February 2024 arXiv papers — page 112

Alexander A. Marchuk, Ilia V. Chugunov, George A. Gontcharov, Aleksandr V. Mosenkov

Spiral structure can contribute significantly to a galaxy's luminosity. However, only rarely are proper photometric models of spiral arms used in decompositions. As we show in the previous work, including the spirals as a separate component in a photometric model of a galaxy would both allow to obtain their structural parameters, and reduce the systematic er

Harley Wiltzer, Jesse Farebrother, Arthur Gretton, Yunhao Tang

This paper contributes a new approach for distributional reinforcement learning which elucidates a clean separation of transition structure and reward in the learning process. Analogous to how the successor representation (SR) describes the expected consequences of behaving according to a given policy, our distributional successor measure (SM) describes the

Matan Atzmon, Jiahui Huang, Francis Williams, Or Litany

Integrating a notion of symmetry into point cloud neural networks is a provably effective way to improve their generalization capability. Of particular interest are $E(3)$ equivariant point cloud networks where Euclidean transformations applied to the inputs are preserved in the outputs. Recent efforts aim to extend networks that are $E(3)$ equivariant, to a

Marcello Bernardara, Enrico Fatighenti, Grzegorz Kapustka, Michał Kapustka

We study Fano fourfolds of K3 type with a conic bundle structure. We construct direct geometrical links between these fourfolds and hyperK\"ahler varieties. As a result we describe families of nodal surfaces that can be seen as generalisations of Kummer quartic surfaces. Each of these families actually arises through two families of Fano fourfolds, whose con

Jean Dolbeault, Maria J. Esteban, Alessio Figalli, Rupert Frank

In this paper, we present recent stability results with explicit and dimensionally sharp constants and optimal norms for the Sobolev inequality and for the Gaussian logarithmic Sobolev inequality obtained by the authors in [24]. The stability for the Gaussian logarithmic Sobolev inequality was obtained as a byproduct of the stability for the Sobolev inequali

Junhao Zheng, Shengjie Qiu, Qianli Ma

Large Language Models (LLMs) have achieved remarkable success across various tasks, yet their ability to learn incrementally without forgetting remains underexplored. Incremental learning (IL) is crucial as it enables models to acquire new knowledge while retaining previously learned information, akin to human learning. Existing benchmarks for IL are insuffi

Jean-François Burnol

We obtain for the Kempner series (i.e. harmonic series where certain digits are excluded from all denominators, for example the digit 9 in base 10) new representations as geometrically convergent series. The coefficients for these representations involve the power sums on the allowed digits and the moments of an explicitly described measure on the unit inter

Huan Zhou, Ralf Schneider

A susceptible-exposed-infected-removed (SEIR) model assumes spatial-/time-varying coefficients to model the effect of non-pharmaceutical interventions (NPIs) on the regional and temporal distribution of COVID-19 disease epidemics. A significant challenge in using such model is their fast and accurate calibration to observed data from geo-referenced hospitali

Muhammad Kashif, Emman Sychiuco, Muhammad Shafique

In this paper, we conduct a comprehensively analyze the influence of different quantum noise gates, including Phase Flip, Bit Flip, Phase Damping, Amplitude Damping, and the Depolarizing Channel, on the performance of HyQNNs. Our results reveal distinct and significant effects on HyQNNs training and validation accuracies across different probabilities of noi

Martijn de Vos, Akash Dhasade, Jade Garcia Bourrée, Anne-Marie Kermarrec

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, with or without coordination beforehand, and their strategy for sampling appropriate data points. We theo

Juan M. Miramont, Rémi Bardenet, Pierre Chainais, Francois Auger

Signal processing in the time-frequency plane has a long history and remains a field of methodological innovation. For instance, detection and denoising based on the zeros of the spectrogram have been proposed since 2015, contrasting with a long history of focusing on larger values of the spectrogram. Yet, unlike neighboring fields like optimization and mach

António Filgueiras, Eduardo R. B. Marques, Luís M. B. Lopes, Miguel Marques

Machine-learning techniques, especially deep convolutional neural networks, are pivotal for image-based identification of biological species in many Citizen Science platforms. In this paper, we describe the construction of a dataset for the Portuguese native flora based on publicly available research-grade datasets, and the derivation of a high-accuracy mode

Roope Anttila, Balázs Bárány, Antti Käenmäki

We study the level sets of prevalent H\"older functions. For a prevalent $\alpha$-H\"older function on the unit interval, we show that the upper Minkowski dimension of every level set is bounded from above by $1-\alpha$ and Lebesgue positively many level sets have Hausdorff dimension equal to $1-\alpha$.

