March 2020 arXiv papers — page 33

Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu

Learning-to-rank (LTR) has become a key technology in E-commerce applications. Most existing LTR approaches follow a supervised learning paradigm from offline labeled data collected from the online system. However, it has been noticed that previous LTR models can have a good validation performance over offline validation data but have a poor online performan

Katie Gittins, Bernard Helffer

We consider the cases where there is equality in Courant's nodal domain theorem for the Laplacian with a Robin boundary condition on the square. In our previous two papers, we treated the cases where the Robin parameter $h>0$ is large, small respectively. In this paper we investigate the case where $h<0\,$.

Sunghun Joung, Seungryong Kim, Hanjae Kim, Minsu Kim

Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, we introduce a learnable module, cylindri

Sepideh Kianfar, Jorrit Leenaarts, Sanja Danilovic, Jaime de la Cruz Rodríguez

Broad-band images of the solar chromosphere in the Ca II H&K line cores around active regions are covered with fine bright elongated structures called bright fibrils. The mechanisms that form these structures and cause them to appear bright are still unknown. We aim to investigate the physical properties, such as temperature, line-of-sight velocity, and micr

S. I. Ipatov

Migration of planetesimals from the feeding zone of the terrestrial planets, which was divided into seven regions depending on the distance to the Sun, was simulated. The influence of gravity of all planets was taken into account. In some cases, the embryos of the terrestrial planets rather than the planets themselves were considered; their masses were assum

Babak Naderi, Tobias Hossfeld, Matthias Hirth, Florian Metzger

The subjective quality of transmitted speech is traditionally assessed in a controlled laboratory environment according to ITU-T Rec. P.800. In turn, with crowdsourcing, crowdworkers participate in a subjective online experiment using their own listening device, and in their own working environment. Despite such less controllable conditions, the increased us

L. D. Molag

The Muttalib-Borodin ensemble is a probability density function for $n$ particles on the positive real axis that depends on a parameter $\theta$ and a weight $w$. We consider a varying exponential weight that depends on an external field $V$. In a recent article, the large $n$ behavior of the associated correlation kernel at the hard edge was found for $\the

Oliver Goertsches, Panagiotis Konstantis, Leopold Zoller

We classify fibrations of abstract $3$-regular GKM graphs over $2$-regular ones, and show that all fiberwise signed fibrations of this type are realized as the projectivization of equivariant complex rank $2$ vector bundles over quasitoric $4$-folds or $S^4$. We investigate the existence of invariant (stable) almost complex, symplectic, and K\"ahler structur

Maximilian Engel, Marios-Antonios Gkogkas, Christian Kuehn

In this paper we study coupled fast-slow ordinary differential equations (ODEs) with small time scale separation parameter $\epsilon$ such that, for every fixed value of the slow variable, the fast dynamics are sufficiently chaotic with ergodic invariant measure. Convergence of the slow process to the solution of a homogenized stochastic differential equatio

Ge He, Zhongxu Wei, Zhongpei Feng, Xiaodong Yu

We present a newly developed facility, comprised of a combinatorial laser molecular beam epitaxy system and an in-situ scanning tunneling microscopy (STM). This facility aims at accelerating the materials research in a highly efficient way, by advanced high-throughput film synthesis techniques and subsequent fast characterization of surface morphology and el

F. Y. Wang, H. Yu, Y. C. Zou, Z. G. Dai

The nuclide $^{14}$C can be produced in the atmosphere by high energy particles and $\gamma$-rays from high-energy phenomena. Through the carbon cycle, some of $^{14}$CO$_2$ produced in the atmosphere can be retained in annual tree rings. Four events of rapid increase of the $^{14}$C content occurred in AD 775, AD 994, BC 660 and BC 3371 were found. Recently

Mengjia Zhu, Alberto Bemporad, Dario Piga

Automating the calibration of the parameters of a control policy by means of global optimization requires quantifying a closed-loop performance function. As this can be impractical in many situations, in this paper we suggest a semi-automated calibration approach that requires instead a human calibrator to express a preference on whether a certain control po

J. Belletête, A. M. Gainutdinov, J. L. Jacobsen, H. Saleur

We provide a lattice regularization of all topological defects in minimal models CFTs using RSOS and anyonic spin chains. For defects of type $(1,s)$, we connect our result with the "topological symmetry" initially identified in Fibonacci anyons [Phys. Rev. Lett. 98, 160409 (2007)], and the center of the affine Temperley-Lieb algebra discussed in [1811.02551

