November 2020 arXiv papers — page 41

Abdulqader Mahmoud, Frederic Vanderveken, Florin Ciubotaru, Christoph Adelmann

Having multi-output logic gates saves much energy because the same structure can be used to feed multiple inputs of next stage gates simultaneously. This paper proposes novel triangle shape fanout of 2 spin wave Majority and XOR gates; the Majority gate is achieved by phase detection, whereas the XOR gate is achieved by threshold detection. The proposed logi

Sina Molavipour, Germán Bassi, Mladen Čičić, Mikael Skoglund

In an intelligent transportation system, the effects and relations of traffic flow at different points in a network are valuable features which can be exploited for control system design and traffic forecasting. In this paper, we define the notion of causality based on the directed information, a well-established data-driven measure, to represent the effecti

Arkady Pikovsky

I study deterministic dynamics of chiral active particles in two dimensions. Particles are considered as discs interacting with elastic repulsive forces. An ensemble of particles, started from random initial conditions, demonstrates chaotic collisions resulting in their normal diffusion. This chaos is transient, as rather abruptly a synchronous collisionless

Lei Yang, Marcello Sega, Jens Harting

Liquid capillary-bridge formation between solid particles has a critical influence on the rheological properties of granular materials and, in particular, on the efficiency of fluidized bed reactors. The available analytical and semi-analytical methods have inherent limitations, and often do not cover important aspects, like the presence of non-axisymmetric

Cyril Pitrou, Alain Coc, Jean-Philippe Uzan, Elisabeth Vangioni

Recent measurements of the D(p,$\gamma)^3$He, nuclear reaction cross-section and of the neutron lifetime, along with the reevaluation of the cosmological baryon abundance from cosmic microwave background (CMB) analysis, call for an update of abundance predictions for light elements produced during the big-bang nucleosynthesis (BBN). While considered as a pil

Xu Liu, Rong Xue, Heqing Wang, Hao Li

Quantum communication is developed owing to the theoretically proven security of quantum mechanics, which may become the main technique in future information security. However, most studies and implementations are limited to two or several parties. Herein, we propose a fully connected quantum communication network without a trusted node for a large number of

Mathias Bersweiler, Evelyn Pratami Sinaga, Inma Peral, Nozomu Adachi

We combine magnetometry and magnetic small-angle neutron scattering to study the influence of the microstructure on the macroscopic magnetic properties of a nanocrystalline Ni bulk sample, which was prepared by straining via high-pressure torsion. As seen by magnetometry, the mechanical deformation leads to a significant increase of the coercivity compared t

Jochen Meyer, Thomas Fröhlich, Kai von Holdt

In this study, the German "Corona-Warn-App" of the German Federal Government and the Robert-Koch-Institute is examined by means of a non-representative online survey with 1482 participants for reasons of use and non-use. The study provides insights into user behavior with the app during the Corona pandemic, highlights the topic of data protection and how the

Abdulqader Mahmoud, Frederic Vanderveken, Christoph Adelmann, Florin Ciubotaru

To bring Spin Wave (SW) based computing paradigm into practice and develop ultra low power Magnonic circuits and computation platforms, one needs basic logic gates that operate and can be cascaded within the SW domain without requiring back and forth conversion between the SW and voltage domains. To achieve this, SW gates have to possess intrinsic fanout cap

Jian Luo, Jianzong Wang, Ning Cheng, Guilin Jiang

Silent speech interfaces (SSI) has been an exciting area of recent interest. In this paper, we present a non-invasive silent speech interface that uses inaudible acoustic signals to capture people's lip movements when they speak. We exploit the speaker and microphone of the smartphone to emit signals and listen to their reflections, respectively. The extract

Tal Horesh, Yakov Karasik

Let $G$ be a semisimple algebraic group. We develop a machinery for manipulation and manufacture of well-rounded families $\left\{ \mathcal{B}_{T}\right\} _{T>0}\subset G$ as they were defined in a work by A. Gorodnik and A. Nevo. The importance of these types of families is that one can asymptotically count lattice points in them and even obtain an error te

Gerald Baier, Antonin Deschemps, Michael Schmitt, Naoto Yokoya

We synthesize both optical RGB and synthetic aperture radar (SAR) remote sensing images from land cover maps and auxiliary raster data using generative adversarial networks (GANs). In remote sensing, many types of data, such as digital elevation models (DEMs) or precipitation maps, are often not reflected in land cover maps but still influence image content