Daniele Gamba, Bingyu Cui, Alessio Zaccone

The Langevin equation is ubiquitously employed to numerically simulate plasmas, colloids and electrolytes. However, the usual assumption of white noise becomes untenable when the system is subject to an external AC electric field. This is because the charged particles in the system, which provide the thermal bath for the particle transport, become themselves

Amartya Bose

Path integrals have, over the years, proven to be an extremely versatile tool for simulating the dynamics of open quantum systems. The initial limitations of applicability of these methods in terms of the size of the system has steadily been overcome through various developments, making numerical explorations of large systems a more-or-less regular feature.

Nico Kubetschek, Claudia Backes, Stuart Goldie

Rapid and reliable analysis of liquid dispersions of 2D materials is essential for fully harnessing their potential, allowing size and quality validation before subsequent processing or device fabrication. Existing UV-VIS extinction spectroscopy-based metrics, particularly those related to thickness, have shown promise but rely on manual data processing, whi

Delaram Rashadfar, Brandi L. Wooten, Joseph P. Heremans

In ferroelectric materials, an electric field has been shown to change the phonon dispersion sufficiently to alter the lattice thermal conductivity, opening the possibility that a heat gradient could drive a polarization flux, and technologically, also opening a pathway towards voltage-driven, all solid-state heat switching. In this report, we confirm the va

Lothar Nannen, Markus Wess

This paper introduces a method for computing eigenvalues and eigenvectors of a generalized Hermitian, matrix eigenvalue problem. The work is focused on large scale eigenvalue problems, where the application of a direct inverse is out of reach. Instead, an explicit time-domain integrator for the corresponding wave problem is combined with a proper filtering a

Milad Kazemi, Jessica Lally, Ekaterina Tishchenko, Hana Chockler

Our work addresses a fundamental problem in the context of counterfactual inference for Markov Decision Processes (MDPs). Given an MDP path $\tau$, this kind of inference allows us to derive counterfactual paths $\tau'$ describing what-if versions of $\tau$ obtained under different action sequences than those observed in $\tau$. However, as the counterfactua

Orimar Sauri

In this paper we consider the Euler-Maruyama scheme for a class ofstochastic delay differential equations driven by a fractional Brownian motion with index $H\in(0,1)$. We establish the consistency of the scheme and study the rate of convergence of the normalized error process. This is done by checking that the generic rate of convergence of the error proces

Yoonjin Lee, Donghyeok Lim

Let $p$ be an odd prime and $F$ be a number field whose $p$-class group is cyclic. Let $F_{\{\mathfrak{q}\}}$ be the maximal pro-$p$ extension of $F$ which is unramified outside a single non-$p$-adic prime ideal $\mathfrak{q}$ of $F$. In this work, we study the finitude of the Galois group $G_{\{\mathfrak{q}\}}(F)$ of $F_{\{\mathfrak{q}\}}$ over $F$. We prov

Cedric Derstroff, Jannis Brugger, Jannis Blüml, Mira Mezini

Monte-Carlo tree search (MCTS) is an effective anytime algorithm with a vast amount of applications. It strategically allocates computational resources to focus on promising segments of the search tree, making it a very attractive search algorithm in large search spaces. However, it often expends its limited resources on reevaluating previously explored regi

Victor J. H. Trees, Stephan R. de Roode, Job I. Wiltink, Jan Fokke Meirink

Clouds affected by solar eclipses could influence the reflection of sunlight back into space and might change local precipitation patterns. Satellite cloud retrievals have so far not taken into account the lunar shadow, hindering a reliable spaceborne assessment of the eclipse-induced cloud evolution. Here we use satellite cloud measurements during three sol

Philipp Seifer, Daniel Hernández, Ralf Lämmel, Steffen Staab

SPARQL CONSTRUCT queries allow for the specification of data processing pipelines that transform given input graphs into new output graphs. It is now common to constrain graphs through SHACL shapes allowing users to understand which data they can expect and which not. However, it becomes challenging to understand what graph data can be expected at the end of

Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj

Modern machine learning usually involves predictors in the overparameterised setting (number of trained parameters greater than dataset size), and their training yields not only good performance on training data, but also good generalisation capacity. This phenomenon challenges many theoretical results, and remains an open problem. To reach a better understa

