April 2024 arXiv papers — page 13

Halid Ziya Yerebakan, Yoshihisa Shinagawa, Gerardo Hermosillo Valadez

Organ segmentation is a fundamental task in medical imaging since it is useful for many clinical automation pipelines. However, some tasks do not require full segmentation. Instead, a classifier can identify the selected organ without segmenting the entire volume. In this study, we demonstrate a classifier based method to obtain organ labels in real time by

Cindy Vindman, Benjamin Trump, Christopher Cummings, Madison Smith

The convergence of artificial intelligence (AI) and synthetic biology is rapidly accelerating the pace of biological discovery and engineering. AI techniques, such as large language models and biological design tools, are enabling the automated design, build, test, and learning cycles for engineered biological systems. This convergence promises to democratiz

Han Zhou, Yuntian Chen

In multivariate time series forecasting, the Transformer architecture encounters two significant challenges: effectively mining features from historical sequences and avoiding overfitting during the learning of temporal dependencies. To tackle these challenges, this paper deconstructs time series forecasting into the learning of historical sequences and pred

Jiri Horyna, Vit Kratky, Vaclav Pritzl, Tomas Baca

A decentralized swarm approach for the fast cooperative flight of Unmanned Aerial Vehicles (UAVs) in feature-poor environments without any external localization and communication is introduced in this paper. A novel model of a UAV neighborhood is proposed to achieve robust onboard mutual perception and flocking state feedback control, which is designed to de

Bergur Snorrason

A famous result of Siciak is how the Siciak-Zakharyuta functions, sometimes called global extremal functions or pluricomplex Green functions with a pole at infinity, of two sets relate to the Siciak-Zakharyuta function of their cartesian product. In this paper Siciak's result is generalized to the setting of Siciak-Zakharyuta functions with growth given by a

Yuhao Ye, Jinhua Wang, Pan Nie, Huakun Zuo

In graphite, a moderate magnetic field confines electrons and holes into their lowest Landau levels. In the extreme quantum limit, two insulating states with a dome-like field dependence of the their critical temperatures are induced by the magnetic field. Here, we study the evolution of the first dome (below 60 T) under hydrostatic pressure up to 1.7 GPa. W

Wondimagegnhue Tsegaye Tufa, Ilia Markov, Piek Vossen

Toxic language remains an ongoing challenge on social media platforms, presenting significant issues for users and communities. This paper provides a cross-topic and cross-lingual analysis of toxicity in Reddit conversations. We collect 1.5 million comment threads from 481 communities in six languages: English, German, Spanish, Turkish,Arabic, and Dutch, cov

Étienne Fouvry, Peter Koymans

Let $F, G \in \mathbb{Z}[X, Y]$ be binary forms of degree $\geq 3$ with automorphism groups isomorphic to the dihedral group of cardinality $6$ or $12$. We characterize exactly when $F$ and $G$ have the same value set, i.e. $F(\mathbb{Z}^2) = G(\mathbb{Z}^2)$.

Pavel Dvurechensky, Mathias Staudigl

In this paper we theoretically show that interior-point methods based on self-concordant barriers possess favorable global complexity beyond their standard application area of convex optimization. To do that we propose first- and second-order methods for non-convex optimization problems with general convex set constraints and linear constraints. Our methods

S Akansha

Approximation theory plays a central role in numerical analysis, undergoing continuous evolution through a spectrum of methodologies. Notably, Lebesgue, Weierstrass, Fourier, and Chebyshev approximations stand out among these methods. However, each technique possesses inherent limitations, underscoring the critical importance of selecting an appropriate appr

Solène Tarride, Yoann Schneider, Marie Generali-Lince, Mélodie Boillet

PyLaia is one of the most popular open-source software for Automatic Text Recognition (ATR), delivering strong performance in terms of speed and accuracy. In this paper, we outline our recent contributions to the PyLaia library, focusing on the incorporation of reliable confidence scores and the integration of statistical language modeling during decoding. O

Shreya Santra, Kentaro Uno, Gen Kudo, Kazuya Yoshida

This paper presents a novel 3D myopic coverage path planning algorithm for lunar micro-rovers that can explore unknown environments with limited sensing and computational capabilities. The algorithm expands upon traditional non-graph path planning methods to accommodate the complexities of lunar terrain, utilizing global data with local topographic features

