February 2020 arXiv papers

Yu-Wei Fan, Kuan-Wen Lai

Are Fourier-Mukai equivalent cubic fourfolds birationally equivalent? We obtain an affirmative answer to this question for very general cubic fourfolds of discriminant 20, where we produce birational maps via the Cremona transformation defined by the Veronese surface. By studying how these maps act on the cubics known to be rational, we surprisingly found ne

Boris Bukh, Oleksandr Rudenko

Let $a_1,\dotsc,a_n$ be a permutation of $[n]$. Two disjoint order-isomorphic subsequences are called \emph{twins}. We show that every permutation of $[n]$ contains twins of length $Ω(n^{3/5})$ improving the trivial bound of $Ω(n^{1/2})$. We also show that a random permutation contains twins of length $Ω(n^{2/3})$, which is sharp.

Zoe Gonzalez Izquierdo, Tameem Albash, Itay Hen

Motivated by recent experiments in which specific thermal properties of complex many-body systems were successfully reproduced on a commercially available quantum annealer, we examine the extent to which quantum annealing hardware can reliably sample from the thermal state in a specific basis associated with a target quantum Hamiltonian. We address this ques

Adam N. Elmachtoub, Jason Cheuk Nam Liang, Ryan McNellis

We consider the use of decision trees for decision-making problems under the predict-then-optimize framework. That is, we would like to first use a decision tree to predict unknown input parameters of an optimization problem, and then make decisions by solving the optimization problem using the predicted parameters. A natural loss function in this framework

Andrey A Popov, Adrian Sandu, Elias D. Nino-Ruiz, Geir Evensen

The Ensemble Kalman Filters (EnKF) employ a Monte-Carlo approach to represent covariance information, and are affected by sampling errors in operational settings where the number of model realizations is much smaller than the model state dimension. To alleviate the effects of these errors EnKF relies on model-specific heuristics such as covariance localizati

Yalin Liao, Aleksandar Vakanski, Min Xian, David Paul

Recent advances in data analytics and computer-aided diagnostics stimulate the vision of patient-centric precision healthcare, where treatment plans are customized based on the health records and needs of every patient. In physical rehabilitation, the progress in machine learning and the advent of affordable and reliable motion capture sensors have been cond

Aikaterini Aretaki, Efthymios N. Karatzas

This work investigates an elliptic optimal control problem defined on uncertain domains and discretized by a fictitious domain finite element method and cut elements. Key ingredients of the study are to manage cases considering the usually computationally "forbidden" combination of poorly conditioned equation system matrices due to challenging geomet

A. Ricottone, M. S. Rudner, W. A. Coish

We extend non-Hermitian topological quantum walks on a Su-Schrieffer-Heeger (SSH) lattice [M. S. Rudner and L. Levitov, Phys. Rev. Lett. 102, 065703 (2009)] to the case of non-Markovian evolution. This non-Markovian model is established by coupling each unit cell in the SSH lattice to a reservoir formed by a quasi-continuum of levels. We find a topological t

Mirjam Weilenmann, Roger Colbeck

Self-testing usually refers to the task of taking a given set of observed correlations that are assumed to arise via a process that is accurately described by quantum theory, and trying to infer the quantum state and measurements. In other words it is concerned with the question of whether we can tell what quantum black-box devices are doing by looking only

Iztok Fister, Iztok Fister

Association Rule Mining is a machine learning method for discovering the interesting relations between the attributes in a huge transaction database. Typically, algorithms for Association Rule Mining generate a huge number of association rules, from which it is hard to extract structured knowledge and present this automatically in a form that would be suitab

Ketan Mittal, Som Dutta, Paul Fischer

We develop a multirate timestepper for semi-implicit solutions of the unsteady incompressible Navier-Stokes equations (INSE) based on a recently-developed multidomain spectral element method (SEM). For {\em incompressible} flows, multirate timestepping (MTS) is particularly challenging because of the tight coupling implied by the incompressibility constraint

Michal Zamboj

The Hopf fibration mapping circles on a 3-sphere to points on a 2-sphere is well known to topologists. While the 2-sphere is embedded in 3-space, four-dimensional Euclidean space is needed to visualize the 3-sphere. Visualizing objects in 4-space using computer graphics based on their analytical representations has become popular in recent decades. For purel

