November 2024 arXiv papers — page 180

L. Coccato, L. Morelli, A. Pizzella, E. M. Corsini

Context. Kinematically decoupled cores (KDCs) are often found in the centers of early-type galaxies. Aims. We aim to investigate the kinematics, structure, and stellar populations of the KDC residing in the early-type galaxy NGC 4494 to understand its formation. Methods. We used long-slit spectroscopic data obtained with the FORS2 instrument on the VLT to me

Linglan Zhao, Xuerui Zhang, Ke Yan, Shouhong Ding

Continual learning aims to incrementally acquire new concepts in data streams while resisting forgetting previous knowledge. With the rise of powerful pre-trained models (PTMs), there is a growing interest in training incremental learning systems using these foundation models, rather than learning from scratch. Existing works often view PTMs as a strong init

Maxime Kawawa-Beaudan, Srijan Sood, Soham Palande, Ganapathy Mani

We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior. We discuss framing common problems in fields like healthcare, finance, and e-commerce as sequence modeling tasks, and address challenges related to constructing coherent sequen

Qingnan An, Søren Eilers, Guihua Gong, Zhichao Liu

In this paper, we construct a class of ASH algebras of real rank zero and stable rank one which is not K-pure. Then we show the following: (i) There exists a real rank zero inductive limit of 1-dimensional noncommutative CW complexes which is not an A$\mathcal{HD}$ algebra, when $K_1$ is torsion free or has bounded torsion. (ii) Total K-theory is not a compl

Hugo Akitaya, Jean Cardinal, Stefan Felsner, Linda Kleist

We consider facet-Hamiltonian cycles of polytopes, defined as cycles in their skeleton such that every facet is visited exactly once. These cycles can be understood as optimal watchman routes that guard the facets of a polytope. We consider the existence of such cycles for a variety of polytopes, the facets of which have a natural combinatorial interpretatio

Róbert Juhász, Géza Ódor

A two-dimensional lattice of oscillators with identical (zero) intrinsic frequencies and Kuramoto type of interactions with randomly frustrated couplings is considered. Starting the time evolution from slightly perturbed synchronized states, we study numerically the relaxation properties, as well as properties at the stable fixed point which can also be view

E. T. Akhmedov, I. A. Belkovich, D. V. Diakonov, K. A. Kazarnovskii

We consider two approaches to calculate imaginary parts of effective actions in expanding space-times. While the first approach uses Bogolyubov coefficients, the second one uses the functional integral or the Feynman propagator. In eternally expanding space-times these two approaches give different answers for the imaginary parts. The origin of the differenc

Cem Bilaloglu, Tobias Löw, Sylvain Calinon

Virtual fixtures assist human operators in teleoperation settings by constraining their actions. This extended abstract introduces a novel virtual fixture formulation \emph{on surfaces} for tactile robotics tasks. Unlike existing methods, our approach constrains the behavior based on the position on the surface and generalizes it over the surface by consider

Tom Pelletreau-Duris, Ruud van Bakel, Michael Cochez

Deep neural networks (DNNs) achieve state-of-the-art performance on many tasks, but this often requires increasingly larger model sizes, which in turn leads to more complex internal representations. Explainability techniques (XAI) have made remarkable progress in the interpretability of ML models. However, the non-euclidean nature of Graph Neural Networks (G

Jean-François Babadjian, Alessandro Giacomini, Maria Giovanna Mora

This work addresses the question of regularity of solutions to evolutionary (quasi-static and dynamic) perfect plasticity models. Under the assumption that the elasticity set is a compact convex subset of deviatoric matrices, with $C^2$ boundary and positive definite second fundamental form, it is proved that the Cauchy stress admits spatial partial derivati

Xin Zeng, Yuxin Kang, Chunfang Sun, Chunfeng Wu

In this study, we propose a theoretical scheme for achieving long-distance Greenberger-Horne-Zeilinger states in a driven hybrid quantum system. By applying a microwave field to the YIG sphere, we utilize the Kerr effect to induce the squeezing of the magnon, thereby achieving an exponential enhancement of the coupling strength between the magnonic mode and

Petr Pálka, Federico Landini, Dominik Klement, Mireia Diez

In spite of the popularity of end-to-end diarization systems nowadays, modular systems comprised of voice activity detection (VAD), speaker embedding extraction plus clustering, and overlapped speech detection (OSD) plus handling still attain competitive performance in many conditions. However, one of the main drawbacks of modular systems is the need to run

