January 2022 arXiv papers — page 16

Zizhang Wu, Jason Wang, Tianhao Xu, Fan Wang

The owner-member relationship between wheels and vehicles contributes significantly to the 3D perception of vehicles, especially in embedded environments. However, to leverage this relationship we must face two major challenges: i) Traditional IoU-based heuristics have difficulty handling occluded traffic congestion scenarios. ii) The effectiveness and appli

Ivo Vatavuk, Marsela Polić, Ivan Hrabar, Frano Petrić

In this paper we present our hardware design and control approaches for a mobile manipulation platform used in Challenge 2 of the MBZIRC 2020 competition. In this challenge, a team of UAVs and a single UGV collaborate in an autonomous, wall-building scenario, motivated by construction automation and large-scale robotic 3D printing. The robots must be able, a

Márton Naszódi, Konrad J. Swanepoel

We study the contact structure of totally separable} packings of translates of a convex body $K$ in $\mathbb{R}^d$, that is, packings where any two touching bodies have a separating hyperplane that does not intersect the interior of any translate in the packing. The separable Hadwiger number $H_{\text{sep}}(K)$ of $K$ is defined to be the maximum number of t

Tao Yu, Zhizheng Zhang, Cuiling Lan, Yan Lu

For deep reinforcement learning (RL) from pixels, learning effective state representations is crucial for achieving high performance. However, in practice, limited experience and high-dimensional inputs prevent effective representation learning. To address this, motivated by the success of mask-based modeling in other research fields, we introduce mask-based

Abdullah Aydın, Eduard Emelyanov, Svetlana Gorokhova

In this paper, we investigate operators on Riesz algebras, which are continuous with respect to multiplicative modifications of order convergence and relatively uniform convergence. We also introduce and study mo-Lebesgue, mo-$KB$, and mo-Levi operators.

Lifa Zhu, Haining Guan, Changwei Lin, Renmin Han

In feature-learning based point cloud registration, the correct correspondence construction is vital for the subsequent transformation estimation. However, it is still a challenge to extract discriminative features from point cloud, especially when the input is partial and composed by indistinguishable surfaces (planes, smooth surfaces, etc.). As a result, t

Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu

Learning sentence embeddings in an unsupervised manner is fundamental in natural language processing. Recent common practice is to couple pre-trained language models with unsupervised contrastive learning, whose success relies on augmenting a sentence with a semantically-close positive instance to construct contrastive pairs. Nonetheless, existing approaches

Irma Custovic, Nicolas Pocholle, Eric Bourillot, Eric Lesniewska

Significant efforts have been done in last two decades to develop nanoscale spectroscopy techniques owning to their great potential for single-molecule structural detection and in addition, to resolve open questions in heterogeneous biological systems, such as protein-DNA complexes. Applying AFM-IR technique has become a powerful leverage for obtaining simul

Shauli Ravfogel, Michael Twiton, Yoav Goldberg, Ryan Cotterell

Modern neural models trained on textual data rely on pre-trained representations that emerge without direct supervision. As these representations are increasingly being used in real-world applications, the inability to \emph{control} their content becomes an increasingly important problem. We formulate the problem of identifying and erasing a linear subspace

Ayush Bharti, Louis Filstroff, Samuel Kaski

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of information and dimensionality reduction, a

Chi Liu, Zongyuan Ge, Mingguang He, Xiaotong Han

The annotation of disease severity for medical image datasets often relies on collaborative decisions from multiple human graders. The intra-observer variability derived from individual differences always persists in this process, yet the influence is often underestimated. In this paper, we cast the intra-observer variability as an uncertainty problem and in

Max Bolderman, Mircea Lazar, Hans Butler

Performance of model-based feedforward controllers is typically limited by the accuracy of the inverse system dynamics model. Physics-guided neural networks (PGNN), where a known physical model cooperates in parallel with a neural network, were recently proposed as a method to achieve high accuracy of the identified inverse dynamics. However, the flexible na

Robert E. Gaunt, Siqi Li

We establish general upper bounds on the Kolmogorov distance between two probability distributions in terms of the distance between these distributions as measured with respect to the Wasserstein or smooth Wasserstein metrics. These bounds generalise existing results from the literature. To illustrate the broad applicability of our general bounds, we apply t

Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which i

Zhe Liu, Chunyang Chen, Junjie Wang, Yuekai Huang

Mobile apps are indispensable for people's daily life. Complementing with automated GUI testing, manual testing is the last line of defence for app quality. However, the repeated actions and easily missing of functionalities make manual testing time-consuming and inefficient. Inspired by the game candy crush with flashy candies as hint moves for players, we

Robert Nichols, Christian Rathgeb, Pawel Drozdowski, Christoph Busch

In recent years, increasing deployment of face recognition technology in security-critical settings, such as border control or law enforcement, has led to considerable interest in the vulnerability of face recognition systems to attacks utilising legitimate documents, which are issued on the basis of digitally manipulated face images. As automated manipulati

Ziyu Wang, Wenhao Jiang, Yiming Zhu, Li Yuan

Recently, MLP-like vision models have achieved promising performances on mainstream visual recognition tasks. In contrast with vision transformers and CNNs, the success of MLP-like models shows that simple information fusion operations among tokens and channels can yield a good representation power for deep recognition models. However, existing MLP-like mode

Takashi Mori, Masahito Ueda

It has been recognized that heavily overparameterized deep neural networks (DNNs) exhibit surprisingly good generalization performance in various machine-learning tasks. Although benefits of depth have been investigated from different perspectives such as the approximation theory and the statistical learning theory, existing theories do not adequately explai

Pınar Kirezli

Algebraic classification of higher dimensional, shear-free, twist-free, expanding (or non-expanding) spacetime is studied with the limit of $D\rightarrow\infty$. Similar to classification of any arbitrary dimension $D>4$, this spacetime is Type I(b) or more special, according to our calculations. However, thanks to the method of taking the limit of dimension

Michael Eichmair, Thomas Koerber

Let $(M,g)$ be an asymptotically flat Riemannian manifold of dimension $n\geq 3$ with positive mass. We give a short proof based on Lyapunov-Schmidt reduction of the existence of an asymptotic foliation of $(M, g)$ by stable constant mean curvature spheres. Moreover, we show that the geometric center of mass of the foliation agrees with the Hamiltonian cente

Riccardo Piovani, Adriano Tomassini

We prove that the dimension $h^{1,1}_{\overline\partial}$ of the space of Dolbeault harmonic $(1,1)$-forms is not necessarily always equal to $b^-$ on a compact almost complex 4-manifold endowed with an almost Hermitian metric which is not locally conformally almost K\"ahler. Indeed, we provide examples of non integrable, non locally conformally almost K\"ah

Huanchao Zhou, Zhidong Bai, Jiang Hu

Let $ X_{n} $ be $ n\times N $ random complex matrices, $R_{n}$ and $T_{n}$ be non-random complex matrices with dimensions $n\times N$ and $n\times n$, respectively. We assume that the entries of $ X_{n} $ are independent and identically distributed, $ T_{n} $ are nonnegative definite Hermitian matrices and $T_{n}R_{n}R_{n}^{*}= R_{n}R_{n}^{*}T_{n} $. The ge

Junlin Han, Pengfei Fang, Weihao Li, Jie Hong

We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-fre

Michael Eichmair, Thomas Koerber

We refine the Lyapunov-Schmidt analysis developed in our recent paper arxiv:2101.12665 to study the geometric center of mass of the asymptotic foliation by area-constrained Willmore surfaces of initial data for the Einstein field equations. If the scalar curvature of the initial data vanishes at infinity, we show that this geometric center of mass agrees wit

Charles Batty, David Seifert

Let $T$ be a power-bounded operator on a Banach space $X$, $\mathcal{A}$ be a Banach algebra of bounded holomorphic functions on the unit disc $\mathbb{D}$, and assume that there is a bounded functional calculus for the operator $T$, so there is a bounded algebra homomorphism mapping functions $f \in \mathcal{A}$ to bounded operators $f(T)$ on $X$. Theorems

O. S. Bayandina, C. L. Brogan, R. A. Burns, X. Chen

We present the most complete to date interferometric study of the centimeter wavelength methanol masers detected in G358.93-0.03 at the burst and post-burst epochs. A unique, NIR/(sub)mm-dark and FIR-loud MYSO accretion burst was recently discovered in G358.93-0.03. The event was accompanied by flares of an unprecedented number of rare methanol maser transit

Henrik Kalisch, Miguel A. Alejo, Adán J. Corcho, Didier Pilod

We are concerned with numerical approximations of breather solutions for the cubic Whitham equation which arises as a water-wave model for interfacial waves. The model combines strong nonlinearity with the non-local character of the water-wave problem. The equation is non-integrable as suggested by the inelastic interaction of solitary waves. As a non local