João C. Serra, Emanuele Galiffi, Paloma A. Huidobro, John B. Pendry

Photonic systems with time-varying modulations have attracted considerable attention as they allow for the design of non-reciprocal devices without the need for an external magnetic bias. Unlike time-invariant systems, such modulations couple modes with different frequencies. Here, we discuss how this coupling and particle-hole symmetry may lead to the reson

Zi Ye, Tianxiang Chen, Fangyijie Wang, Hanwei Zhang

In pediatric cardiology, the accurate and immediate assessment of cardiac function through echocardiography is crucial since it can determine whether urgent intervention is required in many emergencies. However, echocardiography is characterized by ambiguity and heavy background noise interference, causing more difficulty in accurate segmentation. Present me

Francisco Valdes-Souto, Hector G. Perez-Gonzalez, Carlos A. Perez-Delgado

Quantum engineering seeks to exploit quantum information to build, among others, computing, cybersecurity, and metrology technologies. Quantum Software Engineering (QSE) focuses on the information processing side of these technologies. Historically, quantum (software) engineering has focused on development in controlled research environments and 'in the smal

Zhenwei Xu, Bálint Kaszás, Mattia Cenedese, Giovanni Berti

We use video footage of a water tunnel experiment to construct a two-dimensional reduced-order model of the flapping dynamics of an inverted flag in uniform flow. The model is obtained as the reduced dynamics on an attracting spectral submanifold (SSM) that emanates from the two slowest modes of the unstable fixed point of the flag. Beyond an unstable fixed

Ken Smith, Jordan Webster

There are exactly 35 inequivalent (36, 15, 6) difference sets in nine groups. Eight of the nine groups have a normal Sylow 3-subgroup. We give a straightforward spread construction which explains the 32 inequivalent difference sets in these eight groups. An interesting variation on this construction provides the three difference sets in the ninth group.

Hanna Krasowski, Matthias Althoff

For safe operation, autonomous vehicles have to obey traffic rules that are set forth in legal documents formulated in natural language. Temporal logic is a suitable concept to formalize such traffic rules. Still, temporal logic rules often result in constraints that are hard to solve using optimization-based motion planners. Reinforcement learning (RL) is a

Costantino Delizia, Mikel E. Garciarena, Marialaura Noce

In this paper we revisit the description of all verbal subgroups of the group of automorphisms of a regular rooted tree $\mathcal{T}_d$, for $d>2$ and odd.

Guilherme W. F. Barros, Jenny Häggström

Hazard ratios are frequently reported in time-to-event and epidemiological studies to assess treatment effects. In observational studies, the combination of propensity score weights with the Cox proportional hazards model facilitates the estimation of the marginal hazard ratio (MHR). The methods for estimating MHR are analogous to those employed for estimati

Atri Goswami, Rohn Chatterjee, Sudip Mukherjee

Totally asymmetric simple exclusion process (TASEP) sets the paradigm for one-dimensional driven single file motion. We study a periodic TASEP with two ``road blocks'' or defects of different kinds, one point and another extended, across which particle flows are inhibited. We show how the interplay between particle number conservation and competition between

Preetika Verma, Kokil Jaidka, Svetlana Churina

Large language models (LLMs) play a key role in generating evidence-based and stylistic counter-arguments, yet their effectiveness in real-world applications has been underexplored. Previous research often neglects the balance between evidentiality and style, which are crucial for persuasive arguments. To address this, we evaluated the effectiveness of styli

Daniele Dona, Martin W. Liebeck, Kamilla Rekvényi

Let $G$ be a finite non-abelian simple group, $C$ a non-identity conjugacy class of $G$, and $\Gamma_C$ the Cayley graph of $G$ based on $C \cup C^{-1}$. Our main result shows that in any such graph, there is an involution at bounded distance from the identity.

Chinonso Cynthia Osuji, Thiago Castro Ferreira, Brian Davis

This systematic review undertakes a comprehensive analysis of current research on data-to-text generation, identifying gaps, challenges, and future directions within the field. Relevant literature in this field on datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods is reviewed. Various metho

Christopher J. McDevitt, Xian-Zhu Tang

A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov-Fokker-Planck equation in the absence of any synthetic or experimental data.

