December 2024 arXiv papers — page 114

Grant T. Barkley, Christian Gaetz

Blundell, Buesing, Davies, Veli\v{c}kovi\'c, and Williamson (BBDVW) introduced the notion of a hypercube decomposition of an interval in Bruhat order. They conjectured a recursive formula in terms of this structure which, if shown for all intervals, would imply the Combinatorial Invariance Conjecture of Lusztig and Dyer, for Kazhdan-Lusztig polynomials of th

Yudong Jiang, Baohan Xu, Siqian Yang, Mingyu Yin

Animation has gained significant interest in the recent film and TV industry. Despite the success of advanced video generation models like Sora, Kling, and CogVideoX in generating natural videos, they lack the same effectiveness in handling animation videos. Evaluating animation video generation is also a great challenge due to its unique artist styles, viol

Marco Drewes, Yannis Georis, Claudia Hagedorn, Juraj Klaric

We consider an extension of the Standard Model with three right-handed neutrinos, endowed with a flavour symmetry $G_f$, $G_f=\Delta (3 \, n^2)$ or $G_f=\Delta (6 \, n^2)$, $n \geq 2$, and CP. For large active-sterile mixing, we study the properties of the (nearly mass-degenerate) heavy neutrinos, such as their lifetimes and branching ratios. In doing so, we

Chang Liu, Amin Kamali, Verena Kantere, Calisto Zuzarte

Join order selection is a sub-field of query optimization that aims to find the optimal join order for an SQL query with the minimum cost. The challenge lies in the exponentially growing search space as the number of tables increases, making exhaustive enumeration impractical. Traditional optimizers use static heuristics to prune the search space, but they o

Arthur Chatton, Émilie Pilote, Kevin Assob Feugo, Héloïse Cardinal

Objective: This study sought to compare the drop in predictive performance over time according to the modeling approach (regression versus machine learning) used to build a kidney transplant failure prediction model with a time-to-event outcome. Study Design and Setting: The Kidney Transplant Failure Score (KTFS) was used as a benchmark. We reused the data f

Daniel Köglmayr, Alexander Haluszczynski, Christoph Räth

We present a novel, model-free, and data-driven methodology for controlling complex dynamical systems into previously unseen target states, including those with significantly different and complex dynamics. Leveraging a parameter-aware realization of next-generation reservoir computing (NGRC), our approach accurately predicts system behavior in unobserved pa

P. S. Muraev, D. N. Maksimov, A. R. Kolovsky

We analyze conductance of a two-leg ladder connected with fermionic reservoirs, focusing on the decoherence effect induced by the reservoirs. In the absence of decoherence the system exhibits both bound states in the continuum and Fano resonances. We found that the Fano resonances in transmittance are robust against decoherence, at the same time decoherence

Alessandro Perelli, Carola-Bibiane Schonlieb, Matthias J. Ehrhardt

A challenge in high-dimensional inverse problems is developing iterative solvers to find the accurate solution of regularized optimization problems with low computational cost. An important example is computed tomography (CT) where both image and data sizes are large and therefore the forward model is costly to evaluate. Since several years algorithms from s

Jonas Latz, Aretha L. Teckentrup, Simon Urbainczyk

In image reconstruction, an accurate quantification of uncertainty is of great importance for informed decision making. Here, the Bayesian approach to inverse problems can be used: the image is represented through a random function that incorporates prior information which is then updated through Bayes' formula. However, finding a prior is difficult, as imag

B. S. Basilio, V. G. Kupriyanov, M. A. Kurkov

Lie-Poisson electrodynamics describes the semi-classical limit of non-commutative $U(1)$ gauge theory, characterized by Lie-algebra-type non-commutativity. We focus on the mechanics of a charged point-like particle moving in a given gauge background. First, we derive explicit expressions for gauge-invariant variables representing the particle's position. Sec

Hazel Kim, Tom A. Lamb, Adel Bibi, Philip Torr

Large language models (LLMs) frequently generate confident yet inaccurate responses, introducing significant risks for deployment in safety-critical domains. We present a novel, test-time approach to detecting model hallucination through systematic analysis of information flow across model layers. We target cases when LLMs process inputs with ambiguous or in

Arthur Chatton, Michael Schomaker, Miguel-Angel Luque-Fernandez, Robert W. Platt

Background: Sequential positivity is often a necessary assumption for drawing causal inferences, such as through marginal structural modeling. Unfortunately, verification of this assumption can be challenging because it usually relies on multiple parametric propensity score models, unlikely all correctly specified. Therefore, we propose a new algorithm, call