A. L. Kataev, V. S. Molokoedov

A specific representation of the known one-loop EW correction to the relation between the pole and running $\msbar$-scheme masses of the top-quark through particle masses of the Standard Model is given within the Fleischer-Jegerlehner tadpole scheme, where the vacuum expectation value of the Higgs field is renormalized. The importance of taking into account

Franziska Porkert, Oliver Schlotterer

We present a method to compute the integrands of one-loop Einstein-Yang-Mills amplitudes for any number of external gauge and gravity multiplets. Our construction relies on the double-copy structure of Einstein-Yang-Mills as (super-)Yang-Mills with the so-called YM+$\phi^3$ theory -- pure Yang-Mills coupled to bi-adjoint scalars -- which we implement via one

Yves Meinard, Alexis Tsoukiàs

Decision support is the science and associated practice that consist in providing recommendations to decision makers facing problems, based on available theoretical knowledge and empirical data. Although this activity is often seen as being concerned with solving mathematical problems and conceiving algorithms, it is essentially an empirical and socially fra

Tsan-Ming Wang, Benjamin Magnelli, Eva Schinnerer, Daizhong Liu

To constrain for the first time the mean mass and extent of the molecular gas of a mass-complete sample of $>10^{10}$M$_{\odot}$ main-sequence (MS) galaxies at $0.4<z<3.6$. We apply an innovative $uv$-based stacking analysis to a large set of archival Atacama Large Millimeter/submillimeter Array (ALMA) observations. This stacking analysis provides measuremen

Yang Liu, Xin Zhong

We revisit the 3D Cauchy problem of compressible heat-conducting magnetohydrodynamic equations with vacuum as far field density. By delicate energy method, we derive global existence and uniqueness of strong solutions provided that $(\|\rho_0\|_{L^\infty}+1)\big[\|\rho_0\|_{L^3}+ \|\rho_0\|_{L^\infty}+1)^2\big(\|\sqrt{\rho_0}u_0\|_{L^2}^2 +\|b_0\|_{L^2}^2\bi

Pamina M. Winkler, Thomas S. van Zanten, Satyajit Mayor, Maria F. Garcia-Parajo

Single molecule detection has revolutionised the fields of chemistry and biology by offering powerful ways to study individual molecules under different scenarios. Nanophotonic structures, including plasmonic antennas, significantly overcome the concentration limit at which single molecule events can be observed, enabling their detection at concentrations th

Hurriyet Yuce, Mukunda Mandal, Yenal Yalcinkaya, Denis Andrienko

All-inorganic lead halide perovskite nanocrystals (NCs) show potential in optoelectronic devices, though their stability and efficiency have yet to improve. In this work, we explore the effect of bivalent metal site doping on the optoelectronic properties of CsPbX$_3$ (X = Br, Cl) perovskite NCs. First, the Pb$^{2+}$ ions in pristine CsPbBr$_3$ NC are partia

Laurent Baratchart, Sébastien Fueyo, Jean-Baptiste Pomet

This paper deals with the stability of linear periodic difference delay systems, where the value at time $t$ of a solution is a linear combination with periodic coefficients of its values at finitely many delayed instants $t-\tau_1,\ldots,t-\tau_N$. We establish a necessary and sufficient condition for exponential stability of such systems when the coefficie

Frank-Olaf Schreyer, Isabel Stenger

In this paper we study marked numerical Godeaux surfaces with special bicanonical fibers. Based on our construction method of marked Godeaux surfaces we give a complete characterization for the existence of hyperelliptic bicanonical fibers and torsion fibers. Moreover, we describe how the families of Reid and Miyaoka with torsion $\mathbb{Z}/3\mathbb{Z}$ and

Edric Tam, David Dunson

Laplacian eigenvectors capture natural community structures on graphs and are widely used in spectral clustering and manifold learning. The use of Laplacian eigenvectors as embeddings for the purpose of multiscale graph comparison has however been limited. Here we propose the Embedded Laplacian Discrepancy (ELD) as a simple and fast approach to compare graph

Cynthia Michalkowski, Maziar Veyskarami, Carina Bringedal, Rainer Helmig

For improved operating conditions of a polymer electrolyte membrane (PEM) fuel cell, a sophisticated water management is crucial. Therefore, it is necessary to understand the transport mechanisms of water throughout the cell constituents especially on the cathode side, where the excess water has to be removed. Pore-scale modeling of diffusion layers and gas

