December 2024 arXiv papers — page 43

Hongna Liu, Sidong Chen, Frank Browne

The neutron numbers N = 32 and 34 are new magic numbers suggested in neutron-rich $pf$-shell nuclei. In this article, we discuss the experimental observables and state-of-the-art theoretical calculations that characterize and explain the shell evolution leading to new magic numbers. Particular focus shall be afforded to the experimental progress of the shell

Aswini Kumar Patra, Tejashwini Gajurel

Cotton crops, often called "white gold," face significant production challenges, primarily due to various leaf-affecting diseases. As a major global source of fiber, timely and accurate disease identification is crucial to ensure optimal yields and maintain crop health. While deep learning and machine learning techniques have been explored to address this ch

Evi M. C. Huijben, Sina Amirrajab, Josien P. W. Pluim

Out-of-distribution (OOD) detection is crucial for safely deploying automated medical image analysis systems, as abnormal patterns in images could hamper their performance. However, OOD detection in medical imaging remains an open challenge, and we address three gaps: the underexplored potential of a simple OOD detection model, the lack of optimization of de

Youseung Cho, Eduard Feireisl, Minsuk Yang

We establish various results concerning the uniqueness of zero velocity solutions for the static barotropic Navier--Stokes system. Some of them can be seen as Liouville-type theorems for problems in unbounded physical space.

ATLAS Collaboration

Charged Higgs bosons produced either in top-quark decays or in association with a top-quark, subsequently decaying via $H^{\pm} \to \tau^{\pm}\nu_{\tau}$, are searched for in 140 $\text{fb}^{-1}$ of proton-proton collision data at $\sqrt{s}=13$ TeV recorded with the ATLAS detector. Depending on whether the top-quark produced together with the $H^{\pm}$ decay

Dimitrios Charamaras, Florian K. Richter

Let $Ω(n)$ denote the number of prime factors of a positive integer $n$ counted with multiplicities. We show that for any bounded functions $a,b\colon\mathbb{N}\to\mathbb{C}$, $$\frac{1}{\log{N}}\sum_{n=1}^N \frac{a(Ω(n))b(Ω(n+1))}{n} = \Bigg(\frac{1}{N}\sum_{n=1}^N a(Ω(n))\Bigg)\Bigg(\frac{1}{N}\sum_{n=1}^N b(Ω(n))\Bigg) + \mathrm{o}_{N\to\infty}(1).$$ This

Niklas Reinhardt, Sven Wang, Jakob Zech

We present statistical convergence results for the learning of (possibly) non-linear mappings in infinite-dimensional spaces. Specifically, given a map $G_0:\mathcal X\to\mathcal Y$ between two separable Hilbert spaces, we analyze the problem of recovering $G_0$ from $n\in\mathbb N$ noisy input-output pairs $(x_i, y_i)_{i=1}^n$ with $y_i = G_0 (x_i)+\varepsi

M. Agaoglou, V. J. Garcia-Garrido, U. Harlander, A. M. Mancho

In this paper we study baroclinic waves both from the experimental and the theoretical perspective. We obtain data from a rotating annulus experiment capable of producing a series of baroclinic eddies similar to those found in the mid-latitude atmosphere. We analyze the experimental outputs using two methods. First, we apply a technique that involves filteri

Vignesh Anantharamakrishnan, Márcio M. Taddei

Variational quantum circuits have arisen as an important method in quantum computing. A crucial step of it is parameter optimization, which is typically tackled through gradient-descent techniques. We advantageously explore instead the use of evolutionary algorithms for such optimization, specifically for time-series forecasting. We perform a comparison, for

Konstantin Baune, Johannes Broedel, Federico Zerbini

Inspired by earlier results on recursions for open-string tree-level amplitudes, and by a result of Brown and Dupont relating open- and closed-string tree-level amplitudes via single-valued periods, we identify a recursive relation for closed-string tree-level amplitudes. We achieve this by showing that closed-string analogues of Selberg integrals satisfy th

Danilo Zia, Mario Zitelli, Gonzalo Carvacho, Nicolò Spagnolo

Mode-division multiplexing using multimode optical fibers has been intensively studied in recent years, in order to alleviate the transmission capacity crunch. Moreover, the need for secure information transmission based on quantum encryption protocols leads to investigating the possibility of multiplexing both quantum and classical signals in the same fiber

Keivan Khosroshahi, Philippe Sehier, Sami Mekki, Michael Suppa

Integrated sensing and communication (ISAC) is anticipated to play a crucial role in sixth-generation (6G) mobile communication networks. A significant challenge in ISAC systems is the degradation of localization accuracy due to poor propagation conditions, such as multipath effects and non-line-of-sight (NLoS) scenarios. These conditions result in outlier m