Bipin Kumar, Rajib Chattopadhyay, Manmeet Singh, Niraj Chaudhari

Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spat

V. I. Romanenko, L. P. Yatsenko

We study numerically the evolution of the velocity distribution of atoms under the action of the bichromatic force. The comparison of the time dependencies of the distribution width and the average acceleration of atoms reveal the correlation of these quantities. We show that the estimation of the momentum diffusion coefficient on the basis of the analogy be

Jessica Deuschel, Bettina Finzel, Ines Rieger

Over the past decades the machine and deep learning community has celebrated great achievements in challenging tasks such as image classification. The deep architecture of artificial neural networks together with the plenitude of available data makes it possible to describe highly complex relations. Yet, it is still impossible to fully capture what the deep

Arkady Pikovsky

Synchronization in a population of oscillators with hyperbolic chaotic phases is studied for two models. One is based on the Kuramoto dynamics of the phase oscillators and on the Bernoulli map applied to these phases. This system possesses an Ott-Antonsen invariant manifold, allowing for a derivation of a map for the evolution of the complex order parameter.

Zhao Yuzhi, Po Lai-Man, Wang Xuehui, Liu Kangcheng

There are quite a number of photographs captured under undesirable conditions in the last century. Thus, they are often noisy, regionally incomplete, and grayscale formatted. Conventional approaches mainly focus on one point so that those restoration results are not perceptually sharp or clean enough. To solve these problems, we propose a noise prior learner

Hans Bekaert, Hans Van Winckel, Wim Van Dooren, An Steegen

Young children, students, and adults may have alternative ideas about the motion of the Sun and stars as we observe them in the sky. However, a good understanding of this apparent motion is essential as a starting point to study more advanced astronomical concepts, especially when these include astronomical observations. In this paper, we describe the develo

Liangdong Lu, Wenping Ma, Ruihu Li, Hao Cao

With entanglement-assisted (EA) formalism, arbitrary classical linear codes are allowed to transform into EAQECCs by using pre-shared entanglement between the sender and the receiver. In this paper, based on classical cyclic MDS codes by exploiting pre-shared maximally entangled states, we construct two families of $q$-ary entanglement-assisted quantum MDS c

Aurélien Decelle, Cyril Furtlehner

This review deals with Restricted Boltzmann Machine (RBM) under the light of statistical physics. The RBM is a classical family of Machine learning (ML) models which played a central role in the development of deep learning. Viewing it as a Spin Glass model and exhibiting various links with other models of statistical physics, we gather recent results dealin

Mikhail Gomoyunov

We consider a Cauchy problem for a Hamilton--Jacobi equation with coinvariant derivatives of an order $\alpha \in (0, 1)$. Such problems arise naturally in optimal control problems for dynamical systems which evolution is described by ordinary differential equations with the Caputo fractional derivatives of the order $\alpha$. We propose a notion of a genera

Ioannis D. Apostolopoulos, Mpesiana Tzani

Modern industry requires modern solutions for monitoring the automatic production of goods. Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process. Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been i

Giovanni Soldi, Domenico Gaglione, Nicola Forti, Alessio Di Simone

Maritime surveillance (MS) is crucial for search and rescue operations, fishery monitoring, pollution control, law enforcement, migration monitoring, and national security policies. Since the early days of seafaring, MS has been a critical task for providing security in human coexistence. Several generations of sensors providing detailed maritime information

Emilio T. Maddalena, Paul Scharnhorst, Yuning Jiang, Colin N. Jones

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-parametric kernel regression. By treating each prediction step individually, we dispense with the need of

Shishuo Fu, Zhicong Lin, Yaling Wang

A di-sk tree is a rooted binary tree whose nodes are labeled by $\oplus$ or $\ominus$, and no node has the same label as its right child. The di-sk trees are in natural bijection with separable permutations. We construct a combinatorial bijection on di-sk trees proving the two quintuples $(\LMAX,\LMIN,\DESB,\iar,\comp)$ and $(\LMAX,\LMIN,\DESB,\comp,\iar)$ h

Qiang Sun, Kishan Dholakia, Andrew D Greentree

The optical trapping and manipulation of small particles is an important tool for probing fluid properties at the microscale. In particular, microrheology exploits the manipulation and rotation of micron-scale particles to probe local viscosity, especially where these properties may be perturbed as a function of their local environment, for example in the vi