Kishan Sthankiya, Nagham Saeed, Greg McSorley, Mona Jaber

This survey uncovers the tension between AI techniques designed for energy saving in mobile networks and the energy demands those same techniques create. We compare modeling approaches that estimate power usage cost of current commercial terrestrial next-generation radio access network deployments. We then categorize emerging methods for reducing power usage

Qi Guo, Leyao Wei, Wentao Luo, Shurui Lin

We propose a novel microlensing event search method that differs from either the traditional time domain method, astrometric microlensing, or the parallax microlensing method. Our method assumes that stars with nearly identical "genes" - normalized Spectral Energy Distributions (SED) bear the same luminosity within the intrinsic scatter due to stellar proper

Angus Kan, Benjamin Symons

Exploring low-cost applications is paramount to creating value in early fault-tolerant quantum computers. Here we optimize both gate and qubit counts of recent algorithms for simulating the Fermi-Hubbard model. We further devise and compile algorithms to simulate established models of cuprate and pnictide high-temperature superconductors, which include beyon

Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier, Anna Hedström

We introduce PASTA (Perceptual Assessment System for explanaTion of Artificial Intelligence), a novel human-centric framework for evaluating eXplainable AI (XAI) techniques in computer vision. Our first contribution is the creation of the PASTA-dataset, the first large-scale benchmark that spans a diverse set of models and both saliency-based and concept-bas

Shan Yang, Yongli Zhu

This paper presents a Consensus ADMM-based modeling and solving approach for the stochastic ACOPF. The proposed optimization model considers the load forecasting uncertainty and its induced load-shedding cost via Monte Carlo sampling. The sampled scenarios are reduced using a clustering method combined with simultaneous backward reduction techniques to reduc

Elad Sharony, Heng Yang, Tong Che, Marco Pavone

Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications, such as robot control, autonomous driving, and portfolio management. The performance of local optimization methods in these settings is sensitive to the initial solution: poor initialization can lead to slow convergence or suboptimal soluti

Donghoon Kim, Tomotaka Kuwahara

The entanglement area law is a universal principle that characterizes the information structure in quantum many-body systems and serves as the foundation for modern algorithms based on tensor network representations. Historically, the area law has been well understood under two critical assumptions: short-range interactions and bounded local energy. However,

Maxence Petit

We consider an outward degenerate drifted Brownian motion in the quarter plane with oblique reflections on the boundaries. In this article, we explicitly compute the Laplace transforms of the Green's functions associated with the process. These Laplace transforms are expressed as an infinite sum of products by iterating a functional equation, which is deeply

R Roy, K B Goswami, P K Chattopadhyay, A Saha

This work discuses the effect of the QCD coupling constant ($\alpha_c$) on various physical properties of compact stars in the framework of the Tolman IV potential admitting the equation of state of MIT bag model with nonzero value of mass of strange quark mass ($m_s$). The internal matter, consisting of the deconfined phase of the $3$-flavour quark, is over

Marta Roman, Simone Di Cataldo, Berthold Stöger, Lisa Reisinger

Charge density wave (CDW) orders in YNiC2 are studied by means of combined experimental and computational techniques. On the experimental side, single crystals grown by the floating-zone method were examined by means of X-ray diffraction, as well as transport and thermal techniques. Density functional theory (DFT) calculations founded on the experimentally d

Sam Nelson

We define a family of quiver representation-valued invariants of oriented classical and virtual knots and links associated to a choice of finite quandle $X$, abelian group $A$, set of quandle 2-cocycles $C\subset H^2_Q(x;A)$, choice of coefficient ring $k$ and set of quandle endomorphisms $S\subset \mathrm{Hom}(X,X)$. From this representation we define four

Leon Mächler, Gustav Grimberg, Ivan Ezhov, Manuel Nickel

This paper presents FedPID, our submission to the Federated Tumor Segmentation Challenge 2024 (FETS24). Inspired by FedCostWAvg and FedPIDAvg, our winning contributions to FETS21 and FETS2022, we propose an improved aggregation strategy for federated and collaborative learning. FedCostWAvg is a method that averages results by considering both the number of t

Pablo Monteagudo-Haro, Juan Jose Prieto-Gutierrez

The current situation of open research data in Spanish university repositories is analyzed by means of twelve indicators that allow us to compare them with each other. The twelve self-developed indicators deal with research datasets and institutional policies linked to open access, as well as some of the key characteristics of the repositories. The methodolo