Arijit Nag, Soumen Chakrabarti, Animesh Mukherjee, Niloy Ganguly

Open-source Large Language models (OsLLMs) propel the democratization of natural language research by giving the flexibility to augment or update model parameters for performance improvement. Nevertheless, like proprietary LLMs, Os-LLMs offer poorer performance on low-resource languages (LRLs) than high-resource languages (HRLs), owing to smaller amounts of

Yazen S. Sheet, Mohammed Younis Thanoun, Firas S. Alsharbaty

The traditional industrial communication networks may not meet the requirements of the main smart factory applications together, such as Remote control and safety applications (which considered as strict real time applications) and augmented reality (which consumes wide bandwidth). This work suggests an enhanced communication network to serve an optimum case

Chun Huang

Traditional methods for determining the radius of a 1.4 $M_{\odot}$ neutron star ($R_{1.4}$) rely on specific equations of state (EOS) models that describe various types of dense nuclear matter. This dependence on EOS models can introduce substantial systematic uncertainties, which may exceed the measurement uncertainties when constraining $R_{1.4}$. In this

Astrid an Huef, Dana P. Williams

We characterise when the C*-algebra C*(G) of a locally compact and Hausdorff groupoid G is subhomogeneous, that is, when its irreducible representations have bounded finite dimension; if so we establish a bound for its nuclear dimension in terms of the topological dimensions of the unit space of the groupoid and the spectra of the primitive ideal spaces of t

Giovanni Francesco Diotallevi, Leander Reascos, Mónica Benito

The Schrieffer-Wolff transformation (SWT) is a foundational perturbative method for deriving effective Hamiltonians in quantum systems by systematically eliminating couplings between pairs of energy distant subspaces. Despite recent advancements, the implementation of SWTs for sufficiently complex systems remains computationally challenging and often require

Abu Bakar Dawood, Zhenyu Zhang, Martin Angelmahr, Alberto Arezzo

Palpation of human tissue during Minimally Invasive Surgery is hampered due to restricted access. In this extended abstract, we present a variable stiffness and dynamic force range sensor that has the potential to address this challenge. The sensor utilises light reflection to estimate sensor deformation, and from this, the force applied. Experimental testin

Daniel Centeno, Elie Wolfe

The use of graphical models to represent causal hypotheses has enabled revolutionary progress in the study of the foundations of quantum theory. Here we consider directed acyclic graphs each of which contains both nodes representing observed variables as well as nodes representing latent or hidden variables. When comparing distinct causal structures, a natur

Shah Rukh Qasim, Patrick Owen, Nicola Serra

We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods. It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well longitudinal placement of

Tim Kokkeler, Ilya Tokatly, F. Sebastian Bergeret

We present a quantum transport theory for generic magnetic metals, in which magnetism occurs predominantly due to exchange interactions, such as ferromagnets, antiferromagnets, altermagnets and p-wave magnets. Our theory is valid both for the normal and the superconducting state. We derive the effective low-energy action for each of these materials, where th

Songpengcheng Xia, Yu Zhang, Zhuo Su, Xiaozheng Zheng

Estimating full-body motion using the tracking signals of head and hands from VR devices holds great potential for various applications. However, the sparsity and unique distribution of observations present a significant challenge, resulting in an ill-posed problem with multiple feasible solutions (i.e., hypotheses). This amplifies uncertainty and ambiguity

Kathrin Bringmann, Nikolaos Diamantis

We introduce an extension of the standard cohomology which is characterised by maps that fail to be classical cocycles by products of simpler maps. The construction is motivated by the study of Manin's noncommutative modular symbols and of false theta functions. We use this construction to obtain a cohomological interpretation of important iterated integrals

Krzysztof Ptaszynski, Timur Aslyamov, Massimiliano Esposito

Time-integrated state observables, which quantify the fraction of time spent by the system in a specific pool of states, are important in many fields, such as chemical sensing or the theory of fluorescence spectroscopy. We derive exact identities, called Fluctuation-Response Relations (FRRs), that connect the fluctuations of such observables to their respons

Kai Murai, Yuma Narita, Fuminobu Takahashi, Wen Yin

We investigate the level-crossing phenomenon in two-axion systems, where the mass eigenvalues intersect as the mass of one axion increases with the cooling of the universe. This phenomenon can significantly alter the abundance of axions in the early universe. Our study focuses on its impact on the QCD axion and an axion-like particle, identifying viable regi

