December 2020 arXiv papers — page 72

James R. Wootton, Francis Harkins, Nicholas T. Bronn, Almudena Carrera Vazquez

Quantum computing is a technology that promises to offer significant advantages during the coming decades. Though the technology is still in a prototype stage, the last few years have seen many of these prototype devices become accessible to the public. This has been accompanied by the open-source development of the software required to use and test quantum

Kamila Sala, Anton Doicin, Andrew D. Armour, Tommaso Tufarelli

We exploit local quantum estimation theory to investigate the measurement of linear and quadratic coupling strengths in a driven-dissipative optomechanical system. For experimentally realistic values of the model parameters, we find that the linear coupling strength is considerably easier to estimate than the quadratic one. Our analysis also reveals that the

Allan Lo, Vincent Pfenninger

A $k$-uniform tight cycle is a $k$-uniform hypergraph with a cyclic ordering of its vertices such that its edges are all the sets of size $k$ formed by $k$ consecutive vertices in the ordering. We prove that every red-blue edge-coloured $K_n^{(4)}$ contains a red and a blue tight cycle that are vertex-disjoint and together cover $n-o(n)$ vertices. Moreover,

Santiago Andrés Azcoitia, Nikolaos Laoutaris

Data trading is becoming increasingly popular, as evident by the appearance of scores of Data Marketplaces (DMs) in the last few years. Pricing digital assets is particularly complex since, unlike physical assets, digital ones can be replicated at zero cost, stored, and transmitted almost for free, etc. In most DMs, data sellers are invited to indicate a pri

Ngoc Hoang Anh Mai, Jean-Bernard Lasserre, Victor Magron, Jie Wang

We prove that every semidefinite moment relaxation of a polynomial optimization problem (POP) with a ball constraint can be reformulated as a semidefinite program involving a matrix with constant trace property (CTP). As a result such moment relaxations can be solved efficiently by first-order methods that exploit CTP, e.g., the conditional gradient-based au

Zhengcai Li

This paper proposes a robust self-triggered distributed model predictive control (DMPC) scheme for a family of Discrete-Time linear systems with local (uncoupled) and global (coupled) constraints. To handle the additive disturbance, tube-based method is proposed for the satisfaction of local state and control constraints. Meanwhile, A special form of constra

Gui-Bin Liu, Miao Chu, Zeying Zhang, Zhi-Ming Yu

We have developed a Mathematica program package SpaceGroupIrep which is a database and tool set for irreducible representations (IRs) of space group in BC convention, i.e. the convention used in the famous book "The mathematical theory of symmetry in solids" by C. J. Bradley & A. P. Cracknell. Using this package, elements of any space group, little group, He

Giovanni Falcone, Ágota Figula, Carolin Hannusch

For an (imaginary) hyperelliptic curve $\mathcal{H}$ of genus $g$, we determine a basis of the Riemann-Roch space $\mathcal{L}(D)$, where $D$ is a divisor with positive degree $n$, linearly equivalent to $P_1+\cdots+ P_j+(n-j)\Omega$, with $0 \le j \le g$, where $\Omega$ is a Weierstrass point, taken as the point at infinity. As an application, we determine

Pascal Puech, Iann Gerber, Fabrice Piazza, Marc Monthioux

Two-dimensional (2D) materials are among the most studied ones nowadays, because of their unique properties. These materials are made of, single- or few atom-thick layers assembled by van der Waals forces, hence allowing a variety of stacking sequences possibly resulting in a variety of crystallographic structures as soon as the sequences are periodic. Takin

M. H. Rahman, S. M. Rifaat

To reduce passenger waiting time and driver search friction, ride-hailing companies need to accurately forecast spatio-temporal demand and supply-demand gap. However, due to spatio-temporal dependencies pertaining to demand and supply-demand gap in a ride-hailing system, making accurate forecasts for both demand and supply-demand gap is a difficult task. Fur

Thomas Haubner, Mhd. Modar Halimeh, Andreas Brendel, Walter Kellermann

We introduce a synergistic approach to double-talk robust acoustic echo cancellation combining adaptive Kalman filtering with a deep neural network-based postfilter. The proposed algorithm overcomes the well-known limitations of Kalman filter-based adaptation control in scenarios characterized by abrupt echo path changes. As the key innovation, we suggest to

