June 2020 arXiv papers — page 6

Wenxi Zhao, Dmitriy Korobskiy, George Chacko

A Sleeping Beauty is a publication that is apparently unrecognized for some period of time before experiencing sudden recognition by citation. Various reasons, including resistance to new ideas, have been attributed to such delayed recognition. We examine this phenomenon in the special case of co-citations, which represent new ideas generated through the com

Vishnu Vardhan Nimmalapudi, Ajith Kumar Mengani, Roopa Vuppula, Rahul Jashvantbhai Pandya

Deep learning has made great strides lately with the availability of powerful computing machines and the advent of user-friendly programming environments. It is anticipated that the deep learning algorithms will entirely provision the majority of operations in 6G. One such environment where deep learning can be the right solution is load balancing in future

Hadas Kress-Gazit, Kerstin Eder, Guy Hoffman, Henny Admoni

Robot capabilities are maturing across domains, from self-driving cars, to bipeds and drones. As a result, robots will soon no longer be confined to safety-controlled industrial settings; instead, they will directly interact with the general public. The growing field of Human-Robot Interaction (HRI) studies various aspects of this scenario - from social norm

Xing Huang, Feng-Yu Wang

By using the heat kernel parameter expansion with respect to the frozen SDEs, the intrinsic derivative is estimated for the law of Mckean-Vlasov SDEs with respect to the initial distribution. As an application, the total variation distance between the laws of two solutions is bounded by the Wasserstein distance for initial distributions. These extend some re

Loïc Brevault, Mathieu Balesdent, Ali Hebbal

The design process of complex systems such as new configurations of aircraft or launch vehicles is usually decomposed in different phases which are characterized for instance by the depth of the analyses in terms of number of design variables and fidelity of the physical models. At each phase, the designers have to compose with accurate but computationally i

Nicolas Bousquet, Alice Joffard, Paul Ouvrard

Given a graph $G$ and an integer $k$, a token addition and removal ({\sf TAR} for short) reconfiguration sequence between two dominating sets $D_{\sf s}$ and $D_{\sf t}$ of size at most $k$ is a sequence $S= \langle D_0 = D_{\sf s}, D_1 \ldots, D_\ell = D_{\sf t} \rangle$ of dominating sets of $G$ such that any two consecutive dominating sets differ by the a

G. B. Apolinário, L. Moriconi, R. M. Pereira, V. J. Valadão

Statistical properties of circulation encode relevant information about the multi-scale structure of turbulent cascades. Recent massive computational efforts have posed challenging theoretical issues, as the dependence of circulation moments upon Reynolds numbers and length scales, and the specific shape of the heavy-tailed circulation probability distributi

Yang, Bang, Takuya Murooka, Kwangsoo Kim

Nitrogen-vacancy (NV) centers in diamond work as a quantum electrometer. Using an ensemble state of NV centers, we propose vector electrometry and demonstrate measurements in a diamond electronic device. A transverse electric field applied to the N-V axis under a high voltage was measured while applying a transverse magnetic field. The response of the energy

Xinyan Zhao, Xiao Feng, Haoming Zhong, Jun Yao

The study of question answering has received increasing attention in recent years. This work focuses on providing an answer that compatible with both user intent and conditioning information corresponding to the question, such as delivery status and stock information in e-commerce. However, these conditions may be wrong or incomplete in real-world applicatio

Noud Mooren, Gert Witvoet, Tom Oomen

Motion systems are often subject to disturbances such as cogging, commutation errors, and imbalances, that vary with velocity and appear periodic in time for constant operating velocities. The aim of this paper is to develop a repetitive controller (RC) for disturbances that are not periodic in the time domain, yet occur due to an identical position-domain d

J. Matthias Kahk, Ivar Kuusik, Vambola Kisand, Kevin R. J. Lovelock

Room temperature ionic liquids play an important role in many technological applications and a detailed understanding of their frontier molecular orbitals is required to optimize interfacial barriers, reactivity and stability with respect to electron injection and removal. In this work, we calculate quasiparticle energy levels of ionic liquids using first-pr

Jacob Swambo, Spencer Hommel, Bob McElrath, Bryan Bishop

A bitcoin covenant is a mechanism to enforce conditions on how the control of coins will be transferred in the future. This work introduces deleted-key covenants; using pre-signed transactions with secure key deletion. With this, a general class of covenants are possible without introducing new security risks to bitcoin. There is a range of security models f