Piermario Vitullo, Nicola Rares Franco, Paolo Zunino

Forward uncertainty quantification (UQ) for partial differential equations is a many-query task that requires a significant number of model evaluations. The objective of this work is to mitigate the computational cost of UQ for a 3D-1D multiscale computational model of microcirculation. To this purpose, we present a deep learning enhanced multi-fidelity Mont

Qinghua Tao, Xiangming Xi, Jun Xu, Johan A. K. Suykens

For the linear inverse problem with sparsity constraints, the $l_0$ regularized problem is NP-hard, and existing approaches either utilize greedy algorithms to find almost-optimal solutions or to approximate the $l_0$ regularization with its convex counterparts. In this paper, we propose a novel and concise regularization, namely the sparse group $k$-max reg

Xiaoqiang Liu, Yubin Wang, Zicheng Huang, Boming Xu

Background: Colonoscopy, a crucial diagnostic tool in gastroenterology, depends heavily on superior bowel preparation. ChatGPT, a large language model with emergent intelligence which also exhibits potential in medical applications. This study aims to assess the accuracy and consistency of ChatGPT in using the Boston Bowel Preparation Scale (BBPS) for colono

Andrzej Mizera, Jakub Zarzycki

Cellular reprogramming can be used for both the prevention and cure of different diseases. However, the efficiency of discovering reprogramming strategies with classical wet-lab experiments is hindered by lengthy time commitments and high costs. In this study, we develop a novel computational framework based on deep reinforcement learning that facilitates th

Søren Fournais, Błażej Ruba, Jan Philip Solovej

We study the ground state energy of a gas of $N$ fermions confined to a unit box in $d$ dimensions. The particles interact through a 2-body potential with strength scaled in an $N$-dependent way as $N^{-\alpha}v$, where $\alpha\in \mathbb R$ and $v$ is a function of positive type satisfying a mild regularity assumption. Our focus is on the strongly interacti

Shan Ren, Runxuan Zhang

We study connections between skew-symmetric solutions of the classical Yang-Baxter equation (CYBE) and $\mathcal{O}$-operators of Malcev algebras. We prove that a skew-symmetric solution of the CYBE on a Malcev algebra can be interpreted as an $\mathcal{O}$-operator associated to the coadjoint representation. We show that this connection can be enhanced with

P. North, M. Hayes, M. Millon, A. Verhamme

The radio-quiet quasar SDSS J1240+1455 lies at a redshift of z=3.11, is surrounded by a Ly-alpha blob (LAB), and is absorbed by a proximate damped Ly-alpha system. In order to better define the morphology of the blob and determine its emission mechanism, we gathered deep narrow-band images isolating the Ly-alpha line of this object in linearly polarized ligh

Michael Parfenov

A physically more adequate definition of a quaternionic holomorphic (H-holomorphic) function of one quaternionic variable compared to known ones and a quaternionic generalization of Cauchy-Riemann's equations are presented. At that a class of introduced H-holomorphic functions consists of those quaternionic functions whose left and right derivatives become e

CMS Collaboration

Differential cross sections for top quark pair ($\mathrm{t\bar{t}}$) production are measured in proton-proton collisions at a center-of-mass energy of 13 TeV using a sample of events containing two oppositely charged leptons. The data were recorded with the CMS detector at the CERN Large Hadron Collider and correspond to an integrated luminosity of 138 fb$^{

John M. Campbell

Using the Wolfram NumberTheory package and the Recognize command, together with numerical estimates involving the elliptic lambda and elliptic alpha functions, Bagis and Glasser, in 2013, introduced a conjectural Ramanujan-type series related to the class number $h(-d) = 1$ for a quadratic form with discriminant $d = 163$. This conjectured series is of level

Edwin Lock, Zephyr Qiu, Alexander Teytelboym

We prove that the classic problem of finding a competitive equilibrium in an exchange economy with indivisible goods, money, and unit-demand agents is PPAD-complete. In this "housing market", agents have preferences over the house and amount of money they end up with, but can experience income effects. Our results contrast with the existence of polynomial-ti

Jiasheng Wang, Jeongwoo Lee, Jongchul Chae, Yan Xu

Minifilament (MF) eruption producing small jets and micro-flares is regarded as an important source for coronal heating and the solar wind transients through studies mostly based on coronal observations in the extreme ultraviolet (EUV) and X-ray wavelengths. In this study, we focus on the chromospheric plasma diagnostics of a tiny minifilament in quiet Sun l

Julian Hölz

We study the uniform ergodicity property for non-invertible topological and measure-preserving dynamical systems. It is shown that for topological dynamical systems uniform ergodicity is equivalent to eventually periodicity and that for measure preserving systems it is equivalent to periodicity. To obtain our results, we prove a result on the long-term behav

