July 2022 arXiv papers — page 122

Yihua Ma, Zhifeng Yuan, Shuqiang Xia, Guanghui Yu

Joint communication and sensing (JCAS) is a very promising 6G technology, which attracts more and more research attention. Compared with communication, radar has many unique features in terms of waveform design criteria, self-interference cancellation (SIC), aperture-dependent resolution, and virtual aperture. This paper proposes a novel waveform design name

Quentin Brabant, Gwenole Lecorve, Lina M. Rojas-Barahona

Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the form of coreferences (e.g., via pronoun use) or ellipses, and can make the understanding difficult for automated systems. One way to facilitate the understanding and subsequent tr

Meer Mehran Rashid, Tanu Pittie, Souvik Chakraborty, N. M. Anoop Krishnan

Increased demands for high-performance materials have led to advanced composite materials with complex hierarchical designs. However, designing a tailored material microstructure with targeted properties and performance is extremely challenging due to the innumerable design combinations and prohibitive computational costs for physics-based solvers. In this s

Lucas Backes, Fagner B. Rodrigues

We prove a variational principle for the upper and lower metric mean dimension of level sets \[ \left\{x\in X: \lim_{n\to\infty}\frac{1}{n}\sum_{j=0}^{n-1}\varphi(f^{j}(x))=\alpha\right\} \] associated to continuous potentials $\varphi:X\to \mathbb R$ and continuous dynamics $f:X\to X$ defined on compact metric spaces and exhibiting the specification propert

I. Klincare, M. Tamanis, R. Ferber, E. A. Pazyuk

The relative intensity distribution in the rotationally resolved laser-induced fluorescence spectra belonging to the $E(4)^1\Sigma^+\to (a^3\Sigma^+_1,X^1\Sigma^+)$ band systems of the KCs molecule was analyzed. The experimental intensities in doublet $P$,$R$ progressions assigned to spin-allowed $E\to X$ and spin-forbidden $E\to a$ transitions up to their c

Joseph A. Ball, Haripada Sau

We develop a Sz.-Nagy--Foias-type functional model for a commutative contractive operator tuple $\underline{T} = (T_1, \dots, T_d)$ having $T = T_1 \cdots T_d$ equal to a completely nonunitary contraction. We identify additional invariants ${\mathbb G}_\sharp, {\mathbb W}_\sharp$ in addition to the Sz.-Nagy--Foias characteristic function $\Theta_T$ for the p

Andrey Polyakov, Denis Efimov, Xubin Ping

The paper proposes an algorithm for a discretization (sampled-time implementation) of a homogeneous control preserving the finite-time and nearly fixed-time stability property of the original (sampling-free) system. The sampling period is assumed to be constant. Both single-input and multiple-input cases are considered. The robustness (Input-to-State Stabili

R. Prasad, Ritam Mallick

We have studied the spin-down induced phase transition in cold, isolated neutron stars in this work. After birth, as the star slows down, its central density rises and crosses the critical density of phase transition, and a quark core is seeded inside the star. Intermediate mass stars are more likely to have a quark seeding in their lifetime at birth. Smalle

Lusine Abrahamyan, Nikos Deligiannis

This paper introduces an efficient patch-based computational module, coined Entropy-based Patch Encoder (EPE) module, for resource-constrained semantic segmentation. The EPE module consists of three lightweight fully-convolutional encoders, each extracting features from image patches with a different amount of entropy. Patches with high entropy are being pro

Joakim Rosdahl, Jérémy Blaizot, Harley Katz, Taysun Kimm

We measure escape fractions, $f_{\rm esc}$, of ionizing radiation from galaxies in the SPHINX suite of cosmological radiation-hydrodynamical simulations of reionization, resolving halos with $M_{\rm vir} \gtrapprox 7.5 \times 10^7 \ M_{\odot}$ with a minimum cell width of $\approx 10$ pc. Our new and largest $20$ co-moving Mpc wide volume contains tens of th

Andrew Beckett

We summarise recent work (arXiv:2203.07405 [math.SG]) on the classical result of Kirillov that any simply-connected homogeneous symplectic space of a connected group $G$ is a hamiltonian $\widehat{G}$-space for a one-dimensional central extension $\widehat{G}$ of $G$, and is thus (by a result of Kostant) a cover of a coadjoint orbit of $\widehat{G}$. We emph

Panagiotis Kaklamanos, Nikola Popović

We study the three-timescale dynamics of a model that describes the El Ni\~no Southern Oscillation (ENSO) phenomenon, which was proposed in [A. Roberts, J. Guckenheimer, E. Widiasih, A. Timmermann, and C. K. Jones, Mixed-mode oscillations of El Ni\~no--Southern Oscillation, Journal of the Atmospheric Sciences, 73 (2016), pp. 1755--1766]. While ENSO phenomena