Giuseppe Tinaglia, Alex Zhou

In this paper we study the geometry of complete constant mean curvature (CMC) hypersurfaces immersed in an (n + 1)-dimensional Riemannian manifold N (n = 2, 3 and 4) with sectional curvatures uniformly bounded from below. We generalise radius estimates given by Rosenberg [32] (n = 2) and by Elbert, Nelli and Rosenberg [13] and Cheng [2] (n = 3, 4) to nearly

Ahmad Halimi Razlighi, Maximilian H. V. Tillmann, Edgar Beck, Carsten Bockelmann

In this paper, we explore a multi-task semantic communication (SemCom) system for distributed sources, extending the existing focus on collaborative single-task execution. We build on the cooperative multi-task processing introduced in [1], which divides the encoder into a common unit (CU) and multiple specific units (SUs). While earlier studies in multi-tas

Yuanqi Yao, Gang Wu, Kui Jiang, Siao Liu

Learning a self-supervised Monocular Depth Estimation (MDE) model with great generalization remains significantly challenging. Despite the success of adversarial augmentation in the supervised learning generalization, naively incorporating it into self-supervised MDE models potentially causes over-regularization, suffering from severe performance degradation

Mark Braverman, Or Zamir

Estimating the second frequency moment of a stream up to $(1\pm\varepsilon)$ multiplicative error requires at most $O(\log n / \varepsilon^2)$ bits of space, due to a seminal result of Alon, Matias, and Szegedy. It is also known that at least $\Omega(\log n + 1/\varepsilon^{2})$ space is needed. We prove an optimal lower bound of $\Omega\left(\log \left(n \v

Thomas Monahan, Tianning Tang, Stephen Roberts, Thomas A. A. Adcock

On September 16th, 2023, an anomalous 10.88 mHz seismic signal was observed globally, persisting for 9 days. One month later an identical signal appeared, lasting for another week. Several studies have theorized that these signals were produced by seiches which formed after two landslide generated mega-tsunamis in an East-Greenland fjord. This theory is supp

Giovani Dalla Valle Garcia

Models of inelastic (or pseudo-Dirac) dark matter commonly introduce a gauge symmetry spontaneously broken by the introduction of a dark sector version of the Higgs mechanism. We find that this ubiquitous introduction of two extra fields, a vector and a complex scalar boson, is indeed unnecessary, with only a mass generating real scalar field being actually

Daniel Henrik Nevermann, Claudius Gros

Models for resident infectious diseases, like the SIRS model, may settle into an endemic state with constant numbers of susceptible ($S$), infected ($I$) and recovered ($R$) individuals, where recovered individuals attain a temporary immunity to reinfection. For many infectious pathogens, infection dynamics may also show periodic outbreaks corresponding to a

Nick Lorenzo

In a full 3D context, we study a projectile subject to linear drag, a non-uniform gravitational field, time-dependent wind, and parameterized atmospheric thinning. In this general context, we provide integral solutions, exact to $\mathcal{ O }( \varepsilon )$, for the position and velocity of the projectile, where $\varepsilon$ is a small perturbation parame

Hamideh Hassani, Eric Bousquet, Xu He, Bart Partoens

Polarons can naturally form in materials from the interaction of extra charge carriers with the atomic lattice. Ubiquitous, they are central to various topics and phenomena such as high-T$_c$ superconductivity, electrochromism, photovoltaics, photocatalysis or ion batteries. However, polaron formation remains poorly understood and mostly relies on few histor

Pranathi Rayavaram, Ukaegbu Onyinyechukwu, Maryam Abbasalizadeh, Krishnaa Vellamchetty

This paper presents an educational tool designed to enhance cryptography education for K-12 students, utilizing Kolb's Experiential Learning (EL) model and engaging visual components. Our tool incorporates the four stages of EL -- Concrete Experience, Reflective Observation, Abstract Conceptualization, and Active Experimentation -- to teach key cryptographic

Xingyi Cheng, Bo Chen, Pan Li, Jing Gong

We explore optimally training protein language models, an area of significant interest in biological research where guidance on best practices is limited. Most models are trained with extensive compute resources until performance gains plateau, focusing primarily on increasing model sizes rather than optimizing the efficient compute frontier that balances pe

Gideon Amir, Yaakov Malinovsky

In this note, we extend a recent result on the uniqueness of the maximum score in a classical round-robin tournament to general round-robin tournament models with equally strong players, where the scores take values in $[0,\,1]$.