Fumiya Hori, Hiroyasu Matsudaira, Shunsaku Kitagawa, Kenji Ishida

Frustrated magnetic systems with anisotropic exchange interactions have been recognized as key platforms for discovering exotic quantum states and quasiparticles. In this study, we report the pressure evolution of magnetic structures and quasiparticle excitations in the frustrated semiconductor YbCuS2, characterized by Yb3+ zigzag chains with competing excha

Ting Zhou, Daoyuan Chen, Qirui Jiao, Bolin Ding

Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook the intricacies of emotion, behavior, and cross-modal alignment. We introduce HumanVBench, a comprehensive video benchmark designed to rigorously probe these capabilities across 16

Jie Song, Yue Sun, Ziyun Cai, Liang Xiao

The challenges of road network segmentation demand an algorithm capable of adapting to the sparse and irregular shapes, as well as the diverse context, which often leads traditional encoding-decoding methods and simple Transformer embeddings to failure. We introduce a computationally efficient and powerful framework for elegant road-aware segmentation. Our m

Yeonju Kim, Se Jin Park, Yong Man Ro

Chatbot research is advancing with the growing importance of chatbots in fields that require human interactions, such as customer support and mental health care. Despite these advancements, chatbots still face significant challenges in understanding subtle nuances and managing long conversation histories. To address these issues, our study introduces a dual

Murat Isik, Hiruna Vishwamith, Jonathan Naoukin, I. Can Dikmen

This paper presents the innovative HPCNeuroNet model, a pioneering fusion of Spiking Neural Networks (SNNs), Transformers, and high-performance computing tailored for particle physics, particularly in particle identification from detector responses. Our approach leverages SNNs' intrinsic temporal dynamics and Transformers' robust attention mechanisms to enha

Dibyendu Bala, Sajid Ali, Olaf Kaczmarek, Pavan

Quarkonia, the bound states of heavy quark-antiquark pairs, are important tools for studying the quark-gluon plasma (QGP). In this study, we examine the behavior of in-medium quarkonium bound states in the QGP by analyzing their spectral functions at two temperatures, $T = 220\,\textrm{MeV}$ and $T = 293\,\textrm{MeV}$. We use physics-motivated information t

Jaume Llabrés, Sara Oliver-Bonafoux, Celia Anteneodo, Raúl Toral

We investigate the effects of aging in the noisy voter model considering that the probability to change states decays algebraically with age $\tau$, defined as the time elapsed since adopting the current state. We study the complete aging scenario, which incorporates aging to both mechanisms of interaction: herding and idiosyncratic behavior, and compare it

Noel Fortun, Piolo Gaspar, Editha Jose, Angelyn Lao

This paper focuses on what we call Reaction Network Cardon Dioxide Removal (RNDCR) framework to analyze several proposed negative emissions technologies (NETs) so as to determine when present-day Earth carbon cycle system would exhibit multistationarity (steady-state multiplicity) or possibly monostationarity, that will in effect lower the rising earth tempe

M. R. Jia

We present an event-by-event study of photons produced by the chiral magnetic effect in the early stage of high-energy nuclear collisions. We model the early stage with an evolving, 2+1 dimensional, glasma, initialized via the McLerran-Venugopalan model. The photons are produced from the interplay of the chiral anomaly of quantum chromodynamics and the stron

Shentong Mo

Masked autoencoders (MAE) have recently succeeded in self-supervised vision representation learning. Previous work mainly applied custom-designed (e.g., random, block-wise) masking or teacher (e.g., CLIP)-guided masking and targets. However, they ignore the potential role of the self-training (student) model in giving feedback to the teacher for masking and

Theodoros Tsiolakis, Nikolaos Pavlidis, Vasileios Perifanis, Pavlos Efraimidis

Cellular traffic forecasting is a critical task that enables network operators to efficiently allocate resources and address anomalies in rapidly evolving environments. The exponential growth of data collected from base stations poses significant challenges to processing and analysis. While machine learning (ML) algorithms have emerged as powerful tools for

Narutaka Ozawa

We prove that the approximately inner automorphism group of a separable strongly stable von Neumann algebra is contractible in the u-topology. Thus the automorphism group of the hyperfinite type III_1 factor is contractible.