Meryam chaieb, Mohamed Aymen Saied

The microservices architectural style is widely favored for its scalability, reusability, and easy maintainability, prompting increased adoption by developers. However, transitioning from a monolithic to a microservices-based architecture is intricate and costly. In response, we present a novel method utilizing clustering to identify potential microservices

Chen Lin, Liheng Ma, Yiyang Chen, Wanli Ouyang

This study addresses the limitations of the traditional analysis of message-passing, central to graph learning, by defining {\em \textbf{generalized propagation}} with directed and weighted graphs. The significance manifest in two ways. \textbf{Firstly}, we propose {\em Generalized Propagation Neural Networks} (\textbf{GPNNs}), a framework that unifies most

Wei Jie Yeo, Ranjan Satapathy, Erik Cambria

The increasing use of complex and opaque black box models requires the adoption of interpretable measures, one such option is extractive rationalizing models, which serve as a more interpretable alternative. These models, also known as Explain-Then-Predict models, employ an explainer model to extract rationales and subsequently condition the predictor with t

Marvin Bechtold, Johanna Barzen, Frank Leymann, Alexander Mandl

Distributed quantum computing supports combining the computational power of multiple quantum devices to overcome the limitations of individual devices. Circuit cutting techniques enable the distribution of quantum computations via classical communication. These techniques involve partitioning a quantum circuit into smaller subcircuits, each containing fewer

Michel Broniatowski, Wolfgang Stummer

The constrained minimization (respectively maximization) of directed distances and of related generalized entropies is a fundamental task in information theory as well as in the adjacent fields of statistics, machine learning, artificial intelligence, signal processing and pattern recognition. In our previous paper "A precise bare simulation approach to the

Ömer Faruk Doğan

In this paper, via invertible radial differential operators, we characterize the closures of the harmonic Bergman Besov Spaces in the weighted harmonic Bloch spaces on the unit ball of Rn in terms of natural level sets. To this end, we first show that the harmonic Bergman Besov space is contained in the weighted harmonic little Bloch space.

Carsten Hartmann, Annika Jöster

We discuss importance sampling of exit problems that involve unbounded stopping times; examples are mean first passage times, transition rates or committor probabilities in molecular dynamics. The naive application of variance minimization techniques can lead to pathologies here, including proposal measures that are not absolutely continuous to the reference

Muhammad Kashif, Muhammad Shafique

Effective training of Quantum Neural Networks (QNNs) is crucial in the Noisy Intermediate-Scale Quantum (NISQ) era, where noise accelerates the onset of barren plateaus (BPs) and limits scalability. This paper investigates how quantum noise impacts QNN trainability and demonstrates that careful selection of qubit measurement observables can mitigate these ef

F. Della Pietra

The aim of this paper is to obtain optimal estimates for the first Robin eigenvalue of the anisotropic $p$-Laplace operator, namely: \[ \lambda_F(\beta,\Omega)=\lambda_{F}(p,\beta,\Omega)= \min_{\psi\in W^{1,p}(\Omega)\setminus\{0\} } \frac{\int_\Omega F(\nabla \psi)^p dx +\beta\int_{\partial\Omega}|\psi|^p F(\nu_{\Omega}) d\mathcal H^{N-1} }{\int_\Omega|\ps

Shaeke Salman, Md Montasir Bin Shams, Xiuwen Liu, Lingjiong Zhu

Transformer-based models have dominated natural language processing and other areas in the last few years due to their superior (zero-shot) performance on benchmark datasets. However, these models are poorly understood due to their complexity and size. While probing-based methods are widely used to understand specific properties, the structures of the repres

Camilo Chacón Sartori, Christian Blum, Gabriela Ochoa

The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into STNWeb. This is a web-based tool for the generation of Search Trajectory Networks (STNs), which are visualizati

Yaron Oz, Sebastian Waeber, Amos Yarom

We study the turbulent dynamics of a relativistic (2 + 1)-dimensional fluid placed in a stochastic gravitational potential. We demonstrate that the dynamics of the fluid can be obtained using a dual holographic description realized by an asymptotically Anti-de Sitter black brane driven by a random boundary metric. Using the holographic duality we study the i

Yangxin Fan, Raymond Wieser, Laura Bruckman, Roger French

We propose a novel Spatio-Temporal Graph Neural Network empowered trend analysis approach (ST-GTrend) to perform fleet-level performance degradation analysis for Photovoltaic (PV) power networks. PV power stations have become an integral component to the global sustainable energy production landscape. Accurately estimating the performance of PV systems is cr