Shimian Zhang, Qiuhong Lu

In the rapidly advancing field of robotics, the fusion of state-of-the-art visual technologies with mobile robotic arms has emerged as a critical integration. This paper introduces a novel system that combines the Segment Anything model (SAM) -- a transformer-based visual foundation model -- with a robotic arm on a mobile platform. The design of integrating

Jan D. A. Vuik, Daan M. Pool, Y. Vardar

This study explores the impact of whole-body vibrations induced by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens. Electrovibration holds promise as a technology for providing tactile feedback on future touchscreens, addressing usability challenges in vehicle cockpits. However, its

Huasheng Xie

The plasma dispersion function $Z(s)$ is a fundamental complex special integral function widely used in the field of plasma physics. The simplest and most rapid, yet accurate, approach to calculating it is through rational or equivalent multi-pole expansions. In this work, we summarize the numerical coefficients that are practically useful to the community.

Sourav Dey, Amaresh Jaiswal, Hiranmaya Mishra

The strongly interacting matter created in relativistic heavy-ion collisions possesses several conserved quantum numbers, such as baryon number, strangeness, and electric charge. The diffusion process of these charges can be characterized by a diffusion matrix that describes the mutual influence of the diffusion of various charges. We derive the Kubo relatio

S. Jafarzadeh, L. A. C. Schiavo, V. Fedun, S. K. Solanki

Solar pores are intense concentrations of magnetic flux that emerge through the Sun's photosphere. When compared to sunspots, they are much smaller in diameter and hence can be impacted and buffeted by neighbouring granular activity to generate significant magnetohydrodynamic (MHD) wave energy flux within their confines. However, observations of solar pores

Raul Perea-Causin, Samuel Brem, Fabian Buchner, Yao Lu

Doped van der Waals heterostructures host layer-hybridized trions, i.e. charged excitons with layer-delocalized constituents holding promise for highly controllable optoelectronics. Combining a microscopic theory with photoluminescence (PL) experiments, we demonstrate the electrical tunability of the trion energy landscape in naturally stacked WSe$_2$ bilaye

Aimeric Colléaux, David Langlois, Karim Noui

We consider, in Minkowski spacetime, higher-order Maxwell Lagrangians with terms quadratic in the derivatives of the field strength tensor, and study their degrees of freedom. Using a 3+1 decomposition of these Lagrangians, we extract the kinetic matrix for the components of the electric field, corresponding to second time derivatives of the gauge field. If

Shichuan Chen, Luohan Wang, Kota Hayashi, Kyohei Kawaguchi

We study the merger of black hole-neutron star (BH-NS) binaries in numerical relativity, focusing on the properties of the remnant disk and the ejecta, varying the mass of compactness of the NS and the mass and spin of the BH. We find that within the precision of our numerical simulations, the remnant disk mass and ejecta mass normalized by the NS baryon mas

Yu Tang Liu, Nilaksh Singh, Aamir Ahmad

Deep reinforcement learning (DRL) has shown remarkable success in simulation domains, yet its application in designing robot controllers remains limited, due to its single-task orientation and insufficient adaptability to environmental changes. To overcome these limitations, we present a novel adaptive agent that leverages transfer learning techniques to dyn

J. E. Abrão, E. Santos, J. L. Costa, J. G. S. Santos

We investigate anomalous spin and orbital Hall phenomena in antiferromagnetic (AF) materials via orbital pumping experiments. Conducting spin and orbital pumping experiments on YIG/Pt/Ir20Mn80 heterostructures, we unexpectedly observe strong spin and orbital anomalous signals in an out-of-plane configuration. We report a sevenfold increase in the signal of t

Rita Giuliano, Georges Grekos, Ladislav Misik

In this paper we present a new formulation of the Beurling-Malliavin density (Proposition 1). Then we consider the upper Polya density and show how its existence is connected with the concept of subadditivity; moreover, by means of some quantities introduced for proving Proposition 1, a theorem is presented that clarifies the connection between the upper Pol

Philippe Brax, Pierre Brun

We study the effects of an oscillating axion field on the pressure between two metallic plates. We consider the situation where a magnetic field parallel to the plates is present and show that the electric field induced by the coupling of the axion to photons leads to resonances. When the boundary plates are perfect conductors, the resonances are infinitely