He Zhang, Santosh Kumar, Yu-Ping Huang

We propose and demonstrate a photon-efficient optical classifier to overcome the Rayleigh limit in spatial resolution. It utilizes mode-selective sum frequency generation and single-pixel photon detection to resolve closely spaced incoherent sources based on photon counting statistics. Super-resolving and photon efficient, this technique can find application

Štěpán Holub, Štěpán Starosta

We provide a reformulation and a formalization of the classical result by Juhani Karhumäki characterizing intersections of two languages of the form $\{x,y\}^*\cap \{u,v\}^*$. We use the terminology of morphisms which allows to formulate the result in a shorter and more transparent way, and we formalize the result in the proof assistant Isabelle/HOL.

Daiki Koyama, Yuji Okawa, Nanami Suzuki

In the AdS/CFT correspondence we consider correlation functions of gauge-invariant operators on the gauge theory side, which we obtain in the low-energy limit of the open string sector. To investigate this low-energy limit we consider the action of open bosonic string field theory including source terms for gauge-invariant operators and classically integrate

P. Z. Zhao, K. Z. Li, G. F. Xu, D. M. Tong

The main challenges in achieving high-fidelity quantum gates are to reduce the influence of control errors caused by imperfect Hamiltonians and the influence of decoherence caused by environment noise. To overcome control errors, a promising proposal is nonadiabatic holonomic quantum computation, which has attracted much attention in both theories and experi

Miloud Mekkaoui, Ahmed Jellal, Hocine Bahlouli

We study Dirac fermions in gapped graphene that are subjected to a magnetic field and a potential barrier harmonically oscillating in time. The tunneling modes inside the gap and the associated Goos-Hänchen (GH) shifts are analytically investigated. We show that the GH shifts in transmission for the central band and the first two sidebands change sign at the

Yuval Bahat, Gregory Shakhnarovich

Machine learning plays an increasingly significant role in many aspects of our lives (including medicine, transportation, security, justice and other domains), making the potential consequences of false predictions increasingly devastating. These consequences may be mitigated if we can automatically flag such false predictions and potentially assign them to

Paul McCloud

Risk-neutral pricing dictates that the discounted derivative price is a martingale in a measure equivalent to the economic measure. The residual ambiguity for incomplete markets is here resolved by minimising the entropy of the price measure from the economic measure, subject to mark-to-market constraints, following arguments based on the optimisation of por

Hannu Reittu, Ville Kotovirta, Lasse Leskelä, Hannu Rummukainen

The use of quantum computing in graph community detection and regularity checking related to Szemeredi's Regularity Lemma (SRL) are demonstrated with D-Wave Systems' quantum annealer and simulations. We demonstrate the capability of quantum computing in solving hard problems relevant to big data. A new community detection algorithm based on SRL is al

Jana Bogdanoska, Denis Burgarella

Studying the UV dust attenuation, as well as its relation to other galaxy parameters such as the stellar mass, plays an important role in multi-wavelength research. This work relates the dust attenuation to the stellar mass of star forming galaxies, and its evolution with redshift. A sample of galaxies with an estimate of the dust attenuation computed from t

V. M. Shapoval, M. D. Adzhymambetov, Yu. M. Sinyukov

The recent results on the main soft observables, including hadron and photon yields and particle number ratios, $p_T$ spectra, flow harmonics, as well as the femtoscopy radii, obtained within the integrated hydrokinetic model (iHKM) for high-energy heavy-ion collisions are reviewed and re-examined. The cases of different nuclei colliding at different energie

Sascha Trostorff, Marcus Waurick

We discuss the issue of maximal regularity for evolutionary equations with non-autonomous coefficients. Here evolutionary equations are abstract partial-differential algebraic equations considered in Hilbert spaces. The catch is to consider time-dependent partial differential equations in an exponentially weighted Hilbert space. In passing, one establishes t

Diego F. Torres, Shuang-Nan Zhang

Insight-HXMT is the first Chinese X-ray astronomical mission, launched successfully on June 15, 2017, from China's Jiuquan Satellite Launch Center. Insight-HXMT was designed to have a broad energy coverage in X-rays, from 1-250 keV, with excellent timing and adequate energy resolution at soft X-rays, and the largest effective area at hard X-rays. Here, w

Armin Tavakoli, Nicolas Gisin, Cyril Branciard

Network Bell experiments give rise to a form of quantum nonlocality that conceptually goes beyond Bell's theorem. We investigate here the simplest network, known as the bilocality scenario. We depart from the typical use of the Bell State Measurement in the network central node and instead introduce a family of symmetric iso-entangled measurement bases t