Siyun Liang, Sen Wang, Kunyi Li, Michael Niemeyer

3D Gaussian Splatting has recently gained traction for its efficient training and real-time rendering. While its vanilla representation is mainly designed for view synthesis, recent works extended it to scene understanding with language features. However, storing additional high-dimensional features per Gaussian for semantic information is memory-intensive,

Stefan Zitz, Andrea Scagliarini, Johan Roenby

Rivulets and droplets are naturally appearing shapes when small amounts of liquid are deposited on a partially wettable substrate. Here we study, by means of numerical simulations, the dewetting dynamics of a ring-rivulet on substrates with various contact angles and wettability patterns. In particular, we consider, beyond the homogeous case, an annular band

Emanuele Tirrito, Xhek Turkeshi, Piotr Sierant

Quantum state complexity metrics, such as anticoncentration and nonstabilizerness, or ``magic'', offer key insights into many-body physics, information scrambling, and quantum computing. Anticoncentration and equilibration of magic resources under dynamics of random quantum circuits occur at times scaling logarithmically with system size, a prediction that i

Jovan Odavić, Michele Viscardi, Alioscia Hamma

Entanglement and stabilizer entropy are both involved in the onset of complex behavior in quantum many-body systems. Their interplay is at the root of complexity of simulability, scrambling, thermalization and typicality. In this work, we study the dynamics of entanglement, stabilizer entropy, and the anti-flatness of the entanglement spectrum after a quantu

J. -L. Smirr, P. Manset, Ç. Ö. Girit

Noisy voltage sources can be a limiting factor for fundamental physics experiments as well as for device applications in quantum information, mesoscopic circuits, magnetometry, and other fields. The best commercial DC voltage sources can be programmed to approximately six digits and have intrinsic noise in the microvolt range. On the other hand the noise lev

Zachary Gardner, Keerthi Madapusi

We use the newly developed stacky prismatic technology of Drinfeld and Bhatt-Lurie to give a uniform, group-theoretic construction of smooth stacks $\mathrm{BT}^{G,\mu}_{n}$ attached to a smooth affine group scheme $G$ over $\mathbb{Z}_p$ and $1$-bounded cocharacter $\mu$, verifying a recent conjecture of Drinfeld. This can be viewed as a refinement of resul

Tanushree Shah, Jonathan Simone

We consider tight contact structures on plumbed 3-manifolds with no bad vertices. We discuss how one can count the number of tight contact structures with zero Giroux torsion on such 3-manifolds and explore conditions under which Giroux torsion can be added to these tight contact structures without making them overtwisted. We give an explicit algorithm to co

Senlin Cheng, Haopeng Sun

Interactive segmentation aims to extract objects of interest from an image based on user-provided clicks. In real-world applications, there is often a need to segment a series of images featuring the same target object. However, existing methods typically process one image at a time, failing to consider the sequential nature of the images. To overcome this l

Benjamin Commeau, Kevin Player

This paper investigates the feasibility of mapping non-local, sparse, diagonal forms of quantum Hamiltonians to local forms via eigenbasis permutations. We prove that such a mapping is not always possible, definitively refuting the "Quasiparticle Locality Conjecture." This refutation is achieved by establishing a lower bound, denoted $G_m$, on the number of

Gianni Dal Maso, Irene Fonseca, Giovanni Leoni

This paper addresses a two-dimensional sharp interface variational model for solid-state dewetting of thin films with surface energies, introduced by Wang, Jiang, Bao, and Srolovitz in \cite{jiang2016solid}. Using the $H^{-1}$-gradient flow structure of the evolution law, short-time existence for a surface diffusion evolution equation with curvature regulari

Jerzy Kakol, Arkady Leiderman

For locally convex spaces, we systematize several known equivalent definitions of Fr\'echet (G\^ ateaux) Differentiability Spaces and Asplund (Weak Asplund) Spaces. As an application, we extend the classical Mazur's theorem as follows: Let $E$ be a separable Baire locally convex space and let $Y$ be the product $\prod_{\alpha\in A} E_{\alpha}$ of any family

Timour Ichmoukhamedov, James Hinns, David Martens

A rapidly developing application of LLMs in XAI is to convert quantitative explanations such as SHAP into user-friendly narratives to explain the decisions made by smaller prediction models. Evaluating the narratives without relying on human preference studies or surveys is becoming increasingly important in this field. In this work we propose a framework an

Nikolai Warner, Jack Kolb, Meera Hahn, Vighnesh Birodkar

In this paper we focus on inserting a given human (specifically, a single image of a person) into a novel scene. Our method, which builds on top of Stable Diffusion, yields natural looking images while being highly controllable with text and pose. To accomplish this we need to train on pairs of images, the first a reference image with the person, the second