Naweiluo Zhou, Yiannis Georgiou, Li Zhong, Huan Zhou

Containerisation demonstrates its efficiency in application deployment in cloud computing. Containers can encapsulate complex programs with their dependencies in isolated environments, hence are being adopted in HPC clusters. HPC workload managers lack micro-services support and deeply integrated container management, as opposed to container orchestrators (e

Xiao-Wei Tang, Shuowen Zhang, Changsheng You, Xin-Lin Huang

In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image reso

Ali R. Baghirzade

Hardly any other area of research has recently attracted as much attention as machine learning (ML) through the rapid advances in artificial intelligence (AI). This publication provides a short introduction to practical concepts and methods of machine learning, problems and emerging research questions, as well as an overview of the participants, an overview

Sophie Gruenbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka

We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique for constructing a tight Reachtube (an over-approximation of the set of reachable states over a given time-hor

Daniel Elambo Atonge, Shokhista Ergasheva, Artem Kruglov, Giancarlo Succi

The paper demonstrates the Windows data collectordevelopment process with the built back-end from the require-ments gathering stage till the implementation and testing phase.Each phase throughout the development life cycle of the systemis defined in details. The whole system idea and the objectivesbehind developing this kind of framework is described in earl

Yanli Li, Jing Ma, Fanshu Fang

The implication and contagion effect of emotion cannot be ignored in rumor spreading. This paper sheds light on how DMs'emotional type and intensity affect rumor spreading. Based on the theory of RDEU and evolutionary game, we construct an evolutionary game model of rumor spreading by considering emotions, which takes netizens and the government as the core

Markus Geisenhofer, Florian Kummer, Martin Oberlack

We present a sub-cell accurate shock-fitting technique using a high-order extended discontinuous Galerkin (XDG) method, where a computational cell of the background grid is cut into two cut-cells at the shock position. Our technique makes use of a sharp interface description where the shock front is implicitly defined by means of the zero iso-contour of a le

Bert Moons, Parham Noorzad, Andrii Skliar, Giovanni Mariani

Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces. To remedy this, we present DONNA (Distilling Optimal Neural Network Architectures), a novel pipeline for rapid, scalable and diverse NAS, that scales to many user scenarios. DONNA consists

Shuji Shinohara, Nobuhito Manome, Yoshihiro Nakajima, Yukio Pegio Gunji

L\'evy walks are found in the migratory behaviour patterns of various organisms, and the reason for this phenomenon has been much discussed. We use simulations to demonstrate that learning causes the changes in confidence level during decision-making in non-stationary environments, and results in L\'evy-walk-like patterns. One inference algorithm involving c

Agnieszka Kuźmicz, Marek Jamrozy

We present the largest sample of giant radio quasars (GRQs), which are defined as having a projected linear size greater than 0.7 Mpc. The sample consists of 272 GRQs, of which 174 are new objects discovered through cross-matching the NRAO VLA Sky Survey (NVSS) and the Sloan Digital Sky Survey 14$^{\rm th}$ Data Release Quasar Catalogue (DR14Q) and confirmed

Yinren Shou, Defeng Kong, Pengjie Wang, Zhusong Mei

We demonstrate the high-efficiency generation of water-window soft x-ray emissions from polyethylene nanowire array targets irradiated by femtosecond laser pulses at the intensity of 4*10^19 W/cm^2. The experimental results indicate more than one order of magnitude enhancement of the water-window x-ray emissions from the nanowire array targets compared to th

Dawon Ahn, Jun-Gi Jang, U Kang

Given a time-evolving tensor with missing entries, how can we effectively factorize it for precisely predicting the missing entries? Tensor factorization has been extensively utilized for analyzing various multi-dimensional real-world data. However, existing models for tensor factorization have disregarded the temporal property for tensor factorization while

Depen Morwani, Rahul Vashisht, Harish G. Ramaswamy

Recent papers have shown that sufficiently overparameterized neural networks can perfectly fit even random labels. Thus, it is crucial to understand the underlying reason behind the generalization performance of a network on real-world data. In this work, we propose several measures to predict the generalization error of a network given the training data and

Vincent Lemaire, Oumaima Alaoui Ismaili, Antoine Cornuéjols, Dominique Gay

Supervised classification can be effective for prediction but sometimes weak on interpretability or explainability (XAI). Clustering, on the other hand, tends to isolate categories or profiles that can be meaningful but there is no guarantee that they are useful for labels prediction. Predictive clustering seeks to obtain the best of the two worlds. Starting