Daniel Wagner, Didier Henrion, Martin Hromčík

Occupation measures and linear matrix inequality (LMI) relax-ations (called the moment sums of squares or Lasserre hierarchy) are state-of-the-art methods for verification and validation (VV) in aerospace. In this document, we extend these results to a full F-16 closed-loop nonlinear dutch roll polynomial model complete with model reference adaptive control

Junbo Yin, Wenguan Wang, Qinghao Meng, Ruigang Yang

Current popular online multi-object tracking (MOT) solutions apply single object trackers (SOTs) to capture object motions, while often requiring an extra affinity network to associate objects, especially for the occluded ones. This brings extra computational overhead due to repetitive feature extraction for SOT and affinity computation. Meanwhile, the model

Matteo Saveriano

Non-linear dynamical systems represent a compact, flexible, and robust tool for reactive motion generation. The effectiveness of dynamical systems relies on their ability to accurately represent stable motions. Several approaches have been proposed to learn stable and accurate motions from demonstration. Some approaches work by separating accuracy and stabil

Ekin Ozman, Samir Siksek

Recent attempts at studying the Fermat equation over number fields have uncovered an unexpected and powerful connection with $S$-unit equations. In this expository paper we explain this connection and its implications for the asymptotic Fermat conjecture.

Ron Litman, Oron Anschel, Shahar Tsiper, Roee Litman

Scene Text Recognition (STR), the task of recognizing text against complex image backgrounds, is an active area of research. Current state-of-the-art (SOTA) methods still struggle to recognize text written in arbitrary shapes. In this paper, we introduce a novel architecture for STR, named Selective Context ATtentional Text Recognizer (SCATTER). SCATTER util

Alexander I. Bufetov, Boris Solomyak

A sufficient condition for a substitution automorphism to have pure singular spectrum is given in terms of the top Lyapunov exponent of the associated spectral cocycle. As a corollary, singularity of the spectrum is established for an infinite family of self-similar interval exchange transformations.

Narcisse Bang Mbang, Emmanuel Fouotsa, Celestin Lele

Efficient computations of pairings with Miller Algorithm have recently received a great attention due to the many applications in cryptography. In this work, we give formulae for the optimal Ate pairing in terms of elliptic nets associated to twisted Barreto-Naehrig (BN) curve, Barreto-Lynn-Scott(BLS) curves and Kachisa-Schaefer-Scott(KSS) curves considered

Rui She, Pingyi Fan

In terms of Generative Adversarial Networks (GANs), the information metric to discriminate the generative data from the real data, lies in the key point of generation efficiency, which plays an important role in GAN-based applications, especially in anomaly detection. As for the original GAN, there exist drawbacks for its hidden information measure based on

S. L. Cherkas, V. L. Kalashnikov

We argue that the problem of time is not a crucial issue inherent in the quantum picture of the universe evolution. On the minisuperspace model example with the massless scalar field, we demonstrate four approaches to the description of quantum evolution, which give similar results explicitly. The relevance of these approaches to building a quantum theory of

Osman Gulseven

In this article, we examine whether the gold market returns show abnormally positive or negative returns in some months of the calendar year. The statistical analysis and the decomposition techniques suggest that gold prices show some seasonal behavior during the turn of the year. We observe a strong cyclical behavior in gold markets during the turn-of-the-y

Jakramate Bootkrajang

Ridge regression is a well established regression estimator which can conveniently be adapted for classification problems. One compelling reason is probably the fact that ridge regression emits a closed-form solution thereby facilitating the training phase. However in the case of high-dimensional problems, the closed-form solution which involves inverting th

Guo Lu, Chunlei Cai, Xiaoyun Zhang, Li Chen

Recently, learning based video compression methods attract increasing attention. However, the previous works suffer from error propagation due to the accumulation of reconstructed error in inter predictive coding. Meanwhile, the previous learning based video codecs are also not adaptive to different video contents. To address these two problems, we propose a

Julian Bernhard, Alois Knoll

A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted with a variety of behavioral variations and limited knowledge about the true behaviors of observed agents. The practicability of existing works addressing this challenge is being li

Xing Yang, Tingfeng Yi, Yan Zhang, Huaizhen Li

We have assembled the historical light curves of the BL Lac Object 3FGL J0449.4-4350 at optical and gamma-ray bands, the time spanning about 10 years, analyzed the periodic variability of the light curves by using four different methods (Lomb-Scargle periodogram, REDFIT38, Jurkevich and DACF). We detected a marginally possible quasi-periodic oscillation (QPO

Dan Ciubotaru, Marcelo De Martino, Philippe Meyer

We define a pair of symplectic Dirac operators $(D^+,D^-)$ in an algebraic setting motivated by the analogy with the algebraic orthogonal Dirac operators in representation theory. We work in the settings of $\mathbb Z/2$-graded quadratic Lie algebras $\mathfrak g=\mathfrak k+\mathfrak p$ and of graded affine Hecke algebras $\mathbb H$.