Klaus Kroencke, Markus Wolff

We construct asymptotic foliations of asymtotically Schwarzschildean lightcones by surfaces of constant spacetime mean curvature (STCMC). Our construction is motivated by the approach of Huisken-Yau for the Riemannian setting in employing a geometric flow. We prove that initial data within a sufficient a-priori class converges exponentially to an STCMC surfa

Mohammed Furqan, Raahid Bin Khaja, Rayyan Habeeb

Bridging linguistic gaps fosters global growth and cultural exchange. This study addresses the challenges of Roman Urdu -- a Latin-script adaptation of Urdu widely used in digital communication -- by creating a novel parallel dataset comprising 75,146 sentence pairs. Roman Urdu's lack of standardization, phonetic variability, and code-switching with English

Zixi Liang, Guowei Xu, Haifeng Wu, Ye Huang

Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the oversimplified explicit representations th

Chao Zeng, Songwei Liu, Shu Yang, Fangmin Chen

Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a novel compression technique tailored for LLMs. Traditional methods typically focus exclusively on either quantization or sparsification, but relying on a single strategy often resul

Amr Salem, Anirban Mondal

Kidney stones are a common and debilitating health issue, and genetic factors play a crucial role in determining susceptibility. While Genome-Wide Association Studies (GWAS) have identified numerous single nucleotide polymorphisms (SNPs) linked to kidney stone risk, translating these findings into effective clinical tools remains a challenge. In this study,

Mingyang Song, Mao Zheng

Query Optimization (QO) has become essential for enhancing Large Language Model (LLM) effectiveness, particularly in Retrieval-Augmented Generation (RAG) systems where query quality directly determines retrieval and response performance. This survey provides a systematic and comprehensive analysis of query optimization techniques with three principal contrib

Elliot Kaplan

Let $\mathbb{T}$ be the differential field of logarithmic-exponential transseries. We consider the expansion of $\mathbb{T}$ by the binary map that sends a real number $r$ and a positive transseries $f$ to the transseries $f^r$. Building on recent work of Aschenbrenner, van den Dries, and van der Hoeven, we show that this expansion is model complete, and we

Juan Manuel Diaz, Marcelo E. Rubio

We explore dynamical features of the maximally symmetric nonlinear extension of classical electromagnetism, recently proposed in the literature as ``ModMax'' electrodynamics. This family of theories is the only one that preserves all the symmetries of Maxwell's theory, having applications in the study of regular black hole solutions and supersymmetry. The pu

Sadhana Singh, Manisha Priyadarsini, Sharanjeet Singh, Ilya Sergeev

In the present work, spin orientation and variation of the strength of coupling in [Co/Pt]ML/Fe multilayer have been investigated as a function of the thickness of the Fe layer. [Co/Pt]ML/Fe multilayer has orthogonal anisotropy with the Fe layer, and [Co/Pt]ML has in-plane magnetic anisotropy and perpendicular anisotropy, respectively. Measurements are perfo

Peter Grünwald, Yunda Hao, Akshay Balsubramani

We consider growth-optimal e-variables with maximal e-power, both in an absolute and relative sense, for simple null hypotheses for a $d$-dimensional random vector, and multivariate composite alternatives represented as a set of $d$-dimensional means $\meanspace_1$. These include, among others, the set of all distributions with mean in $\meanspace_1$, and th

I. Carmeli, Vladimiro Mujica, Gregory Leitus, Pini Shechter

The work investigates enantiomers of chiral organo-metallic particles that exhibit a collective memory effect. Under the influence of magnetic field millions of particles in solution form macroscopic shapes and when dispersed again at zero field they return to their original shape. The charge and magnetic behaviors of the particles are strongly dependent on

Klara Kramer

This study evaluates the performance of TF-IDF weighting, averaged Word2Vec embeddings, and BERT embeddings for document similarity scoring across two contrasting textual domains. By analysing cosine similarity scores, the methods' strengths and limitations are highlighted. The findings underscore TF-IDF's reliance on lexical overlap and Word2Vec's superior

Veronika Baumann, Ämin Baumeler, Eleftherios-Ermis Tselentis

In standard quantum theory, the causal relations between operations are fixed. One can relax this notion by allowing for dynamical arrangements, where operations may influence the causal relations of future operations, as certified by violation of fixed-order inequalities, e.g., the k-cycle inequalities. Another, non-causal, departure further relaxes these l

Wei Liu, Stanislau U. Piatrusha, Lena Fürst, Lukas Lunczer

We measured the microwave response of a HgTe quantum well Josephson junction embedded into an RF SQUID loop which is inductively coupled to a superconducting resonator. The side-contacted devices studied here operate in bulk transport mode, with a separation between the superconducting contacts smaller than both the estimated carrier mean free path and super