Joseph A. Ball, Haripada Sau

A classical result of Sz.-Nagy asserts that a Hilbert space contraction operator $T$ can be dilated to a unitary $\cU$. A more general multivariable setting for these ideas is the setup where (i) the unit disk is replaced by a domain $\Omega$ contained in ${\mathbb C}^d$, (ii) the contraction operator $T$ is replaced by a commuting tuple $\bfT = (T_1, \dots,

Wenda Zhang, Michal Dovčiak, Michal Bursa, Vladimír Karas

High-sensitivity X-ray polarimetric observations of black hole X-ray binaries, which will soon become available with the launches of space-borne X-ray observatories with sensitive X-ray polarimeters, will be able to put independent constraints on the black hole as well as the accretion flow, and possibly break degeneracies that cannot be resolved by spectral

Ambrish Rawat, James Requeima, Wessel Bruinsma, Richard Turner

Machine unlearning refers to the task of removing a subset of training data, thereby removing its contributions to a trained model. Approximate unlearning are one class of methods for this task which avoid the need to retrain the model from scratch on the retained data. Bayes' rule can be used to cast approximate unlearning as an inference problem where the

Erkka Theodor Haapasalo

We study generating joint measurements by operating on the input quantum state with a broadcasting channel followed by local measurements on the two outputs of the broadcasting channel. Although, due to perfect broadcasting or cloning being impossible, this scheme cannot generate perfect joint observables for all pairs of quantum observables, we study for wh

Markus Haug Ana Cristina Franco Da Silva, Stefan Wagner

Nowadays, the correct use of cryptography libraries is essential to ensure the necessary information security in different kinds of applications. A common practice in software development is the use of static application security testing (SAST) tools to analyze code regarding security vulnerabilities. Most of these tools are designed to run separately from d

Daniel Pister, Kilian Irländer, Dennis Westerbeck, Jürgen Schnack

Molecular magnetic toroidal moments are molecule-based structures of quantum spins that are expected to boost magnetic storage technology and quantum computing. We study selected fictitious but typical examples of single-molecule toroidal magnet behavior, discuss the essence of the concept and clarify inappropriate or even wrong assignments of physical prope

F. Rizzuti, R. Hirschi, C. Georgy, W. D. Arnett

Our understanding of stellar structure and evolution coming from one-dimensional (1D) stellar models is limited by uncertainties related to multi-dimensional processes taking place in stellar interiors. 1D models, however, can now be tested and improved with the help of detailed three-dimensional (3D) hydrodynamics models, which can reproduce complex multi-d

Antoine Danchin, Oriane Pagani-Azizi, Gabriel Turinici, Ghozlane Yahiaoui

The interplay between the virus, infected cells and the immune responses to SARS-CoV-2 is still under debate. Extending the basic model of viral dynamics we propose here a formal approach to describe the neutralizing versus weakly (or non-)neutralizing scenarios and compare with the possible effects of antibody-dependent enhancement (ADE). The theoretical mo

M. Shadmangohar, S. M. S. Movahed

Relying on the excursion set theory, we compute the number density of local extrema and crossing statistics versus the threshold for the stock market indices. Comparing the number density of excursion sets calculated numerically with the theoretical prediction for the Gaussian process confirmed that all data sets used in this paper have a surplus (almost lac

Jiyu Shen, Jiajun Mo, Zeyi Lu, Chenying Gong

Sophisticated spin instruments require high-precision spin control. In this study, we accurately study the intrinsic magnetic properties of the strongly disordered system NdFe0.5Cr0.5O3 through molecular field models combined with ASD theory. The three constituent sub-magnetic phases of the system are separated, and their magnetization contributions are calc

Yoshihiko Susuki, Kohei Eto, Naoto Hiramatsu, Atsushi Ishigame

This paper addresses a control problem on air-conditioning systems in buildings that is regarded as a control practice of nonlinear distributed-parameter systems. Specifically, we consider the design of a controller for suppressing an oscillatory response of in-room temperature field. The main idea in this paper is to apply the emergent theory of Koopman ope

Durmus Demir

Tunneling time, time needed for a quantum particle to tunnel through a potential energy barrier, can be measured by a duration marker. One such marker is spin reorientation due to Larmor precession. With a weak magnetic field in $z$ direction, the Larmor clock reads two times, $\tau_y$ and $\tau_z$, for a potential energy barrier along the $y$ axis. The prob