Donghoon Kim, Tomotaka Kuwahara

The area law for entanglement entropy fundamentally reflects the complexity of quantum many-body systems, demonstrating ground states of local Hamiltonians to be represented with low computational complexity. While this principle is well-established in one-dimensional systems, little is known beyond 1D cases, and attempts to generalize the area law on infini

Jim Zhao, Sidak Pal Singh, Aurelien Lucchi

The Gauss-Newton (GN) matrix plays an important role in machine learning, most evident in its use as a preconditioning matrix for a wide family of popular adaptive methods to speed up optimization. Besides, it can also provide key insights into the optimization landscape of neural networks. In the context of deep neural networks, understanding the GN matrix

Amitai Yacobi, Ofir Lindenbaum, Uri Shaham

Multi-view representation learning (MvRL) has garnered substantial attention in recent years, driven by the increasing demand for applications that can effectively process and analyze data from multiple sources. In this context, graph Laplacian-based MvRL methods have demonstrated remarkable success in representing multi-view data. However, these methods oft

Hugo Chardon, Matthieu Lerasle, Jaouad Mourtada

Logistic regression is a classical model for describing the probabilistic dependence of binary responses to multivariate covariates. We consider the predictive performance of the maximum likelihood estimator (MLE) for logistic regression, assessed in terms of logistic risk. We consider two questions: first, that of the existence of the MLE (which occurs when

Robert Fonod, Haechan Cho, Hwasoo Yeo, Nikolas Geroliminis

This paper presents a framework for extracting georeferenced vehicle trajectories from high-altitude drone imagery, addressing key challenges in urban traffic monitoring and the limitations of traditional ground-based systems. Our approach integrates several novel contributions, including a tailored object detector optimized for high-altitude bird's-eye view

Kishan Sthankiya, Keith Briggs, Mona Jaber, Richard G. Clegg

Recent sustainability drives place energy-consumption metrics in centre-stage for the design of future radio access networks (RAN). At the same time, optimising the trade-off between performance and system energy usage by machine-learning (ML) is an approach that requires large amounts of granular RAN data to train models, and to adapt in near realtime. In t

Fucheng Warren Zhu, Connor T. Jerzak, Adel Daoud

Earth Observation (EO) data are increasingly used in policy analysis by enabling granular estimation of conditional average treatment effects (CATE). However, a challenge in EO-based causal inference is determining the scale of the input satellite imagery -- balancing the trade-off between capturing fine-grained individual heterogeneity in smaller images and

Mingyu Zhu, Hongcheng Ni, Jian Wu

The dynamics of light-matter interactions in the realm of strong-field ionization has been a focal point and has attracted widespread interest. We present the eTraj.jl program package, designed to implement established classical/semiclassical trajectory-based methods to determine the photoelectron momentum distribution resulting from strong-field ionization

Emel Altas, Bayram Tekin

Cotton gravity was recently introduced as a higher derivative extension of General Relativity. The field equations of the theory involve the rank-3 Cotton tensor. Here we show that all solutions of the theory, including the black holes, have vanishing conserved charges, i.e. mass and angular momentum. This result implies that either the theory is unphysical

Riccardo Graziosi, Massimiliano Ronzani, Andrei Buliga, Chiara Di Francescomarino

In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated accuracy in reproducing the features of the selected processes. However, current DL generative models are limited in their ability to adapt the learned distributions to generate data samples based on specif

Nguyen Bin, Vicente Lorenzo

Examples of algebraic surfaces of general type with maximal Picard number are not abundant in the literature. Moreover, most known examples either possess low invariants, lie near the Noether line $K^2=2\chi-6$ or are somewhat scattered. A notable exception is Persson's sequence of double covers of the projective plane with maximal Picard number, whose invar

Jaidev S. Ram, Sishu Shankar Muni, Igor A. Shepelev

We present numerical results for the effects of influence by high-amplitude periodic pulse series on a network of nonlocally coupled Hindmarsh-Rose neurons with 2D geometry of the topology. We consider the case when the pulse amplitude is larger than the amplitude of oscillations in the autonomous network for a wide range of pulse frequencies. An initial reg