Stefan Klus, Feliks Nüske, Sebastian Peitz

Koopman operator theory has been successfully applied to problems from various research areas such as fluid dynamics, molecular dynamics, climate science, engineering, and biology. Applications include detecting metastable or coherent sets, coarse-graining, system identification, and control. There is an intricate connection between dynamical systems driven

W. Bednarek, A. Śmiałkowski

Long gamma-ray bursts (GRBs) are at present well confirmed sites of acceleration of particles to relativistic energies due to observations of gamma-ray emission in the GeV-TeV energy range. We consider a scenario in which the mechanism accelerating electrons is also responsible for acceleration of hadrons in the GRB jets to multi-PeV energies. Since progenit

Iakovos Androulidakis, Omar Mohsen, Robert Yuncken

We extend the classical regularity theorem of elliptic operators to maximally hypoelliptic differential operators. More precisely, given vector fields $X_1,\ldots,X_m$ on a smooth manifold which satisfy H\"ormander's bracket generating condition, we define a principal symbol for \textit{any} linear differential operator. Our symbol takes into account the vec

Carlo Albert, Simone Ulzega, Firat Ozdemir, Fernando Perez-Cruz

For stochastic models with intractable likelihood functions, approximate Bayesian computation offers a way of approximating the true posterior through repeated comparisons of observations with simulated model outputs in terms of a small set of summary statistics. These statistics need to retain the information that is relevant for constraining the parameters

Shichao Zhang, Jiaye Li, Wenzhen Zhang, Yongsong Qin

Data representation is usually a natural form with their attribute values. On this basis, data processing is an attribute-centered calculation. However, there are three limitations in the attribute-centered calculation, saying, inflexible calculation, preference computation, and unsatisfactory output. To attempt the issues, a new data representation, named a

Kenta Ohira, Toru Ohira

Recently, we have studied a delay differential equation which has a coefficient that is a linear function of time. The equation has shown the oscillatory transient dynamics appear and disappear as the delay is increased between zero to asymptotically large delay. We here propose and study another equation that shows similar transient oscillations. It has an

Paul Ryan

This article, based on the author's PhD thesis, reviews recent advancements in the field of quantum integrability, in particular the separation of variables (SoV) program for high-rank integrable spin chains and the boost mechanism for solving the Yang-Baxter equation. We begin with a general overview of quantum integrable systems with special emphasis on th

Alexandros-Apostolos A. Boulogeorgos, Angeliki Alexiou, Marco Di Renzo

In this paper, we analyze the performance of a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) wireless system that is affected by mixture-gamma small-scale fading, stochastic disorientation, and misalignment, as well as transceivers hardware imperfections. First, we statistically characterize the end-to-end channel for both c

Rasmus Berg Palm, Miguel González-Duque, Shyam Sudhakaran, Sebastian Risi

In nature, the process of cellular growth and differentiation has lead to an amazing diversity of organisms -- algae, starfish, giant sequoia, tardigrades, and orcas are all created by the same generative process. Inspired by the incredible diversity of this biological generative process, we propose a generative model, the Variational Neural Cellular Automat

Md. Zaved Iqubal Ahmed, Nidul Sinha, Souvik Phadikar, Ebrahim Ghaderpour

Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automati

Patricia Díaz de Alba, Luisa Fermo, Federica Pes, Giuseppe Rodriguez

Overdetermined systems of first kind integral equations appear in many applications. When the right-hand side is discretized, the resulting finite-data problem is ill-posed and admits infinitely many solutions. We propose a numerical method to compute the minimal-norm solution in the presence of boundary constraints. The algorithm stems from the Riesz repres

Aljosha Köcher, Alexander Hayward, Alexander Fay

Today's production systems are complex networks of cyber-physical systems which combine mechanical and electronic parts with software and networking capabilities. To the inherent complexity of such systems additional complexity arises from the context in which these systems operate. Manufacturing companies need to be able to adapt their production to ever ch

Cheng-Jun Xia, Bao Yuan Sun, Toshiki Maruyama, Wen-Hui Long

Considering the effects of charge screening, we propose a new numerical recipe within the framework of Thomas-Fermi approximation, where the properties of nuclear matter throughout a vast density range can be obtained self-consistently. Assuming spherical and cylindrical approximations for the Wigner-Seitz cell, typical nuclear matter structures (droplet, ro