Thierry Gourieux, Raphaël Leone

This article focuses on an important quantity that will be called the Rund-Trautman function. It already plays a central role in Noether's theorem since its vanishing characterizes a symmetry and leads to a conservation law. The main aim of the paper is to show how, in the realm of classical mechanics, an 'almost' vanishing Rund-Trautman function accompanyin

Anastasiia Sheveleva, Pierre Colman, Christophe Finot

Based on the space-time duality of light, we numerically demonstrate that temporal dispersion grating couplers can generate from a single pulse an array of replicas of equal amplitude. The phase-only profile of the temporal grating is optimized by a genetic algorithm that takes into account the optoelectronic bandwidth limitations of the setup.

Orestis Friderikos, Emmanuel Baranger, Marc Olive, David Néron

This work considers the stability of Proper Orthogonal Decomposition (POD) basis interpolation on Grassmann manifolds for parametric Model Order Reduction (pMOR) in hyperelasticity. The article contribution is mainly about stability conditions, all defined from strong mathematical background. We show how the stability of interpolation can be lost if certain

Ashish Cherukuri, Ashish R. Hota

We study stochastic optimization problems with chance and risk constraints, where in the latter, risk is quantified in terms of the conditional value-at-risk (CVaR). We consider the distributionally robust versions of these problems, where the constraints are required to hold for a family of distributions constructed from the observed realizations of the unc

Andres Pinilla, Jaime Garcia, William Raffe, Jan-Niklas Voigt-Antons

A cluster of research in Affective Computing suggests that it is possible to infer some characteristics of users' affective states by analyzing their electrophysiological activity in real-time. However, it is not clear how to use the information extracted from electrophysiological signals to create visual representations of the affective states of Virtual Re

Jiangqi Wu, Linjie Wen, Peter L Green, Jinglai Li

Many real-world problems require one to estimate parameters of interest, in a Bayesian framework, from data that are collected sequentially in time. Conventional methods for sampling from posterior distributions, such as {Markov Chain Monte Carlo} can not efficiently address such problems as they do not take advantage of the data's sequential structure. To t

Weiwei Zhu, Haoran Xue, Jiangbin Gong, Yidong Chong

The recent discoveries of higher-order topological insulators (HOTIs) have shifted the paradigm of topological materials, which was previously limited to topological states at boundaries of materials, to those at boundaries of boundaries, such as corners . So far, all HOTI realisations have assumed static equilibrium described by time-invariant Hamiltonians,

Heather E. Logan, Stefano Moretti, Diana Rojas-Ciofalo, Muyuan Song

We demonstrate a new type of cancellation of contributions to the electron and neutron electric dipole moments (EDMs) that occurs in three Higgs doublet models (3HDMs) when CP violation appears in the charged Higgs sector. The cancellation becomes exact when the two physical charged Higgs bosons in the model are degenerate in mass. Depending on the model par

Louis Desreumaux, Vincent Lemaire

Active learning aims to reduce annotation cost by predicting which samples are useful for a human expert to label. Although this field is quite old, several important challenges to using active learning in real-world settings still remain unsolved. In particular, most selection strategies are hand-designed, and it has become clear that there is no best activ

Waldemar Hergett, Christoph Neef, Hans-Peter Meyer, Rüdiger Klingeler

The high-pressure optical floating-zone method enables single crystal growth of the Pmnb high-temperature phase of Li2FeSiO4. The influence of growth conditions on crystal quality, phase homogeneity, and impurity formation in Li2FeSiO4 is studied. The use of different starting materials, i.e., either the P121/n1 or the Pmn21 polymorph, as well as optimizatio

Lihu Chen, Gaël Varoquaux, Fabian M. Suchanek

Biomedical entity linking aims to map biomedical mentions, such as diseases and drugs, to standard entities in a given knowledge base. The specific challenge in this context is that the same biomedical entity can have a wide range of names, including synonyms, morphological variations, and names with different word orderings. Recently, BERT-based methods hav

Kory D. Johnson, Mathias Beiglböck, Manuel Eder, Annemarie Grass

A primary quantity of interest in the study of infectious diseases is the average number of new infections that an infected person produces. This so-called reproduction number has significant implications for the disease progression. There has been increasing literature suggesting that superspreading, the significant variability in number of new infections c