M. Argudo-Fernández, C. Gómez Hernández, S. Verley, A. Zurita

In this work we investigate the effects of the environment on the evolution of void galaxies. In particular, we study their morphology and explore its dependence with their location within the void where the galaxies reside, as well as with properties of the void, such as void size or galaxy number-density. The sample of void galaxies that we use in this stu

M. Chernyakova, D. Malyshev, B. van Soelen, A. Finn Gallagher

PSR B1259-63 is a gamma-ray binary system with a 48 ms radio pulsar orbiting around an O9.5Ve star, LS 2883, in a highly eccentric ~3.4 yr long orbit. Close to the periastron the system is detected from radio up to the TeV energies due to the interaction of LS 2883 and pulsar's outflows. The observations of last 4 periastra passages taken in 2010-2021 demons

Kenan Weber, Christine Preisach

Common challenges in fault diagnosis include the lack of labeled data and the need to build models for each domain, resulting in many models that require supervision. Transfer learning can help tackle these challenges by learning cross-domain knowledge. Many approaches still require at least some labeled data in the target domain, and often provide unexplain

Santiago Acevedo, Alex Rodriguez, Alessandro Laio

In real-world data, information is stored in extremely large feature vectors. These variables are typically correlated due to complex interactions involving many features simultaneously. Such correlations qualitatively correspond to semantic roles and are naturally recognized by both the human brain and artificial neural networks. This recognition enables, f

Abdulkadir Celikkanat, Andres R. Masegosa, Thomas D. Nielsen

Obtaining effective representations of DNA sequences is crucial for genome analysis. Metagenomic binning, for instance, relies on genome representations to cluster complex mixtures of DNA fragments from biological samples with the aim of determining their microbial compositions. In this paper, we revisit k-mer-based representations of genomes and provide a t

Kola Ayonrinde

Sparse autoencoders (SAEs) are a promising approach to extracting features from neural networks, enabling model interpretability as well as causal interventions on model internals. SAEs generate sparse feature representations using a sparsifying activation function that implicitly defines a set of token-feature matches. We frame the token-feature matching as

Jędrzej Hodor, Hoang La, Piotr Micek, Clément Rambaud

A vertex coloring $\varphi$ of a graph $G$ is $p$-centered if for every connected subgraph $H$ of $G$, either $\varphi$ uses more than $p$ colors on $H$, or there is a color that appears exactly once on $H$. We prove that for every fixed positive integer $t$, every $K_t$-minor-free graph admits a $p$-centered coloring using $\mathcal{O}(p^{t-1})$ colors.

M. Weżgowiec, M. Jamrozy, K. T. Chyży, M. J. Hardcastle

The appearance of the jets and lobes of some radio galaxies makes it difficult to assign them to a known class of objects. This is often due to the activity of the central engine and/or interactions with the environment, as well as projection effects. We analyse the radio data for an apparently asymmetric radio source 4C70.19, which is associated with the gi

Yiheng Zhu, Jialu Wu, Qiuyi Li, Jiahuan Yan

Inverse protein folding is a fundamental task in computational protein design, which aims to design protein sequences that fold into the desired backbone structures. While the development of machine learning algorithms for this task has seen significant success, the prevailing approaches, which predominantly employ a discriminative formulation, frequently en

Aniruddha Ghosh, Ujjal Debnath

Black holes are the fascinating objects in the universe. They represent extreme deformations in spacetime geometry. Here, we construct f(P) gravity and the first example of static-spherically symmetric black hole solution in f(P) gravity and discuss their thermodynamics. Using the numerical approach and series solution, we discover the solution and demonstra

Guimin Hu, Zirui Zhao, Lukas Heilmann, Yasemin Vardar

Designing and displaying haptic signals with sensory and emotional attributes can improve the user experience in various applications. Free-form user language provides rich sensory and emotional information for haptic design (e.g., ``This signal feels smooth and exciting''), but little work exists on linking user descriptions to haptic signals (i.e., languag

Zichen Song, Yuxin Wu, Sitan Huang, Zhongfeng Kang

The evaluation of layer importance in deep learning has been an active area of research, with significant implications for model optimization and interpretability. Recently, large language models (LLMs) have gained prominence across various domains, yet limited studies have explored the functional importance and performance contributions of individual layers