Dongchan Lee, Konstantin Turitsyn, Jean-Jacques Slotine

We present an algorithm for robust model predictive control with consideration of uncertainty and safety constraints. Our framework considers a nonlinear dynamical system subject to disturbances from an unknown but bounded uncertainty set. By viewing the system as a fixed point of an operator acting over trajectories, we propose a convex condition on control

Sangdon Park, Osbert Bastani, James Weimer, Insup Lee

Reliable uncertainty estimates are an important tool for helping autonomous agents or human decision makers understand and leverage predictive models. However, existing approaches to estimating uncertainty largely ignore the possibility of covariate shift--i.e., where the real-world data distribution may differ from the training distribution. As a consequenc

Hongzhuo Liang, Chuangchuang Zhou, Shuang Li, Xiaojian Ma

Robust and accurate estimation of liquid height lies as an essential part of pouring tasks for service robots. However, vision-based methods often fail in occluded conditions while audio-based methods cannot work well in a noisy environment. We instead propose a multimodal pouring network (MP-Net) that is able to robustly predict liquid height by conditionin

Raghav G. Jha

We consider one-plaquette unitary matrix model at finite $N$ using exact expression of the partition function for both SU($N$) and U($N$) groups.

Alon Z. Shapira, Hannes Uecker, Arik Yochelis

Stationary periodic patterns are widespread in natural sciences, ranging from nano-scale electrochemical and amphiphilic systems to mesoscale fluid, chemical and biological media and to macro-scale vegetation and cloud patterns. Their formation is usually due to a primary symmetry breaking of a uniform state to stripes, often followed by secondary instabilit

Eliya Nachmani, Yossi Adi, Lior Wolf

We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the

Martin Bridgeman, Jeffrey Brock, Kenneth Bromberg

In this paper, we use the Weil-Petersson gradient flow for renormalized volume to study the space $CC(N;S,X)$ of convex cocompact hyperbolic structures on the relatively acylindrical 3-manifold $(N;S)$. Among the cases of interest are the deformation space of an acylindrical manifold and the Bers slice of quasi-Fuchsian space associated to a fixed surface. T

Aaron Lou, Isay Katsman, Qingxuan Jiang, Serge Belongie

Recent advances in deep representation learning on Riemannian manifolds extend classical deep learning operations to better capture the geometry of the manifold. One possible extension is the Fréchet mean, the generalization of the Euclidean mean; however, it has been difficult to apply because it lacks a closed form with an easily computable derivative. In

Nian Yao, Zhiqiu Li, Zhichao Ling, Junfeng Lin

In this paper, we study the asymptotic behaviors of implied volatility of an affine jump-diffusion model. Let log stock price under risk-neutral measure follow an affine jump-diffusion model, we show that an explicit form of moment generating function for log stock price can be obtained by solving a set of ordinary differential equations. A large-time large

Xinyang Zhou, Yue Chen, Zhiyuan Liu, Changhong Zhao

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multi

Biagio Ricceri

Here is one of the results obtained in this paper: Let $Ω\subset {\bf R}^n$ be a smooth bounded domain, let $q>1$, with $q<{{n+2}\over {n-2}}$ if $n\geq 3$ and let $λ_1$ be the first eigenvalue of the problem $$\cases{-Δu=λu & in $Ω$ \cr & \cr u=0 & on $\partialΩ$\ .\cr}$$ Then, for every $λ>λ_1$ and for every convex set $S\subseteq L^{\infty}(Ω)$ dense in $

Luis C. Lamb, Artur Garcez, Marco Gori, Marcelo Prates

Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for

Rashik Shadman, M. G. Sarwar Murshed, Edward Verenich, Alvaro Velasquez

The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets. We describe a transfer learning use case for a domain with a data-starved regime, having fewer than 100 labeled target samples. We evaluate the effectiveness of c

Fabio Botelho

The first part of this article develops a variational formulation for relativistic mechanics. The results are established through standard tools of variational analysis and differential geometry. The novelty here is that the main motion manifold has a $n+1$ dimensional range. It is worth emphasizing in a first approximation we have neglected the self-interac