Manuel Meier, Berken Utku Demirel, Christian Holz

Multi-channel photoplethysmography (PPG) sensors have found widespread adoption in wearable devices for monitoring cardiac health. Channels thereby serve different functions -- whereas green is commonly used for metrics such as heart rate and heart rate variability, red and infrared are commonly used for pulse oximetry. In this paper, we introduce a novel me

Prakash Aryan

This paper presents a novel approach to fine-tuning the Qwen2-1.5B model for Arabic language processing using Quantized Low-Rank Adaptation (QLoRA) on a system with only 4GB VRAM. We detail the process of adapting this large language model to the Arabic domain, using diverse datasets including Bactrian, OpenAssistant, and Wikipedia Arabic corpora. Our method

Shuyang Liu, Ruiqiu Zheng, Yunhang Shen, Ke Li

Semi-supervised learning (SSL) assumes that neighbor points lie in the same category (neighbor assumption), and points in different clusters belong to various categories (cluster assumption). Existing methods usually rely on similarity measures to retrieve the similar neighbor points, ignoring cluster assumption, which may not utilize unlabeled information s

Jiansong Li, Heping Wang

Consider the numerical integration $${\rm Int}_{\mathbb S^d,w}(f)=\int_{\mathbb S^d}f({\bf x})w({\bf x}){\rm d}\sigma({\bf x}) $$ for weighted Sobolev classes $BW_{p,w}^r(\mathbb S^d)$ with a Dunkl weight $w$ and weighted Besov classes $BB_\gamma^\Theta(L_{p,w}(\mathbb S^d))$ with the generalized smoothness index $\Theta $ and a doubling weight $w$ on the un

Martin Bohnert

The Kodaira dimension of a nondegenerate toric hypersurface can be computed from the dimension of the Fine interior of its Newton polytope according to recent work of Victor Batyrev, where the Fine interior of the Newton polytope is the subpolytope consisting of all points which have an integral distance of at least $1$ to all integral supporting hyperplanes

Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho

The emergence of Vision-Language Models (VLMs) is a significant advancement in integrating computer vision with Large Language Models (LLMs) to enhance multi-modal machine learning capabilities. However, this progress has also made VLMs vulnerable to sophisticated adversarial attacks, raising concerns about their reliability. The objective of this paper is t

Martin Hanek, Jan Papež, Jakub Šístek

We deal with accelerating the solution of a sequence of large linear systems solved by preconditioned conjugate gradient method (PCG). The sequence originates from time-stepping within a simulation of an unsteady incompressible flow. We apply a pressure correction scheme and focus on the solution of the Poisson problem for the pressure corrector. Its scalabl

Laura Manduchi, Antoine Wehenkel, Jens Behrmann, Luca Pegolotti

Whole-body hemodynamics simulators, which model blood flow and pressure waveforms as functions of physiological parameters, are now essential tools for studying cardiovascular systems. However, solving the corresponding inverse problem of mapping observations (e.g., arterial pressure waveforms at specific locations in the arterial network) back to plausible

Haoyuan Zhang, Xiangyu Zhu, Li Gao, Jiawei Pan

With the rapid growth usage of face recognition in people's daily life, face anti-spoofing becomes increasingly important to avoid malicious attacks. Recent face anti-spoofing models can reach a high classification accuracy on multiple datasets but these models can only tell people "this face is fake" while lacking the explanation to answer "why it is fake".

Manuel Meier, Berken Utku Demirel, Christian Holz

Reflective photoplethysmography (PPG) has become the default sensing technique in wearable devices to monitor cardiac activity via a person's heart rate (HR). However, PPG-based HR estimates can be substantially impacted by factors such as the wearer's activities, sensor placement and resulting motion artifacts, as well as environmental characteristics such

Vladislav Bushmakin, Oliver von Berg, Colin Sauerzapf, Sreehari Jayaram

Scalable quantum networks rely on optical connections between long-lived qubits to distribute entanglement. Tin vacancies in diamond have emerged as promising long-lived qubits, offering extended spin coherence times at liquid helium temperatures and spin-dependent, highly coherent optical transitions for effective photon-based communication. Connecting remo

Manuel Meier, Christian Holz

Smartwatches have become popular for monitoring physiological parameters outside clinical settings. Using reflective photoplethysmography (PPG) sensors, such watches can non-invasively estimate heart rate (HR) in everyday environments and throughout a patient's day. However, achieving consistently high accuracy remains challenging, particularly during moment

Danielle Saunders, Steve DeNeefe

Neural Machine Translation (NMT) models can be specialized by domain adaptation, often involving fine-tuning on a dataset of interest. This process risks catastrophic forgetting: rapid loss of generic translation quality. Forgetting has been widely observed, with many mitigation methods proposed. However, the causes of forgetting and the relationship between

Geraldo Gomes, Pedro Freire, Jaroslaw E. Prilepsky, Sergei K. Turitsyn

We present a novel time-domain chromatic dispersion equalizer, implemented on FPGA, eliminating pre-processing and multipliers, achieving up to 54.3% energy savings over 80-1280 km with a simple, low-power design.