Makoto Fukushima, Shusuke Eshita, Hiroshige Fukuhara

Color-word associations play a fundamental role in human cognition and design applications. Large Language Models (LLMs) have become widely available and have demonstrated intelligent behaviors in various benchmarks with natural conversation skills. However, their ability to replicate human color-word associations remains understudied. We compared multiple g

Ziwei Zhan, Wenkuan Zhao, Yuanqing Li, Weijie Liu

Federated learning (FL) is a collaborative machine learning approach that enables multiple clients to train models without sharing their private data. With the rise of deep learning, large-scale models have garnered significant attention due to their exceptional performance. However, a key challenge in FL is the limitation imposed by clients with constrained

Yuhui Chai, Lili Hu, Danni Quan, Yunyun Yang

This study aimed to investigate the effects of genetic polymorphisms on tacrolimus blood levels and intra-individual variability in recipients of heart transplants during the early post-transplantation period. Demographic information, concomitant medications, daily tacrolimus dose, trough concentration, and physiological and biochemical information of 87 Chi

Ji Won Park, Robert Tibshirani, Kyunghyun Cho

Many risk-sensitive applications require well-calibrated prediction sets over multiple, potentially correlated target variables, for which the prediction algorithm may report correlated errors. In this work, we aim to construct the conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores. Drawing from

Michael Eichmair, Thomas Koerber

The Riemannian Penrose inequality is a fundamental result in mathematical relativity. It has been a long-standing conjecture of G. Huisken that an analogous result should hold in the context of extrinsic geometry. In this paper, we resolve this conjecture and show that the exterior mass $m$ of an asymptotically flat support surface $S\subset\mathbb{R}^3$ wit

Vatchala S, Yogesh C, Yeshwanth Govindarajan, Krithik Raja M

In this study, we introduce a novel multi-modal biometric authentication system that integrates facial, vocal, and signature data to enhance security measures. Utilizing a combination of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), our model architecture uniquely incorporates dual shared layers alongside modality-specific enhanc

Zubeyir Cinkir

We give identities for the voltage and resistance functions on a metrized graph to show how these functions behave under any edge deletion/contraction and the identification of any two vertices. This leads to explicit versions of Rayleigh's Principles on a resistive electrical network. We also establish Euler's Identities for the resistance and the voltage f

Zs. Mátyus, L. Csedreki, Zs. Fülöp, Z. Halász

Silicon isotopic ratios measured in meteoritic presolar grains can provide useful information about the nucleosynthesis origin of these isotopes if the rates of nuclear reactions responsible for their production are known. One of the key reactions determining the Si isotopic abundances is 29Si(p,gamma)30P. Its reaction rate is not known with sufficient preci

Anton Bushuiev, Roman Bushuiev, Olga Pimenova, Nikola Zadorozhny

Generalization beyond training data remains a central challenge in machine learning for biology. A common way to enhance generalization is self-supervised pre-training on large datasets. However, aiming to perform well on all possible proteins can limit a model's capacity to excel on any specific one, whereas experimentalists typically need accurate predicti

Jingwei Liu, He Chen

In this paper, we explore how to schedule multiple users to optimize information freshness in a pull-based wireless network, where the status updates from users are requested by randomly arriving queries at the destination. We use the age of information at query (QAoI) to characterize the performance of information freshness. Such a decision-making problem i

Jie Miao, Jingming Chen, Deshui Yu, Qiaohui Yang

We demonstrate a dual wavelength optical frequency standard based on the dual optical transition modulation transfer spectroscopy (DOTMTS) between different quantum transitions of the rubidium D1 (795 nm) and D2 (780 nm) lines. In a single rubidium atomic ensemble, modulation frequency sidebands from the 780 nm pump beam are simultaneously transferred to bot

Xiaoqing Chen, Siyang Li, Yunlu Tu, Ziwei Wang

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) is a direct communication pathway between the human brain and a computer. Most research so far studied more accurate BCIs, but much less attention has been paid to the ethics of BCIs. Aside from task-specific information, EEG signals also contain rich private information, e.g., use

Masayuki Asaoka, Yushi Nakano, Paulo Varandas, Tomoo Yokoyama

In this paper we study geometrical and dynamical properties of codimension one foliations, by exploring a relation between length averages and ball averages of certain group actions. We introduce a new mechanism, which relies on the group structure itself, to obtain irregular behavior of ball averages for certain non-amenable group actions. Several geometric