Dejan Govc, Ran Levi, Jason P. Smith

Complete digraphs are referred to in the combinatorics literature as tournaments. We consider a family of semi-simplicial complexes, that we refer to as &#34;tournaplexes&#34;, whose simplices are tournaments. In particular, given a digraph $\mathcal{G}$, we associate with it a &#34;flag tournaplex&#34; which is a tournaplex containing the directed flag comp

Pieter Verding, Wim Deferme, Werner Steffen

We have developed a combination of light scattering techniques to study and characterize droplets of a ultrasonic spray printer or coater in flight. For this economically relevant printer there is so far no reliable technique to systematically adjust the experimental parameters. We have combined photon correlation spectroscopy and turbidimetry to determine s

P. Y. Wen, O. V. Ivakhnenko, M. A. Nakonechnyi, B. Suri

We investigate the Landau-Zener-Stuckelberg-Majorana interferometry of a superconducting qubit in a semi-infinite transmission line terminated by a mirror. The transmon-type qubit is at the node of the resonant electromagnetic (EM) field, hiding from the EM field. &#34;Mirror, mirror&#34; briefly describes this system, because the qubit acts as another mirro

Tao Wang, Zheng Gong, Alexey Arefiev

A laser-driven azimuthal plasma magnetic field is known to facilitate electron energy gain from the irradiating laser pulse. The enhancement is due to changes in the orientation between the laser electric field and electron velocity caused by magnetic field deflections. Transverse electron confinement is critical for realizing this concept experimentally. We

Mark Kamsma

We introduce the framework of AECats (abstract elementary categories), generalising both the category of models of some first-order theory and the category of subsets of models. Any AEC and any compact abstract theory (&#34;cat&#34;, as introduced by Ben-Yaacov) forms an AECat. In particular, we find applications in positive logic and continuous logic: the c

Mohammadreza F. Imani, David R. Smith, Philipp del Hougne

Achieving the very special condition of perfect absorption (PA) in a complex scattering enclosure promises to enable a wealth of applications in secure communication, precision sensing, wireless power transfer, analog signal processing and random lasing. Consequently, a lot of recent research effort was dedicated to proposing wave-front shaping protocols to

Ziping Liu, Joyce C. Mhlanga, Richard Laforest, Paul-Robert Derenoncourt

Tumor segmentation in oncological PET is challenging, a major reason being the partial-volume effects that arise due to low system resolution and finite voxel size. The latter results in tissue-fraction effects, i.e. voxels contain a mixture of tissue classes. Conventional segmentation methods are typically designed to assign each voxel in the image as belon

Mohsen Sadatsafavi, Paramita Saha-Chaudhuri, John Petkau

The performance of risk prediction models is often characterized in terms of discrimination and calibration. The Receiver Operating Characteristic (ROC) curve is widely used for evaluating model discrimination. When evaluating the performance of a risk prediction model in a new sample, the shape of the ROC curve is affected by both case-mix and the postulate

Jiaju Zhang, Pasquale Calabrese, Marcello Dalmonte, M. A. Rajabpour

We carry out a comprehensive comparison between the exact modular Hamiltonian and the lattice version of the Bisognano-Wichmann (BW) one in one-dimensional critical quantum spin chains. As a warm-up, we first illustrate how the trace distance provides a more informative mean of comparison between reduced density matrices when compared to any other Schatten $

J. Maurice Rojas, Yuyu Zhu

We reveal a complexity chasm, separating the trinomial and tetranomial cases, for solving univariate sparse polynomial equations over certain local fields. First, for any fixed field $K\in\{\mathbb{Q}_2,\mathbb{Q}_3,\mathbb{Q}_5,\ldots\}$, we prove that any polynomial $f\in\mathbb{Z}[x]$ with exactly $3$ monomial terms, degree $d$, and all coefficients havin

Perry T. Mahon, J. E. Sipe

We extend a field theoretic approach for the investigation of the electronic charge-current density response of crystalline systems to arbitrary applied electromagnetic fields. The approach leads to the introduction of microscopic polarization and magnetization fields, as well as free charge and current densities, the dynamics of which are described by a lat

Lawrence Reeves, Peter Scott, Gadde Swarup

Analogues of JSJ decompositions were developed for Poincaré duality pairs in [19]. These decompositions depend only on the group. Our focus will be on describing the edge splittings of these decompositions more precisely. We use our results to compare these decompositions with two other closely related decompositions.