Bartłomiej Polaczyk, Jacek Cyranka

We study the overparametrization bounds required for the global convergence of stochastic gradient descent algorithm for a class of one hidden layer feed-forward neural networks, considering most of the activation functions used in practice, including ReLU. We improve the existing state-of-the-art results in terms of the required hidden layer width. We intro

M. Shamanth, Russel Mathias, Dr Vijayalakshmi MN

: Deep learning methodologies have been used to create applications that can cause threats to privacy, democracy and national security and could be used to further amplify malicious activities. One of those deep learning-powered applications in recent times is synthesized videos of famous personalities. According to Forbes, Generative Adversarial Networks(GA

Christopher Jelich, Wenchang Zhao, Haibo Chen, Steffen Marburg

In this work, two fast multipole boundary element formulations for the linear time-harmonic acoustic analysis of finite periodic structures are presented. Finite periodic structures consist of a bounded number of unit cell replications in one or more directions of periodicity. Such structures can be designed to efficiently control and manipulate sound waves

Gianluca Napoletano, Raffaello Foldes, Enrico Camporeale, Giancarlo de Gasperis

In recent years, ensemble modeling has been widely employed in space weather to estimate uncertainties in forecasts. We here focus on the ensemble modeling of CME arrival times and arrival velocities using a drag-based model, which is well-suited for this purpose due to its simplicity and low computational cost. Although ensemble techniques have previously b

Matthew Middlehurst, Anthony Bagnall

There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC). However, a commonly asked question by real world practitioners and data scientists less familiar with the research topic, is whether the complexity of the algorithms considered state of the art is really necessary. Many times the first a

Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu

Deep learning models as an emerging topic have shown great progress in various fields. Especially, visualization tools such as class activation mapping methods provided visual explanation on the reasoning of convolutional neural networks (CNNs). By using the gradients of the network layers, it is possible to demonstrate where the networks pay attention durin

Cedric Richter, Heike Wehrheim

Single statement bugs are one of the most important ingredients in the evaluation of modern bug detection and automatic program repair methods. By affecting only a single statement, single statement bugs represent a type of bug often overlooked by developers, while still being small enough to be detected and fixed by automatic methods. With the rise of data-

Dawid Bernaciak, Jim E. Griffin

We introduce a Loss Discounting Framework for model and forecast combination which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme which allows a flexible specification of the dynamics of the model weights.

Jungnam Park, Sehee Min, Phil Sik Chang, Jaedong Lee

Understanding the relation between anatomy andgait is key to successful predictive gait simulation. Inthis paper, we present Generative GaitNet, which isa novel network architecture based on deep reinforce-ment learning for controlling a comprehensive, full-body, musculoskeletal model with 304 Hill-type mus-culotendons. The Generative Gait is a pre-trained,

Vicenç Méndez, Axel Masó-Puigdellosas, Daniel Campos

We consider a walker moving in a one-dimensional interval with absorbing boundaries under the effect of Markovian resettings to the initial position. The walker's motion follows a random walk characterized by a general waiting time distribution between consecutive short jumps. We investigate the existence of an optimal reset rate, which minimizes the mean ex

Giordano De Marzo, Andrea Gabrielli, Andrea Zaccaria, Luciano Pietronero

Many natural and socio-economic systems are characterized by power-law distributions that make the occurrence of extreme events not negligible. Such events are sometimes referred to as Black Swans, but a quantitative definition of a Black Swan is still lacking. Here, by leveraging on the properties of Zipf-Mandelbrot law, we investigate the relations between

Piotr Klukowski, Roland Riek, Peter Güntert

Nuclear Magnetic Resonance (NMR) spectroscopy is one of the major techniques in structural biology with over 11,800 protein structures deposited in the Protein Data Bank. NMR can elucidate structures and dynamics of small and medium size proteins in solution, living cells, and solids, but has been limited by the tedious data analysis process. It typically re

Marina Ishchenko, Peter Berczik, Nina Kharchenko

The purpose of the present work is a detailed investigation of the dynamical evolutionof Collinder 135 and UBC 7 star clusters. We present a set of dynamical numerical simulationsusing realistic star clusterN-body modeling technique with the forward integration of thestar-by-star cluster models to the present day, based on best-available 3D coordinates and v