José Pereira dos Reis, Fernando Brito e Abreu, Glauco de Figueiredo Carneiro, Craig Anslow

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells de

M Lansade

We show that a weighted manifold which admits a relative Faber Krahn inequality admits the Fefferman Phong inequality V $\psi$, $\psi$ $\le$ CV $\psi$ 2 , with the constant depending on a Morrey norm of V , and we deduce from it a condition for a L 2 Hardy inequality to holds, as well as conditions for Schr{\"o}dinger operators to be positive. We also obtain

Kenneth Lomas, Dave Cliff

In seeking to explain aspects of real-world economies that defy easy understanding when analysed via conventional means, Nobel Laureate Robert Shiller has since 2017 introduced and developed the idea of Narrative Economics, where observable economic factors such as the dynamics of prices in asset markets are explained largely as a consequence of the narrativ

Vaibhav Tiwari

Gravitational waves inform about the probable distances at which an observed signal originated. This information when combined over multiple observations is used in the modeling of the redshift evolution of the merger rate. This is an important aspect of binary black hole population analysis which is expected to have close ties with the star formation histor

Gabriel Rivière

On the canonical $2$-sphere and for Schr{\"o}dinger eigenfunctions, we obtain a simple geometric criterion on the potential under which we can improve, near a given point and for every $p\neq 6$, Sogge's estimates by a power of the eigenvalue. This criterion can be formulated in terms of the critical points of the Radon transform of the potential and it is i

Paul-Emile Paradan

Let Z be the real part of a K{\"a}hler Hamiltonian manifold M. The O'Shea-Sjamaar's Theorem tells us that the moment polytope Delta(Z) corresponds to the anti-invariant part of the Kirwan polytope Delta(M). The purpose of the present paper is to explain how to parameterize the equations of the facets of Delta(Z) in terms of real Ressayre's pairs of Z.

Stephanos Yerolatsitis, András Kufcsák, Katjana Ehrlich, Harry A. C. Wood

Using the shifted-excitation Raman difference spectroscopy technique and an optical fibre featuring a negative curvature excitation core and a coaxial ring of high numerical aperture collection cores, we have developed a portable, background and fluorescence free, endoscopic Raman probe. The probe consists of a single fibre with a diameter of less than 0.25

Rishi Rabheru, Hazim Hanif, Sergio Maffeis

This paper presents DeepTective, a deep learning approach to detect vulnerabilities in PHP source code. Our approach implements a novel hybrid technique that combines Gated Recurrent Units and Graph Convolutional Networks to detect SQLi, XSS and OSCI vulnerabilities leveraging both syntactic and semantic information. We evaluate DeepTective and compare it to

Stefan-Cristian Nechita, Roland Toth, Dhruv Khandelwal, Maarten Schoukens

Data-driven modeling of nonlinear dynamical systems often require an expert user to take critical decisions a priori to the identification procedure. Recently an automated strategy for data driven modeling of \textit{single-input single-output} (SISO) nonlinear dynamical systems based on \textit{Genetic Programming} (GP) and \textit{Tree Adjoining Grammars}

Prosper Ngabonziza, Yi Wang, Peter A. van Aken, Joachim Maier

Ion conducting materials are critical components of batteries, fuel cells, and devices such as memristive switches. Analytical tools are therefore sought that allow the behavior of ions in solids to be monitored and analyzed with high spatial resolution and in real time. In principle, inelastic tunneling spectroscopy offers these capabilities. However, as it

Yvonne Pachmayer

This article gives an overview of recent highlights from experimental measurements of heavy-ion collisions at ultra-relativistic energies: Measurements of electroweak probes constrain both the initial collision geometry and the nuclear parton distribution functions. Results from soft particle production show that the abundance of light-flavour hadrons from p

Hangrui Bi, Hengyi Wang, Chence Shi, Jian Tang

Reaction prediction is a fundamental problem in computational chemistry. Existing approaches typically generate a chemical reaction by sampling tokens or graph edits sequentially, conditioning on previously generated outputs. These autoregressive generating methods impose an arbitrary ordering of outputs and prevent parallel decoding during inference. We dev

Igor Sokolović, Giada Franceschi, Zhichang Wang, Jian Xu

The surfaces of perovskite oxides affect their functional properties, and while a bulk-truncated (1$\times$1) termination is generally assumed, its existence and stability is controversial. Here, such a surface is created by cleaving the prototypical SrTiO$_3$(001) in ultra-high vacuum, and its response to thermal annealing is observed. Atomically resolved n