Andrey V. Panov

The nonlinear optical properties of the high index nanoparticles are boosted at the anapole state. Researchers intensively study this phenomenon as promising for various applications. In this work, the nonlinear optical Kerr effect of disordered and square lattice metasurfaces of GaP nanodisks is investigated as a function of the disk size in the vicinity of

Ibrahim Jawarneh, Nesreen Alsharman

In this paper, different categories of the arch fingerprint are set up in a general dynamical system model using ordinary differential equations. We study its global dynamics and analyze the existence and stability of equilibria. Numerical simulations using Maple show the matching between real images of categories of arch fingerprint and phase portraits of t

Chaoyue Liu, Libin Zhu, Mikhail Belkin

The success of deep learning is due, to a large extent, to the remarkable effectiveness of gradient-based optimization methods applied to large neural networks. The purpose of this work is to propose a modern view and a general mathematical framework for loss landscapes and efficient optimization in over-parameterized machine learning models and systems of n

Boris Muzellec, Kanji Sato, Mathurin Massias, Taiji Suzuki

Gradient Langevin dynamics (GLD) and stochastic GLD (SGLD) have attracted considerable attention lately, as a way to provide convergence guarantees in a non-convex setting. However, the known rates grow exponentially with the dimension of the space. In this work, we provide a convergence analysis of GLD and SGLD when the optimization space is an infinite dim

Gautam Krishna, Co Tran, Mason Carnahan, Yan Han

In this paper we demonstrate predicting electroencephalograpgy (EEG) features from acoustic features using recurrent neural network (RNN) based regression model and generative adversarial network (GAN). We predict various types of EEG features from acoustic features. We compare our results with the previously studied problem on speech synthesis using EEG and

Massimo Bartoletti, Maurizio Murgia, Roberto Zunino

BitML is a process calculus to express smart contracts that can be run on Bitcoin. One of its current limitations is that, once a contract has been stipulated, the participants cannot renegotiate its terms: this prevents expressing common financial contracts, where funds have to be added by participants at run-time. In this paper, we extend BitML with a new

Sashank Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett

Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated sett

Paul Irofti, Andra Băltoiu

We investigate the possibilities of employing dictionary learning to address the requirements of most anomaly detection applications, such as absence of supervision, online formulations, low false positive rates. We present new results of our recent semi-supervised online algorithm, TODDLeR, on a anti-money laundering application. We also introduce a novel u

Ki-Seok Kim

We derive an effective dual holographic Einstein-Maxwell theory, applying renormalization group transformations to interacting Dirac fermions in a recursive way. In particular, we show how both background metric tensor and U(1) gauge fields become dynamical to describe the renormalization group flows of coupling functions and order parameter fields of the co

David R. Patton, Kieran D. Wilson, Colin J. Metrow, Sara L. Ellison

We use the IllustrisTNG cosmological hydrodynamical simulations to investigate how the specific star formation rates (sSFRs) of massive galaxies $(M_* > 10^{10} M_\odot)$ depend on the distance to their closest companions. We estimate sSFR enhancements by comparing with control samples that are matched in redshift, stellar mass, local density and isolation,

Xiaoyang Guo, Anuj Srivastava

Past approaches for statistical shape analysis of objects have focused mainly on objects within the same topological classes, e.g., scalar functions, Euclidean curves, or surfaces, etc. For objects that differ in more complex ways, the current literature offers only topological methods. This paper introduces a far-reaching geometric approach for analyzing sh

Zack Carson, Kent Yagi

Gravitational waves from extreme gravity events such as the coalescence of two black holes in a binary system fill our observable universe, bearing with them the underlying theory of gravity driving their process. One compelling alternative theory of gravity -- known as Einstein-dilaton Gauss-Bonnet gravity motivated by string theory -- describes the presenc

David Arnas

This work introduces a linearized analytical model for the study of the dynamic of satellites in near circular orbits under the effects of the atmospheric drag. This includes the evaluation of the station keeping required for each satellite subjected to a control box strategy, and also the study of the dynamic of tandem formations between two or more satelli