Taiyong Chen, Yahui Jiang, Marco Squassina, Jianjun Zhang

In this paper, we are concerned with the coupled nonlinear Schr\"{o}dinger system \begin{align*} \begin{cases} -\varepsilon^{2}\Delta u+a(x)u=\mu_{1}u^{3}+\beta v^{2}u \ \ \ \ \mbox{in}\ \mathbb{R}^{N},\\ -\varepsilon^{2}\Delta v+b(x)v=\mu_{2}v^{3}+\beta u^{2}v \ \ \ \ \ \mbox{in}\ \mathbb{R}^{N}, \end{cases} \end{align*} where $1\leq N\leq3$, $\mu_{1},\mu_{

Wiesław Kopeć, Cezary Biele, Monika Kornacka, Grzegorz Pochwatko

We propose a one-day transdisciplinary creative workshop in the broad area of HCI focused on multiple opportunities of incorporating participatory design into research and industry practice. This workshop will become a venue to share experiences and novel ideas in this area. At the same time, we will brainstorm and explore frontiers of HCI related to engagin

Wataru Namiki, Daiki Nishioka, Yu Yamaguchi, Takashi Tsuchiya

Physical reservoir computing, which is a promising method for the implementation of highly efficient artificial intelligence devices, requires a physical system with nonlinearity, fading memory, and the ability to map in high dimensions. Although it is expected that spin wave interference can perform as highly efficient reservoir computing in some micromagne

Christopher Lloyd

The times of minima of the bright, eccentric eclipsing binary RR Lyncis are re-investigated. From the TESS data it is found that significant differences in the eclipse timings, particularly for the primary minima, are probably due to small, irregular changes in the eclipse profile due to pulsations, rather than light-curve asymmetries.

Francisco Cruz, Charlotte Young, Richard Dazeley, Peter Vamplew

Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in reinforcement learning and robotic scenarios, to better understand the robot decision-making process. Previous work, however, has been widely focused on providing technical explanati

O. B. Zaslavskii

The BSW effect implies that the energy $E_{c.m.}$ in the center of mass frame of two particles colliding near a black hole can become unbounded. Usually, it is assumed that particles move along geodesics or electrogeodesics. Instead, we consider another version of this effect. One particle is situated at rest near a static, generally speaking, distorted blac

D. N. Voskresensky

A broad range of problems associated with phase transitions in systems characterized by the strong interaction between particles and with formation of structures is reviewed. A general phenomenological mean-field model is constructed describing phase transitions of the first and the second order to the $k_0=0$ and $\vec{k}_0\neq 0$ states. Due to fluctuation

Feini Yan, Shaogang Xu, Chao He, Changchun He

While the growth of germanene has been claimed on many substrates, the exact crystal structures remain controversial. Here, we systematically explore the possible structures formed by Ge deposition onto Al(111) surface by combining density-functional theory (DFT) and global optimization algorithm. We show that, by high-level random-phase approximation (RPA)

Yi Gu, Yoshito Otake, Keisuke Uemura, Mazen Soufi

We propose a method for estimating the bone mineral density (BMD) from a plain x-ray image. Dual-energy X-ray absorptiometry (DXA) and quantitative computed tomography (QCT) provide high accuracy in diagnosing osteoporosis; however, these modalities require special equipment and scan protocols. Measuring BMD from an x-ray image provides an opportunistic scre

Sebastian Stock, Atif Mashkoor, Alexander Egyed

Our lives become increasingly dependent on safety- and security-critical systems, so formal techniques are advocated for engineering such systems. One of such techniques is validation obligations that enable formalizing requirements early in development to ensure their correctness. Furthermore, validation obligations help hold requirements consistent in an e

Ivan Rubachev, Artem Alekberov, Yury Gorishniy, Artem Babenko

Recent deep learning models for tabular data currently compete with the traditional ML models based on decision trees (GBDT). Unlike GBDT, deep models can additionally benefit from pretraining, which is a workhorse of DL for vision and NLP. For tabular problems, several pretraining methods were proposed, but it is not entirely clear if pretraining provides c

Sean E. Lake, Chao-Wei Tsai

When training a machine learning classifier on data where one of the classes is intrinsically rare, the classifier will often assign too few sources to the rare class. To address this, it is common to up-weight the examples of the rare class to ensure it isn't ignored. It is also a frequent practice to train on restricted data where the balance of source typ

Jasmin Bogatinovski, Gjorgji Madjarov, Sasho Nedelkoski, Jorge Cardoso

Artificial Intelligence for IT Operations (AIOps) describes the process of maintaining and operating large IT systems using diverse AI-enabled methods and tools for, e.g., anomaly detection and root cause analysis, to support the remediation, optimization, and automatic initiation of self-stabilizing IT activities. The core step of any AIOps workflow is anom