Artur Bichs, Kostiantyn V. Yershov, Volodymyr P. Kravchuk

Here we perform a systematic analysis of equilibrium solutions for Neel and Bloch skyrmions on a cylindrical shell and demonstrate that both experience curvature-induced deformation. Considering the curvature as a small perturbation and applying the first-order perturbation theory we decompose the deformation on the radial symmetrical and elliptical parts. T

Mathieu Giroux, Sean Molesky, Raphael St-Gelais, Jacob J. Krich

Near-field thermophotovoltaics (NFTPV) systems have significant potential for waste heat recovery applications, with both high theoretical efficiency and power density, up to 40% and $11 \ \mathrm{W/cm^{2}}$ at 900 K. Yet experimental demonstrations have only achieved up to 14% efficiency and modest power densities (i.e., $0.75 \ \mathrm{W/cm^{2}}$). While e

Antonio F. Rotundo, Paolo Perinotti, Alessandro Bisio

It is known that the same physical system can be described by different effective theories depending on the scale at which it is observed. In this work, we formulate a prescription for finding the unitary that best approximates the large scale dynamics of a quantum system evolving discretely in time, as it is the case for digital quantum simulators. We consi

Tom Matty Bo Asmussen, Roman Höllwieser, Francesco Knechtli, Tomasz Korzec

We calculate the hadronic scales $r_0$, $r_1$ and their ratio $r_0/r_1$ on $N_{\rm f}=2+1$ flavor QCD ensembles generated by the CLS consortium. These scales are determined from a tree-level improved definition of the static force on the lattice, which we measure using Wilson loops. Our analysis involves various continuum and chiral extrapolations of data th

Veronica Bitonti, Bishal Deb, Alan D. Sokal

We introduce some classes of increasing labeled and multilabeled trees, and we show that these trees provide combinatorial interpretations for certain Thron-type continued fractions with coefficients that are quasi-affine of period 2. Our proofs are based on bijections from trees to labeled Motzkin or Schr\"oder paths; these bijections extend the well-known

William Fisher, Qiong Zhang, Lulu Kang, Xinwei Deng

We consider the optimal experimental design problem of allocating subjects to treatment or control when subjects participate in multiple, separate controlled experiments within a short time-frame and subject covariate information is available. Here, in addition to subject covariates, we consider the dependence among the responses coming from the subject's ra

Priyanka Sharma, Ashutosh Singh, Om Prakash

This work introduces a novel approach to constructing DNA codes from linear codes over a non-chain extension of $\mathbb{Z}_4$. We study $(\text{\textbaro},\mathfrak{d}, \gamma)$-constacyclic codes over the ring $\mathfrak{R}=\mathbb{Z}_4+\omega\mathbb{Z}_4, \omega^2=\omega,$ with an $\mathfrak{R}$-automorphism $\text{\textbaro}$ and a $\text{\textbaro}$-der

Paula Daudén-Oliver, David Agost-Beltran, Emilio Sansano-Sansano, Valero Laparra

Image quality databases are used to train models for predicting subjective human perception. However, most existing databases focus on distortions commonly found in digital media and not in natural conditions. Affine transformations are particularly relevant to study, as they are among the most commonly encountered by human observers in everyday life. This D

Delphine Moussard

We give a new proof of Laudenbach and Po\'enaru's theorem, which states that any diffeomorphism of the boundary of a 4-dimensional 1-handlebody extends to the whole handlebody. Our proof is based on the cassification of Heegaard splittings of double handlebodies and a result of Cerf on diffeomorphisms of the 3-ball. Further, we extend this theorem to 4-dimen

Jiapeng Tang, Davide Davoli, Tobias Kirschstein, Liam Schoneveld

We propose a novel approach for reconstructing animatable 3D Gaussian avatars from monocular videos captured by commodity devices like smartphones. Photorealistic 3D head avatar reconstruction from such recordings is challenging due to limited observations, which leaves unobserved regions under-constrained and can lead to artifacts in novel views. To address

Jaehyeon Kim, Taehong Moon, Keon Lee, Jaewoong Cho

We introduce ResGen, an efficient Residual Vector Quantization (RVQ)-based generative model for high-fidelity generation with fast sampling. RVQ improves data fidelity by increasing the number of quantization steps, referred to as depth, but deeper quantization typically increases inference steps in generative models. To address this, ResGen directly predict

Xiao Zhang, Qianru Meng, Johan Bos

Open-domain semantic parsing remains a challenging task, as neural models often rely on heuristics and struggle to handle unseen concepts. In this paper, we investigate the potential of large language models (LLMs) for this task and introduce Retrieval-Augmented Semantic Parsing (RASP), a simple yet effective approach that integrates external symbolic knowle

Gerhard Hiss, Rafał Lutowski

We introduce a conjecture of Dekimpe, De Rock and Penninckx and sketch some major steps in its proof. This text presents an extended account of the talk of the first author given at the conference Ischia Group Theory 2024.