Bernardo J. Zubillaga, Mateus F. B. Granha, André L. M. Vilela, Chao Wang

This work investigates the effects of complex networks on the collective behavior of a three-state opinion formation model in economic systems. Our model considers two distinct types of investors in financial markets: noise traders and fundamentalists. Financial states evolve via probabilistic dynamics that include economic strategies with local and global i

Andy Lücking, Alexander Henlein, Alexander Mehler

The current multimodal turn in linguistic theory leaves a crucial question unanswered: what is the meaning of iconic gestures, and how does it compose with speech meaning? We argue for a separation of linguistic and visual levels of meaning and introduce a spatial gesture semantics that closes this gap. Iconicity is differentiated into three aspects: Firstly

Andrzej Chlebicki

We investigate the precision of the numerical implementation of the functional renormalization group based on extracting the eigenvalues from the linearized RG transformation. For this purpose, we implement the LPA and $O(\partial^2)$ orders of the derivative expansion for the three-dimensional $O(N)$ models with $N~\in~\{1,2,3\}$. We identify several catego

Mélodie Boillet, Solène Tarride, Manon Blanco, Valentin Rigal

This paper presents a complete processing workflow for extracting information from French census lists from 1836 to 1936. These lists contain information about individuals living in France and their households. We aim at extracting all the information contained in these tables using automatic handwritten table recognition. At the end of the Socface project,

Constantinos Psomas, Konstantinos Ntougias, Nikita Shanin, Dongfang Xu

Wireless information and energy transfer (WIET) represents an emerging paradigm which employs controllable transmission of radio-frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned 6G use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such a

Shijie Zhou, Yang Luan, Xuzhe Qian, Wei Lin

Investigating the network stability or synchronization dynamics of multi-agent systems with time delays is of significant importance in numerous real-world applications. Such investigations often rely on solving the transcendental characteristic equations (TCEs) obtained from linearization of the considered systems around specific solutions. While stability

Aidi Yang, Fa Peng Huang

In recent years, an increasing number of studies have focused on using gravitational waves to explore axions and the dynamics of Peccei-Quinn symmetry breaking at high energy scales in the early universe. To accurately quantify the capability of specific gravitational wave experiments to probe the axion properties, it is crucial to perform precise calculatio

Xi Xin, Giles Hooker, Fei Huang

The adoption of artificial intelligence (AI) across industries has led to the widespread use of complex black-box models and interpretation tools for decision making. This paper proposes an adversarial framework to uncover the vulnerability of permutation-based interpretation methods for machine learning tasks, with a particular focus on partial dependence (

Roberto Hazenfratz, Paramita Barai, Gustavo A. Lanfranchi, Anderson Caproni

A fundamental question regarding the evolution of dwarf spheroidal galaxies is the identification of the key physical mechanisms responsible for gas depletion. Here, we focus on the study of stellar feedback in isolated dwarf spheroidal galaxies, by performing numerical simulations using a modified version of the SPH code GADGET-3. The Milky Way satellite Le

Xin Zhang, Junfeng Bai, Bowen Liu, Tong Zhu

The intermolecular potential plays crucial roles in real-fluid interactions away from the ideal-gas equilibrium, such as supercritical fluid, high-enthalpy fluid, plasma interactions, etc. We propose a Boltzmann-weighted Full-dimensional (BWF) potential model for real-fluid computations. It includes diverse intermolecular interactions so as to determine the

Pascal Fernsel, Željko Kereta, Alexander Denker

The incorporation of generative models as regularisers within variational formulations for inverse problems has proven effective across numerous image reconstruction tasks. However, the resulting optimisation problem is often non-convex and challenging to solve. In this work, we show that score-based generative models (SGMs) can be used in a graduated optimi

Andy Crabtree, Tom Lodge, Alan Chamberlain, Neelima Sailaja

This paper introduces a novel methodological approach for surfacing the acceptability and adoption challenges that confront future and emerging technologies from the perspective of mundane action, in which they will ultimately be embedded and used. This novel approach configures design fiction as a breaching experiment to surface taken for granted background

Zacharias Chrysidis, Stefanos-Iordanis Papadopoulos, Symeon Papadopoulos, Panagiotis C. Petrantonakis

Automated fact-checking (AFC) is garnering increasing attention by researchers aiming to help fact-checkers combat the increasing spread of misinformation online. While many existing AFC methods incorporate external information from the Web to help examine the veracity of claims, they often overlook the importance of verifying the source and quality of colle

Sebastián Higuera, María Camila Ramírez, Armando Reyes

In this paper, we study the uniform dimension and the associated prime ideals of induced modules over skew PBW extensions.