Wenchao Ge, M. Suhail Zubairy

Quantifying nonclassicality of a bosonic mode is an important but challenge task in quantum optics. Recently, the first nonclassicality measure based on the concept of operational resource theory has been proposed [Phys. Rev. Research 2, 023400 (2020)], which shows several crucial properties as a resource for quantum metrology. Here we apply the measure to e

Mohammad Pouranvari

We studied numerically the distribution of the entanglement Hamiltonian eigenvalues in two one-dimensional free fermion models and the typical three-dimensional Anderson model. We showed numerically that this distribution depends on the phase of the system: In the delocalized phase it is centered around very small values and in the localized phase, picks of

Carlos A. Perez-Delgado, Hector G. Perez-Gonzalez

We set down the principles behind a modeling language for quantum software. We present a minimal set of extensions to the well-known Unified Modeling Language (UML) that allows it to effectively model quantum software. These extensions are separate and independent of UML as a whole. As such they can be used to extend any other software modeling language, or

Yang Xiang, Liming Shi, Jesper Lisby Højvang, Morten Højfeldt Rasmussen

In this paper, we propose a novel supervised single-channel speech enhancement method combing the the Kullback-Leibler divergence-based non-negative matrix factorization (NMF) and hidden Markov model (NMF-HMM). With the application of HMM, the temporal dynamics information of speech signals can be taken into account. In the training stage, the sum of Poisson

Roderick Bloem, Peter Gjøl Jensen, Bettina Könighofer, Kim Guldstrand Larsen

Erroneous behaviour in safety critical real-time systems may inflict serious consequences. In this paper, we show how to synthesize timed shields from timed safety properties given as timed automata. A timed shield enforces the safety of a running system while interfering with the system as little as possible. We present timed post-shields and timed pre-shie

Hamid Hezari, Steve Zelditch

This paper is part of a series concerning the isospectral problem for an ellipse. In this paper, we study Cauchy data of eigenfunctions of the ellipse with Dirichlet or Neumann boundary conditions. Using many classical results on ellipse eigenfunctions, we determine the microlocal defect measures of the Cauchy data of the eigenfunctions. The ellipse has inte

Simon Davidson, Stephen B. Furber, Oliver Rhodes

Spike Timing Dependent Plasticity is form of learning that has been demonstrated in real cortical tissue, but attempts to use it for artificial systems have not produced good results. This paper seeks to remedy this with two significant advances. The first is the development a simple stochastic learning rule called cyclic STDP that can extract patterns encod

Kate Brown, Thomas Bland, Paolo Comaron, Nick P. Proukakis

The quenched dynamics of an ultracold homogeneous atomic two-dimensional Bose gas subjected to periodic quenches across the Berezinskii-Kosterlitz-Thouless (BKT) phase transition are discussed. Specifically, we address the effect of periodic cycling of the effective atomic interaction strength between a thermal disordered state above, and a highly ordered st

M. Ali Khan, Metin Uyanik

In a 1983 paper, Yannelis-Prabhakar rely on Michael's selection theorem to guarantee a continuous selection in the context of the existence of maximal elements and equilibria in abstract economies. In this tribute to Nicholas Yannelis, we root this paper in Chapter II of Yannelis' 1983 Rochester Ph.D. dissertation, and identify its pioneering applica

Toshiyuki Kobayashi

Geometric symmetry induces symmetries of function spaces, and the latter yields a clue to global analysis via representation theory. In this note we summarize recent developments on the general theory about how geometric conditions affect representation theoretic properties on function spaces, with focus on multiplicities and spectrum.

Zhongxiang Dai, Yizhou Chen, Kian Hsiang Low, Patrick Jaillet

This paper presents a recursive reasoning formalism of Bayesian optimization (BO) to model the reasoning process in the interactions between boundedly rational, self-interested agents with unknown, complex, and costly-to-evaluate payoff functions in repeated games, which we call Recursive Reasoning-Based BO (R2-B2). Our R2-B2 algorithm is general in that it

Florian Dupuy, Olivier Mestre, Mathieu Serrurier, Mohamed Chafik Bakkay

Cloud cover is crucial information for many applications such as planning land observation missions from space. It remains however a challenging variable to forecast, and Numerical Weather Prediction (NWP) models suffer from significant biases, hence justifying the use of statistical post-processing techniques. In this study, ARPEGE (Météo-France global NWP)