Congcong Li, Peng Shi, Luping Du, Xiaocong Yuan

Optical spin angular momenta in a confined electromagnetic field exhibit remarkable difference with their free space counterparts, in particular, the optical transverse spin that is locked with the energy propagating direction lays the foundation for many intriguing physical effects such as unidirectional transportation, quantum spin Hall effect, photonic Sk

Dian-Wu Yue, Ha H. Nguyen, Yu Sun

As a means to control wireless propagation environments, the use of emerging and novel intelligent reflecting surfaces (IRS) is envisioned to enhance and broaden many applications in future wireless networks. This paper is concerned with a point-to-point IRS-assisted millimeter-wave (mmWave) system in which the IRS consists of multiple subsurfaces, each havi

Ki-Seok Kim

First, we reformulate RG transformations in a recursive way with introduction of an order-parameter field. As a result, we manifest the RG flow of an effective field theory through the emergence of an extra dimensional space, where both RG $β-$functions of coupling functions and RG flow equation of the order-parameter field appear in the resulting effective

Zhi Zhang, Yang Liu, Jan Sieber

Lyapunov exponents are a widely used tool for studying dynamical systems. When calculating Lyapunov exponents for piecewise-smooth systems with time-delayed arguments one faces a lack of continuity in the variational problem. This paper studies how to build a variational equation for the efficient construction of Jacobians along trajectories of the delayed n

Amadeus Oertel, Titus Cieslewski, Davide Scaramuzza

In this paper, we propose to augment image-based place recognition with structural cues. Specifically, these structural cues are obtained using structure-from-motion, such that no additional sensors are needed for place recognition. This is achieved by augmenting the 2D convolutional neural network (CNN) typically used for image-based place recognition with

Emilio Pisanty, Marcelo F. Ciappina, Maciej Lewenstein

High-harmonic generation - the emission of high-frequency radiation by the ionization and subsequent recombination of an atomic electron driven by a strong laser field - is widely understood using a quasiclassical trajectory formalism, derived from a saddle-point approximation, where each saddle corresponds to a complex-valued trajectory whose recombination

Runfa Chen, Wenbing Huang, Binghui Huang, Fuchun Sun

Unsupervised image-to-image translation is a central task in computer vision. Current translation frameworks will abandon the discriminator once the training process is completed. This paper contends a novel role of the discriminator by reusing it for encoding the images of the target domain. The proposed architecture, termed as NICE-GAN, exhibits two advant

Nidhi Rathee, Arghya Mukherjee, R. M. G. M. Trines, Sudip Sengupta

The effect of electron temperature on the space-time evolution of nonlinear plasma oscillations in an inhomogeneous plasma is studied using a one-dimensional particle-in-cell (PIC) code. In contrast to the conventional wisdom, it is found that for an inhomogeneous plasma, there exists a critical value of electron temperature beyond which wave breaking does n

Sara Faridi, Mayada Shahada

We consider the following question: if a simplicial complex $Δ$ has $d$-homology, then does the corresponding $d$-cycle always induce cycles of smaller dimension that are not boundaries in $Δ$? We provide an answer to this question in a fixed dimension. We use the breaking of homology to show the subadditivity property for the maximal degrees of syzygies of

Neal Madras, Justin M. Troyka

We introduce a new boundedness condition for affine permutations, motivated by the fruitful concept of periodic boundary conditions in statistical physics. We study pattern avoidance in bounded affine permutations. In particular, we show that if $τ$ is one of the finite increasing oscillations, then every $τ$-avoiding affine permutation satisfies the bounded

Preeti Suman Dash, Salila Das, Prabir K. Mukherjee

We present an improved analysis of the phase transitions in rare earth superconductor using Ginzburg-Landau theory. Our work is based on the systematic study of critical field and superconducting order parameter in the presence of localized magnet due to rare-earth atom. We present the different phases that can occur and analyze the conditions of phase trans