Angelos-Christos Anadiotis, Oana Balalau, Catarina Conceicao, Helena Galhardas

Digital data is a gold mine for modern journalism. However, datasets which interest journalists are extremely heterogeneous, ranging from highly structured (relational databases), semi-structured (JSON, XML, HTML), graphs (e.g., RDF), and text. Journalists (and other classes of users lacking advanced IT expertise, such as most non-governmental-organizations,

Patrick Fiorenza, Corrado Bongiorno, Filippo Giannazzo, Santi Alessandrino

In this paper, SiO2 layers deposited on 4H-SiC and subjected to different post deposition annealing (PDA) in NO and N2O were studied to identify the key factors influencing the channel mobility and threshold voltage stability in 4H-SiC MOSFETs. In particular, PDA in NO gave a higher channel mobility (55 cm2V-1s-1) than PDA in N2O (20 cm2V-1s-1), and the subt

Haoran Wang, Cheng Yang

Information diffusion prediction is a fundamental task which forecasts how an information item will spread among users. In recent years, deep learning based methods, especially those based on recurrent neural networks (RNNs), have achieved promising results on this task by treating infected users as sequential data. However, existing methods represent all pr

Marc Vuffray, Carleton Coffrin, Yaroslav A. Kharkov, Andrey Y. Lokhov

Drawing independent samples from high-dimensional probability distributions represents the major computational bottleneck for modern algorithms, including powerful machine learning frameworks such as deep learning. The quest for discovering larger families of distributions for which sampling can be efficiently realized has inspired an exploration beyond esta

R. Iaria, A. Sanna, T. Di Salvo, A. F. Gambino

The dipping source XB 1916-053 is a compact binary system with an orbital period of 50 min harboring a neutron star. Using ten new {\it Chandra} observations and one {\it Swift/XRT} observation, we are able to extend the baseline of the orbital ephemeris; this allows us to exclude some models that explain the dip arrival times. The Chandra observations provi

Masahiko G. Yamada, Satoshi Fujimoto

The Kitaev model is a remarkable spin model with gapped and gapless spin liquid phases, which are potentially realized in iridates and $\alpha$-RuCl$_3$. In the recent experiment of $\alpha$-RuCl$_3$, the signature of a nematic transition to the gapped toric code phase, which breaks the $C_3$ symmetry of the system, has been observed through the angle depend

Aleksandra Malysheva, Daniel Kudenko, Aleksei Shpilman

Learning to produce efficient movement behaviour for humanoid robots from scratch is a hard problem, as has been illustrated by the "Learning to run" competition at NIPS 2017. The goal of this competition was to train a two-legged model of a humanoid body to run in a simulated race course with maximum speed. All submissions took a tabula rasa approach to rei

Rozaliya Amirova, Sergey Masyagin, Anastasia Reprintseva, Giancarlo Succi

Nowadays, Computer Science tightly entered all spheres of human activity. To improve quality and speed of development process, it is important to help programmers improve their working conditions. This paper proposes a vision on exploring this issue and presents in conjunction a factor that has been claimed multiple time to affect the effectiveness of softwa

Anastasia Gaydashenko, Daniel Kudenko, Aleksei Shpilman

Robot navigation through crowds poses a difficult challenge to AI systems, since the methods should result in fast and efficient movement but at the same time are not allowed to compromise safety. Most approaches to date were focused on the combination of pathfinding algorithms with machine learning for pedestrian walking prediction. More recently, reinforce

Rani Hod, Michael Krivelevich, Tobias Müller, Alon Naor

In the $\left(1:b\right)$ component game played on a graph $G$, two players, Maker and Breaker, alternately claim~$1$ and~$b$ previously unclaimed edges of $G$, respectively. Maker's aim is to maximise the size of a largest connected component in her graph, while Breaker is trying to minimise it. We show that the outcome of the game on the binomial random gr

Devesh P. Sariya, Ing-Guey Jiang, Li-Hsin Su, Li-Chin Yeh

Considering the importance of investigating the transit timing variations (TTVs) of transiting exoplanets, we present a follow-up study of HAT-P-12b. We include six new light curves observed between 2011 and 2015 from three different observatories, in association with 25 light curves taken from the published literature. The sample of the data used, thus cove