Peng-Fei Han, Yi Zhang

Shallow water waves phenomena in nature attract the attention of scholars and play an important role in fields such as tsunamis, tidal waves, solitary waves, and hydraulic engineering. Hereby, fortheshallowwaterwavesphenomenainvariousnaturalenvironments, westudytheKdV-Calogero-Bogoyavlenskii-Schiff (KdV-CBS) equation. Based on the binary Bell polynomial theo

Xu Tu, Siqi Feng, Jiajun Li, Yangguang Xing

Here we employ the quasi-bound state in the continuum (quasi-BIC) resonance in all-dielectric metasurfaces for efficient nonlinear processes in consideration of the backward frequency conversion. We theoretically study the second-harmonic generation (SHG) from symmetry-broken AlGaAs metasurfaces and reveal the efficiency enhancement empowered by high-$Q$ qua

Liying Gao, Bingliang Jiao, Peng Wang, Shizhou Zhang

Sketch-based image retrieval (SBIR) associates hand-drawn sketches with their corresponding realistic images. In this study, we aim to tackle two major challenges of this task simultaneously: i) zero-shot, dealing with unseen categories, and ii) fine-grained, referring to intra-category instance-level retrieval. Our key innovation lies in the realization tha

Matin Fallahi, Patricia Arias-Cabarcos, Thorsten Strufe

Extended Reality (XR) technologies are becoming integral to daily life. However, password-based authentication in XR disrupts immersion due to poor usability, as entering credentials with XR controllers is cumbersome and error-prone. This leads users to choose weaker passwords, compromising security. To improve both usability and security, we introduce a mul

Philippe Gaucher

We identify Grandis' directed spaces as a full reflective subcategory of the category of multipointed $d$-spaces. When the multipointed $d$-space realizes a precubical set, its reflection coincides with the standard realization of the precubical set as a directed space. The reflection enables us to extend the construction of the natural system of topological

Aleksander B. G. Christiansen, Eva Rotenberg, Teresa Anna Steiner, Juliette Vlieghe

Differential privacy is the gold standard in the problem of privacy preserving data analysis, which is crucial in a wide range of disciplines. Vertex colouring is one of the most fundamental questions about a graph. In this paper, we study the vertex colouring problem in the differentially private setting. To be edge-differentially private, a colouring algor

Reza G. Shirazi, Vladimir V. Rybkin, Michael Marthaler, Dmitry S. Golubev

We apply the analytically solvable model of two electrons in two orbitals to diradical molecules, characterized by two unpaired electrons. The effect of the doubly occupied and empty orbitals is taken into account by means of random phase approximation (RPA). We show that in the static limit the direct RPA leads to the renormalization of the parameters of th

Yali Zheng, Yingqing Xiao

In this paper, we study the spectrality of a class of Moran measures $\mu_{\mathcal{P},\mathcal{D}}$ on $\mathbb{R}$ generated by $\{(p_n,\mathcal{D}_n)\}_{n=1}^{\infty}$, where $\mathcal{P}=\{p_n\}_{n=1}^{\infty}$ is a sequence of positive integers with $p_n>1$ and $\mathcal{D}=\{\mathcal{D}_{n}\}_{n=1}^{\infty}$ is a sequence of digit sets of $\mathbb{N}$

Ming Ni, Rong-Long Ma, Zhen-Zhen Kong, Ning Chu

To realize large-scale quantum information processes, an ideal scheme for two-qubit operations should enable diverse operations with given hardware and physical interaction. However, for spin qubits in semiconductor quantum dots, the common two-qubit operations, including CPhase gates, SWAP gates, and CROT gates, are realized with distinct parameter regions

Jiahui Wei, Elsa Dupraz, Philippe Mary

The design of communication systems dedicated to machine learning tasks is one key aspect of goal-oriented communications. In this framework, this article investigates the interplay between data reconstruction and learning from the same compressed observations, particularly focusing on the regression problem. We establish achievable rate-generalization error

Yao Wang, Yuqi Kong, Wenzheng Chi, Lining Sun

The natural interaction between robots and pedestrians in the process of autonomous navigation is crucial for the intelligent development of mobile robots, which requires robots to fully consider social rules and guarantee the psychological comfort of pedestrians. Among the research results in the field of robotic path planning, the learning-based socially a