Sergei M. Kuzenko, Michael Ponds, Emmanouil S. N. Raptakis

We propose generalised $\mathcal{N}=1$ superconformal higher-spin (SCHS) gauge multiplets of depth $t$, $\Upsilon_{\alpha(n)\dot{\alpha}(m)}^{(t)}$, with $n\geq m \geq 1$. At the component level, for $t>2$ they contain generalised conformal higher-spin (CHS) gauge fields with depths $t-1$, $t$ and $t+1$. The supermultiplets with $t=1$ and $t=2$ include both

Jin-Yu Liu, Zhe-Ming You

In this paper, we construct the supersymmetric spinning polynomials. These are orthogonal polynomials that serve as an expansion basis for the residue or discontinuity of four-point scattering amplitudes, respecting four-dimensional super Poincare invariance. The polynomials are constructed by gluing on-shell supersymmetric three-point amplitudes of one mass

Joel Chaskalovic, Franck Assous

In this paper we propose a new generation of probability laws based on the generalized Beta prime distribution to estimate the relative accuracy between two Lagrange finite elements $P_{k_1}$ and $P_{k_2}, (k_1<k_2)$. Since the relative finite element accuracy is usually based on the comparison of the asymptotic speed of convergence when the mesh size $h$ go

A. Reguitti, M. L. Pumo, P. A. Mazzali, A. Pastorello

In this work, we present photometric and spectroscopic data of the low-luminosity Type IIP supernova (SN) 2018hwm. The object shows a faint ($M_r=-15$ mag) and very long ($\sim$130 days) plateau, followed by a 2.7 mag drop in the $r$-band to the radioactive tail. The first spectrum shows a blue continuum with narrow Balmer lines, while during the plateau the

J. T. Schneider, J. Despres, S. J. Thomson, L. Tagliacozzo

Whether long-range interactions allow for a form of causality in non-relativistic quantum models remains an open question with far-reaching implications for the propagation of information and thermalization processes. Here, we study the out-of-equilibrium dynamics of the one-dimensional transverse Ising model with algebraic long-range exchange coupling. Usin

Dario Tamascelli

The chain mapping of structured environments is a most powerful tool for the simulation of open quantum system dynamics. Once the environmental bosonic or fermionic degrees of freedom are unitarily rearranged into a one dimensional structure, the full power of Density Matrix Renormalization Group (DMRG) can be exploited. Beside resulting in efficient and num

Joel Chaskalovic, Franck Assous

We propose a numerical validation of a probabilistic approach applied to estimate the relative accuracy between two Lagrange finite elements $P_k$ and $P_m, (k<m)$. In particular, we show practical cases where finite element $P_{k}$ gives more accurate results than finite element $P_{m}$. This illustrates the theoretical probabilistic framework we recently d

Thor V. A. N. Olesen, Dennis T. T. Nguyen, Rasmus Berg Palm, Sebastian Risi

Planning is a powerful approach to reinforcement learning with several desirable properties. However, it requires a model of the world, which is not readily available in many real-life problems. In this paper, we propose to learn a world model that enables Evolutionary Planning in Latent Space (EPLS). We use a Variational Auto Encoder (VAE) to learn a compre

Fred Rowley

This paper records some apparently new results for the partition of integer intervals [1, n] into weakly sum-free subsets. These were produced using a method closely related to that used by Schur in 1917. New lower bounds can be produced in this way for partitions of unlimited size. The asymptotic growth rate of the lower bounds, as the number of subsets inc

Gennady N. Chuev, Marina V. Fedotova, Marat Valiev

Site density functional theory (SDFT) provides a rigorous framework for statistical mechanics analysis of inhomogeneous molecular liquids. The key defining feature of these systems is the presence of two very distinct interactions scales (intra- and inter-molecular), and as such proper description of both effects is critical to the accuracy of the calculatio

Mark C. H. de Jong, Can Onur Avci, Aleš Hrabec, Pietro Gambardella

We show that the coupling between two ferromagnetic layers separated by a nonmagnetic spacer can be used to control the depinning of domain walls and induce unidirectional domain wall propagation. We investigated CoFeB/Ti/CoFeB trilayers where the easy axis of the magnetization of the top CoFeB layer is out-of-plane and that of the bottom layer is in-plane.