Akshay Ajagekar, Fengqi You

Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks

G. C. Katsimiga, S. I. Mistakidis, T. M. Bersano, M. K. H. Ome

We report on the static and dynamical properties of multiple dark-antidark solitons (DADs) in two-component, repulsively interacting Bose-Einstein condensates. Motivated by experimental observations involving multiple DADs, we present a theoretical study which showcases that bound states consisting of dark (antidark) solitons in the first (second) component

O. Barnabà, A. Menegolli, R. Nardò, M. Pirola

Large volume Liquid Argon Time Projection Chambers (LAr-TPC) are used and proposed for neutrino physics and rare event search. Most of these detectors make use of the scintillation light of liquid argon for trigger purposes. Two different approaches can be adopted to provide these detectors with an effective trigger system, relying upon analog or digital pro

Cynthia Arias, Francisco Tello-Ortiz, Ernesto Contreras

In this work we investigate the extra packing of mass within the framework of gravitational decoupling by means of Minimal Geometric Deformation approach. It is shown that, after a suitable set of the free parameters involved, the like--Tolman IV solution extended by Minimal Geometric Deformation not only acquire extra packing of mass but it corresponds to a

Zhilei Liu, Yunpeng Wu, Le Li, Cuicui Zhang

Previous research on face restoration often focused on repairing a specific type of low-quality facial images such as low-resolution (LR) or occluded facial images. However, in the real world, both the above-mentioned forms of image degradation often coexist. Therefore, it is important to design a model that can repair LR occluded images simultaneously. This

Wolfgang Rudolf Bauer

We present a rigorous mathematical approach of two species channel transport within the framework of stochastic thermodynamics, which explains the impact of interparticle in-channel interactions. Different from mean field approaches, the model explicitly conserves spatial correlations by considering stochastic transitions between the channel&#39;s occupation

John Hopfensperger

Let $G$ be a locally compact amenable group, $TLIM(G)$ the topological left-invariant means on $G$, and $TLIM_0(G)$ the limit points of Folner-nets. I show that $TLIM_0(G) = TLIM(G)$ unless $G$ is $σ$-compact non-unimodular, in which case $TLIM_0(G) \neq TLIM(G)$. This improves a 1970 result of Chou and a 2009 result of Hindman and Strauss. I consider the an

Fabien Cléry, Gerard van der Geer

We describe the ring of modular forms of degree 2 in characteristic 2 using its relation with curves of genus 2.

Cao Vien Phung, Jasenka Dizdarevic, Admela Jukan

REST HTTP is the communication protocol of choice for software developers today. In IoT systems with unreliable connectivity, however, a stateless protocol like REST HTTP needs to send a request message multiple times, and it only stops the retransmissions when an acknowledgement arrives at the sender. In our previous work, we studied the usage of random lin

Pramod Padmanabhan, Fumihiko Sugino, Diego Trancanelli

Unitary braiding operators can be used as robust entangling quantum gates. We introduce a solution-generating technique to solve the $(d,m,l)$-generalized Yang-Baxter equation, for $m/2\leq l \leq m$, which allows to systematically construct such braiding operators. This is achieved by using partition algebras, a generalization of the Temperley-Lieb algebra

Yizhi Zhao

The adversarial wiretap channel (AWTC) model is a secure communication model in which adversary can directly read and write the transmitted bits in legitimate communication with fixed fractions. In this paper we propose a secure polar coding scheme to provide secure and reliable communication over the AWTC model. For the adversarial reading and writing actio

Cao Vien Phung, Anna Engelmann, Thomas Kuerner, Admela Jukan

In this paper, we propose a novel solution that can improve the quality of THz transmission with systematic random linear network coding (sRLNC) and a low-bitrate auxiliary channel. To minimize complexity of channel coding, we complement a generic low complexity FEC code by a low complexity sRLNC. To increase the overall throughput of THz transmission, we pr

Miel Sharf

Lately, data-driven control has become a widespread area of research. A few recent big-data based approaches for data-driven control of nonlinear systems try to use classical input-output techniques to design controllers for systems for which only a finite number of (input-output) samples are known. These methods focus on using the given data to compute boun

Raphael C. Assier, I. David Abrahams

We introduce and study a new canonical integral, denoted $I_{+-}^{\varepsilon}$, depending on two complex parameters $α_1$ and $α_2$. It arises from the canonical problem of wave diffraction by a quarter-plane, and is heuristically constructed to capture the complex field near the tip and edges. We establish some region of analyticity of this integral in $\m