G. S. Adkins, U. D. Jentschura

Three-loop electronic vacuum-polarization corrections due to irreducible diagrams are evaluated for two-body muonic ions with nuclear charge numbers 1 <= Z <= 6. The corrections are of order alpha^3 (Zalpha)^2 m_r, where alpha is the fine-structure constant and m_r is the reduced mass. Numerically, the energy corrections are found to be of the same order-of-

Ege Yiğit Çelik, Selma Tekir

Local citation recommendation (LCR) suggests a set of papers for a citation placeholder within a given context. The task has evolved as generative approaches have become more promising than the traditional pre-fetch and re-rank-based state-of-the-art approaches. This paper introduces citation-specific pre-training within an encoder-decoder architecture, wher

Anna Kołos, Katarzyna Lorenc, Emilia Wiśnios, Agnieszka Karlińska

The surge in online content has created an urgent demand for robust detection systems, especially in non-English contexts where current tools demonstrate significant limitations. We present forePLay, a novel Polish language dataset for erotic content detection, featuring over 24k annotated sentences with a multidimensional taxonomy encompassing ambiguity, vi

Adrian Azzarelli, Nantheera Anantrasirichai, David R Bull

Novel view synthesis (NVS) has shown significant promise for applications in cinematographic production, particularly through the exploitation of Neural Radiance Fields (NeRF) and Gaussian Splatting (GS). These methods model real 3D scenes, enabling the creation of new shots that are challenging to capture in the real world due to set topology or expensive e

Yang Hou, Qiuling Yue, Lujia Chai, Guozhao Liao

Backdoor attacks pose an important security threat to textual large language models. Exploring textual backdoor attacks not only helps reveal the potential security risks of models, but also promotes innovation and development of defense mechanisms. Currently, most textual backdoor attack methods are based on a single trigger. For example, inserting specific

Vahideh Hayyolalam, Abdulrezzak Zekiye, Hamza Abuzahra, Oznur Ozkasap

Peer-to-peer (P2P) energy trading, Smart Grids (SG), and electric vehicle energy management are integral components of the Internet of Energy (IoE) field. The integration of Software-Defined Networks (SDNs) and Blockchain (BC) technologies into the IoE domain offers potential benefits that have only been studied in the literature in a few works. In this pape

Germán Molpeceres, Thanh Nguyen, Yasuhiro Oba, Naoki Watanabe

We sought to determine which are the main hydrogenation paths of acetaldehyde (CH3CHO). As a partially unsaturated molecule, CH3CHO can have links with more hydrogenated species, like ethanol (C2H5OH) or with more unsaturated ones, like ketene (H2CCO). We used highly accurate quantum chemical calculations to determine the reaction rate constants for the CH3C

Tobias Sägesser, Shreyans Jain, Pavel Hrmo, Alexander Ferk

Single-atom quantum sensors offer high spatial resolution and high sensitivity to electric and magnetic fields. Among them, trapped ions offer exceptional performance in sensing electric fields, which has been used in particular to probe these in the proximity of metallic surfaces. However, the flexibility of previous work was limited by the use of radio-fre

Badaru I. Olumuyiwa, The Anh Han, Zia U. Shamszaman

This research presents an innovative approach to cancer diagnosis and prediction using explainable Artificial Intelligence (XAI) and deep learning techniques. With cancer causing nearly 10 million deaths globally in 2020, early and accurate diagnosis is crucial. Traditional methods often face challenges in cost, accuracy, and efficiency. Our study develops a

Eric Opdam, Valerio Toledano-Laredo

This is the text of a talk given by the first author at the Harish-Chandra centenary meeting held in Allahabad in October 2023. It reviews Harish-Chandra's isomorphism and its many applications to representation theory and mathematical physics. It also announces the existence and uniqueness of nonsymmetric shift operators for an arbitrary root system. These