Martin Skrodzki, Ulrich Reitebuch, Eric Zimmermann

In his 1952 paper "The chemical basis of morphogenesis", Alan M. Turing presented a model for the formation of skin patterns. While it took several decades, the model has been validated by finding corresponding natural phenomena, e.g. in the skin pattern formation of zebrafish. More surprising, seemingly unrelated pattern formations can also be studi

M. Gromniak, N. Gessert, T. Saathoff, A. Schlaefer

Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside the tissue. For this purpose, fiber-optical sensors can be directly integrated into the needle tip. Optical coherence tomography (OCT) can be used to image tissue. Here, we study how

Sourav Koner, Dhiren Kumar Basnet

We provide an alternative proof that the finite rational linear combination of radicals, under certain constraint, are linearly independent over $\mathbb{Q}$.

Aidyn Kassymov, Michael Ruzhansky, Berikbol T. Torebek

In this paper in the cylindrical domain we consider a fractional elliptic operator with Dirichlet conditions. We prove, that the first eigenvalue of the fractional elliptic operator is minimised in a circular cylinder among all cylindrical domains of the same Lebesgue measure. This inequality is called the Rayleigh-Faber-Krahn inequality. Also, we give Lyapu

Di Wu, Qi Tang, Yongle Zhao, Ming Zhang

The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications. There are two kinds of quantization methods, training-based quantization and post-training quantization. Training-based approach suffers from a cumbersome training process, while post-training quantization may lead to unacceptable accuracy d

Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen

Neural network scaling has been critical for improving the model quality in many real-world machine learning applications with vast amounts of training data and compute. Although this trend of scaling is affirmed to be a sure-fire approach for better model quality, there are challenges on the path such as the computation cost, ease of programming, and effici

Toshiyuki Kobayashi, Birgit Speh

We consider branching laws for the restriction of some irreducible unitary representations $Π$ of $G=O(p,q)$ to its subgroup $H=O(p-1,q)$. In Kobayashi (arXiv:1907.07994), the irreducible subrepresentations of $O(p-1,q)$ in the restriction of the unitary $Π|_{O(p-1,q) }$ are determined. By considering the restriction of packets of irreducible representations

Vincenzo Orlando, Iván Lombardero, Mercedes Gabás, Neftali Nuñez

A temperature accelerated life test on Upright Metamorphic Ga0.37In0.63P/Ga0.83In0.17As/Ge triple-junction solar cells has been carried out. The acceleration has been accomplished by subjecting the solar cells to temperatures (125, 145 and 165°C) significantly higher than the nominal working temperature inside a concentrator (90°C), while the nominal photo-c

Siyuan Liu, Abdalla Swikir, Majid Zamani

The security in information-flow has become a major concern for cyber-physical systems (CPSs). In this work, we focus on the analysis of an information-flow security property, called opacity. Opacity characterizes the plausible deniability of a system's secret in the presence of a malicious outside intruder. We propose a methodology of checking a notion

N. Matsubara, E. Nocerino, K. Kamazawa, O. K. Forslund

The high-pressure synthesized quasi-one-dimensional compounds NaMn$_2$O$_4$ and Li$_{0.92}$Mn$_2$O$_4$ are both antiferromagnetic insulators, and here their atomic and magnetic structures were investigated using neutron powder diffraction. The present crystal structural analyses of NaMn2O4 reveal that Mn3+/Mn4+ charge-ordering state exist even at low tempera

Sukru Acitas, Peter Filzmoser, Birdal Senoglu

In linear regression, the least squares (LS) estimator has certain optimality properties if the errors are normally distributed. This assumption is often violated in practice, partly caused by data outliers. Robust estimators can cope with this situation and thus they are widely used in practice. One example of robust estimators for regression are adaptive m

Chih-Yao Chen, Josef Rizell, Godwill Mbiti Kanyolo, Titus Masese

Natural abundance, impressive chemical characteristics and economic feasibility have rekindled the appeal for rechargeable sodium (Na) batteries as a practical solution for the growing energy demand, environmental sustainability and energy independence. However, the scarcity of viable positive electrode materials remains a huge impediment to the actualizatio

Kirill Neklyudov, Max Welling, Evgenii Egorov, Dmitry Vetrov

Markov Chain Monte Carlo (MCMC) is a computational approach to fundamental problems such as inference, integration, optimization, and simulation. The field has developed a broad spectrum of algorithms, varying in the way they are motivated, the way they are applied and how efficiently they sample. Despite all the differences, many of them share the same core