Thorsten Deilmann, Peter Krüger, Michael Rohlfing

The effect of a magnetic field on the optical absorption in semiconductors has been measured experimentally and modeled theoretically for various systems in previous decades. We present a new first-principles approach to systematically determine the response of excitons to magnetic fields, i.e. exciton $g$ factors. By utilizing the $GW$-Bethe-Salpeter equati

V. G. Bornyakov, V. V. Braguta, A. A. Nikolaev, R. N. Rogalyov

The transverse and longitudinal gluon propagators in the Landau gauge are studied in the two-color lattice QCD at nonzero quark chemical potential $μ_q$. Parameterization of the momentum dependence of the propagators is provided for all values of chemical potential under study. We find that the longitudinal propagator is infrared suppressed at nonzero $μ_q$

Yu Kobayashi, Hideaki Iiduka

This paper proposes a conjugate-gradient-based Adam algorithm blending Adam with nonlinear conjugate gradient methods and shows its convergence analysis. Numerical experiments on text classification and image classification show that the proposed algorithm can train deep neural network models in fewer epochs than the existing adaptive stochastic optimization

Wenjie Song, Xiansi Wang, Chenglong Jia, Xiangrong Wang

We investigate the transfer and control of nonreciprocity through magnons themselves in permalloy thin films deposited on surface oxide silicon substrate. Evidences of nonreciprocal emergence of hybridized dipole exchange magnons (spin waves) at two permalloy surfaces are provided by studying magnon transmission and asymmetry, via Brillouin light scattering

Kang Wei, Jun Li, Ming Ding, Chuan Ma

Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from the shared models uploaded by MTs. To ad

R. Chan, M. F. A. da Silva, V. H. Satheeshkumar

In the present work we analyze all the possible spherically symmetric exterior vacuum solutions allowed by the Einstein-Aether theory with static aether. We show that there are four classes of solutions corresponding to different values of a combination of the free parameters, $c_{14}=c_1+c_4$, which are: $ 0 < c_{14}<2$, $c_{14} < 0$, $c_{14}=2$ and $c_{14}

Padraig Corcoran

A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use o

Tianrui Zhao, Sebastien Ourselin, Tom Vercauteren, Wenfeng Xia

Image transmission through multimode optical fibers has been an area of immense interests driven by the demand for miniature endoscopes in biomedicine and higher speed and capacity in telecommunications. Conventionally, a complex-valued transmission matrix is obtained experimentally to link the input and output light fields of a multimode fiber for image ret

Stanislav Volkov

Fix some $p\in[0,1]$ and a positive integer $n$. The discrete Bak-Sneppen model is a Markov chain on the space of zero-one sequences of length $n$ with periodic boundary conditions. At each moment of time a minimum element (typically, zero) is chosen with equal probability, and is then replaced together with both its neighbours by independent Bernoulli(p) ra

Satoru Odake

Corresponding to a certain Wronskian identity, we present two types of new Casoratian identities. We apply these identities to the Darboux transformations of quantum mechanical systems. The Wronskian identity is applied to the ordinary quantum mechanics, and the two Casoratian identities are applied to the discrete quantum mechanics with pure imaginary and r

Yutao Hu, Xiaolong Jiang, Xuhui Liu, Baochang Zhang

Most of the recent advances in crowd counting have evolved from hand-designed density estimation networks, where multi-scale features are leveraged to address the scale variation problem, but at the expense of demanding design efforts. In this work, we automate the design of counting models with Neural Architecture Search (NAS) and introduce an end-to-end se

Linlin Liu, Jianfei Yu, Junming Huang, Feng Xia

Modern science is dominated by scientific productions from teams. A recent finding shows that teams with both large and small sizes are essential in research, prompting us to analyze the extent to which a country&#39;s scientific work is carried out by big/small teams. Here, using over 26 million publications from Web of Science, we find that China&#39;s res

Congqi Cao, Yanning Zhang

Few-shot learning is a fundamental and challenging problem since it requires recognizing novel categories from only a few examples. The objects for recognition have multiple variants and can locate anywhere in images. Directly comparing query images with example images can not handle content misalignment. The representation and metric for comparison are crit