Jiyao Wang, Zehua Peng, Yijia Zhang, Dengbo He

Traffic flow prediction plays a critical role in the intelligent transportation system, and it is also a challenging task because of the underlying complex Spatio-temporal patterns and heterogeneities evolving across time. However, most present works mostly concentrate on solely capturing Spatial-temporal dependency or extracting implicit similarity graphs,

Hyungjun Joo, Hyeonggeun Han, Sehwan Kim, Sangwoo Hong

As the use of machine learning models has increased, numerous studies have aimed to enhance fairness. However, research on the intersection of fairness and explainability remains insufficient, leading to potential issues in gaining the trust of actual users. Here, we propose a novel module that constructs a fair latent space, enabling faithful explanation wh

Hao Wang, Hao Li, Junda Zhu, Xinyuan Wang

Large Language Models (LLMs) are susceptible to generating harmful content when prompted with carefully crafted inputs, a vulnerability known as LLM jailbreaking. As LLMs become more powerful, studying jailbreak methods is critical to enhancing security and aligning models with human values. Traditionally, jailbreak techniques have relied on suffix addition

V. Vasylyk, V. L. Makarov

An exponentially convergent numerical method for solving a differential equation with a right-hand fractional Riemann-Liouville time-derivative and an unbounded operator coefficient in Banach space is proposed and analysed for a homogeneous/inhomogeneous equation of the Hardy-Tichmarsh type. We employ a solution representation by the Danford-Cauchy integral

Shi-Yu Li, Song-Shan Luo, Zhong-Wen Feng

This study investigates photon orbits around Kerr-MOG black holes. The equation of photon of motion around the Kerr-MOG black hole is derived by solving the Hamilton-Jacobi equation, expressed as a sixth-order polynomial involving the inclination angle $v$, the rotation parameter $u$, and the deformation parameter $\alpha$ that characterizes modified gravity

Gopal Chandra Santra, Philipp Hauke

Understanding how physical systems are influenced by disorder is a fundamental challenge in quantum science. Addressing its effects often involves numerical averaging over a large number of samples, and it is not always easy to gain an analytical handle on exploring the effect of disorder. In this work, we derive exact solutions for disorder-averaged dynamic

Mike Nguyen, Nicole Mücke

We introduce the neural tangent kernel (NTK) regime for two-layer neural operators and analyze their generalization properties. For early-stopped gradient descent (GD), we derive fast convergence rates that are known to be minimax optimal within the framework of non-parametric regression in reproducing kernel Hilbert spaces (RKHS). We provide bounds on the n

Dylan jayabahu, Parthipan Siva

Human action detection using privacy-preserving mmWave radar sensors is studied for its applications in healthcare and home automation. Unlike existing research, limited to simulations in controlled environments, we present a real-world mmWave radar dataset with baseline results for human action detection.

Maria V. Efremova, Lotte Boer, Laurenz Edelmann, Lieke Ruijs

Particles with high anisotropy in their magnetic properties and shape are of increasing interest for mechanobiology, where transducing a remotely applied magnetic field vector to a local mechanical response is crucial. An outstanding challenge is quantifying the mechanical torque of a single nanoparticle, typically in the range of atto- to femto-Newton-meter

P. A. Palicio, A. Recio-Blanco, T. Tepper-Garcia, E. Poggio

Among the various implications of the spiral arms, it has been observed in the Milky Way disc that the distribution of radial actions from the Gaia Data Release 3 exhibits structures that may be related to the spiral arms. Our goal is to investigate the relationship between regions of low radial action identified in simulated discs and the location of the sp

Andrey Saveliev, Rafael Alves Batista, Feodor Mishin

Lorentz invariance violation is a feature of several quantum gravity models in which Lorentz symmetry is broken at high energies, leading to potential changes in particle behavior and interactions. In this study, we investigate vacuum Cherenkov radiation, a reaction in which an electron spontaneously emits a photon. This process, forbidden when considering u

Konstantin Emil Thiel, Paavo Sattler, Arne C Bathke, Georg Zimmermann

Analysis of covariance is a crucial method for improving precision of statistical tests for factor effects in randomized experiments. However, existing solutions suffer from one or more of the following limitations: (i) they are not suitable for ordinal data (as endpoints or explanatory variables); (ii) they require semiparametric model assumptions; (iii) th

Oren Barkan, Yehonatan Elisha, Jonathan Weill, Noam Koenigstein

Two prominent challenges in explainability research involve 1) the nuanced evaluation of explanations and 2) the modeling of missing information through baseline representations. The existing literature introduces diverse evaluation metrics, each scrutinizing the quality of explanations through distinct lenses. Additionally, various baseline representations