Yuting Liu, Huibo Hong, Xiao Xiang, Runai Quan

A dynamic temperature compensation method is presented to stabilize the wavelength of the entangled biphoton source, which is generated via the spontaneous parametric down-conversion based on a MgO: PPLN waveguide. Utilizing the dispersive Fourier transformation technique combined with a digital proportional-integral-differential algorithm, the small amount

Andy Crabtree, Glenn McGarry, Lachlan Urquhart

Abstract. The risks AI presents to society are broadly understood to be manageable through general calculus, i.e., general frameworks designed to enable those involved in the development of AI to apprehend and manage risk, such as AI impact assessments, ethical frameworks, emerging international standards, and regulations. This paper elaborates how risk is a

Oswaldo Ludwig

This paper introduces a new biologically-inspired training method named Continual Learning through Adjustment Suppression and Sparsity Promotion (CLASSP). CLASSP is based on two main principles observed in neuroscience, particularly in the context of synaptic transmission and Long-Term Potentiation (LTP). The first principle is a decay rate over the weight a

Giorgos Giannopoulos, Dimitris Sacharidis, Nikolas Theologitis, Loukas Kavouras

Fairness is steadily becoming a crucial requirement of Machine Learning (ML) systems. A particularly important notion is subgroup fairness, i.e., fairness in subgroups of individuals that are defined by more than one attributes. Identifying bias in subgroups can become both computationally challenging, as well as problematic with respect to comprehensibility

Sidharth Ranjan, Titus von der Malsburg

Dependency length minimization is a universally observed quantitative property of natural languages. However, the extent of dependency length minimization, and the cognitive mechanisms through which the language processor achieves this minimization remain unclear. This research offers mechanistic insights by postulating that moving a short preverbal constitu

Iryna Stepanenko, Zhishuo Huang, Liviu Ungur, Dimitrios Bessas

A large number of osmium complexes with osmium in different oxidation states (II, III, IV, VI) have been reported recently to exhibit good antiproliferative activity in cancer cell lines. Herein, we demonstrate new opportunities offered by $^{187}$Os nuclear forward scattering (NFS) and nuclear inelastic scattering (NIS) of synchrotron radiation for characte

Grischa Liebel, Jil Klünder, Regina Hebig, Christopher Lazik

Purpose: Software modelling and Model-Driven Engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write throu

Fabian Biester, Mohamed Abdelaal, Daniel Del Gaudio

Machine learning's influence is expanding rapidly, now integral to decision-making processes from corporate strategy to the advancements in Industry 4.0. The efficacy of Artificial Intelligence broadly hinges on the caliber of data used during its training phase; optimal performance is tied to exceptional data quality. Data cleaning tools, particularly those

Maurice Koch, Nelusa Pathmanathan, Daniel Weiskopf, Kuno Kurzhals

Image thumbnails are a valuable data source for fixation filtering, scanpath classification, and visualization of eye tracking data. They are typically extracted with a constant size, approximating the foveated area. As a consequence, the focused area of interest in the scene becomes less prominent in the thumbnail with increasing distance, affecting image-b

Seonhee Cho, Choonghan Kim, Jiho Lee, Chetan Chilkunda

Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data

Christian G. Boehmer, Rafael Ferraro, Franco Fiorini

We introduce a new class of two dimensional gravity models using ideas motivated by the Teleparallel Equivalent of General Relativity. This leads to a rather natural formulation of a theory that has close links with Jackiw-Teitelboim gravity. After introducing the theory and discussing its vacuum solutions, we present the Hamiltonian analysis. This implies t

Sebastian Arnold, Georgios Gavrilopoulos, Benedikt Schulz, Johanna Ziegel

In most prediction and estimation situations, scientists consider various statistical models for the same problem, and naturally want to select amongst the best. Hansen et al. (2011) provide a powerful solution to this problem by the so-called model confidence set, a subset of the original set of available models that contains the best models with a given le

Christoph Treude, Marco A. Gerosa, Igor Steinmacher

Newcomers to a software project must overcome many barriers before they can successfully place their first code contribution, and they often struggle to find information that is relevant to them. In this work, we argue that much of the information needed by newcomers already exists, albeit scattered among many different sources, and that many barriers can be