Vlado Valkovic, Jasmina Obhodas

The phenomenon of life is discussed within a framework of its origin as defined by four hypotheses. The 1. hypothesis says: Life, as we know, is (H-C-N-O) based and relies on the number of bulk (Na-Mg-P-S-Cl-K-Ca) and trace elements (Cr-Mn-Fe-Co-Ni-Cu-Zn-Se-Mo-I-W, and possibly Li-B-F-Si-V-As). It originated when the element abundance curve of the living mat

Viktor Daropoulos, Matthias Augustin, Joachim Weickert

Digital image inpainting refers to techniques used to reconstruct a damaged or incomplete image by exploiting available image information. The main goal of this work is to perform the image inpainting process from a set of sparsely distributed image samples with the Smoothed Particle Hydrodynamics (SPH) technique. As, in its naive formulation, the SPH techni

Daphné Lemasquerier, Benjamin Favier, Michael Le Bars

This paper is associated with a poster winner of a 2019 American Physical Society Division of Fluid Dynamics (DFD) Milton van Dyke Award for work presented at the DFD Gallery of Fluid Motion. The original poster is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2019.GFM.P0015.

Yaoman Li, Irwin King

Graphs play an important role in many applications. Recently, Graph Neural Networks (GNNs) have achieved promising results in graph analysis tasks. Some state-of-the-art GNN models have been proposed, e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), etc. Despite these successes, most of the GNNs only have shallow structure. This ca

Fadi Sun, Jinwu Ye

A strongly spin-orbital coupled systems could be in a magnetic ordered phase at zero field. However, a Zeeman field could drive it into different quantum or topological phases. In this work, starting from general symmetry principle, we construct various effective actions to study all these quantum phases and phase transitions which take different forms depen

Ekraam Sabir, Ayush Jaiswal, Wael AbdAlmageed, Prem Natarajan

Fake news often involves semantic manipulations across modalities such as image, text, location etc and requires the development of multimodal semantic forensics for its detection. Recent research has centered the problem around images, calling it image repurposing -- where a digitally unmanipulated image is semantically misrepresented by means of its accomp

Jorge J. Betancor, Lourdes Rodríguez-Mesa

In this paper we study higher order Riesz transforms associated with the inverse Gaussian measure given by $\pi ^{n/2}e^{|x|^2}dx$ on $\mathbb{R}^n$. We establish $L^p(\mathbb{R}^n,e^{|x|^2}dx)$-boundedness properties and obtain representations as principal values singular integrals for the higher order Riesz transforms. New characterizations of the Banach s

Kai Hou Yip, Quentin Changeat, Nikolaos Nikolaou, Mario Morvan

Deep learning algorithms are growing in popularity in the field of exoplanetary science due to their ability to model highly non-linear relations and solve interesting problems in a data-driven manner. Several works have attempted to perform fast retrievals of atmospheric parameters with the use of machine learning algorithms like deep neural networks (DNNs)

Heyman J., Lester D., Le Borgne T

Steady laminar flows through porous media spontaneously generate Lagrangian chaos at pore scale, with qualitative implications for a range of transport, reactive and biological processes. The characterization and understanding of mixing dynamics in these opaque environments is an outstanding challenge. We address this issue by developing a novel technique ba

Tuukka Korhonen

We show that a graph with $n$ vertices and vertex cover of size $k$ has at most $4^k + n$ potential maximal cliques. We also show that for each positive integer $k$, there exists a graph with vertex cover of size $k$, $O(k^2)$ vertices, and $\Omega(4^k)$ potential maximal cliques. Our results extend the results of Fomin, Liedloff, Montealegre, and Todinca [A

Patrick Chang, Etienne Pienaar, Tim Gebbie

Time and the choice of measurement time scales is fundamental to how we choose to represent information and data in finance. This choice implies both the units and the aggregation scales for the resulting statistical measurables used to describe a financial system. It also defines how we measure the relationship between different traded instruments. As we mo

Adeline Kabacinski, Kosta Oubrerie, Jean-Philippe Goddet, Julien Gautier

We report a straightforward method to control main spatio-temporal couplings in a CPA laser chain system using a specially designed chromatic doublet in a divergent beam configuration. The centering of the doublet allows for the control of the spatial chirp of the CPA laser chain, while its longitudinal position in the divergent beam enables the control of t