Galin N. Gyulchev, Ayush Roy, Lucas G. Collodel, Petya G. Nedkova

We study the shadows cast by rotating hairy black holes with two non-trivial time-periodic scalar fields having a non-flat Gaussian curvature of the target space spanned by the scalar fields. Such black holes are a viable alternative to the Kerr black hole, having a much more complicated geodesic structure and resulting shadows. We investigate how a nontrivi

Freddy Heppell, Mehmet E. Bakir, Kalina Bontcheva

As Large Language Models become more proficient, their misuse in coordinated disinformation campaigns is a growing concern. This study explores the capability of ChatGPT with GPT-3.5 to generate short-form disinformation claims about the war in Ukraine, both in general and on a specific event, which is beyond the GPT-3.5 knowledge cutoff. Unlike prior work,

Cary Coglianese, Colton R. Crum

Fervent calls for more robust governance of the harms associated with artificial intelligence (AI) are leading to the adoption around the world of what regulatory scholars have called a management-based approach to regulation. Recent initiatives in the United States and Europe, as well as the adoption of major self-regulatory standards by the International O

Chuanqi Zhu, Yuichiro Koizumi

The kinetics of interfaces in alloy solidification pose a classic free boundary problem. This paper introduces an approach that amalgamates the distinctive characteristics of sharp and diffuse interface models. The motion of the diffuse interface is governed by the phase-field equation featuring a traveling wave function [I. Steinbach, Modell. Simul. Mater.

Kamaludin Dingle, Mohammad Alaskandarani, Boumediene Hamzi, Ard A. Louis

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. This phenomenon is known as \emph{simplicity bias}. By viewing the parameters of dynamical systems as inputs, and resulting (digitised) trajectories as outputs, we study simplicity bi

Ngoc Linh Nguyen, Hung The Dang, Tien Lam Pham, Thi Minh Hoa Nghiem

In this study, we employ a first-principles approach to conduct a comprehensive investigation of the properties of nine common native point defects in cubic boron nitride. This analysis combines standard semi-local and dielectric hybrid density-exchange-correlation functional calculations, encompassing vacancies, interstitials, antisites, and their complexes

Ricardo Rodrigues

We evaluated all two-loop conformal integrals of scalar half-BPS six-point functions in $\mathcal{N} = 4$ SYM restricted to a configuration where all points lie on a line. Moreover, we also computed some of these integrals in the kinematical limit where adjacent points become null-separated. Our results can serve as cross-checks for future works which obtain

Erica Forzinetti, Marco L. Della Vedova, Stefano Pasta, Milena Santerini

We examined four case studies in the context of hate speech on Twitter in Italian from 2019 to 2020, aiming at comparing the classification of the 3,600 tweets made by expert pedagogists with the automatic classification made by machine learning algorithms. Pedagogists used a novel classification scheme based on seven indicators that characterize hate. These

Franco Flandoli, Andrea Papini, Marco Rehmeier

We show that, in one spatial and arbitrary jump dimension, the averaged solution of a Marcustype SPDE with pure jump L\'evy transport noise satisfies a dissipative deterministic equation involving a fractional Laplace-type operator. To this end, we identify the correct associated L\'evy measure for the driving noise. We consider this a first step in the dire

R. Seppi, J. Comparat, V. Ghirardini, C. Garrel

The spatial distribution of galaxy clusters provides a reliable tracer of the large-scale distribution of matter in the Universe. The clustering signal depends on intrinsic cluster properties and cosmological parameters. The ability of eROSITA onboard Spectrum-Roentgen-Gamma (SRG) to discover galaxy clusters allows probing the association of extended X-ray e

E. Artis, V. Ghirardini, E. Bulbul, S. Grandis

The evolution of the cluster mass function traces the growth of the linear density perturbations and can be utilized for constraining the parameters of cosmological and alternative gravity models. In this context, we present new constraints on potential deviations from general relativity by investigating the Hu-Sawicki parametrization of the f(R) gravity wit

V. Ghirardini, E. Bulbul, E. Artis, N. Clerc

The cluster mass function traces the growth of linear density perturbations and provides valuable insights into the growth of structures, the nature of dark matter, and the cosmological parameters governing the Universe. The primary science goal of eROSITA, on board the {\it Spectrum Roentgen Gamma (SRG)} mission, launched in 2019, is to constrain cosmology