Ziyi Xi, Hao Lin, Weiqi Luo

With the development of computer graphics technology, the images synthesized by computer software become more and more closer to the photographs. While computer graphics technology brings us a grand visual feast in the field of games and movies, it may also be utilized by someone with bad intentions to guide public opinions and cause political crisis or soci

Sinisa Stekovic, Mahdi Rad, Alireza Moradi, Friedrich Fraundorfer

We propose a novel method applicable in many scene understanding problems that adapts the Monte Carlo Tree Search (MCTS) algorithm, originally designed to learn to play games of high-state complexity. From a generated pool of proposals, our method jointly selects and optimizes proposals that minimize the objective term. In our first application for floor pla

Jean-Baptiste Gorce, Eric Falcon

We investigate experimentally three-dimensional (3D) hydrodynamic turbulence at scales larger than the forcing scale. We manage to perform a scale separation between the forcing scale and the container size by injecting energy into the fluid using centimetric magnetic particles. We measure the statistics of the fluid velocity field at scales larger than the

Csilla Bujtás, Pakanun Dokyeesun, Sandi Klavžar

We study a recently introduced two-person combinatorial game, the $(a,b)$-monochromatic clique transversal game which is played by Alice and Bob on a graph $G$. As we observe, this game is equivalent to the $(b,a)$-biased Maker-Breaker game played on the clique-hypergraph of $G$. Our main results concern the threshold bias $a_1(G)$ that is the smallest integ

Jill-Jênn Vie, Tomas Rigaux, Sein Minn

Institutions collect massive learning traces but they may not disclose it for privacy issues. Synthetic data generation opens new opportunities for research in education. In this paper we present a generative model for educational data that can preserve the privacy of participants, and an evaluation framework for comparing synthetic data generators. We show

Kevin K. M. Hickson, Jean-Christophe Loison

Atomic oxygen in its first excited singlet state, O(1 D), is an important species in the photochemistry of several planetary atmospheres and has been predicted to be a potentially important reactive species on interstellar ices. Here, we report the results of a kinetic study of the reactions of O(1 D) with methanol, CH3OH, and acetonitrile, CH3CN, over the 5

Marianna D'Amato, Qi Ying Tan, Quentin Glorieux, Alberto Bramati

Quantum photonics technologies like wavelength division multiplexing (WDM) for high-rate quantum key distribution require narrowband, spectrally tunable single photon emitters. Physical methods that rely on the application of large mechanical strain to epitaxial quantum dots or electric and magnetic fields to color centers in 2D metal dichalcogenides provide

Andrea Wagner, Firdevs Ulus, Birgit Rudloff, Gabriela Kováčová

This paper is concerned with solution algorithms for general convex vector optimization problems (CVOPs). So far, solution concepts and approximation algorithms for solving CVOPs exist only for bounded problems [Ararat et al. 2022, Doerfler et al. 2021, Loehne et al. 2014]. They provide a polyhedral inner and outer approximation of the upper image that have

Lisa J. Hofer, Leonhard Held

There are over 55 different ways to construct a confidence respectively credible interval (CI) for the binomial proportion. Methods to compare them are necessary to decide which should be used in practice. The interval score has been suggested to compare prediction intervals. This score is a proper scoring rule that combines the coverage as a measure of cali

Maryam Shahabikargar

Mental health disorders may cause severe consequences on all the countries' economies and health. For example, the impacts of the COVID-19 pandemic, such as isolation and travel ban, can make us feel depressed. Identifying early signs of mental health disorders is vital. For example, depression may increase an individual's risk of suicide. The state-of-the-a

Stefano Dafarra, Giulio Romualdi, Daniele Pucci

The paper presents a planner to generate walking trajectories by using the centroidal dynamics and the full kinematics of a humanoid robot. The interaction between the robot and the walking surface is modeled explicitly via new conditions, the \emph{Dynamical Complementarity Constraints}. The approach does not require a predefined contact sequence and genera

Chun Yang, Shicai Fan

The goal of a recommendation system is to model the relevance between each user and each item through the user-item interaction history, so that maximize the positive samples score and minimize negative samples. Currently, two popular loss functions are widely used to optimize recommender systems: the pointwise and the pairwise. Although these loss functions

Andrius Grigutis, Jonas Jankauskas, Jonas Šiaulys

In this work we set up the distribution function of $\mathcal{M}:=\sup_{n\geqslant1}\sum_{i=1}^{n}{(Z_i-1)}$, where the random walk $\sum_{i=1}^{n}Z_i, n\in\mathbb{N},$ is generated by $N$ periodically occurring distributions and the integer-valued and non-negative random variables $Z_1,\,Z_2,\,\ldots$ are independent. The considered random walk generates so

Vasiliki Bitsouni, Nikolaos Gialelis, Dan-Stefan Marinescu

We first introduce the generic versions of the fraction rules for monotonicity, i.e. the one that involves integrals known as the Gromov theorem and the other that involves derivatives known as L'H\^opital rule for monotonicity, which we then extend to high order antiderivatives and derivatives, respectively.