Kishan Gupta, Srikanth Korse, Bernd Edler, Guillaume Fuchs

Frequency domain processing, and in particular the use of Modified Discrete Cosine Transform (MDCT), is the most widespread approach to audio coding. However, at low bitrates, audio quality, especially for speech, degrades drastically due to the lack of available bits to directly code the transform coefficients. Traditionally, post-filtering has been used to

Davide Canali

Skyline and Top-k are two of the most important methods to extract information from datasets, but both come with their drawbacks, that's why lately some new technics that try to mix the features of the two have been studied. In this survey three new operators are analysed, F-Skyline, ORU/ORD, and ${\epsilon}$-Skyline. After giving the main ideas behind those

Chaofan Pan, Manuel Pinto, Y. H. Xia

Algebraic dichotomy is a generalization of an exponential dichotomy (Lin, JDE2009). This paper gives a version of Hartman-Grobman linearization theorem assuming that linear system admits an algebraic dichotomy, which generalizes the Palmer's linearization theorem. Besides, we prove that the homeomorphism in the linearization theorem (and has a H\"{o}lder con

Surjeet Singh Choudhary, Saurabh Shrivastava

In this paper we study maximal and square functions associated with bilinear Bochner-Riesz means at the critical index. In particular, we prove that they satisfy weighted estimates from $L^{p_1}(w_1)\times L^{p_2}(w_2)\rightarrow L^p(v_w)$ for bilinear weights $(w_1,w_2)\in A_{\vec{P}}$ where $p_1,p_2>1$ and $\frac{1}{p_1}+\frac{1}{p_2}=\frac{1}{p}$. Also, w

Alfonso Maiellaro, Antonio Marino, Fabrizio Illuminati

Identifying entanglement-based order parameters characterizing topological systems, in particular topological superconductors and topological insulators, has remained a major challenge for the physics of quantum matter in the last two decades. Here we show that the end-to-end, long-distance, bipartite squashed entanglement between the edges of a many-body sy

Gregory J. Clark, Felipe Thomaz, Andrew Stephen

Graphs (i.e., networks) have become an integral tool for the representation and analysis of relational data. Advances in data gathering have lead to multi-relational data sets which exhibit greater depth and scope. In certain cases, this data can be modeled using a hypergraph. However, in practice analysts typically reduce the dimensionality of the data (whe

Nikita Nikulsin, Rohan Ramasamy, Matthias Hoelzl, Florian Hindenlang

Although the basic concept of a stellarator was known since the early days of fusion research, advances in computational technology have enabled the modelling of increasingly complicated devices, leading up to the construction of Wendelstein 7-X, which has recently shown promising results. This recent success has revived interest in the nonlinear 3D MHD mode

Zuoyu Yan, Tengfei Ma, Liangcai Gao, Zhi Tang

Topological features based on persistent homology capture high-order structural information so as to augment graph neural network methods. However, computing extended persistent homology summaries remains slow for large and dense graphs and can be a serious bottleneck for the learning pipeline. Inspired by recent success in neural algorithmic reasoning, we p

Wolfgang Mauerer, Stefanie Scherzinger

Various fields of science face a reproducibility crisis. For quantum software engineering as an emerging field, it is therefore imminent to focus on proper reproducibility engineering from the start. Yet the provision of reproduction packages is almost universally lacking. Actionable advice on how to build such packages is rare, particularly unfortunate in a

Oscar Castillo-Felisola, Bastian Grez, Oscar Orellana, Jose Perdiguero

In this work, we explore a three-dimensional formulation of the polynomial affine model of gravity, which is a model that extends general relativity by relaxing the equivalence principle through the exclusion of the metric from the set of fundamental fields. In particular, in an attempt to gain insight of the role of the torsion and nonmetricity in the gravi

Hiroshi Miki, Satoshi Tsujimoto, Luc Vinet

The Darboux transformations of Krawtchouk polynomials are investigated and all possible exceptional Krawtchouk polynomials obtainable from a single-step Darboux transformation are considered. The properties of these exceptional Krawtchouk polynomials including the Diophantine ones and the recurrence relations are obtained.