Peixiao Wang, Hui Ren, Xinyan Zhu, Xiaokang Fu

Studying the spatiotemporal distribution of SARS-CoV-2 infections among healthcare workers (HCWs) can aid in protecting them from exposure. Existing studies related to HCW infections have emphasized infection rates and protective measures. However, the spatiotemporal patterns and related external environmental factors of HCW infections remain unclear. To fil

Eric Aristidi, Yan Fantéï-Caujolle, Aziz Ziad, Julien Chabé

The Generalised Differential Image Motion Monitor (GDIMM) was proposed a few years ago as a new generation instrument for turbulence monitoring. It measures integrated parameters of the optical turbulence, i.e the seeing, isoplanatic angle, scintillation index, coherence time and wavefront coherence outer scale. GDIMM is based on a fully automatic small tele

Aleksei Shpilman, Dmitry Boikiy, Marina Polyakova, Daniel Kudenko

Microtubule networks (MTs) are a component of a cell that may indicate the presence of various chemical compounds and can be used to recognize properties such as treatment resistance. Therefore, the classification of MT images is of great relevance for cell diagnostics. Human experts find it particularly difficult to recognize the levels of chemical compound

Mateusz Denys

A numerical agent-based spin model of financial markets, based on the Potts model from statistical mechanics, with a novel interpretation of the spin variable (as regards financial-market models) is presented. In this model, a value of the spin variable is only the agent's opinion concerning current market situation, which he communicates to his nearest neig

Alexander I. Lebedev

The stability of a high-symmetry $P4mm$ polar phase in seventeen short-period ferroelectric perovskite superlattices with polar discontinuity is studied from first principles within the density-functional theory. It is shown that in most superlattices this phase exhibits either the ferroelectric instability or the antiferrodistortive one, or both of them. Fo

Ivan Sosin, Daniel Kudenko, Aleksei Shpilman

Movement control of artificial limbs has made big advances in recent years. New sensor and control technology enhanced the functionality and usefulness of artificial limbs to the point that complex movements, such as grasping, can be performed to a limited extent. To date, the most successful results were achieved by applying recurrent neural networks (RNNs)

Daniele Garzoni, Nick Gill

For a finite group $G$, let $m_I(G)$ denote the largest possible cardinality of a minimal invariable generating set of $G$. We prove an upper and a lower bound for $m_I(S_n)$, which show in particular that $m_I(S_n)$ is asymptotic to $n/2$ as $n\rightarrow \infty$.

Toni Annala

The purpose of this article is to show that the bivariant algebraic $A$-cobordism groups considered previously by the author are independent of the chosen base ring $A$. This result is proven by analyzing the bivariant ideal generated by the so called snc relations, and, while the alternative characterization we obtain for this ideal is interesting by itself

Prashanth Kumar Kasarla, Pitamber Singh Patwal, Hitesh Kumar L. Adalja, Satya Narain Mathur

Near-infrared Imager Spectrometer and Polarimeter (NISP) is a camera, an intermediate resolution spectrograph and an imaging polarimeter being developed for upcoming 2.5m telescope of Physical Research Laboratory at Mount Abu, India. NISP is designed to work in the Near-IR (0.8-2.5 micron) using a H2RG detector. Collimator and camera lenses would transfer th

A. D. N. James, M. Sekania, S. B. Dugdale, L. Chioncel

We calculated the magnetic Compton profiles (MCPs) of Ni using density functional theory supplemented by electronic correlations treated within dynamical mean-field theory (DMFT). We present comparisons between the theoretical and experimental MCPs. The theoretical MCPs were calculated using the KKR method with the perturbative spin-polarized T-matrix fluctu

Simon D Duque Anton, Daniel Fraunholz, Daniel Schneider

The internet landscape is growing and at the same time becoming more heterogeneous. Services are performed via computers and networks, critical data is stored digitally. This enables freedom for the user, and flexibility for operators. Data is easier to manage and distribute. However, every device connected to a network is potentially susceptible to cyber at

Kathryn Garside, Aida Gjoka, Robin Henderson, Hollie Johnson

Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version

Dingwei Li, Qinglong Chang, Lixue Pang, Yanfang Zhang

Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. In this paper, we propose a high efficient FL method equipped with the double head design aiming for personalization optimization over non-IID dataset, and the gradual model sharing