Tomokatsu Onaga, Fabio Caccioli, Teruyoshi Kobayashi

Trading activities in financial systems create various channels through which systemic risk can propagate. An important contagion channel is financial fire sales, where a bank failure causes asset prices to fall due to asset liquidation, which in turn drives further bank defaults, triggering the next rounds of liquidation. This process can be considered as c

İsmail Ş. Güloğlu, Gülin Ercan

Let $A$ be a group acting by automorphisms on the group $G.$ \textit{The commuting graph $\Gamma(G,A)$ of $A$-orbits} of this action is the simple graph with vertex set $\{x^{A} : 1\ne x \in G \}$, the set of all $A$-orbits on $G\setminus \{1\}$, where two distinct vertices $x^{A}$ and $y^{A}$ are joined by an edge if and only if there exist $x_{1}\in x^{A}$

Luca Bertaccini, Gianna Paulin, Tim Fischer, Stefan Mach

Low-precision formats have recently driven major breakthroughs in neural network (NN) training and inference by reducing the memory footprint of the NN models and improving the energy efficiency of the underlying hardware architectures. Narrow integer data types have been vastly investigated for NN inference and have successfully been pushed to the extreme o

Charles Bertucci, Alekos Cecchin

This paper studies the convergence of mean field games with finite state space to mean field games with a continuous state space. We examine a space discretization of a diffusive dynamics, which is reminiscent of the Markov chain approximation method in stochasctic control, but also of finite difference numerical schemes; time remains continuous in the discr

Jiashuo Yu, Junfu Pu, Ying Cheng, Rui Feng

Although audio-visual representation has been proved to be applicable in many downstream tasks, the representation of dancing videos, which is more specific and always accompanied by music with complex auditory contents, remains challenging and uninvestigated. Considering the intrinsic alignment between the cadent movement of dancer and music rhythm, we intr

Valentina Amitrano, Alessandro Roggero, Piero Luchi, Francesco Turro

It is well known that the neutrino flavor in extreme astrophysical environments changes under the effect of three contributions: the vacuum oscillation, the interaction with the surrounding matter, and the collective oscillations due to interactions between different neutrinos. The latter adds a non-linear contribution to the equations of motion, making the

A. Zuevsky

We introduce and study a $K$-theory of twisted bundles for associative algebras $A(\mathfrak g)$ of formal series with an infinite-Lie algebra coefficients over arbitrary compact topological spaces. Fibers of such bundles are given by elements of algebraic completion of the space of all formal series in complex parameters, sections are provided by rational f

Raffaele Fiorentini, Thomas Tarenzi, Raffaello Potestio

In recent years, a few multiple-resolution modelling strategies have been proposed, in which functionally relevant parts of a biomolecule are described with atomistic resolution, while the remainder of the system is concurrently treated using a coarse-grained model. In most cases, the parametrisation of the latter requires lengthy reference all-atom simulati

Mareike Dressler, André Uschmajew, Venkat Chandrasekaran

The decomposition or approximation of a linear operator on a matrix space as a sum of Kronecker products plays an important role in matrix equations and low-rank modeling. The approximation problem in Frobenius norm admits a well-known solution via the singular value decomposition. However, the approximation problem in spectral norm, which is more natural fo

Taiki Idomoto, Takao Suzuki

Recently, we formulated the $q$-Garnier system and its variations as translations of an extended affine Weyl group of type $A^{(1)}_{2n+1}\times A^{(1)}_1\times A^{(1)}_1$. On the other hand, those systems admit particular solutions in terms of the basic hypergeometric series ${}_{n+1}\phi_n$. In this article, we investigate an action of the extended affine

Kinga Skorupska, Anna Jaskulska, Rafał Masłyk, Julia Paluch

In this exploratory study we evaluated the engagement, performance and preferences of older adults who interacted with different citizen science tasks. Out of 40 projects recently active on the Zooniverse platform we selected top ones to be represented by 8 diverse, yet standardized, microtasks, 2 in each category of image, audio, text and pattern recognitio

