Context. The Galactic ASKAP collaboration (GASKAP) is undertaking an HI emission survey of the 21cm line to map the Magellanic system and the Galactic plane with the Australian Square Kilometre Array Pathfinder (ASKAP). One of the first areas observed in the Pilot Phase I of the survey was the Small Magellanic Cloud (SMC). Previous surveys of the SMC have un

Bartosz Sobolewski

Let $\mathsf{s}(n)$ denote the sum of binary digits of an integer $n \geq 0$. In the recent years there has been interest in the behavior of the differences $\mathsf{s}(n+t)-\mathsf{s}(n)$, where $t \geq 0$ is an integer. In particular, Spiegelhofer and Wallner showed that for $t$ whose binary expansion contains sufficiently many blocks of $\mathtt{1}$s the

Wantong Huang, Yuguo Yin, Haicheng Lin, Wei Chen

Many-body ground states of imbalanced Fermi gas have been studied both theoretically and experimentally for several decades because of their fundamental significance in condensed matter physics, cold atom physics and nuclear physics. The Sarma state, a gapless spin-polarized superfluid, is one of those long sought-after exotic ground states of spin imbalance

Hanchun Jiang, Toshiya Namikawa

We explore the impact of the reionization history on examining the shape of the power spectrum of the primordial gravitational waves (PGWs) with the cosmic microwave background (CMB) polarization. The large-scale CMB generated from the reionization epoch is important in probing the PGWs from all-sky experiments, such as LiteBIRD. The reionization model has b

Sarah L. Thomson, Quentin Renau, Diederick Vermetten, Emma Hart

Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation algorithms. Local optima networks (LONs) have been especially dominant in the literature and capture an approximation of local optima and their connectivity in the landscape. However,

Xuekai Wei, Junyu Zhang, Qinlin Hu, Mingliang Zhou\\Yong Feng

Current full-reference image quality assessment (FR-IQA) methods often fuse features from reference and distorted images, overlooking that color and luminance distortions occur mainly at low frequencies, whereas edge and texture distortions occur at high frequencies. This work introduces a pioneering training-free FR-IQA method that accurately predicts image

Chengting Yu, Shu Yang, Fengzhao Zhang, Hanzhi Ma

Quantization-aware training (QAT) is a common paradigm for network quantization, in which the training phase incorporates the simulation of the low-precision computation to optimize the quantization parameters in alignment with the task goals. However, direct training of low-precision networks generally faces two obstacles: 1. The low-precision model exhibit

Aiwen Jiang, Hourong Chen, Zhiwen Chen, Jihua Ye

Recent efforts on image restoration have focused on developing "all-in-one" models that can handle different degradation types and levels within single model. However, most of mainstream Transformer-based ones confronted with dilemma between model capabilities and computation burdens, since self-attention mechanism quadratically increase in computational com

Mikko Impiö, Philipp M. Rehsen, Jenni Raitoharju

The amount of image datasets collected for environmental monitoring purposes has increased in the past years as computer vision assisted methods have gained interest. Computer vision applications rely on high-quality datasets, making data curation important. However, data curation is often done ad-hoc and the methods used are rarely published. We present a m

Junteng Yao, Tuo Wu, Liaoshi Zhou, Ming Jin

In this paper, we analyze the role of fluid antenna systems (FAS) in multi-user systems with hardware impairments (HIs). Specifically, we investigate a scenario where a base station (BS) equipped with multiple fluid antennas communicates with multiple users (CUs), each equipped with a single fluid antenna. Our objective is to maximize the minimum communicati

Paula Hähndel, Christoph Möller, Rebecca Waldecker

In this article we prove results about finite soluble groups that act with fixity 2 or 3.