Hayk L. Gevorgyan, Lekdar A. Gevorgian

The radiation emitted at zero angle by a microbunched positron bunch undergoing planar channeling in a crystalline undulator (CU) is studied. The bunch energy is assumed to be far above the threshold for radiation generation in the dispersive CU medium. Besides the usual ``hard'' undulator radiation produced by channeling oscillations (channeling undulator r

Lili Ködmön, Anqi Li, Ji Zeng

For a bipartite graph $H$, its linear threshold is the smallest real number $\sigma$ such that every bipartite graph $G = (U \sqcup V, E)$ with unbalanced parts $|V| \gtrsim |U|^\sigma$ and without a copy of $H$ must have a linear number of edges $|E| \lesssim |V|$. We prove that the linear threshold of the complete bipartite subdivision graph $K_{s,t}'$ is

Zehao Chen, Rong Pan

Scalable Vector Graphics (SVG) are essential XML-based formats for versatile graphics, offering resolution independence and scalability. Unlike raster images, SVGs use geometric shapes and support interactivity, animation, and manipulation via CSS and JavaScript. Current SVG generation methods face challenges related to high computational costs and complexit

Aliasghar Baziar, Navid Parsa

This exhaustive investigation is dedicated to delving into the intricate legal aspects that underlie the inefficiency in the advancement and utilization of sustainable energies, with a primary focus on the dynamic landscape of China and carefully selected representative nations. In an era where the global community increasingly acknowledges the pressing need

Ralf Schützhold, William G. Unruh

By gluing together two sections of flat space-time in different metric representations (with the Minkowski metric representing the region far away from the black hole and the Rindler metric modeling the vicinity of the horizon), we construct a simplified toy model for black-hole evaporation. The simple structure of this toy model allows us to construct exact

Alfonso Artigue, Luis Ferrari, Jorge Groisman

In this paper we apply techniques from nonstandard analysis to study expansive dynamical systems. Among other results, we provide a necessary and sufficient condition for an expansive homeomorphism on a compact metric space to admit doubly-asymptotic points in terms of the decay of expansivity constants of the powers of the system.

Antonello Ceravola, Frank Joublin

This paper presents HyperGraphOS, a significant innovation in the domain of operating systems, specifically designed to address the needs of scientific and engineering domains. This platform aims to combine model-based engineering, graph modeling, data containers, and documents, along with tools for handling computational elements. HyperGraphOS functions as

Sergey G. Bobkov

Basic general properties are considered for the Fisher-type information involving higher order derivatives. They are used to explore various properties of probability densities and to derive Stam-type inequalities.

You Wu, Mengfang Sun, Hongye Zheng, Jinxin Hu

This document presents an in-depth examination of stock market sentiment through the integration of Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU), enabling precise risk alerts. The robust feature extraction capability of CNN is utilized to preprocess and analyze extensive network text data, identifying local features and patterns. The e

Haowei Wang, Rupeng Zhang, Junjie Wang, Mingyang Li

Tool-calling has changed Large Language Model (LLM) applications by integrating external tools, significantly enhancing their functionality across diverse tasks. However, this integration also introduces new security vulnerabilities, particularly in the tool scheduling mechanisms of LLM, which have not been extensively studied. To fill this gap, we present T

Abderrahim Boussaïri, Brahim Chergui, Zaineb Sarir, Mohamed Zouagui

An $n\times n$ real matrix $Q$ is quasi-orthogonal if $Q^{\top}Q=qI_{n}$ for some positive real number $q$. If $M$ is a principal sub-matrix of a quasi-orthogonal matrix $Q$, we say that $Q$ is a quasi-orthogonal extension of $M$. In a recent work, the authors have investigated this notion for the class of real skew-symmetric matrices. Using a different appr

Zhendong Guo

Batch effects are inevitable in large-scale metabolomics. Prior to formal data analysis, batch effect correction (BEC) is applied to prevent from obscuring biological variations, and batch effect evaluation (BEE) is used for correction assessment. However, existing BEE algorithms neglect covariances between the variables, and existing BEC algorithms might fa