Deog Ki Hong, Wei-Chen Lin, Dong-han Yeom

By utilizing the thin shell approximation, we investigate the behavior of radial timelike geodesics in a black hole to white hole bouncing scenario with a mass (de-)amplification relation. We show that those geodesics lose energy after crossing the transition surface if the white hole mass is less than the black hole mass and vice versa. That is, the bounded

Patrick Héas, Frédéric Cérou, Mathias Rousset

Atmospheric motion vectors (AMVs) extracted from satellite imagery are the only wind observations with good global coverage. They are important features for feeding numerical weather prediction (NWP) models. Several Bayesian models have been proposed to estimate AMVs. Although critical for correct assimilation into NWP models, very few methods provide a thor

Tristan Suzanne, Julien Deschamps, Marc Georgelin, Gwenn Boedec

Thin elastic two-dimensionnal systems under compressive stresses may relieve part of their stretching energy by developing out of plane undulations. We investigate experimentally and theoretically the indentation of an elastic disk supported by a circular ring and show that compressive stresses are relieved via two different routes : either developing \texti

Ijeoma Amuche Chikwendu, Kulevome Delanyo Kwame Bensah, Chiagoziem Chima Ukwuoma, Chukwuebuka Joseph Ejiyi

The Distributed Diffusion Kalman Filter (DDKF) algorithm in all its magnitude has earned great attention lately and has shown an elaborate way to address the issue of distributed optimization over networks. Estimation and tracking of a single state vector collectively by nodes have been the point of focus. In reality, however, there are several multi-task-or

Jiashun Chen, Donghuan Lu, Yu Zhang, Dong Wei

Recently, deep-learning-based approaches have been widely studied for deformable image registration task. However, most efforts directly map the composite image representation to spatial transformation through the convolutional neural network, ignoring its limited ability to capture spatial correspondence. On the other hand, Transformer can better characteri

M. Griggio, L. R. Bedin, R. Raddi, N. Reindl

We present an astrometric and photometric wide-field study of the Galactic open star cluster M37 (NGC 2099). The studied field was observed with ground-based images covering a region of about four square degrees in the Sloan-like filters ugi. We exploited the Gaia catalogue to calibrate the geometric distortion of the large field mosaics, developing software

Anthony DiGiovanni, Nicolas Macé, Jesse Clifton

The evolution of preferences that account for other agents' fitness, or other-regarding preferences, has been modeled with the "indirect approach" to evolutionary game theory. Under the indirect evolutionary approach, agents make decisions by optimizing a subjective utility function. Evolution may select for subjective preferences that differ from the fitnes

Bihui Zou, Zihe Liang, Zhiming Cui, Kai Xiao

Future active metamaterials for reconfigurable structural applications require fast, untethered, reversible, and reprogrammable (multimodal) transformability with shape locking. Herein, we aim to construct and demonstrate a magneto-thermomechanical tool that enables a single material system to transform with untethered, reversible, low-powered reprogrammable

Alexander Shlapunov

The initial problem for the Navier-Stokes type equations over ${\mathbb R}^n \times [0,T]$, $n\geq 2$, with a positive time $T$ in the spatially periodic setting is considered. First, we prove that the problem induces an open injective continuous mapping on scales of specially constructed function spaces of Bo\-chner-Sobolev type over the $n\,$-dimensional t

Vitaliy Afanasyev, Chen Ran, Yuri Ozhigov, You Jiangchuan

The singlet state of a system of two two-level atoms changes smoothly, remaining dark, as the Hamiltonian TC is slowly deformed, despite the inapplicability of the adiabatic theorem to this case. In this case, there is a small probability of emission of free photons, which does not depend on the smoothness of the deformation of the Hamiltonian. The effect of

Eliseo Luongo

We consider in a smooth and bounded two dimensional domain the convergence in the $L^2$ norm, uniformly in time, of the solution of the stochastic second-grade fluid equations with transport noise and no-slip boundary conditions to the solution of the corresponding Euler equations. We prove, that assuming proper regularity of the initial conditions of the Eu

N. Bijas, Chakali Eswaraiah, Jia-Wei Wang, Jessy Jose

Dust polarization observations at optical wavelengths help to understand the dust grain properties and trace the plane-of-the-sky component of the magnetic field. In this study, we make use of the $I$-band polarization data acquired from AIMPOL along with the distances ($d$) and extinction ($A_{V}$) data to study the variation of polarization fraction ($P$)

Gabriele Lagani, Claudio Gennaro, Hannes Fassold, Giuseppe Amato

Learning algorithms for Deep Neural Networks are typically based on supervised end-to-end Stochastic Gradient Descent (SGD) training with error backpropagation (backprop). Backprop algorithms require a large number of labelled training samples to achieve high performance. However, in many realistic applications, even if there is plenty of image samples, very