M. Cadoni, M. Tuveri, A. P. Sanna

It is a tantalising possibility that quantum gravity (QG) states remaining coherent at astrophysical, galactic and cosmological scales could exist and that they could play a crucial role in understanding macroscopic gravitational effects. We explore, using only general principles of General Relativity, quantum and statistical mechanics, the possibility of us

A. Petralia, E. Alei, G. Aresu, D. Locci

The Milky Way Galaxy is literally teeming with exoplanets; thousands of planets have been discovered, with thousands more planet candidates identified. Terrestrial-like planets are quite common around other stars, and are expected to be detected in large numbers in the future. Such planets are the primary targets in the search for potentially habitable condi

Vincent Dutordoir, Nicolas Durrande, James Hensman

We introduce a new class of inter-domain variational Gaussian processes (GP) where data is mapped onto the unit hypersphere in order to use spherical harmonic representations. Our inference scheme is comparable to variational Fourier features, but it does not suffer from the curse of dimensionality, and leads to diagonal covariance matrices between inducing

Heunchul Lee, Maksym Girnyk, Jaeseong Jeong

In this paper, we propose a deep reinforcement learning (RL)-based precoding framework that can be used to learn an optimal precoding policy for complex multiple-input multiple-output (MIMO) precoding problems. We model the precoding problem for a single-user MIMO system as an RL problem in which a learning agent sequentially selects the precoders to serve t

Furkan Ozcelik, Ugur Alganci, Elif Sertel, Gozde Unal

Convolutional Neural Networks (CNN)-based approaches have shown promising results in pansharpening of satellite images in recent years. However, they still exhibit limitations in producing high-quality pansharpening outputs. To that end, we propose a new self-supervised learning framework, where we treat pansharpening as a colorization problem, which brings

Erion Çano, Maurizio Morisio

The fabulous results of convolution neural networks in image-related tasks, attracted attention of text mining, sentiment analysis and other text analysis researchers. It is however difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. In this paper we pr

Grzegorz J. Blinowski, Paweł Piotrowski

Common Vulnerabilities and Exposures database (CVE) is one of the largest publicly available source of software and hardware vulnerability data and reports. In this work we analyze the CVE database in the context of IoT device and system vulnerabilities. We introduce a real-world based classification of IoT systems. Then, we employ a SVM algorithm on selecte

Kunming Luo, Chuan Wang, Nianjin Ye, Shuaicheng Liu

Occlusion is an inevitable and critical problem in unsupervised optical flow learning. Existing methods either treat occlusions equally as non-occluded regions or simply remove them to avoid incorrectness. However, the occlusion regions can provide effective information for optical flow learning. In this paper, we present OccInpFlow, an occlusion-inpainting

Andrew Panteli

The inconsistency of the observed rotational velocity curve of the Milky Way with the current theory of gravitational dynamics is well documented. The curve implies the presence of significantly more mass, or dark matter, than is observable. Current models estimate the amount of dark matter to be of the order of 85% of total matter. This paper explores relat

Wolfgang Domcke, Andrzej L. Sobolewski, Cody W. Schlenker

We present a conspectus of recent joint spectroscopic and computational studies which provided novel insight into the photochemistry of hydrogen-bonded complexes of the heptazine (Hz) chromophore with hydroxylic substrate molecules (water and phenol). It was found that a functionalized derivative of Hz, tri-anisole-heptazine (TAHz), can photooxidize water an

Linde S. Hesse, Pim A. de Jong, Josien P. W. Pluim, Veronika Cheplygina

Early detection of lung cancer has been proven to decrease mortality significantly. A recent development in computed tomography (CT), spectral CT, can potentially improve diagnostic accuracy, as it yields more information per scan than regular CT. However, the shear workload involved with analyzing a large number of scans drives the need for automated diagno

Xiaolong Li

We prove that the moduli of continuity of viscosity solutions to fully nonlinear parabolic partial differential equations are viscosity subsolutions of suitable parabolic equations of one space variable. As applications, we obtain sharp Lipschitz bounds and gradient estimates for fully nonlinear parabolic equations with bounded initial data, via comparison w

Niclas Technau, Nadav Yesha

A well known result in the theory of uniform distribution modulo one (which goes back to Fejér and Csillag) states that the fractional parts $\{n^α\}$ of the sequence $(n^α)_{n\ge1}$ are uniformly distributed in the unit interval whenever $α>0$ is not an integer. For sharpening this knowledge to local statistics, the $k$-level correlation functions of the se