Daphné Lemasquerier, Giulio Facchini, Benjamin Favier, Michael Le Bars

Jupiter's dynamics shapes its cloud patterns but remains largely unknown below this natural observational barrier. Unraveling the underlying three-dimensional flows is thus a primary goal for NASA's ongoing Juno mission that was launched in 2011. Here, we address the dynamics of large Jovian vortices using laboratory experiments complemented by theoretical a

He Zhu, Dianbo Liu

The concept of disinformation is to use fake messages to confuse people in order to protect the real information. This strategy can be adapted into data science to protect valuable private and sensitive data. Huge amount of private data are being generated from personal devices such as smart phone and wearable in recent years. Being able to utilize these per

László Csató

We analyse a mathematical model of seeding for sports contests with round-robin qualifying tournaments. The standard seeding system based on coefficients measuring the historical performance of the teams is shown to be unfair as it might potentially punish a team for its better results by having to face stronger opponents on average in the next stage. Major

Dario Benedetti, Razvan Gurau, Sabine Harribey

We consider a multi-scalar field theory with either short-range or long-range free action and with quartic interactions that are invariant under $O(N_1)\times O(N_2) \times O(N_3)$ transformations, of which the scalar fields form a tri-fundamental representation. We study the renormalization group fixed points at two loops at finite $N$ and in various large-

Alexander Khodjamirian, Blaženka Melić, Yu-Ming Wang, Yan-Bing Wei

We revisit the calculation of the strong couplings $D^*D\pi$ and $B^*B\pi$ from the QCD light-cone sum rules using the pion light-cone distribution amplitudes. The accuracy of the correlation function, calculated from the operator product expansion near the light-cone, is upgraded by taking into account the gluon radiative corrections to the twist-3 terms. T

Rachel Heyard, Hanna Hottenrott

This study investigates the impact of competitive project-funding on researchers' publication outputs. Using detailed information on applicants at the Swiss National Science Foundation (SNSF) and their proposals' evaluation, we employ a case-control design that accounts for individual heterogeneity of researchers and selection into treatment (e.g. funding).

Sang Youl Lee, Vassily Olegovich Manturov, Igor Mikhailovich Nikonov

The groups $G_n^k$ were defined by V. O. Manturov in order to describe dynamical systems in configuration systems. In the paper we consider two applications of this theory: we define a biquandle structure on the groups $G_n^k$, and construct a homomorphism from the surface singular braid monoid to the group $G_n^2$.

Toru Hirata, Youichi Yamakawa, Seiichiro Onari, Hiroshi Kontani

The transition metal dichalcogenide 1T-TaS2 attract growing attention because of the formation of rich density-wave (DW) and superconducting transitions. However, the origin of the incommensurate DW state at the highest temperature (~ 550 K), which is "the parent state" of the rich physical phenomena, is still uncovered. Here, we present a natural explanatio

Junyi An, Fengshan Liu, Jian Zhao, Furao Shen

As a popular machine learning method, neural networks can be used to solve many complex tasks. Their strong generalization ability comes from the representation ability of the basic neuron model. The most popular neuron is the MP neuron, which uses a linear transformation and a non-linear activation function to process the input successively. Inspired by the

Zhuo Xu, Wenhao Yu, Alexander Herzog, Wenlong Lu

General contact-rich manipulation problems are long-standing challenges in robotics due to the difficulty of understanding complicated contact physics. Deep reinforcement learning (RL) has shown great potential in solving robot manipulation tasks. However, existing RL policies have limited adaptability to environments with diverse dynamics properties, which

Rajeswari Roy Chowdhury, Samik DuttaGupta, Chandan Patra, Oleg A. Tretiakov

Two-dimensional (2D) van der Waals (vdW) magnetic materials have attracted a lot of attention owing to the stabilization of long-range magnetic order down to atomic dimensions, and the prospect of novel spintronic devices with unique functionalities. The clarification of the magnetoresistive properties and its correlation to the underlying magnetic configura

Eklavya Sharma

We give an $\alpha(1+\epsilon)$-approximation algorithm for solving covering LPs, assuming the presence of a $(1/\alpha)$-approximation algorithm for a certain optimization problem. Our algorithm is based on a simple modification of the Plotkin-Shmoys-Tardos algorithm (MOR 1995). We then apply our algorithm to $\alpha(1+\epsilon)$-approximately solve the con