M. G. Signorelli, G. H. Hoffstaetter

We present a new method for minimizing the effects of radiative depolarization in electron storage rings by use of a minimal number of special vertical orbit bumps. The bumps can be used to minimize the effects of radiative depolarization while simultaneously maintaining other common benefits of vertical orbits, e.g. transverse coupling and vertical dispersi

C. Branchina, V. Branchina, F. Contino, A. Pernace

Considering (euclidean) quantum gravity in the Einstein-Hilbert truncation, we calculate the one-loop effective action $\Gamma^{1l}_{\rm grav}$ using a spherical background. Usually, this calculation is performed resorting to proper-time regularization within the heat kernel expansion and gives rise to quartically and quadratically UV-sensitive contributions

Yair Schiff, Subham Sekhar Sahoo, Hao Phung, Guanghan Wang

Diffusion models for continuous data gained widespread adoption owing to their high quality generation and control mechanisms. However, controllable diffusion on discrete data faces challenges given that continuous guidance methods do not directly apply to discrete diffusion. Here, we provide a straightforward derivation of classifier-free and classifier-bas

Ludmila Carone, Rory Barnes, Lena Noack, Katy L. Chubb

We investigate the impact of CO$_2$ on TRAPPIST-1 e, f and g during the magma ocean stage. These potentially habitable rocky planets are currently the most accessible for astronomical observations. A constraint on the volatile budget during the magma ocean stage is a link to planet formation and also needed to judge their habitability. We perform simulations

S. Barland, O. D'Huys, R. Veltz

We analyze experimentally and theoretically the response of a network of spiking nodes to external perturbations. The experimental system consists of an array of semiconductor lasers that are adaptively coupled through an optoelectronic feedback signal. This coupling signal can be tuned from one to all to globally coupled and makes the network collectively e

Azarmidokht Gholamipour-Shirazi, Endre Joachim Lerheim Mossige

The flavor and aroma development in fermented foods is intricately tied to the mixing dynamics during fermentation. This review explores how variations in mixing influence the physical, chemical, and microbial interactions within fermentation systems, ultimately affecting sensory characteristics such as flavor and aroma. Factors like rheology, shear forces,

Rory Bowens, Jarron Leisenring, Michael R. Meyer, Taylor L. Tobin

We present results from commissioning observations of the mid-IR instrument, MIRAC-5, on the 6.5-m MMT telescope. MIRAC-5 is a novel ground-based instrument that utilizes a state-of-the-art GeoSnap (2 - 13 microns) HgCdTe detector with adaptive optics support from MAPS to study protoplanetary disks, wide-orbit brown dwarfs, planetary companions in the contra

Louis Richard, Yuri V. Khotyaintsev, Cecilia Norgren, Konrad Steinvall

We investigate electron heating by magnetic-field-aligned electric fields ($E_\parallel$) during anti-parallel magnetic reconnection in the Earth's magnetotail. Using a statistical sample of 140 reconnection outflows, we infer the acceleration potential associated with $E_\parallel$ from the shape of the electron velocity distribution functions. We show that

Deniz Najafi, Hamza Errahmouni Barkam, Mehrdad Morsali, SungHeon Jeong

Neuro-symbolic Artificial Intelligence (AI) models, blending neural networks with symbolic AI, have facilitated transparent reasoning and context understanding without the need for explicit rule-based programming. However, implementing such models in the Internet of Things (IoT) sensor nodes presents hurdles due to computational constraints and intricacies.

Tobias Lorenz, Marta Kwiatkowska, Mario Fritz

Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems. While extensive effort has gone into empirical mitigations, the evolving nature of attacks and the complexity of data require a more principled, provable approach to robustly learn on such data - and to understand how perturbations inf

Tobias Meggendorfer, Maximilian Weininger, Patrick Wienhöft

Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In robust MDP (RMDP), every action is associated with an uncertainty set of probability distributions, modelling that transition probabilities are not known precisely. Based on the known theoretical connection to stochastic games, we

Akram Zaytar, Girmaw Abebe Tadesse, Caleb Robinson, Eduardo G. Bendito

Acquiring, processing, and visualizing geospatial data requires significant computing resources, especially for large spatio-temporal domains. This challenge hinders the rapid discovery of predictive features, which is essential for advancing geospatial modeling. To address this, we developed Similarity Search (Sims), a no-code web tool that allows users to