I. Saccheo, A. Bongiorno, E. Piconcelli, L. Zappacosta

We aim at characterizing the X-ray-to-optical/near-infrared broad-band emission of luminous QSOs in the first Gyr of cosmic evolution to understand whether they exhibit differences compared to the lower-\textit{z} QSO population. Our goal is also to provide for these objects a reliable and uniform catalog of SED fitting derivable properties such as bolometri

Thodoris Betsas, Andreas Georgopoulos, Anastasios Doulamis, Pierre Grussenmeyer

In this paper an exhaustive review and comprehensive analysis of recent and former deep learning methods in 3D Semantic Segmentation (3DSS) is presented. In the related literature, the taxonomy scheme used for the classification of the 3DSS deep learning methods is ambiguous. Based on the taxonomy schemes of 9 existing review papers, a new taxonomy scheme of

Mathieu Colin, Tatsuya Watanabe

This paper is devoted to the study of the nonlinear Schr\"odinger-Poisson system with a doping profile. We are interested in the existence of ground state solutions by considering the minimization problem on a Nehari-Pohozaev set. The presence of a doping profile causes several difficulties, especially in the proof of the uniqueness of a maximum point of a f

Riham M. Hilal, Haitham El-Hussieny, Ayman A. Nada

The pursuit of realism in cinema has driven significant advancements in animatronics, where the integration of mechatronics, a multidisciplinary field that combines mechanical engineering, electronics, and computer science, plays a pivotal role in enhancing the functionality and realism of animatronics. This interdisciplinary approach facilitates smoother ch

Gabriel Picavet, Martine Picavet L'Hermitte

We are working in the category of commutative unital rings and denote by $\mathrm U(R)$ the group of units of a nonzero ring $R$. An extension of rings $R\subseteq S$, satisfying $\mathrm U(R)=R \cap\mathrm U(S)$ is usually called local. This paper is devoted to the study of ring extensions such that $\mathrm U(R)=\mathrm U(S)$, that we call strongly local.

Felipe Galarce, Douglas R. Q. Pacheco

Variable viscosity arises in many flow scenarios, often imposing numerical challenges. Yet, discretisation methods designed specifically for non-constant viscosity are few, and their analysis is even scarcer. In finite element methods for incompressible flows, the most popular approach to allow equal-order velocity-pressure interpolation are residual-based s

Idris Zakariyya, Linda Tran, Kaushik Bhargav Sivangi, Paul Henderson

Human motion analysis offers significant potential for healthcare monitoring and early detection of diseases. The advent of radar-based sensing systems has captured the spotlight for they are able to operate without physical contact and they can integrate with pre-existing Wi-Fi networks. They are also seen as less privacy-invasive compared to camera-based s

Madeline Navarro, Sergio Rozada, Antonio G. Marques, Santiago Segarra

This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transition probabilities on multi-dimensional st

David Soriano-Paños

Antigenic escape constitutes the main mechanism allowing rapidly evolving viruses to achieve endemicity. Beyond granting immune escape, empirical evidence also suggests that mutations of viruses might increase their inter-host infectiousness. While both mechanisms are well-studied individually, their combined effects on viral endemicity remain to be explored

Paul Tod

I review the twistor theory construction of stationary and axisymmetric, Lorentzian signature solutions of the Einstein vacuum equations and the related toric Ricci-flat metrics of Riemannian signature, \cite{W,MW,F,FW}. The construction arises from the Ward construction \cite{W2} of anti-self-dual Yang-Mills fields as holomorphic vector bundles on twistor s

Andreas Triantafyllopoulos, Yannik Terhorst, Iosif Tsangko, Florian B. Pokorny

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records to app-based interventions. The recent emergence of large language models (LLMs), both proprietary and open-source ones, represents a major new opportunity on that front. Yet there

Ahmed R. Sadik, Christian Goerick, Manuel Muehlig

A Multi Robot System (MRS) is the infrastructure of an intelligent cyberphysical system, where the robots understand the need of the human, and hence cooperate together to fulfill this need. Modeling an MRS is a crucial aspect of designing the proper system architecture, because this model can be used to simulate and measure the performance of the proposed a

Preetish Kakkar, Hariharan Ragothaman

Volumetric video, the capture and display of three-dimensional (3D) imagery, has emerged as a revolutionary technology poised to transform the media landscape, enabling immersive experiences that transcend the limitations of traditional 2D video. One of the key challenges in this domain is the efficient delivery of these high-bandwidth, data-intensive volume