Sourendu Gupta, Rishi Sharma

Lattice measurements provide adequate information to fix the parameters of long distance effective field theories in Euclidean time. Using such a theory, we examine the analytic continuation of long distance correlation functions of composite operators at finite temperature from Euclidean to Minkowski space time. We show through an explicit computation that

In-Jae Yu, Wonhyuk Ahn, Seung-Hun Nam, Heung-Kyu Lee

Convolutional neural networks (CNN) for image steganalysis demonstrate better performances with employing concepts from high-level vision tasks. The major employed concept is to use data augmentation to avoid overfitting due to limited data. To augment data without damaging the message embedding, only rotating multiples of 90 degrees or horizontally flipping

Yang-Hui He

Calabi-Yau spaces, or Kahler spaces admitting zero Ricci curvature, have played a pivotal role in theoretical physics and pure mathematics for the last half-century. In physics, they constituted the first and natural solution to compactification of superstring theory to our 4-dimensional universe, primarily due to one of their equivalent definitions being th

Guruprasad Hegde, Avinash Nittur Ramesh, Kanchana Vaishnavi Gandikota, Roman Obermaisser

Deep Learning systems have proven to be extremely successful for image recognition tasks for which significant amounts of training data is available, e.g., on the famous ImageNet dataset. We demonstrate that for robotics applications with cheap camera equipment, the low image quality, however,influences the classification accuracy, and freely available datab

Yang-Hui He, Shing-Tung Yau

Graph Laplacians as well as related spectral inequalities and (co-)homology provide a foray into discrete analogues of Riemannian manifolds, providing a rich interplay between combinatorics, geometry and theoretical physics. We apply some of the latest techniques in data science such as supervised and unsupervised machine-learning and topological data analys

Shiheng Liang, Shuyuan Shi, Chuang-Han Hsu, Kaiming Cai

The ability to switch magnetic elements by spin-orbit-induced torques has recently attracted much attention for a path towards high-performance, non-volatile memories with low power consumption. Realizing efficient spin-orbit-based switching requires harnessing both new materials and novel physics to obtain high charge-to-spin conversion efficiencies, thus m

Bing Tan, Jingjing Fan, Xiaolong Qin

The objective of this research is to explore a convex feasibility problem, which consists of a monotone variational inequality problem and a fixed point problem. We introduce four inertial extragradient algorithms that are motivated by the inertial method, the subgradient extragradient method, the Tseng's extragradient method and the Mann-type method end

Noga Alon, Andrei Graur

We provide approximation algorithms for two problems, known as NECKLACE SPLITTING and $ε$-CONSENSUS SPLITTING. In the problem $ε$-CONSENSUS SPLITTING, there are $n$ non-atomic probability measures on the interval $[0, 1]$ and $k$ agents. The goal is to divide the interval, via at most $n (k-1)$ cuts, into pieces and distribute them to the $k$ agents in an ap

Thomas Simpson, Dimitrios Giagopoulos, Vasilis Dertimanis, Eleni Chatzi

Dynamic substructuring (DS) methods encompass a range of techniques to decompose large structural systems into multiple coupled subsystems. This decomposition has the principle benefit of reducing computational time for dynamic simulation of the system. In this context, DS methods may form an essential component of hybrid simulation, wherein they can be used

Marcella Marconi, Giulia De Somma, Vincenzo Ripepi, Roberto Molinaro

On the basis of recently computed nonlinear convective pulsation models of Galactic Cepheids, spanning wide ranges of input stellar parameters, we derive theoretical mass-dependent Period-Wesenheit relations in the Gaia bands, namely $G$, $G_{BP}$ and $G_{RP}$, that are found to be almost independent of the assumed efficiency of super-adiabatic convection. T

N. Nardetto, A. Salsi, D. Mourard, V. Hocde

The surface brightness - color relationship (SBCR) is a poweful tool for determining the angular diameter of stars from photometry. It was for instance used to derive the distance of eclipsing binaries in the Large Magellanic Cloud (LMC), which led to its distance determination with an accuracy of 1%. We calibrate the SBCR for red giant stars in the 2.1 < V-

D. Baroch, J. C. Morales, I. Ribas, E. Herrero

Context. Variability caused by stellar activity represents a challenge to the discovery and characterization of terrestrial exoplanets and complicates the interpretation of atmospheric planetary signals. Aims. We aim to use a detailed modeling tool to reproduce the effect of active regions on radial velocity measurements, which aids the identification of the

Du Xiaorui, Yavuzhan Erdem, Immanuel Schweizer, Cristian Axenie

Perceptual learning enables humans to recognize and represent stimuli invariant to various transformations and build a consistent representation of the self and physical world. Such representations preserve the invariant physical relations among the multiple perceived sensory cues. This work is an attempt to exploit these principles in an engineered system.