Yannis Oudghiri

In this article, we prove two new versions of a theorem proven by Efron in [Efr65]. Efron's theorem says that if a function $\phi : \mathbb{R}^2 \rightarrow \mathbb{R}$ is non-decreasing in each argument then we have that the function $s \mapsto \mathbb{E}[\phi(X,Y)|X+Y=s]$ is non-decreasing. We name restricted Efron's theorem a version of Efron's theorem wh

Nathalie Ayi, Nastassia Pouradier Duteil

In this paper, we study a model for opinion dynamics where the influence weights of agents evolve in time via an equation which is coupled with the opinions' evolution. We explore the natural question of the large population limit with two approaches: the now classical mean-field limit and the more recent graph limit. After establishing the existence and uni

Michał Parniak, Ivan Galinskiy, Timo Zwettler, Eugene S. Polzik

Laser phase noise remains a limiting factor in many experimental settings, including metrology, time-keeping, as well as quantum optics. Hitherto this issue was addressed at low frequencies, ranging from well below 1 Hz to maximally 100 kHz. However, a wide range of experiments, such as, e.g., those involving nanomechanical membrane resonators, are highly se

Deekshya Roy Sarkar, Amish B. Shah, Alka Singh, Pitamber Singh Patwal

NISP, a multifaceted near-infrared instrument for the upcoming 2.5m IR telescope at MIRO Gurushikhar, Mount Abu, Rajasthan, India is being developed at PRL, Ahmedabad. NISP will have wide (FOV = 10' x 10') field imaging, moderate (R=3000) spectroscopy and imaging polarimetry operating modes. It is designed based on 0.8 to 2.5 micron sensitive, 2048 X 2048 Hg

Jianan Li, Xuemei Xie, Zhifu Zhao, Yuhan Cao

Graph Convolutional Networks (GCNs), which model skeleton data as graphs, have obtained remarkable performance for skeleton-based action recognition. Particularly, the temporal dynamic of skeleton sequence conveys significant information in the recognition task. For temporal dynamic modeling, GCN-based methods only stack multi-layer 1D local convolutions to

Ricard Durall, Kalun Ho, Franz-Josef Pfreundt, Janis Keuper

Generative adversarial networks are the state of the art approach towards learned synthetic image generation. Although early successes were mostly unsupervised, bit by bit, this trend has been superseded by approaches based on labelled data. These supervised methods allow a much finer-grained control of the output image, offering more flexibility and stabili

D. Banerjee, S. Krishna Prasad, V. Pant, J. A. McLaughlin

Modern observatories have revealed the ubiquitous presence of magnetohydrodynamic waves in the solar corona. The propagating waves (in contrast to the standing waves) are usually originated in the lower solar atmosphere which makes them particularly relevant to coronal heating. Furthermore, open coronal structures are believed to be the source regions of sol

Xi Chen, Mikhail E. Kandel, Gabriel Popescu

In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first

Calum Ross, Norisuke Sakai, Muneto Nitta

We determine exactly the phase structure of a chiral magnet in one spatial dimension with the Dzyaloshinskii-Moriya (DM) interaction and a potential that is a function of the third component of the magnetization vector, $n_3$, with a Zeeman (linear with the coefficient $B$) term and an anisotropy (quadratic with the coefficient $A$) term, constrained so that

Linhu Li, Sen Mu, Ching Hua Lee, Jiangbin Gong

Topologically quantized response is one of the focal points of contemporary condensed matter physics. While it directly results in quantized response coefficients in quantum systems, there has been no notion of quantized response in classical systems thus far. This is because quantized response has always been connected to topology via linear response theory

Kun Xu, Chengze Liu, Yipeng Jing, Marcin Sawicki

We report a giant red central disk galaxy in the XMM-LSS north region. The region is covered with a rich variety of multiband photometric and spectroscopic observations. Using the photometric data of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) and spectroscopic observation of the Baryon Oscillation Spectroscopic Survey (BOSS), we find that the

Subrat Sahu, Rajan Jha

We report a new gateway to enhance the sponatnesous emission rate of single quantum emitters by introducing a cavity in tilted nanofiber Bragg grating (TNFBG). We found that light emitter's coupling efficiency into the tilted grating structure's guided mode is 65%, with 70 grating periods. This novel tilted structure has a high Q factor ~ 1408.71 and low mod