Guo-Dong Zhao, Xingen Liu, Wei Ren, Xiaona Zhu

Hafnium dioxide (HfO2) is a promising ferroelectric (FE) material for achieving high-density nonvolatile memory and neuromorphic computing, due to its compatibility with the mainstream integrated circuit technology and the surprisingly enhanced ferroelectricity by reduced thickness. The FE switching dynamics is essential to the device performance, but the co

Kinga Skorupska, Radosław Nielek, Wiesław Kopeć

In this article we propose a unique framework for designing attractive and engaging crowdsourcing systems for older adults, which is called AFFORCE (Actionable Framework For Crowdsourcing Experiences). We first categorize and map mitigating factors and barriers to crowdsourcing for older adults to finally discuss, present and combine system elements addressi

Hechao Liu, Yufei Huang

For a graph $G$ with vertex set $V_{G}$ and edge set $E_{G}$, the symmetric division deg index is defined as $SDD(G)=\sum\limits_{uv\in E_{G}}(\frac{d_{u}}{d_{v}}+\frac{d_{v}}{d_{u}})$, where $d_{u}$ denotes the degree of vertex $u$ in $G$. In 2018, Furtula et al. confirmed the quality of SDD index exceeds that of some more popular VDB indices, in particular

Jumon Nozaki, Tatsuya Kawahara, Kenkichi Ishizuka, Taiichi Hashimoto

Conventional automatic speech recognition systems do not produce punctuation marks which are important for the readability of the speech recognition results. They are also needed for subsequent natural language processing tasks such as machine translation. There have been a lot of works on punctuation prediction models that insert punctuation marks into spee

C. Verbeke, B. Schmieder, P. Démoulin, S. Dasso

Coronal mass ejections (CMEs) are large scale eruptions observed close to the Sun. They are travelling through the heliosphere and possibly interacting with the Earth environment creating interruptions or even damaging new technology instruments. Most of the time their physical conditions (velocity, density, pressure) are only measured in situ at one point i

Ravi Tomar

In this paper, we prove a combination theorem for a relatively acylindrical graph of relatively hyperbolic groups (Theorem 1.1). Here, we are extending the technique of [Tom21] and constructing Bowditch boundary of the fundamental group of graph of groups. Suppose G(Y) is a graph of relatively hyperbolic groups such that edge groups are relatively quasi-conv

Eric Simonnet, Mickaël D. Chekroun

We propose a flexible machine-learning framework for solving eigenvalue problems of diffusion operators in moderately large dimension. We improve on existing Neural Networks (NNs) eigensolvers by demonstrating our approach ability to compute (i) eigensolutions for non-self adjoint operators with small diffusion (ii) eigenpairs located deep within the spectru

Harish Kishnani, Rijubrata Kundu, Sumit Chandra Mishra

Given integers $k,l\geq 2$, where either $l$ is odd or $k$ is even, let $n(k,l)$ denote the largest integer $n$ such that each element of $A_n$ is a product of $k$ many $l$-cycles. In 2008, M. Herzog, G. Kaplan and A. Lev proved that if $k,l$ both are odd, $3\mid l$ and $l>3$, then $n(k,l)=\frac{2}{3}kl$. They further conjectured that if $k$ is even and $3\m

Christina D. Kreisch, Minsu Park, Erminia Calabrese, Francis-Yan Cyr-Racine

We use data from the Atacama Cosmology Telescope (ACT) DR4 to search for the presence of neutrino self-interaction in the cosmic microwave background. Consistent with prior works, the posterior distributions we find are bimodal, with one mode consistent with $\Lambda$CDM and one where neutrinos strongly self-interact. By combining ACT data with large-scale i

Hao Chen

Private Information Retrieval (PIR) was first proposed by B. Chor, O. Goldreich, E. Kushilevitz and M. Sudan in their 1995 FOCS paper. For MDS coded distributed storage system private information retrieval was proposed and the capacity of PIR schemes for MDS coded distributed storage was studied. Star product PIR schemes from general coded distributed storag

Zhuo Huang, Xiaobo Xia, Li Shen, Bo Han

Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of different types of OOD data: there are benign OOD data that can be properly adapted to enhance the learning performance, while other malign OOD data would severely degenerate the clas

Yi Yao, Roland Willa, Tom Lacmann, Sofia-Michaela Souliou

Understanding the organizing principles of interacting electrons and the emergence of novel electronic phases is a central endeavor of condensed matter physics. Electronic nematicity, in which the discrete rotational symmetry in the electron fluid is broken while the translational one remains unaffected, is a prominent example of such a phase. It has proven