Koyel Majumdar, Florence Jaffrézic, Andrea Rau, Isobel Claire Gormley

Gene expression and DNA methylation are two interconnected biological processes and understanding their relationship is important in advancing understanding in diverse areas, including disease pathogenesis, environmental adaptation, developmental biology, and therapeutic responses. Differential analysis, including the identification of differentially methyla

Tomonori Kouya

Sparse matrices have recently played a significant and impactful role in scientific computing, including artificial intelligence-related fields. According to historical studies on sparse matrix--vector multiplication (SpMV), Krylov subspace methods are particularly sensitive to the effects of round-off errors when using floating-point arithmetic. By employin

Xuanlong Ma, Yuefeng Yang, Liangliang Zhai

Given a finite group $G$ with identity $e$ and a normal subgroup $H$ of $G$, the subgroup sum graph $\Gamma_{G,H}$ (resp. extended subgroup sum graph $\Gamma_{G,H}^+$) of $G$ with respect to $H$ is the graph with vertex set $G$, in which distinct vertices $x$ and $y$ are adjacent whenever $xy\in H\setminus \{e\}$ (resp. $xy\in H$). A group $G$ is said to be

Nikita Lagrange, Herve Isambert

We propose a greedy search-and-score algorithm for ancestral graphs, which include directed as well as bidirected edges, originating from unobserved latent variables. The normalized likelihood score of ancestral graphs is estimated in terms of multivariate information over relevant ``ac-connected subsets'' of vertices, C, that are connected through collider

Li Fu, Shanyong Yu, Siqi Li, Lu Fan

Recent advancements in scaling up models have significantly improved performance in Automatic Speech Recognition (ASR) tasks. However, training large ASR models from scratch remains costly. To address this issue, we introduce UME, a novel method that efficiently Upcycles pretrained dense ASR checkpoints into larger Mixture-of-Experts (MoE) architectures. Ini

Junyang Gou, Arnt-Børre Salberg, Mostafa Kiani Shahvandi, Mohammad J. Tourian

Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the Earth system. In recent years, geoscience and climate scientists have benefited from rapid progress in deep learning to advance the estimation of ECV products with improved accuracy

Mounia Drissi

Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public safety, security, and healthcare. However, these systems inherit biases from their single modalities. This study proposes a systemic framework for analyzing dynamic multimodal bias interactions. Using the MMBias

Yuqi Liang, Jun Luo, Xiaoxi Guo, Jianqi Bi

In product advertising applications, the automated inpainting of backgrounds utilizing AI techniques in product images has emerged as a significant task. However, the techniques still suffer from issues such as inappropriate background and inconsistent product in generated product images, and existing approaches for evaluating the quality of generated produc

Aristotelis P. Sgouros, Markos Arapchatzis, Nektarios N. Lathiotakis, Konstantinos Papagelis

In this work, we present an empirical force field for hexagonal boron nitride (hBN) monolayers, derived via a bottom-up strategy from first principles calculations. We aim to deliver a simple analytical force field for boron nitride which is efficient and applicable to large-scale simulations, without compromising its accuracy. The force field is developed w

Nitin Kumar Chidambaram, Maciej Dołęga, Kento Osuga

We prove that single $G$-weighted $\mathfrak{b}$-Hurwitz numbers with internal faces are computed by refined topological recursion on a rational spectral curve, for certain rational weights $G$. Consequently, the $\mathfrak{b}$-Hurwitz generating function analytically continues to a rational curve. In particular, our results cover the cases of $\mathfrak{b}$

Swarnendu Mandal, Swati Chauhan, Umesh Kumar Verma, Manish Dev Shrimali

We demonstrate a data-driven technique for adaptive control in dynamical systems that exploits the reservoir computing method. We show that a reservoir computer can be trained to predict a system parameter from the time series data. Subsequently, a control signal based on the predicted parameter can be used as feedback to the dynamical system to lead it to a

Junying Lu, Yaojun Chen

Let $H$ be an oriented graph without directed cycle. The oriented Ramsey number of $H$, denoted by $\overrightarrow{r}(H)$, is the smallest integer $N$ such that every tournament on $N$ vertices contains a copy of $H$. Rosenfeld (JCT-B, 1974) conjectured that $\overrightarrow{r}(H)=|H|$ if $H$ is a cycle of sufficiently large order, which was confirmed for $

Linus Heck, Maximilian Gelbrecht, Michael T. Schaub, Niklas Boers

Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they systematically underestimate the noise level inherent in such data, limiting their ability to capture stochastic dynamics accurately. We investigate this underestimation in detail