Roger Huang, Dario Gnani, Carl Grace, Yury Kolomensky

Conventional CMOS technology operated at cryogenic conditions has recently attracted interest for its uses in low-noise electronics. We present one of the first characterizations of 180 nm CMOS technology at a temperature of 100 mK, extracting I/V characteristics, threshold voltages, and transconductance values, as well as observing their temperature depende

Devshree Mandal, Ayushi Vashistha, Amita Das

When a laser field is incident on an overdense plasma it is unable to penetrate inside it. Nevertheless, a part of its energy gets transferred to the electrons through a variety of mechanisms (e.g. vacuum and $\vec{J}\times \vec{B}) heating [1, 2]). The dynamics of these energetic electrons inside the plasma is a field of great interest. It is demonstrated h

Yang Xu, Liping Xin, Xuhui Han, Hongbo Cai

GWAC will have been built an integrated FOV of 5,000 $degree^2$ and have already built 1,800 square $degree^2$. The limit magnitude of a 10-second exposure image in the moonless night is 16R. In each observation night, GWAC produces about 0.7TB of raw data, and the data processing pipeline generates millions of single frame alerts. We describe the GWAC Data

Atsushi J. Nishizawa, Bau-Ching Hsieh, Masayuki Tanaka, Tadafumi Takata

We present a description of the second data release for the photometric redshift (photo-$z$) of the Subaru Strategic Program for the Hyper-Suprime Cam survey. Our photo-$z$ products for the entire area in the Data Release 2 are publicly available, and both our point estimate catalog products and full PDFs can be retrieved from the data release site, \url{htt

Gaomin Tang, Wolfgang Belzig, Ulrich Zülicke, Christoph Bruder

A superconductor subject to electromagnetic irradiation in the terahertz range can show amplitude oscillations of its order parameter. However, coupling this so-called Higgs mode to the charge current is notoriously difficult. We propose to achieve such a coupling in a particle-hole-asymmetric configuration using a DC-voltage-biased normal-metal--superconduc

Michael Gimelfarb, Scott Sanner, Chi-Guhn Lee

In reinforcement learning, agents that consider the context, or current state, when selecting source policies for transfer have been shown to outperform context-free approaches. However, none of the existing approaches transfer knowledge contextually from model-based learners to a model-free learner. This could be useful, for instance, when source policies a

Blake A. Wetherton, Jan Egedal, Peter K. Montag, Ari Le

In this paper, we derive a new drift-kinetic method for estimating gradients in the plasma properties through a velocity space distribution at a single point. The gradients are intrinsically related to agyrotropic features of the distribution function. This method predicts the gradients in the magnetized distribution function, and can predict gradients of ar

Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci

Image animation consists of generating a video sequence so that an object in a source image is animated according to the motion of a driving video. Our framework addresses this problem without using any annotation or prior information about the specific object to animate. Once trained on a set of videos depicting objects of the same category (e.g. faces, hum

Serena Vinciguerra, Coenraad J. Neijssel, Alejandro Vigna-Gómez, Ilya Mandel

Be X-ray binaries (BeXRBs) consist of rapidly rotating Be stars with neutron star companions accreting from the circumstellar emission disk. We compare the observed population of BeXRBs in the Small Magellanic Cloud with simulated populations of BeXRB-like systems produced with the COMPAS population synthesis code. We focus on the apparently higher minimal m

Shinya Sato

The effects of localized source currents on Earth&#39;s magnetotelluric (MT) responses have been reported in the literature in terms of the changes in period and subsurface structure. The focus in this study is on the bias within the MT responses arising from variations in the vertical and horizontal distances of the source current. The MT responses at 20 an

Denis A. Ivanov, Tatiana Yu. Ivanova, Santiago F. Caballero-Benitez, Igor B. Mekhov

Feedback is a general idea of modifying system behaviour depending on the measurement outcomes. It spreads from natural sciences, engineering, and artificial intelligence to contemporary classical and rock music. Recently, feedback has been suggested as a tool to induce phase transitions beyond the dissipative ones and tune their universality class. Here, we