Marius Appel, Edzer Pebesma

Technological developments and open data policies have made large, global environmental datasets accessible to everyone. For analysing such datasets, including spatiotemporal correlations using traditional models based on Gaussian processes does not scale with data volume and requires strong assumptions about stationarity, separability, and distance measures

Bertrand Martinez, Emmanuel d&#39;Humières, Laurent Gremillet

By means of particle-in-cell numerical simulations, we investigate the emission of high-energy photons in laser-plasma interactions under ultrahigh-intensity conditions relevant to multi-petawatt laser systems. We first examine the characteristics of synchrotron radiation from laser-driven plasmas of varying density and size. In particular, we show and expla

Marie-Claude Arnaud

We introduce a notion of weak Denjoy subsystem (WDS) that generalizes the Aubry-Mather Cantor sets to diffeomorphisms of manifolds. We explain how a rotation number can be associated to such a WDS. Then we build in any horseshoe a continuous one parameter family of such WDS that is indexed by its rotation number. Looking at the inverse problem in the setting

Andrea Fioraldi

In this thesis, we introduce the idea of combining symbolic execution with dynamic analysis for reverse engineering. Differently from DSE, we devise an approach where the reverse engineer can use a debugger to drive and inspect a concrete execution engine of the application code and then, when needed, transfer the execution into a symbolic executor in order

E. I. Vorobyov, V. G. Elbakyan, K. Omukai, T. Hosokawa

The early evolution of protostellar disks with metallicities in the $Z=1.0-0.01~Z_\odot$ range was studied with a particular emphasis on the strength of gravitational instability and the nature of protostellar accretion in low-metallicity systems. Numerical hydrodynamics simulations in the thin-disk limit were employed that feature separate gas and dust temp

Yuan Quan, Longfei Shi, Jialei Liu, Jiazhi Ma

Radar detection and communication can be operated simultaneously in joint radar-communication (JRC) system. In this paper, we propose a bistatic JRC system which is applicable in multi-path environments. Basing on a novel joint waveform, a joint detection process is designed for both target detection and channel estimation. Meanwhile, a low-cost channel equa

Pierre Humbert, Batiste Le Bars, Ludovic Minvielle, Nicolas Vayatis

In this paper, we introduce a robust nonparametric density estimator combining the popular Kernel Density Estimation method and the Median-of-Means principle (MoM-KDE). This estimator is shown to achieve robustness to any kind of anomalous data, even in the case of adversarial contamination. In particular, while previous works only prove consistency results

Nandan Kumar Jha, Rajat Saini, Sparsh Mittal

Knowledge distillation (KD) is generally considered as a technique for performing model compression and learned-label smoothing. However, in this paper, we study and investigate the KD approach from a new perspective: we study its efficacy in training a deeper network without any residual connections. We find that in most of the cases, non-residual student n

Shuyuan Xiao, Xing Wang, Junyi Duan, Chaobiao Zhou

Recent progress in nanophotonics is driven by the desire to engineer light-matter interaction in two-dimensional (2D) materials using high-quality resonances in plasmonic and dielectric structures. Here, we demonstrate a link between the radiation control at critical coupling and the metasurface-based bound states in the continuum (BIC) physics, and develop

Juan Luis Gonzalez Bello, Soomin Seo, Munchurl Kim

We present a novel color-aware perceptual (CAP) loss for learning the task of pan-sharpening. Our CAP loss is designed to focus on the deep features of a pre-trained VGG network that are more sensitive to spatial details and ignore color information to allow the network to extract the structural information from the PAN image while keeping the color from the

Gi-Sang Cheon, Ji-Hwan Jung, Bumtle Kang, Hana Kim

The notion of a Riordan graph was introduced recently, and it is a far-reaching generalization of the well-known Pascal graphs and Toeplitz graphs. However, apart from a certain subclass of Toeplitz graphs, nothing was known on independent sets in Riordan graphs. In this paper, we give exact enumeration and lower and upper bounds for the number of independen