Alexander Rothkopf, W. A. Horowitz, Jan Nordström

We present a novel solution procedure for initial boundary value problems. The procedure is based on an action principle, in which coordinate maps are included as dynamical degrees of freedom. This reparametrization invariant action is formulated in an abstract parameter space and an energy density scale associated with the space-time coordinates separates t

Fatemeh Haghsheno, Mohammad Mehrafarin

The emergence of order from initial disordered movement in self-propelled collective motion is an instance of nonequilibrium phase transition, which is known to be first order in the thermodynamic limit. Here, we introduce a multiplicative scalar noise model of collective motion as a modification of the original Vicsek model, which more closely mimics the pa

Manel Aloui, Hasna Chouikhi, Ghaith Chaabane, Haithem Kchaou

In recent years, Large Language Models have revolutionized the field of natural language processing, showcasing an impressive rise predominantly in English-centric domains. These advancements have set a global benchmark, inspiring significant efforts toward developing Arabic LLMs capable of understanding and generating the Arabic language with remarkable acc

Francois Foucart, Matthew D. Duez, Lawrence E. Kidder, Harald P. Pfeiffer

Predicting the properties of the matter ejected during and after a neutron star merger is crucial to our ability to use electromagnetic observations of these mergers to constrain the masses of the neutron stars, the equation of state of dense matter, and the role of neutron star mergers in the enrichment of the Universe in heavy elements. Our ability to reli

Rieke Müller, Mohamed Abdelaal, Davor Stjelja

Data drifts pose a critical challenge in the lifecycle of machine learning (ML) models, affecting their performance and reliability. In response to this challenge, we present a microbenchmark study, called D3Bench, which evaluates the efficacy of open-source drift detection tools. D3Bench examines the capabilities of Evidently AI, NannyML, and Alibi-Detect,

Paul Cibier, Jean-Guy Mailly

Various approaches have been proposed for providing efficient computational approaches for abstract argumentation. Among them, neural networks have permitted to solve various decision problems, notably related to arguments (credulous or skeptical) acceptability. In this work, we push further this study in various ways. First, relying on the state-of-the-art

Lin Zhang, Dade Wu, Ming-Jing Zhao, Hua Nan

The uncertainty relation is a fundamental concept in quantum theory, plays a pivotal role in various quantum information processing tasks. In this study, we explore the additive uncertainty relation pertaining to two or more observables, in terms of their variance,by utilizing the generalized Gell-Mann representation in qudit systems. We find that the tight

Annie Hu, Samuel Stockman, Xun Wu, Richard Wood

Early and timely prediction of patient care demand not only affects effective resource allocation but also influences clinical decision-making as well as patient experience. Accurately predicting patient care demand, however, is a ubiquitous challenge for hospitals across the world due, in part, to the demand's time-varying temporal variability, and, in part

Fred B. Holt

In 2016 Lemke Oliver and Soundararajan examined the gaps between the first hundred million primes and observed biases in their distributions modulo 10. Given our work on the evolution of the populations of various gaps across stages of Eratosthenes sieve, the observed biases are totally expected. The biases observed by Lemke Oliver and Soundararajan are a wo

Yifei Gao, Kerui Ren, Jie Ou, Lei Wang

Recent advancements in 3D Gaussian Splatting (3D-GS) have established new benchmarks for rendering quality and efficiency in 3D reconstruction. However, 3D-GS faces critical limitations when generating novel views that significantly deviate from those encountered during training. Moreover, issues such as dilation and aliasing arise during zoom operations. Th

Christophe Cassens, Bernd Meyer-Hoppe, Ernst Rasel, Carsten Klempt

Interferometers based on ultra-cold atoms enable an absolute measurement of inertial forces with unprecedented precision. However, their resolution is fundamentally restricted by quantum fluctuations. Improved resolutions with entangled or squeezed atoms were demonstrated in internal-state measurements for thermal and quantum-degenerate atoms and, recently,

Juliane Haug, Oliver Schüle, Fabian Wunder

We present a novel semi-analytical method for parton evolution. It is based on constructing a family of analytic functions spanning $x$-space which is closed under the considered evolution equation. Using these functions as a basis, the original integro-differential evolution equation transforms into a system of coupled ordinary differential equations, which

Marcus Vaktnäs, Rostyslav Kozhan

We investigate polynomials that satisfy simultaneous orthogonality conditions with respect to several measures on the unit circle. We generalize the direct and inverse Szeg\H{o} recurrence relations, identify the analogues of the Verblunsky coefficients, and prove the Christoffel$\unicode{x2013}$Darboux formula. These results stand directly in analogue with