Xiaoqing Chen, Ziwei Wang, Dongrui Wu

Machine learning has achieved great success in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Most existing BCI studies focused on improving the decoding accuracy, with only a few considering the adversarial security. Although many adversarial defense approaches have been proposed in other application domains such as computer vision, prev

Guoqing Wang, Zeyu Sun, Zhihao Gong, Sixiang Ye

Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. However, the rapid development of foundational LLMs such as the non-reasoning model GPT-4o and the reasoning model o1 raises questions about the continued effectiveness of these prompt

Moritz Klawin, Lorenzo Ducci, M. Mirac Serim, Andrea Santangelo

The symbiotic X-ray binary GX 1+4 possesses a number of peculiar properties that have been studied since the early 1970s. In particular, the orbital period has been a point of debate for many years, until radial velocity measurements were able to settle the debate. These radial velocity findings have so far not been confirmed using X-ray data, even though mu

Jukka Ruohonen, Adam Alami

Regression bugs refer to situations in which something that worked previously no longer works currently. Such bugs have been pronounced in the Linux kernel. The paper focuses on regression bug tracking in the kernel by considering the time required to fix regression bugs. The dataset examined is based on the regzbot automation framework for tracking regressi

Marcel Krüger, David Gilbert, Torsten Wolfgang Kuhlen, Tim Gerrits

One core aspect of immersive visualization labs is to develop and provide powerful tools and applications that allow for efficient analysis and exploration of scientific data. As the requirements for such applications are often diverse and complex, the same applies to the development process. This has led to a myriad of different tools, frameworks, and appro

Manqi Xu, Ye Guo, Hongbin Sun

Managing and unlocking the flexibility hidden in electric vehicles (EVs) has emerged as a critical yet challenging task towards low-carbon power and energy systems. This paper focuses on the online bidding and dispatch strategies for an EV aggregator (EVA) in a joint energy-regulation market, considering EVs' flexibility contributions and compensations. A me

Felix Anand Epp, Jesse Haapoja, Matti Nelimarkka

Like any form of technology, social media services embed values. To examine how societal values may be present in these systems, we focus on exploring political ideology as a value system. We organised four co-design workshops with political representatives from five major parties in Finland to investigate what values they would incorporate into social media

Allan Grønlund, Kasper Green Larsen

Achieving a provable exponential quantum speedup for an important machine learning task has been a central research goal since the seminal HHL quantum algorithm for solving linear systems and the subsequent quantum recommender systems algorithm by Kerenidis and Prakash. These algorithms were initially believed to be strong candidates for exponential speedups

Fan Wu, Muhammad Bilal, Haolong Xiang, Heng Wang

Railway Turnout Machines (RTMs) are mission-critical components of the railway transportation infrastructure, responsible for directing trains onto desired tracks. For safety assurance applications, especially in early-warning scenarios, RTM faults are expected to be detected as early as possible on a continuous 7x24 basis. However, limited emphasis has been

Xue Jiang, Lubin Meng, Xinru Chen, Yifan Xu

Electroencephalogram-based motor imagery (MI) classification is an important paradigm of non-invasive brain-computer interfaces. Common spatial pattern (CSP), which exploits different energy distributions on the scalp while performing different MI tasks, is very popular in MI classification. Convolutional neural networks (CNNs) have also achieved great succe

Jin Li, Ye Luo, Xiaowei Zhang

This paper examines how spillover effects in A/B testing can impede organizational progress and develops strategies for mitigating these challenges. We identify a phenomenon termed ``seesaw experimentation'', where a firm's overall performance paradoxically deteriorates despite achieving continuous improvements in measured A/B testing metrics. Seesaw experim

Louis Vialar, Jämes Ménétrey, Valerio Schiavoni, Pascal Felber

Using cloud-based applications comes with privacy implications, as the end-user looses control over their data. While encrypting all data on the client is possible, it largely reduces the usefulness of database management systems (DBMS) that are typically built to efficiently query large quantities of data. We present BlindexTEE, a new component that sits be

Jonas Zausinger, Lars Pennig, Anamarija Kozina, Sean Sdahl

While language models have exceptional capabilities at text generation, they lack a natural inductive bias for emitting numbers and thus struggle in tasks involving quantitative reasoning, especially arithmetic. One fundamental limitation is the nature of the cross-entropy (CE) loss, which assumes a nominal scale and thus cannot convey proximity between gene