Odd Erik Gundersen, Odd Cappelen, Martin Mølnå, Nicklas Grimstad Nilsen

A reproducibility crisis has been reported in science, but the extent to which it affects AI research is not yet fully understood. Therefore, we performed a systematic replication study including 30 highly cited AI studies relying on original materials when available. In the end, eight articles were rejected because they required access to data or hardware t

Naia Ormaza-Zulueta, Steve Miller, Zia Mehrabi

Agricultural workers play a vital role in the global economy and food security by cultivating, transporting, and processing food for populations worldwide. Despite their importance, detailed spatial data on the global agricultural workforce have remained scarce. Here, we present a new gridded dataset that maps the global distribution of agricultural workers

Carlota M. Cuesta, Francisco de la Hoz

In this paper we present a non-recursive direct solver, based on the Bartels-Stewart algorithm, for $N$-dimensional Sylvester tensor equations. The method relies only on Schur decompositions of the coefficient matrices and reduces the computation to a single sequential sweep over tensor entries, making it entirely independent of the dimension $N$. Its main a

Anastasia Doikou, Marzia Mazzotta, Paola Stefanelli

We study solutions of the parametric set-theoretic reflection equation from an algebraic perspective by employing recently derived generalizations of the familiar shelves and racks, called parametric (p)-shelves and racks. Generic invertible solutions of the set-theoretic reflection equation are also obtained by a suitable parametric twist. The twist leads t

Jiaming Ji, Jiayi Zhou, Hantao Lou, Boyuan Chen

Reinforcement learning from human feedback (RLHF) has proven effective in enhancing the instruction-following capabilities of large language models; however, it remains underexplored in the cross-modality domain. As the number of modalities increases, aligning all-modality models with human intentions -- such as instruction following -- becomes a pressing ch

Yuanfei Lin, Zekun Xing, Xuyuan Han, Matthias Althoff

Complying with traffic rules is challenging for automated vehicles, as numerous rules need to be considered simultaneously. If a planned trajectory violates traffic rules, it is common to replan a new trajectory from scratch. We instead propose a trajectory repair technique to save computation time. By coupling satisfiability modulo theories with set-based r

MAGIC Collaboration, S. Abe, J. Abhir, A. Abhishek

The BL Lacertae object VER J0521+211 underwent a notable flaring episode in February 2020. A short-term monitoring campaign, led by the MAGIC (Major Atmospheric Gamma Imaging Cherenkov) collaboration, covering a wide energy range from radio to very-high-energy (VHE, 100 GeV < E < 100 TeV) gamma rays was organised to study its evolution. These observations re

Xinyue Chen, Miaojing Shi, Zijian Zhou, Lianghua He

Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typically employ a two-phase training scheme, involving base class pre-training followed by novel class fine-tuning, to learn the classifiers for ba

Chenyi Zhang, Zhenhao Zhang, Wei Zhang, Tian Zeng

Exploring moral dilemmas allows individuals to navigate moral complexity, where a reversal in decision certainty, shifting toward the opposite of one's initial choice, could reflect open-mindedness and less rigidity. This study probes how nonverbal emotional cues from conversational agents could influence decision certainty in moral dilemmas. While existing

Yaroslav Shopa, Maciej Kolwas, Daniel Jakubczyk, Gennadiy Derkachov

We have investigated the optically measurable properties of single evaporating microdroplets of suspensions containing up-converting luminescent nanoparticles (Gd2O3:Er3+), levitated in a linear electrodynamic trap. These microdroplets served as spherical optical resonators, with their resonance properties influenced by the distribution and interactions of n

Simon Lang, Mihai Alexe, Mariana C. A. Clare, Christopher Roberts

Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as they permit to estimate the probability of weather events by representing the sources of uncertainties and accounting for the day-to-day variability of error growth in the atmosphe

Tornike Tsereteli, Daniel Ruffinelli, Simone Paolo Ponzetto

Questions within surveys, called survey items, are used in the social sciences to study latent concepts, such as the factors influencing life satisfaction. Instead of using explicit citations, researchers paraphrase the content of the survey items they use in-text. However, this makes it challenging to find survey items of interest when comparing related wor

Jorge Alda, Alejandro Mir, Siannah Penaranda

We present an analysis on flavour anomalies in semileptonic rare $B$-meson decays using an effective field theory approach and assuming that new physics affects only one generation in the interaction basis and non-universal mixing effects are generated by the rotation to the mass basis. A global fit to experimental data is performed, focusing on LFU ratios $

Shuai Wang, Peter Bloem, Joe Raad, Frank van Harmelen

Large knowledge graphs capture information of a large number of entities and their relations. Among the many relations they capture, class subsumption assertions are usually present and expressed using the \texttt{rdfs:subClassOf} construct. From our examination, publicly available knowledge graphs contain many potentially erroneous cyclic subclass relations