Peng Li, Baijiang Lv, Yuan Fang, Wei Guo

Using angle-resolved photoemission spectroscopy (ARPES) and low-energy electron diffraction (LEED), together with density-functional theory (DFT) calculation, we report the formation of charge density wave (CDW) and its interplay with the Kondo effect and topological states in CeSbTe. The observed Fermi surface (FS) exhibits parallel segments that can be wel

Miao Yang, Akitanoshou Wong, Hongbin Zhu, Haifeng Wang

Federated learning (FL) is a promising technique that enables a large amount of edge computing devices to collaboratively train a global learning model. Due to privacy concerns, the raw data on devices could not be available for centralized server. Constrained by the spectrum limitation and computation capacity, only a subset of devices can be engaged to tra

Dennis Heinig, Tim de Laat, Timo Siebenand

We study the group $C^*$-algebras $C^*_{L^{p+}}(G)$ - constructed from $L^p$-integrability properties of matrix coefficients of unitary representations - of locally compact groups $G$ acting on (semi-)homogeneous trees of sufficiently large degree. These group $C^*$-algebras lie between the universal and the reduced group $C^*$-algebra. By directly investiga

Roberto J. Cier, Sergio Rojas, Victor M. Calo

In this paper, we describe a stable finite element formulation for advection-diffusion-reaction problems that allows for robust automatic adaptive strategies to be easily implemented. We consider locally vanishing, heterogeneous, and anisotropic diffusivities, as well as advection-dominated diffusion problems. The general stabilized finite element framework

Ramchandra Joshi, Raviraj Joshi

Natural language processing (NLP) techniques have become mainstream in the recent decade. Most of these advances are attributed to the processing of a single language. More recently, with the extensive growth of social media platforms focus has shifted to code-mixed text. The code-mixed text comprises text written in more than one language. People naturally

Yisheng Song, Liqun Qi

The most general scalar potential of two real scalar fields and a Higgs boson is a quartic homogeneous polynomial about 3 variables, which defines a 4th order 3 dimensional symmetric tensor. Hence, the boundedness from below of such a scalar potential involves the positive (semi-)definiteness of the corresponding tensor. So, we mainly discuss analytical expr

Zehua Zhang, David Crandall

We present a novel technique for self-supervised video representation learning by: (a) decoupling the learning objective into two contrastive subtasks respectively emphasizing spatial and temporal features, and (b) performing it hierarchically to encourage multi-scale understanding. Motivated by their effectiveness in supervised learning, we first introduce

Zheng Dang, Fei Wang, Mathieu Salzmann

While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2.5D sensor in a scene. The challenges of this scenario include the fact that most measurements are outliers depicting the object's surrounding context, and the mismatch between the

Pierre Barrat-Charlaix, Anna Paola Muntoni, Kai Shimagaki, Martin Weigt

Boltzmann machines (BM) are widely used as generative models. For example, pairwise Potts models (PM), which are instances of the BM class, provide accurate statistical models of families of evolutionarily related protein sequences. Their parameters are the local fields, which describe site-specific patterns of amino-acid conservation, and the two-site coupl

Rajesh Dachiraju

This paper addresses the problem of approximating a function of bounded variation from its scattered data. Radial basis function(RBF) interpolation methods are known to approximate only functions in their native spaces, and to date, there has been no known proof that they can approximate functions outside the native space associated with the particular RBF b

Lars Lien Ankile, Morgan Feet Heggland, Kjartan Krange

The purpose of this paper is to design a solution to the problem of facial recognition by use of convolutional neural networks, with the intention of applying the solution in a camera-based home-entry access control system. More specifically, the paper focuses on solving the supervised classification problem of taking images of people as input and classifyin

Ziang Long, Penghang Yin, Jack Xin

Quantized or low-bit neural networks are attractive due to their inference efficiency. However, training deep neural networks with quantized activations involves minimizing a discontinuous and piecewise constant loss function. Such a loss function has zero gradients almost everywhere (a.e.), which makes the conventional gradient-based algorithms inapplicable

Tong Shen, Wenlong Cai, Yan Lin, Shuo Zhang

In this paper, multiple reconfigurable intelligent surfaces (RIS) aided secure precise wireless transmission (SPWT) schemes are proposed in the three-dimensional (3D) wireless communication scenario. Unavailable direct path channels from transmitter to receivers are considered when the direct paths are obstructed by obstacles. Then, multiple RISs are utilize