N. Clerc, J. Comparat, R. Seppi, E. Artis

Characterising galaxy cluster populations from catalog of sources selected in astronomical surveys requires knowledge of sample incompleteness, known as selection function. The first All-Sky Survey (eRASS1) by eROSITA onboard Spectrum Roentgen Gamma (SRG) has enabled the collection of large samples of galaxy clusters detected in the soft X-ray band over the

Florian Kleinebreil, Sebastian Grandis, Tim Schrabback, Vittorio Ghirardini

We aim to participate in the calibration of the X-ray photon count rate to halo mass scaling relation of galaxy clusters selected in the first eROSITA All-Sky Survey on the Western Galactic Hemisphere (eRASS1) using KiDS-1000 weak-lensing (WL) data. We measure the radial shear profiles around eRASS1 galaxy clusters using background galaxies in KiDS-1000, as

S. Grandis, V. Ghirardini, S. Bocquet, C. Garrel

Number counts of galaxy clusters across redshift are a powerful cosmological probe, if a precise and accurate reconstruction of the underlying mass distribution is performed -- a challenge called mass calibration. With the advent of wide and deep photometric surveys, weak gravitational lensing by clusters has become the method of choice to perform this measu

A. Liu, E. Bulbul, M. Kluge, V. Ghirardini

Superclusters of galaxies mark the large-scale overdense regions in the Universe. Superclusters provide an ideal environment to study structure formation and to search for the emission of the intergalactic medium such as cosmic filaments and WHIM. In this work, we present the largest-to-date catalog of X-ray-selected superclusters identified in the first SRG

M. Kluge, J. Comparat, A. Liu, F. Balzer

The first SRG/eROSITA All-Sky Survey (eRASS1) provides the largest intracluster medium-selected galaxy cluster and group catalog covering the western galactic hemisphere. Compared to samples selected purely on X-ray extent, the sample purity can be enhanced by identifying cluster candidates using optical and near-infrared data from the DESI Legacy Imaging Su

E. Bulbul, A. Liu, M. Kluge, X. Zhang

Clusters of galaxies can be used as powerful probes to study astrophysical processes on large scales, test theories of the growth of structure, and constrain cosmological models. The driving science goal of the SRG/eROSITA All-Sky Survey (eRASS) is to assemble a large sample of X-ray-selected clusters with a well-defined selection function to determine the e

Asaf Liberman, Oron Levy, Soroush Shahi, Cori Tymoszek Park

Personal devices have adopted diverse authentication methods, including biometric recognition and passcodes. In contrast, headphones have limited input mechanisms, depending solely on the authentication of connected devices. We present Moonwalk, a novel method for passive user recognition utilizing the built-in headphone accelerometer. Our approach centers o

Guy Bar-Shalom, Beatrice Bevilacqua, Haggai Maron

In the realm of Graph Neural Networks (GNNs), two exciting research directions have recently emerged: Subgraph GNNs and Graph Transformers. In this paper, we propose an architecture that integrates both approaches, dubbed Subgraphormer, which combines the enhanced expressive power, message-passing mechanisms, and aggregation schemes from Subgraph GNNs with a

Nikos Irges, Stylianos Kastrinakis

We introduce a discrete, graph theoretic approach to conformal field theory correlators. In a certain basis, called the squid basis, the correlator of N scalar operators can be expressed as the determinant of a natural, conformally covariant metric on a weighted graph, called the squid graph. We present the construction of this metric and discuss its possibl

Kazushi Okada, Takuma Kikutsuji, Kei-ichi Okazaki, Toshifumi Mori

The present work shows that the free energy landscape associated with alanine dipeptide isomerization can be effectively represented by specific interatomic distances without explicit reference to dihedral angles. Conventionally, two stable states of alanine dipeptide in vacuum, i.e., C$7_{\mathrm{eq}}$ ($\beta$-sheet structure) and C$7_{\mathrm{ax}}$ (left

Riccardo Cristoferi, Gabriele Fissore

We consider a model to describe stable configurations in epitaxial growth of crystals in the two dimensional case, and in the regime of linearized elasticity. The novelty is that the model also takes into consideration the adatom density on the surface of the film. These are behind the main mechanisms of crystal growth and formation of islands (or quantum do

Abdou Majeed Alidou, Júlia Baligács, Max Hahn-Klimroth, Jan Hązła

Polarization and unexpected correlations between opinions on diverse topics (including in politics, culture and consumer choices) are an object of sustained attention. However, numerous theoretical models do not seem to convincingly explain these phenomena. This paper is motivated by a recent line of work, studying models where polarization can be explained