Michael M. Schein, Amir Shoan

We present a method for computing upper bounds on the systolic length of certain Riemann surfaces uniformized by congruence subgroups of hyperbolic triangle groups, admitting congruence Hurwitz curves as a special case. The uniformizing group is realized as a Fuchsian group and a convenient finite generating set is computed. The upper bound is derived from t

Fengli Gao, Huicai Zhong

Large batch size training in deep neural networks (DNNs) possesses a well-known 'generalization gap' that remarkably induces generalization performance degradation. However, it remains unclear how varying batch size affects the structure of a NN. Here, we combine theory with experiments to explore the evolution of the basic structural properties, including g

Tim Krake, Stefan Reinhardt, Marcel Hlawatsch, Bernhard Eberhardt

Dynamic Mode Decomposition (DMD) is a data-driven and model-free decomposition technique. It is suitable for revealing spatio-temporal features of both numerically and experimentally acquired data. Conceptually, DMD performs a low-dimensional spectral decomposition of the data into the following components: The modes, called DMD modes, encode the spatial con

Noah P. Mitchell, Ari M. Turner, William T. M. Irvine

Networks of interacting gyroscopes have proven to be versatile structures for understanding and harnessing finite-frequency topological excitations. Spinning components give rise to band gaps and topologically protected wave transport along the system's boundaries, whether the gyroscopes are arranged in a lattice or in an amorphous configuration. Here, we ex

Miroslav Rac, Michal Kompan, Maria Bielikova

One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of this domain force researchers to opt for session-based recommendation methods, from which nearest-neighbors-based (SkNN) approaches have been shown to be competitive with and even out

Hoai-Minh Nguyen

We consider the nonlinear Korteweg-de Vries (KdV) equation in a bounded interval equipped with the Dirichlet boundary condition and the Neumann boundary condition on the right. It is known that there is a set of critical lengths for which the solutions of the linearized system conserve the $L^2$-norm if their initial data belong to a finite dimensional subsp

Angus Dempster, Daniel F. Schmidt, Geoffrey I. Webb

Until recently, the most accurate methods for time series classification were limited by high computational complexity. ROCKET achieves state-of-the-art accuracy with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to train a linear class

Yichao Zhou, Yu Yan, Rujun Han, J. Harry Caufield

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of model

Chen Xing, Wenhao Liu, Caiming Xiong

Fitting complex patterns in the training data, such as reasoning and commonsense, is a key challenge for language pre-training. According to recent studies and our empirical observations, one possible reason is that some easy-to-fit patterns in the training data, such as frequently co-occurring word combinations, dominate and harm pre-training, making it har

Christoph Meier, Sebastian L. Fuchs, A. John Hart, Wolfgang A. Wall

Laser-based metal processing including welding and three dimensional printing, involves localized melting of solid or granular raw material, surface tension-driven melt flow and significant evaporation of melt due to the applied very high energy densities. The present work proposes a weakly compressible smoothed particle hydrodynamics formulation for thermo-

Vincent Claveau

A well-known way to improve the performance of document retrieval is to expand the user's query. Several approaches have been proposed in the literature, and some of them are considered as yielding state-of-the-art results in IR. In this paper, we explore the use of text generation to automatically expand the queries. We rely on a well-known neural generativ

Margarita Akhmejanova, Konstantin Olmezov, Aleksei Volostnov, Ilya Vorobyev

The Wiener index $W(G)$ of a connected graph $G$ is a sum of distances between all pairs of vertices of $G$. In 1991, \v{S}olt\'{e}s formulated the problem of finding all graphs $G$ such that for every vertex $v$ the equation $W(G)=W(G-v)$ holds. The cycle $C_{11}$ is the only known graph with this property. In this paper we consider the following relaxation

Suddhasattwa Brahma, Che-Yu Chen, Dong-han Yeom

The lack of rotating black hole models, which are typically found in nature, in loop quantum gravity (LQG) substantially hinders the progress of testing LQG from observations. Starting with a non-rotating LQG black hole as a seed metric, we construct a rotating spacetime using the revised Newman-Janis algorithm. The rotating solution is non-singular everywhe

Guang-Jing Song, Michael K. Ng, Xiongjun Zhang

One of the key problems in tensor completion is the number of uniformly random sample entries required for recovery guarantee. The main aim of this paper is to study $n_1 \times n_2 \times n_3$ third-order tensor completion based on transformed tensor singular value decomposition, and provide a bound on the number of required sample entries. Our approach is