Zelin Zang, Siyuan Li, Di Wu, Ge Wang

Manifold learning (ML) aims to seek low-dimensional embedding from high-dimensional data. The problem is challenging on real-world datasets, especially with under-sampling data, and we find that previous methods perform poorly in this case. Generally, ML methods first transform input data into a low-dimensional embedding space to maintain the data's geometri

C. E. Alissandrakis

Classic solar models put the Chromosphere-Corona Transition Region (CCTR} at $\sim2$Mm above the $\tau_{5000} = 1$ level, whereas rMHD models place it in a wider range of heights. Observational verification is scarce. We review and discuss recent results from various instruments and spectral domains. In SDO and TRACE images spicules appear in emission in the

Aner Shalev, Agata Smoktunowicz

Let $A$ be a brace of cardinality $p^{n}$ where $p>n+1$ is prime, and let $ann (p^{2})$ be the set of elements of additive order at most $p^{2}$ in this brace. We construct a pre-Lie ring related to the brace $A/ann(p^{2})$. In the case of strongly nilpotent braces of nilpotency index $k<p$ the brace $A/ann(p^{2})$ can be recovered by applying the constructi

Zixuan Huang, Beixiong Zheng, Rui Zhang

Intelligent reflecting surface (IRS) has emerged as a promising technique to control wireless propagation environment for enhancing the communication performance cost-effectively. However, the rapidly time-varying channel in high-mobility communication scenarios such as vehicular communication renders it challenging to obtain the instantaneous channel state

Morris Brooks, Robert Seiringer

We study the Fr\"ohlich polaron model in $\mathbb{R}^3$, and establish the subleading term in the strong coupling asymptotics of its ground state energy, corresponding to the quantum corrections to the classical energy determined by the Pekar approximation.

Baptiste Ferrero, Ludovic Bellon

The two output signals of quadrature phase interferometers allow to benefit both from the high sensitivity of interferometry (working inside a fringe) and from an extended input range (counting fringes). Their calibration to reach a linear output is traditionally performed using Heydemann's correction, which involves fitting one output versus the other by an

F. Götze, A. Tikhomirov

We derive estimates for the largest and smallest singular values of sparse rectangular $N\times n$ random matrices, assuming $\lim_{N,n\to\infty}\frac nN=y\in(0,1)$. We consider a model with sparsity parameter $p_N$ such that $Np_N\sim \log^{\alpha }N$ for some $\alpha>1$, and assume that the moments of the matrix elements satisfy the condition $\mathbf E|X_

V. B. Kiran Kumar, P. C. Vinaya

In a recent article, Dumitru Popa proved an operator version of the Korovkin theorem. We recall the quantitative version of the Korovkin theorem obtained by O. Shisha and B. Mond in 1968. In this paper, we obtain a quantitative estimate for the operator version of the Korovkin theorem obtained by Dumitru Popa. We also consider various examples where the oper

Abhijit Anand, Jurek Leonhardt, Koustav Rudra, Avishek Anand

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine tuning. This paper proposes a simple yet effective method to improve ranking performance on smaller datasets usi

Linus Hägg, Martin Berggren

Accurate simulations of sound propagation in narrow geometries need to account for viscous and thermal losses. In this respect, effective boundary conditions that model viscothermal losses in frequency-domain acoustics have recently gained in popularity. Here, we investigate the time-domain analogue of one such boundary condition. We find that the thermal pa

Pavan Vynatheya, Adrian S. Hamers, Rosemary A. Mardling, Earl P. Bellinger

We present two approaches to determine the dynamical stability of a hierarchical triple-star system. The first is an improvement on the Mardling-Aarseth stability formula from 2001, where we introduce a dependence on inner orbital eccentricity and improve the dependence on mutual orbital inclination. The second involves a machine learning approach, where we

Ira Wolfson, Utkarsh Kumar, Ido Ben-Dayan, Ram Brustein

We perform a Bayesian analysis for small field models of inflation, using the most recent datasets produced by Planck`18, ACTPol, and BICEP3. We employ Artificial Neural Networks (ANN) to perform analyses with model coefficients, instead of their proxy slow-roll parameters. The ANN connects the models with their projected scalar index $n_s$ and index running

Pyae Sone Aung, Yu Min Park, Yan Kyaw Tun, Zhu Han

In the realm of wireless communications in 5G, 6G and beyond, deploying unmanned aerial vehicle (UAV) has been an innovative approach to extend the coverage area due to its easy deployment. Moreover, reconfigurable intelligent surface (RIS) has also emerged as a new paradigm with the goals of enhancing the average sum-rate as well as energy efficiency. By co