Afrida Alam, Kevin A. Pimbblet, Yjan A. Gordon

Next generations of radio surveys are expected to identify tens of millions of new sources, and identifying and classifying their morphologies will require novel and more efficient methods. Self-Organising Maps (SOMs), a type of unsupervised machine learning, can be used to address this problem. We map 251,259 multi-Gaussian sources from Rapid ASKAP Continuu

Daojun Liang, Haixia Zhang, Dongfeng Yuan, Minggao Zhang

The number of categories of instances in the real world is normally huge, and each instance may contain multiple labels. To distinguish these massive labels utilizing machine learning, eXtreme Label Classification (XLC) has been established. However, as the number of categories increases, the number of parameters and nonlinear operations in the classifier al

Haopeng Sun, Yingwei Zhang, Lumin Xu, Sheng Jin

Segmentation of ultra-high resolution (UHR) images is a critical task with numerous applications, yet it poses significant challenges due to high spatial resolution and rich fine details. Recent approaches adopt a dual-branch architecture, where a global branch learns long-range contextual information and a local branch captures fine details. However, they s

Jakob Thumm, Julian Balletshofer, Leonardo Maglanoc, Luis Muschal

Autonomous robots are projected to significantly augment the manual workforce, especially in repetitive and hazardous tasks. For a successful deployment of such robots in human environments, it is crucial to guarantee human safety. State-of-the-art approaches to ensure human safety are either too conservative to permit a natural human-robot collaboration or

David K. Campbell, Ian Bouche, Abhishek Som, David J. Bishop

Since its first description in 1948, the Casimir effect has been studied extensively. Standard arguments for its existence hinge on the elimination of certain modes of the electromagnetic field because of the boundary conditions in the Casimir cavity. As such, it has been suggested that the ground state energy of the vacuum within the cavity may be reduced c

Hung Nguyen, Quang Qui-Vinh Nguyen, Khoi Nguyen, Rang Nguyen

Given an input video of a person and a new garment, the objective of this paper is to synthesize a new video where the person is wearing the specified garment while maintaining spatiotemporal consistency. Although significant advances have been made in image-based virtual try-on, extending these successes to video often leads to frame-to-frame inconsistencie

Sabrina Sacerdoti

One of the far detectors of the Deep Underground Neutrino Experiment (DUNE) will be instrumented with a Vertical Drift Liquid Argon Time Projection Chamber (VD LArTPC). It will also be equipped with a Photo-Detection System (PDS), which determines the time of interaction of non-beam events, as well as contributing to the energy reconstruction. The characteri

Haomiao Liu, Hao Xu, Chuhuai Yue, Bo Ma

Unknown Object Detection (UOD) aims to identify objects of unseen categories, differing from the traditional detection paradigm limited by the closed-world assumption. A key component of UOD is learning a generalized representation, i.e. objectness for both known and unknown categories to distinguish and localize objects from the background in a class-agnost

Mariia Legenkaia, Laurent Bourdieu, Rémi Monasson

Principal Component Analysis (PCA) is one of the most used tools for extracting low-dimensional representations of data, in particular for time series. Performances are known to strongly depend on the quality (amount of noise) and the quantity of data. We here investigate the impact of heterogeneities, often present in real data, on the reconstruction of low

Botao Li, Tim Weber, Umut Kose, Matthew Franks

Plastic scintillators are widely used for the detection of elementary particles, and 3D reconstruction of particle tracks is achieved by segmenting the detector into 3D granular structures. In this study, we present a novel prototype fabricated by additive manufacturing, consisting of a 5 x 5 x 5 array of 1 cm3 plastic scintillator cubes, each optically isol

Geoffroy Oudoumanessah, Thomas Coudert, Carole Lartizien, Michel Dojat

Magnetic Resonance Fingerprinting (MRF) is an emerging technology with the potential to revolutionize radiology and medical diagnostics. In comparison to traditional magnetic resonance imaging (MRI), MRF enables the rapid, simultaneous, non-invasive acquisition and reconstruction of multiple tissue parameters, paving the way for novel diagnostic techniques.

Bram Boeykens, David Dudal, Thomas Oosthuyse

We reconsider the Equivalence Theorem from an algebraic viewpoint, using an extended BRST symmetry. This version of the Equivalence Theorem is then used to reexpress the Abelian Higgs model action, originally written in terms of undesirable gauge variant field excitations, in terms of gauge-invariant, physical variables, corresponding to the Fr\"ohlich-Morch

Mykola Krasnoshchok

We consider the Laplace equation in a cracked plane with a nonclassical boundary conditions. This problem arises as a model of the flow in the fractured media. The main result is the theorem of existence and uniqueness of a solution in weighted Sobolev spaces.