Laura Wenderoth, Konstantin Hemker, Nikola Simidjievski, Mateja Jamnik

Integrating AI in healthcare can greatly improve patient care and system efficiency. However, the lack of explainability in AI systems (XAI) hinders their clinical adoption, especially in multimodal settings that use increasingly complex model architectures. Most existing XAI methods focus on unimodal models, which fail to capture cross-modal interactions cr

Haruki Furukawa, Sacha Ployet, Ronnie Rodgers

We compute the electrical conductivities at non-zero frequency in a top-down holographic model of a Weyl semimetal, consisting of $\mathcal{N}=4$ supersymmetric $\mathrm{SU}(N_c)$ Yang--Mills theory coupled to $\mathcal{N}=2$ hypermultiplets with mass $m$, subject to an applied axial vector field $b$. The model exhibits a first-order phase transition between

Joshua B. Moore, Hugo P. Stackhouse, Ben D. Fulcher, Sahand Mahmoodian

Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has motivated researchers to also apply the MPS

Giacomo Colombo, Alessio Figalli

It is a well-known conjecture in $β$-models and in their discrete counterpart that, generically, external potentials should be ``off-critical&#39;&#39; (or, equivalently, ``regular&#39;&#39;). Exploiting the connection between minimizing measures and thin obstacle problems, we give a positive answer to this conjecture.

N. J. Guth, O. Vallhagen, P. Helander, A. Tresnjic

We detail here a semi-analytical model for the pellet rocket effect, which describes the acceleration of pellets in a fusion plasma due to asymmetries in the heat flux reaching the pellet surface and the corresponding ablation rate. This effect was shown in experiments to significantly modify the pellet trajectory, and projections for reactor scale devices i

A. Civit-Bertran, S. Futatani, Y. Suzuki, J. Dominguez-Palacios

A new three-dimensional, non-linear Magnetohydrodynamics (MHD) model has been extended in MIPS code, incorporating parallel heat diffusivity. The model has been benchmarked against the former MHD model used in MIPS code. A preliminary study of the core density collapse event (CDC) observed in the Large Helical Device (LHD) plasma has been performed using the

Tengfei Ma, Yujie Chen, Liang Wang, Xuan Lin

Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in inferring such entities through knowledge subgraph reasoning, they suffer from (i) the semantic inconsistencies of similar relations, and (ii)

Juan-Manuel Torres-Moreno, Juan-José Guzmán-Landa, Graham Ranger, Martha Lorena Avendaño Garrido

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to b

Cheng Wang, Ziyang Feng, Pin Zhang, Manjiang Cao

Electromyography (EMG) signals are widely used in human motion recognition and medical rehabilitation, yet their variability and susceptibility to noise significantly limit the reliability of myoelectric control systems. Existing recognition algorithms often fail to handle unfamiliar actions effectively, leading to system instability and errors. This paper p

Maximilian Fischer, Florian M. Hauptmann, Robin Peretzke, Paul Naser

Although advances in brain surgery techniques have led to fewer postoperative complications requiring Intensive Care Unit (ICU) monitoring, the routine transfer of patients to the ICU remains the clinical standard, despite its high cost. Predictive Gradient Boosted Trees based on clinical data have attempted to optimize ICU admission by identifying key risk

Boyan Torosov, Bohdan Kulchytskyy, Florian Hopfmueller, John Gunderson

We present a gradient-based method to construct high-fidelity, two-qubit quantum gates in a system consisting of two transmon qubits coupled via a tunable coupler. In particular, we focus on single flux quantum (SFQ) pulses as a promising and scalable alternative to traditional control schemes that use microwave electronics. We develop a continuous embedding

Ivan Biočić, Daniel E. Cedeño-Girón, Bruno Toaldo

In this paper, a method to exactly sample the trajectories of inverse subordinators (in the sense of the finite-dimensional distributions), jointly with the undershooting or overshooting process, is provided. The method applies to general strictly increasing subordinators. The (random) running times of these algorithms have finite moments and explicit bounds

Francesco De Sclavis, Giuseppe Galano, Aldo Glielmo, Matteo Nardelli

Stablecoins are digital assets designed to maintain a stable value, typically pegged to traditional currencies. Despite their growing prominence, many stablecoins have struggled to consistently meet stability expectations, and their underlying mechanisms often remain opaque and challenging to analyze. This paper focuses on the DAI stablecoin, which combines