Stefano Marini, Costantino Medori, Mauro Nacinovich

We investigate the nondegeneracy of higher order Levi forms on weakly nondegenerate homogeneous $CR$ manifolds. Improving previous results, we prove that general orbits of real forms in complex flag manifolds have order less or equal $3$ and the compact ones less or equal~$2$. Finally we construct by Lee extensions weakly nondegenerate $CR$ vector bundles wi

Amit Deshpande, Rameshwar Pratap

The subspace approximation problem with outliers, for given $n$ points in $d$ dimensions $x_{1},\ldots, x_{n} \in R^{d}$, an integer $1 \leq k \leq d$, and an outlier parameter $0 \leq α\leq 1$, is to find a $k$-dimensional linear subspace of $R^{d}$ that minimizes the sum of squared distances to its nearest $(1-α)n$ points. More generally, the $\ell_{p}$ su

Deepak K. Gupta, Efstratios Gavves, Arnold W. M. Smeulders

Occlusion is one of the most difficult challenges in object tracking to model. This is because unlike other challenges, where data augmentation can be of help, occlusion is hard to simulate as the occluding object can be anything in any shape. In this paper, we propose a simple solution to simulate the effects of occlusion in the latent space. Specifically,

Yuliang Chen, Changlong Hu, Guobin Zhang, Chongwen Zou

The p-n junctions based on typical semiconductors are the elementary units for the modern electronic devices and chip industry. While the rectification property of those p-n junction is usually fixed once the unit is fabricated. Here, we proposed an adjustable n-WO3/p-GaN heterojunction with controllable electronic properties. For the prepared n-WO3/p-GaN he

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

We consider a multi-armed bandit problem specified by a set of Gaussian or Bernoulli distributions endowed with a unimodal structure. Although this problem has been addressed in the literature (Combes and Proutiere, 2014), the state-of-the-art algorithms for such structure make appear a forced-exploration mechanism. We introduce IMED-UB, the first forced-exp

Giuseppe Bevilacqua, Valerio Biancalana, Marco Consumi, Yordanka Dancheva

The presence of a weak remanence in Ultra-Low-Field (ULF) NMR sample containers is investigated on the basis of proton precession. The high-sensitivity magnetometer used for the NMR detection, enables simultaneously the measurement of the static field produced in the sample proximity by ferromagnetic contaminants. The presence of the latter is studied by hig

H. Garcilazo, A. Valcarce

We use an existing model of the $ΛΛN - ΞNN$ three-body system based in two-body separable interactions to study the $(I,J^P)=(1/2,1/2^+)$ three-body channel. For the $ΛΛ$, $ΞN$, and $ΛΛ- ΞN$ amplitudes we have constructed separable potentials based on the most recent results of the HAL QCD Collaboration. They are characterized by the existence of a resonance

Seshadri Basava, Katrin Mang, Mirjam Walloth, Thomas Wick

In this work, we consider pressurized phase-field fracture problems in nearly and fully incompressible materials. To this end, a mixed form for the solid equations is proposed. To enhance the accuracy of the spatial discretization, a residual-type error estimator is developed. Our algorithmic advancements are substantiated with several numerical tests that a

Mangilal Choudhary

The low temperature plasma technology has been emerged as an alternative environmental friendly technology that has a potential to replace the traditional technologies in various sectors such as textile industries, agriculture, food industries and waste water treatment. The future prospective of the low temperature plasma technology on the life of rural Indi

Shaun Lung, Kai Wang, Khosro Zangeneh Kamali, Jihua Zhang

Birefringent materials or nanostructures that introduce phase differences between two linear polarizations underpin the operation of wave plates for polarization control of light. Here we develop metasurfaces realizing a distinct class of complex-birefringent wave plates, which combine polarization transformation with a judiciously tailored polarization-depe

Vithursan Thangarasa, Thomas Miconi, Graham W. Taylor

Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge. However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process. Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is no

Jyoti Narwariya, Pankaj Malhotra, Vishnu TV, Lovekesh Vig

Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure high operational availability. We note that most complex machinery has a well-documented and readily accessible underlying structure capturing the inter-dependencies between sub-sy

V. V. Bobylev, A. T. Bajkova

We consider 36 candidates for close (within 1 pc) encounters with the Solar system. These stars have been selected in accordance with the results of an analysis of their motion obtained by various authors. For most stars from this list the kinematic characteristics have been taken from the Gaia DR2 catalogue. The parameters of the encounters of these stars w