Yuto Minami, Eiichiro Komatsu

We search for evidence of parity-violating physics in the Planck 2018 polarization data, and report on a new measurement of the cosmic birefringence angle, $\beta$. The previous measurements are limited by the systematic uncertainty in the absolute polarization angles of the Planck detectors. We mitigate this systematic uncertainty completely by simultaneous

Adrian B. Culver, Natan Andrei

We extend the general formalism discussed in the previous paper [A. B. Culver and N. Andrei, Phys. Rev. B 103, 195106 (2021)] to two models with charge fluctuations: the interacting resonant level model and the Anderson impurity model. In the interacting resonant level model, we find the exact time-evolving wavefunction and calculate the steady state impurit

Mutsuo Oka

We consider the exponent of \L ojasiewicz inequality $\|\partial\,f(\mathbf z)\| \ge c |f(\mathbf z|^\theta$ for two classes of analytic functions and we will give an explicit estimation for $\theta$. First we consider certain non-degenerate functions which is not convenient. In \S 3.4, we give an example of a polynomial for which $\theta_0(f)$ is not consta

Udayan Ganguly, Sandip Lashkare, Swaroop Ganguly

Modern semiconductors innovation has a strong relation to scale and skill. While India has a significant demand for semiconductors, it has a daunting challenge to create a semiconductor ecosystem. Yet, India has quietly come a long way. Starting with Centers of Excellence in Nanoelectronics (CENs) initiated in 2006 and broad science and technology funding, I

Zhao-Yu Li

By combining the {\it LAMOST} and {\it Gaia} data, we investigate the vertical phase mixing across the Galactic disk. Our results confirm the existence of the phase space snail shells (or phase spirals) from 6 to 12 kpc. We find that grouping stars by the guiding radius ($R_{g}$), instead of the present radius ($R$) further enhances the snail shell signal in

Yuxin Xie, Siming Liu

Via evaluation of the Lyapunov exponent, we report the discovery of three prominent sets of phase space regimes of quasi-periodic orbits of charged particles trapped in a dipole magnetic field. Besides the low energy regime that has been studied extensively and covers more than 10% in each dimension of the phase space of trapped orbits, there are two sets of

Morgane Austern, Vasilis Syrgkanis

One of the most commonly used methods for forming confidence intervals for statistical inference is the empirical bootstrap, which is especially expedient when the limiting distribution of the estimator is unknown. However, despite its ubiquitous role, its theoretical properties are still not well understood for non-asymptotically normal estimators. In this

Shridhar Velhal, Suresh Sundaram

This paper addresses the problem of restricted airspace protection from invaders using the cooperative multi-UAV system. The objective is to detect and capture the invaders cooperatively by a team of homogeneous UAVs (called evaders)before invaders enter the restricted airspace. The problem of restricted airspace protection problem is formulated as a Multi-U

Takuto Kanamori, Hiromu Miyazaki, Kenji Kise

In this paper, we propose a high-performance RISC-V soft processor with an efficient fetch unit supporting the compressed instructions targeting on FPGA. The compressed instruction extension in RISC-V can reduce the program size by about 25%. But it needs a complicated logic for the instruction fetch unit and has a significant impact on performance. We propo

Jinlu Liu, Liang Song, Yongqiang Qin

Few-shot segmentation is a challenging task that aims to segment objects of new classes given scarce support images. In the inductive setting, existing prototype-based methods focus on extracting prototypes from the support images; however, they fail to utilize semantic information of the query images. In this paper, we propose Bi-level Optimization (BiOpt),

Luman Qu, Davis Unruh, Gergely T. Zimanyi

We simulated electron transport across a binary nanocrystal solid (BNS) of PbSe NCs with diameters of 6.5nm and 5.1nm. We used our Hierarchical Nanoparticle Transport Simulator HINTS to model the transport in these BNSs. The mobility exhibits a minimum at a Large-NC-fraction f_LNC=0.25. The mobility minimum is deep at T=80K and partially smoothed at T=300K.