Xianjun Ma, Yonggang Zhou, Qi Luo, Yihan Ma

In this paper, a proof-of-concept study of a $1$-bit wideband reconfigurable intelligent surface (RIS) comprising planar tightly coupled dipoles (PTCD) is presented. The developed RIS operates at subTHz frequencies and a $3$-dB gain bandwidth of $27.4\%$ with the center frequency at $102$ GHz is shown to be obtainable via full-wave electromagnetic simulation

Filip Kaśkosz, Kajetan Koperwas, Andrzej Grzybowski, Marian Paluch

In this work, we question the generally accepted statement that the character of intermolecular interactions can be directly determined from the scaling exponent. Based on detailed studies of polyatomic molecular systems with precisely defined and purely repulsive intermolecular potential, we show that the value of the density scaling exponent evidently diff

Carmen Delgado, María Canales, Jorge Ortín, José Ramón Gállego

Sensor network virtualization enables the possibility of sharing common physical resources to multiple stakeholder applications. This paper focuses on addressing the dynamic adaptation of already assigned virtual sensor network resources to respond to time varying application demands. We propose an optimization framework that dynamically allocate application

Ning Chang, Zelong Yuan, Yunpeng Wang, Jianchun Wang

The discrete direct deconvolution model (D3M) is developed for the large-eddy simulation (LES) of turbulence. The D3M is a discrete approximation of previous direct deconvolution model studied by Chang et al. ["The effect of sub-filter scale dynamics in large eddy simulation of turbulence," Phys. Fluids 34, 095104 (2022)]. For the first type model D3M-1, the

Nikita Gabdullin

Autoencoders (AE) are simple yet powerful class of neural networks that compress data by projecting input into low-dimensional latent space (LS). Whereas LS is formed according to the loss function minimization during training, its properties and topology are not controlled directly. In this paper we focus on AE LS properties and propose two methods for obta

Lizong Dong, Qinggai Mi, Siyu Zhou, Yuetang Yang

Using a laser-based ranging method for precise environmental 3D sensing, LiDAR has numerous applications in science and industry. However, conventional LiDAR face challenges in simultaneously achieving high ranging precision and fast measurement rates, which limits their applicability in more precise fields, such as aerospace, smart healthcare and beyond. By

Tim Büchner, Oliver Mothes, Orlando Guntinas-Lichius, Joachim Denzler

Analyzing facial features and expressions is a complex task in computer vision. The human face is intricate, with significant shape, texture, and appearance variations. In medical contexts, facial structures and movements that differ from the norm are particularly important to study and require precise analysis to understand the underlying conditions. Given

P. Hernández-Gómez, D. Martín-González, C. Torres, J. M. Muñoz

Single phase multiferroics in which ordered magnetic and ferroelectricity coexist, are of great interest for new multifunctional devices, and among them Y-type hexaferrites are good candidates. Transverse susceptibility measurements, which have been proved to be a versatile tool to study singular properties of bulk and nanoparticle magnetic systems, have bee

Otero Sanchez Alvaro, Lopez Ramos Juan Antonio

We show that a previously introduced key exchange based on a congruence-simple semiring action is not secure by providing an attack that reveals the shared key from the distributed public information for any of such semirings

Hiroki Marumo, Sunyoung Kim, Makoto Yamashita

We study T-semidefinite programming (SDP) relaxation for constrained polynomial optimization problems (POPs). T-SDP relaxation for unconstrained POPs was introduced by Zheng, Huang and Hu in 2022. In this work, we propose a T-SDP relaxation for POPs with polynomial inequality constraints and show that the resulting T-SDP relaxation formulated with third-orde

Muhammad Waleed, Abdul Rauf, Murtaza Taj

The process of camera calibration involves estimating the intrinsic and extrinsic parameters, which are essential for accurately performing tasks such as 3D reconstruction, object tracking and augmented reality. In this work, we propose a novel constraints-based loss for measuring the intrinsic (focal length: $(f_x, f_y)$ and principal point: $(p_x, p_y)$) a

Timo Langstrof, Alex R. Sabau

These days, software development and security go hand in hand. Numerous techniques and strategies are discussed in the literature that can be applied to guarantee the incorporation of security into the software development process. In this paper the main ideas of secure software development that have been discussed in the literature are outlined. Next, a dat