Sirinun Chaipunko, Watthanaree Ammawat, Keerathi Oanmun, Wanvipha Hongnaphadol

As cognitive interventions for older adults evolve, modern technologies are increasingly integrated into their development. This study investigates the efficacy of augmented reality (AR)-based physical-cognitive training using an interactive game with Kinect motion sensor technology on older individuals at risk of mild cognitive impairment. Utilizing a prete

William Linz, Linyuan Lu, Zhiyu Wang

The spread of a graph $G$ is the difference between the largest and smallest eigenvalue of the adjacency matrix of $G$. In this paper, we consider the family of graphs which contain no $K_{s,t}$-minor. We show that for any $t\geq s \geq 2$ and sufficiently large $n$, there is an integer $\xi_{t}$ such that the extremal $n$-vertex $K_{s,t}$-minor-free graph a

Rajat K. Doshi

This study investigates the application of PointNet and PointNet++ in the classification of LiDAR-generated point cloud data, a critical component for achieving fully autonomous vehicles. Utilizing a modified dataset from the Lyft 3D Object Detection Challenge, we examine the models' capabilities to handle dynamic and complex environments essential for auton

David Villanova-Aparisi, Solène Tarride, Carlos-D. Martínez-Hinarejos, Verónica Romero

Information Extraction processes in handwritten documents tend to rely on obtaining an automatic transcription and performing Named Entity Recognition (NER) over such transcription. For this reason, in publicly available datasets, the performance of the systems is usually evaluated with metrics particular to each dataset. Moreover, most of the metrics employ

Thomas Guerneve, Stephanos Loizou, Andrea Munafo, Pierre-Yves Mignotte

The performance of Automated Recognition (ATR) algorithms on side-scan sonar imagery has shown to degrade rapidly when deployed on non benign environments. Complex seafloors and acoustic artefacts constitute distractors in the form of strong textural patterns, creating false detections or preventing detections of true objects. This paper presents two online

T. Dumont, E. Monpribat, S. Courtin, A. Choplin

Nuclear reactions drive the stellar evolution and contribute to the stellar and galactic chemicals abundances. New determinations of the nuclear reaction rates for key fusion reactions of stellar evolution are now available, paving the way to improved stellar model predictions. We explore the impact of new C12+C12 reaction rates for massive stars evolution,

Siran Li, Zijiu Lyu, Hao Ni, Jiajie Tao

A central question in rough path theory is characterising the law of stochastic processes on path spaces. It is established in [I. Chevyrev & T. Lyons, Characteristic functions of measures on geometric rough paths, Ann. Probab. 44 (2016), 4049--4082] that the characteristic function of a probability measure on group-like elements, which is a subspace of the

Karolina Kulis, Sarah Baatout, Kevin Tabury, Joanna Polanska

A number of studies have indicated a potential association between prenatal exposure to radiation and late mental disabilities. This is believed to be due to long-term developmental changes and functional impairment of the central nervous system following radiation exposure during gestation. This study conducted a bioinformatics analysis on transcriptomic pr

M. Mehmandoost, V. V. Dobrovitski

Progress in fabrication of semiconductor and superconductor qubits has greatly diminished the number of decohering defects, thus decreasing the devastating low-frequency $1/f$ noise and extending the qubits' coherence times (dephasing time $T_2^*$ and the echo decay time $T_2$). However, large qubit-to-qubit variation of the coherence properties remains a pr

Václav Blažej, Dušan Knop, Jan Pokorný, Šimon Schierreich

We study the Equitable Connected Partition (ECP for short) problem, where we are given a graph G=(V,E) together with an integer p, and our goal is to find a partition of V into p parts such that each part induces a connected sub-graph of G and the size of each two parts differs by at most 1. On the one hand, the problem is known to be NP-hard in general and

S. R. Dhanush, A. Subramaniam, S. Subramanian

The internal kinematics of the Large Magellanic Cloud (LMC) disk have been modeled by several studies using different tracers with varying coverage, resulting in a range of parameters. Here, we modeled the LMC disk using 1705 star clusters and field stars, based on a robust Markov Chain Monte Carlo (MCMC) method, using the Gaia DR3 data. The dependency of mo

Oliver Withington, Michael Cook, Laurissa Tokarchuk

The evaluation of procedural content generation (PCG) systems for generating video game levels is a complex and contested topic. Ideally, the field would have access to robust, generalisable and widely accepted evaluation approaches that can be used to compare novel PCG systems to prior work, but consensus on how to evaluate novel systems is currently limite

Zihan Li, Shaocheng Liu, Qi Chen

In this paper, we classify all G-symmetric almost entropic regions according to their Shannon-tightness, that is, whether they can be fully characterized by Shannon-type inequalities, where G is a permutation group of degree 6 or 7.