Pablo M. Berná, Hung Viet Chu

In 1999, S. V. Konyagin and V. N. Temlyakov introduced the so-called Thresholding Greedy Algorithm. Since then, there have been many interesting and useful characterizations of greedy-type bases in Banach spaces. In this article, we study and extend several characterizations of greedy and almost greedy bases in the literature. Along the way, we give various

Huan Zhou, Xuding Zhu

The concept of DP-coloring of a graph is a generalization of list coloring introduced by Dvo\v{r}\'{a}k and Postle in 2015. Multiple DP-coloring of graphs, as a generalization of multiple list coloring, was first studied by Bernshteyn, Kostochka and Zhu in 2019. This paper proves that planar graphs without 3-cycles and normally adjacent 4-cycles are $(7m, 2m

Furkan Eris, Marcia S. Louis, Kubra Eris, Jose L. Abellan

Over the years, processor throughput has steadily increased. However, the memory throughput has not increased at the same rate, which has led to the memory wall problem in turn increasing the gap between effective and theoretical peak processor performance. To cope with this, there has been an abundance of work in the area of data/instruction prefetcher desi

Marcin Lewicki, Tomasz Kajdanowicz, Piotr Bródka, Janusz Sobecki

The main purpose of this article was to create a model and simulate the profitability conditions of an interactive presentation system (IPS) with the recommender system (RS) used in the kiosk. 90 million simulations have been run in Python with SymPy to address the problem of discount recommendation offered to the clients according to their usage of the IPS.

I. M. Flór, A. Lacerda-Santos, G. Fleury, P. Roulleau

Recent experiments have shown that electronic Mach-Zehnder interferometers of unprecedented fidelities could be built using a graphene pn junction in the quantum Hall regime. In these junctions, two different edge states corresponding to two different valley configurations are spatially separated and form the two arms of the interferometer. The observed sepa

V. G. Klochkova, V. E. Panchuk, M. V. Yushkin

Over the past two decades the echelle spectrograph NES of the 6-m telescope was used to perform high resolution spectroscopy of far evolved stars spanning a wide range of initial masses. The studies cover a diversity of stars with high mass-loss rates during the preceding and current stages of evolution. All these stars have extended atmospheres and structur

Lianmin Zheng, Zhuohan Li, Hao Zhang, Yonghao Zhuang

Alpa automates model-parallel training of large deep learning (DL) models by generating execution plans that unify data, operator, and pipeline parallelism. Existing model-parallel training systems either require users to manually create a parallelization plan or automatically generate one from a limited space of model parallelism configurations. They do not

Rodrigo T. Sato Martín de Almagro

In this paper, high-order numerical integrators on homogeneous spaces will be presented as an application of nonholonomic partitioned Runge-Kutta Munthe-Kaas (RKMK) methods on Lie groups. A homogeneous space $M$ is a manifold where a group $G$ acts transitively. Such a space can be understood as a quotient $M \cong G/H$, where $H$ a closed Lie subgroup, is t

Mia Carrola, Amir Asadi, Han Zhang, Dimitrios G. Papageorgiou

With an exponential rise in the popularity and availability of additive manufacturing (AM), a large focus has been directed toward research in this topic's movement, while trying to distinguish themselves from similar works by simply adding nanomaterials to their process. Though nanomaterials can add impressive properties to nanocomposites (NCs), there are e

B. T. T. Wong

This paper follows the previous work on generalized abelian gauge field theory of higher-order derivatives under rotor model and extends the study to the most generalized non-abelian case. We find that the rotor mechanism from the abelian case applies nicely to the non-abelian case under the Lorentz gauge condition. Under the rotor mechanism, the gauge field

Haowei He, Jingzhao Zhang, Yanan Wang, Benben Jiang

Electric vehicles (EVs) play an important role in reducing carbon emissions. As EV adoption accelerates, safety issues caused by EV batteries have become an important research topic. In order to benchmark and develop data-driven methods for this task, we introduce a large and comprehensive dataset of EV batteries. Our dataset includes charging records collec

Bendaoud Fayssal, Abdennebi Marwen, Didi Fedoua

Heterogeneous Wireless Networks HWNs are combined networks made of different Radio Access Technologies RAT. Next-Generation Networks NGN will provide high bandwidth connectivity and high data throughput with smooth support for the user's QoS requirements, in this context, users with multi-interface terminals will be able to connect to different wireless tech

Florian Mouret, Alexandre Hippert-Ferrer, Frédéric Pascal, Jean-Yves Tourneret

This paper tackles the problem of missing data imputation for noisy and non-Gaussian data. A classical imputation method, the Expectation Maximization (EM) algorithm for Gaussian mixture models, has shown interesting properties when compared to other popular approaches such as those based on k-nearest neighbors or on multiple imputations by chained equations