Mohammed Sabbah, Robbie Mears, Kerrianne Harrington, William J. Wadsworth

Supercontinuum generation in the ultraviolet spectral region is challenging in solid-core optical fibres due to solarization and photodarkening. Antiresonant hollow-core fibres have overcome this limitation and have been shown to guide ultraviolet light at sufficient intensity for ultraviolet spectral broadening through nonlinear optics in the filling gas. H

A. L. Hewitt, M. Pitkin, I. M. Hook

The braking index, $n$, of a pulsar is a measure of its angular momentum loss and the value it takes corresponds to different spin-down mechanisms. For a pulsar spinning down due to gravitational wave emission from the principal mass quadrupole mode alone, the braking index would equal exactly 5. Unfortunately, for millisecond pulsars, it can be hard to meas

Sanne van Kempen, Jaron Sanders, Fiona Sloothaak, Maarten G. Wolf

Motivated by applications in service systems, we consider queueing systems where each customer must be handled by a server with the right skill set. We focus on optimizing the routing of customers to servers in order to maximize the total payoff of customer--server matches. In addition, customer--server dependent payoff parameters are assumed to be unknown a

Nada Ayman, Shaimaa Alaa, Mohamed Hussein, Ali Hamdi

Asthmatic patients are very frequently affected by the quality of air, climatic conditions, and traffic density during outdoor activities. Most of the conventional routing algorithms, such as Dijkstra's algorithm, usually fail to consider these health dimensions, hence resulting in suboptimal or risky recommendations. Here, the health-aware heuristic framewo

Octavio Roncero, Pablo del Mazo-Sevillano

We present MADWAVE3, a FORTRAN90 code designed for quantum time dependent wave packet propagation in triatomic systems. This program allows the calculation of state-to-state probabilities for inelastic and reactive collisions, as well as photodissociation processes, over one or multiple coupled diabatic electronic states. The code is highly parallelized usin

Jimmie Adriazola, Katarzyna Roszak

We develop a numerical methodology for the computation of entanglement measures for mixed quantum states. Using the well-known Schr\"odinger-HJW theorem, the computation of convex roof entanglement measures is reframed as a search for unitary matrices; a nonconvex optimization problem. To address this non-convexity, we modify a genetic algorithm, known in th

Maya Gussmann, Carsten Kirkeby, Lars Rönnegård

Contagious mastitis pathogens can be transmitted through milking. However, previously published simulation models, such as MiCull, have not directly taken this into account. We have reimplemented the MiCull model to model transmission of contagious mastitis pathogens through milking in a milking parlor. This additional complexity requires a substantial incre

Xin Peng, Shangwen Wang, Yihao Qin, Bo Lin

Software vulnerability detection is crucial for high-quality software development. Recently, some studies utilizing Graph Neural Networks (GNNs) to learn the graph representation of code in vulnerability detection tasks have achieved remarkable success. However, existing graph-based approaches mainly face two limitations that prevent them from generalizing w

Federico Julian Camerota Verdù, Lorenzo Castelli, Luca Bortolussi

We introduce Limited Rollout Beam Search (LRBS), a beam search strategy for deep reinforcement learning (DRL) based combinatorial optimization improvement heuristics. Utilizing pre-trained models on the Euclidean Traveling Salesperson Problem, LRBS significantly enhances both in-distribution performance and generalization to larger problem instances, achievi

Francesco Battistoni, Enrico Miglierina

We provide new conditions under which the alternating projection sequence converges in norm for the convex feasibility problem where a linear subspace with finite codimension $N\geq 2$ and a lattice cone in a Hilbert space are considered. The first result holds for any Hilbert lattice, assuming that the orthogonal of the linear subspace admits a basis made b

Simon Wein, Marco Riebel, Lisa-Marie Brunner, Caroline Nothdurfter

Resting-state fMRI captures spontaneous neural activity characterized by complex spatiotemporal dynamics. Various metrics, such as local and global brain connectivity and low-frequency amplitude fluctuations, quantify distinct aspects of these dynamics. However, these measures are typically analyzed independently, overlooking their interrelations and potenti

Michael F. Faulkner

Topological phases have been a central focus of condensed-matter physics for over 50 years. Along with many experimental applications, they have provided much intellectual interest due to their characterization via some form of topological ordering, as opposed to the symmetry-breaking ordering of conventional continuous phase transitions. This distinction is