Haeun Yu, Pepa Atanasova, Isabelle Augenstein

Language Models (LMs) acquire parametric knowledge from their training process, embedding it within their weights. The increasing scalability of LMs, however, poses significant challenges for understanding a model's inner workings and further for updating or correcting this embedded knowledge without the significant cost of retraining. This underscores the i

Andrea Serani, Thomas Scholcz, Valentina Vanzi

This scoping review assesses the current use of simulation-based design optimization (SBDO) in marine engineering, focusing on identifying research trends, methodologies, and application areas. Analyzing 277 studies from Scopus and Web of Science, the review finds that SBDO is predominantly applied to optimizing marine vessel hulls, including both surface an

Mauricio A. Diaz, Giorgio Cerro, Srinandan Dasmahapatra, Stefano Moretti

In the attempt to explain possible data anomalies from collider experiments in terms of New Physics (NP) models, computationally expensive scans over their parameter spaces are typically required in order to match theoretical predictions to experimental observations. Under the assumption that anomalies seen at a mass of about 95 GeV by the Large Electron-Pos

Fulai Yao

The energy efficiency optimization of the power generation system and the energy efficiency optimization of the energy consumption system are unified into the same optimization problem, and a simple method to achieve energy efficiency optimization without establishing an accurate mathematical model of the system is proposed. For systems with similar energy e

Clemens Sämann

We give a brief non-technical introduction to non-regular spacetime geometry. In particular, we discuss how curvature, and hence gravity, can be defined without a smooth (differential geometric) calculus.

Fan Wu, Nobby Stevens, Lieven De Strycker, François Rottenberg

This paper presents an optimal calibration scheme and a weighted least squares (LS) localization algorithm for received signal strength (RSS) based visible light positioning (VLP) systems, focusing on the often overlooked impact of light emitting diode (LED) tilt. By optimally calibrating LED tilt and gain, we significantly enhance VLP localization accuracy.

Konstantinos Tsigos, Evlampios Apostolidis, Spyridon Baxevanakis, Symeon Papadopoulos

In this paper we propose a new framework for evaluating the performance of explanation methods on the decisions of a deepfake detector. This framework assesses the ability of an explanation method to spot the regions of a fake image with the biggest influence on the decision of the deepfake detector, by examining the extent to which these regions can be modi

Zhaobo Qi, Shuhui Wang, Weigang Zhang, Qingming Huang

Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision and autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. This reduces the model gener

Arkadiusz Kosior, Karol Gietka, Farokh Mivehvar, Helmut Ritsch

Driven-dissipative light-matter systems can exhibit collective nonequilibrium phenomena due to loss and gain processes on the one hand and effective photon-photon interactions on the other hand. As generic example we study a bosonic lattice system implemented via an array of driven-dissipative coupled nonlinear resonators with linearly increasing resonance f

J. Eckseler, J. Schnack

Research on the emergence of thermodynamics in closed quantum systems under unitary time evolution arrived at the consensus that generic systems equilibrate under rather general assumptions. A new focus of the field is thus on exceptions. Persistent oscillations are one possible hallmark of non-ergodic time evolution. While time-crystalline behavior results

Jesse Beisegel, Ekkehard Köhler, Fabienne Ratajczak, Robert Scheffler

The last in-tree recognition problem asks whether a given spanning tree can be derived by connecting each vertex with its rightmost left neighbor of some search ordering. In this study, we demonstrate that the last-in-tree recognition problem for Generic Search is $\mathsf{NP}$-complete. We utilize this finding to strengthen a complexity result from order th

Zuolin Wei, Tan He, Yangsen Ye, Dachao Wu

To make practical quantum algorithms work, large-scale quantum processors protected by error-correcting codes are required to resist noise and ensure reliable computational outcomes. However, a major challenge arises from defects in processor fabrication, as well as occasional losses or cosmic rays during the computing process, all of which can lead to qubit