Nadir Murru, Giuliano Romeo, Giordano Santilli

In this paper we study the properties of an algorithm for generating continued fractions in the field of p-adic numbers $\mathbb{Q}_p$. First of all, we obtain an analogue of the Galois' Theorem for classical continued fractions. Then, we investigate the length of the preperiod for periodic expansions of square roots. Finally, we prove that there exist infin

Refael Kohen, Or Sheffet

The hybrid-model (Avent et al 2017) in Differential Privacy is a an augmentation of the local-model where in addition to N local-agents we are assisted by one special agent who is in fact a curator holding the sensitive details of n additional individuals. Here we study the problem of machine learning in the hybrid-model where the n individuals in the curato

S. V. Talalov

This article investigates quantum oscillations of a vortex ring with zero thickness that evolves in a cylindrical domain $V = D \times [0,L]$. The symbol $D$ denotes the planar domain which is bounded by some closed connected curve $S$. The quantization scheme of this dynamical system is based on the approach proposed by the author earlier. As result, we fin

Lorenzo Pucci, Enrico Paolini, Andrea Giorgetti

This work investigates a multibeam system for joint sensing and communication (JSC) based on multiple-input multiple-output (MIMO) 5G new radio (NR) waveforms. In particular, we consider a base station (BS) acting as a monostatic sensor that estimates the range, speed, and direction of arrival (DoA) of multiple targets via beam scanning using a fraction of t

Shunsuke Horii

Recently, conditional average treatment effect (CATE) estimation has been attracting much attention due to its importance in various fields such as statistics, social and biomedical sciences. This study proposes a partially linear nonparametric Bayes model for the heterogeneous treatment effect estimation. A partially linear model is a semiparametric model t

Jane Pauline Ramirez, Cesare Stefanini, Giulia De Masi, Donato Romano

Water monitoring is crucial for environmental monitoring, transportation, energy and telecommunication. One of the main problems in aquatic environmental monitoring is biofouling. The simplest method among the current antifouling strategies is the use of wiper technologies like brushes and wipers which apply mechanical pressure. In designing built-in strateg

Markus Bause, Mathias Anselmann, Uwe Köcher, Florin A. Radu

We study the numerical approximation by space-time finite element methods of a multi-physics system coupling hyperbolic elastodynamics with parabolic transport and modeling poro- and thermoelasticity. The equations are rewritten as a first-order system in time. Discretizations by continuous Galerkin methods in time and inf-sup stable pairs of finite element

Leandro Chiarini, Wioletta M. Ruszel

In this article, we study stochastic homogenization of non-homogeneous Gaussian free fields $\Xi^{g,{\bf a}} $ and bi-Laplacian fields $\Xi^{b,{\bf a}}$. They can be characterized as follows: for $f=\delta$ the solution $u$ of $\nabla \cdot \mathbf{a} \nabla u =f$, ${\bf a}$ is a uniformly elliptic random environment, is the covariance of $\Xi^{g,{\bf a}}$.

Xiaowen Zhu, Claude Marchand, Olivier Piquet, Michel Desmons

Compact accelerator-based neutron source facilities are garnering attention and play an important and expanding role in material and engineering sciences, as well as in neutron science education and training. Neutrons are produced by bombarding a low-energy proton beam onto a beryllium or lithium target. In such an accelerator-based neutron source, a radio f

Bendaoud Fayssal, Abdennebi Marwen, Didi Fedoua

The coexistence of different Radio Access Technologies (RATs) in the same area has enabled the researchers to get profit from the available networks by the selection of the best RAT at each moment to satisfy the user requirements. The challenge is to achieve the Always Best Connected (ABC) concept; the main issue is the automatic choice of the suitable Radio

Kuldeep Purohit, Anshul Shah, A. N. Rajagopalan

We present a solution for the goal of extracting a video from a single motion blurred image to sequentially reconstruct the clear views of a scene as beheld by the camera during the time of exposure. We first learn motion representation from sharp videos in an unsupervised manner through training of a convolutional recurrent video autoencoder network that pe

Andrei Bud

For $i\geq2$, we compute the first coefficients of the class $[\overline{D}(\mu;3)]$ in the rational Picard group of the moduli of Prym curves $\overline{\mathcal{R}}_{2i}$, where $D(\mu;3)$ is the divisor parametrizing pairs $[C,\eta]$ for which there exists a degree $2i$ map $\pi\colon C\rightarrow \mathbb{P}^1$ having ramification profile $(2,\ldots,2)$ a