Xiaoming Wang, Hao Wu

We study the Navier-Stokes-Darcy-Boussinesq system that models the thermal convection of a fluid overlying a saturated porous medium in a general decomposed domain. In both two and three spatial dimensions, we first prove the existence of global weak solutions to the initial boundary value problem subject to the Lions and Beavers-Joseph-Saffman-Jones interfa

Han Chen, Yifan Jiang, Murray Loew, Hanseok Ko

Automatic segmentation of infection areas in computed tomography (CT) images has proven to be an effective diagnosis approach for COVID-19. However, due to the limited number of pixel-level annotated medical images, accurate segmentation remains a major challenge. In this paper, we propose an unsupervised domain adaptation based segmentation network to impro

Bin Li, Bo Lu, Yiang Lu, Qi Dou

Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless, the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we her

Changhyun Ahn

In the Grassmannian-like coset model, $\frac{SU(N+M)_k}{SU(N)_k \times U(1)_{k N M (N+M)}}$, Creutzig and Hikida have found the charged spin-$2,3$ currents and the neutral spin-$2,3$ currents previously. In this paper, as an extension of Gaberdiel-Gopakumar conjecture found ten years ago, we calculate the operator product expansion (OPE) between the charged

Jia Jia, Guolei Zhong

Let $X$ be a smooth Fano fourfold admitting a conic bundle structure. We show that $X$ is toric if and only if $X$ admits an amplified endomorphism; in this case, $X$ is a rational variety.

Jingqin Gao, Kaan Ozbay, Michael Marsico

Double parking that often negatively affects traffic operations and safety is not a new phenomenon on urban streets. This study proposes a novel data-driven integrated framework for estimating the actual frequency of double parking so that both microscopic and macroscopic models can be utilized to quantify area-wide impacts in the presence of double parking.

Chad T. Kishimoto, Heather Hodlin, Olexiy Dvornikov

We solved the Quantum Kinetic Equations (QKEs) for an active-sterile neutrino system in the early universe. While on the surface this may seem to be an overly simplistic system, other linear two-state systems can be mapped onto the active-sterile system. In the early universe, we find that solutions to the QKEs are well described by an adiabatic approximatio

Rahul Singh, Qinsheng Zhang, Yongxin Chen

In this paper, we propose an algorithm for estimating the parameters of a time-homogeneous hidden Markov model from aggregate observations. This problem arises when only the population level counts of the number of individuals at each time step are available, from which one seeks to learn the individual hidden Markov model. Our algorithm is built upon expect

Taylor W. Killian, Haoran Zhang, Jayakumar Subramanian, Mehdi Fatemi

Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing hypothetical treatment strategies for septic patients, with a particular focus on offline learning with observational data. In practice, successful RL relies on informative latent states derived from sequential observations to dev

Rostislav Akhmechet, Vyacheslav Krushkal, Michael Willis

Given a link in the thickened annulus, its annular Khovanov homology carries an action of the Lie algebra $\mathfrak{sl}_2$, which is natural with respect to annular link cobordisms. We consider the problem of lifting this action to the stable homotopy refinement of the annular homology. As part of this program, the actions of the standard generators of $\ma

Xiaoxing Wang, Xiangxiang Chu, Yuda Fan, Zhexi Zhang

Albeit being a prevalent architecture searching approach, differentiable architecture search (DARTS) is largely hindered by its substantial memory cost since the entire supernet resides in the memory. This is where the single-path DARTS comes in, which only chooses a single-path submodel at each step. While being memory-friendly, it also comes with low compu

Gyeongsik Moon, Hongsuk Choi, Kyoung Mu Lee

Most 3D human mesh regressors are fully supervised with 3D pseudo-GT human model parameters and weakly supervised with GT 2D/3D joint coordinates as the 3D pseudo-GTs bring great performance gain. The 3D pseudo-GTs are obtained by annotators, systems that iteratively fit 3D human model parameters to GT 2D/3D joint coordinates of training sets in the pre-proc

Maopeng Ran, Juncheng Li, Lihua Xie

This paper investigates the reinforcement learning (RL) based disturbance rejection control for uncertain nonlinear systems having non-simple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the total uncertainty, which represents the perturbation to the nominal system dynamics. Based on the output of the ob

Sai Li, Wang Kang, Xichao Zhang, Tianxiao Nie

Improvements in computing performance have significantly slowed down over the past few years owing to the intrinsic limitations of computing hardware. However, the demand for data computing has increased exponentially. To solve this problem, tremendous attention has been focused on the continuous scaling of Moore's Law as well as the advanced non-von Neumann

Leonardo Colombo, Dimos V. Dimarogonas

We study the reduction of degrees of freedom for the equations that determine necessary optimality conditions for extrema in an optimal control problem for a multiagent system by exploiting the physical symmetries of agents, where the kinematics of each agent is given by a left-invariant control system. Reduced optimality conditions are obtained using techni