Emilie Chouzenoux, Cecile Della Valle, Jean-Christophe Pesquet

We consider a neural network architecture designed to solve inverse problems where the degradation operator is linear and known. This architecture is constructed by unrolling a forward-backward algorithm derived from the minimization of an objective function that combines a data-fidelity term, a Tikhonov-type regularization term, and a potentially nonsmooth

Jiaan Wang, Fandong Meng, Yunlong Liang, Jie Zhou

Recently, O1-like models have emerged as representative examples, illustrating the effectiveness of long chain-of-thought (CoT) in reasoning tasks such as math and coding tasks. In this paper, we introduce DRT, an attempt to bring the success of long CoT to neural machine translation (MT). Specifically, in view of the literature books that might involve simi

Sergi Masot-Llima, Artur Garcia-Saez

Tensor networks are a very powerful data structure tool originating from quantum system simulations. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance exploiting hardware acceleration. As ans\"atze, tensor networks can be used with flexible geometrie

Wenxuan Fang, Junkai Fan, Yu Zheng, Jiangwei Weng

Image dehazing, particularly with learning-based methods, has gained significant attention due to its importance in real-world applications. However, relying solely on the RGB color space often fall short, frequently leaving residual haze. This arises from two main issues: the difficulty in obtaining clear textural features from hazy RGB images and the compl

Elahe Samimi, Antonia Duft, Patrick Adelhardt, Kai Phillip Schmidt

We study the ground-state phase diagram of the spin-$1/2$ Kitaev-Heisenberg model on the bilayer honeycomb lattice with large-scale tensor network calculations based on the infinite projected entangled pair state technique as well as high-order series expansions. We find that beyond various magnetically ordered phases, including ferromagnetic, zigzag, antife

Cillian Cockrell, Margaret-Ann Withington, Harvey L. Devereux, Alin M. Elena

Molten salts are important in a number of energy applications, but the fundamental mechanisms operating in ionic liquids are poorly understood, particularly at higher temperatures. This is despite their candidacy for deployment in solar cells, next-generation nuclear reactors, and nuclear pyroprocessing. We perform extensive molecular dynamics simulations ov

Swaraj Pratim Sarmah, Umananda Dev Goswami

In this study, we investigated the anisotropy of diffusive Ultra-High Energy Cosmic Rays (UHECRs) by employing three cosmological models: two models from the $f(R, T)$ gravity theory and the other is the standard $\Lambda$CDM model. The primary objective of this work was to ascertain the role of the $f(R, T)$ gravity theory in comprehending the anisotropy of

Philippe Lecheminant, Yuya Tanizaki, Keisuke Totsuka

A general strategy is proposed to explore the low-energy properties of two-dimensional nonlinear $\sigma$ models with $\theta$ terms. We demonstrate its application to nonlinear $\sigma$ models with the target space $\text{SU($N$)}$/H, which include $\mathbb{C}P^{N-1}$, complex Grassmannian manifolds as well as the flag $\text{SU($N$)}/\text{U(1)}^{N-1}$ and

Florian Ingels, Camille Marchet, Mikaël Salson

The minimizer of a word of size $k$ (a $k$-mer) is defined as its smallest substring of size $m$ (with $m\leq k$), according to some ordering on $m$-mers. minimizers have been used in bioinformatics -- notably -- to partition sequencing datasets, binning together $k$-mers that share the same minimizer. It is folklore that using the lexicographical order lead

Lindsay Bassman Oftelie, Michele Campisi

We report on the experimental measurement of the work statistics of a genuinely open quantum system using a quantum computer. Such measurement has remained elusive thus far due to the inherent difficulty in measuring the total energy change of a system-bath compound (which is the work) in the open quantum system scenario. We overcome this difficulty by exten

Tim Rolff, Niklas Hypki, Markus Lappe, Frank Steinicke

Due to the still relatively low number of users, acquiring large-scale and multidimensional virtual reality datasets remains a significant challenge. Consequently, VR datasets comparable in size to state-of-the-art collections in natural language processing or computer vision are rare or absent. However, the availability of such datasets could unlock groundb

Dimitris Koutromanos, Dionisis Stefanatos, Emmanuel Paspalakis

Coupled spins form composite quantum systems which play an important role in many quantum technology applications, with an essential task often being the efficient generation of entanglement between two constituent qubits. The simplest such system is a pair of spins-$1/2$ coupled with Ising interaction, and in previous works various quantum control methods s