Wen-Jin Xie, Le Zou, Hong-Bang Liu, Shan-Qin Wang

Early shallow-decaying X-ray afterglows of gamma-ray bursts (GRBs) may be attributed to the dipole radiations of newly-born magnetars. Assuming that the GRB jets powered by magnetars are quasi-universal, we find that the jet structure can be parameterized as a uniform jet with a luminosity of $\log L_{\rm j}/{\rm erg\ s^{-1}}=52.68^{+0.76}_{-0.33}$ (1$\sigma

Soumyabrata Bhattacharjee

COVID-19 is a new type of coronavirus disease which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It originated in China in the month of December 2019 and quickly started to spread within the country. On 31st December 2019, it was first reported to country office of World Health Organization (WHO) in China. Since then, it has spr

Kristo Nugraha Lian, Jian-Sheng Wang

Following the recent research enthusiasm on the effect of geometry on near-field heat transfer (NFHT) enhancement, we present an analysis based on simplified yet highly efficient graphene and nanotube models. Two geometries are considered: that of two parallel infinite "graphene" surfaces and that of a one-dimensional infinite "nanotube" line in parallel wit

Ferenc Bodog, Matyas Mechler, Matyas Koniorczyk, Peter Adam

Detectors inherently capable of resolving photon numbers have undergone a significant development recently, and this is expected to affect multiplexed periodic single-photon sources where such detectors can find their applications. We analyze various spatially and time-multiplexed periodic single-photon source arrangements with photon-number-resolving detect

Jeong Bin Cho, Gunwoo Jung, Kyuheon Kim, Jihun Kim

Highly asymmetric optical properties of b-Ga2O3 are investigated by polarization-dependent photoluminescence (PL) excitation spectroscopy for assessing its potential use for a novel ultraviolet emitter and nonlinear optical material. Based on power-dependent excitation and two-photon depth scan, it is demonstrated that the underlying mechanism for the PL occ

Yixin Cao, Yuping Ke, Hanchun Yuan

Let $H$ be a fixed graph. Given a graph $G$ and an integer $k$, the $H$-free edge modification problem asks whether it is possible to modify at most $k$ edges in $G$ to make it $H$-free. Sandeep and Sivadasan (IPEC 2015) asks whether the paw-free completion problem and the paw-free edge deletion problem admit polynomial kernels. We answer both questions affi

Marc Olive, Boris Desmorat, Boris Kolev, Rodrigue Desmorat

We formulate necessary and sufficient conditions for a unit vector n to generate a plane or axial symmetry of a constitutive tensor. For the elasticity tensor, these conditions consist of two polynomial equations of degree lower than four in the components of n. Compared to Cowin-Mehrabadi conditions, this is an improvement, since these equations involve onl

Rustem Khasanov, Ritu Gupta, Debarchan Das, Alfred Amon

Precise measurements of the thermodynamic critical field ($B_{\rm c}$) in type-I noncentrosymmetric superconductor BeAu were performed by means of the muon-spin rotation/relaxation technique. The temperature evolution of $B_{\rm c}$ can not be described within the single gap scenario and it requires the presence of at least two different types of the superco

Andreas F. Holmsen, Seunghun Lee

Given a graph $G$ on the vertex set $V$, the non-matching complex of $G$, $\mathsf{NM}_k(G)$, is the family of subgraphs $G' \subset G$ whose matching number $\nu(G')$ is strictly less than $k$. As an attempt to generalize the result by Linusson, Shareshian and Welker on the homotopy types of $\mathsf{NM}_k(K_n)$ and $\mathsf{NM}_k(K_{r,s})$ to arbitrary gra

Jin-Fu Chen, Ying Li, Hui Dong

The finite-time isothermal process is fundamental in quantum thermodynamics yet complicated with combination of changing control parameters and the interaction with the thermal bath. Such complexity prevents the direct application of the traditional thermodynamics measurement of the relevant quantities. In this paper, we provide a discrete-step method to sep

Farbod Taymouri, Marcello La Rosa, Sarah Erfani, Zahra Dasht Bozorgi

Predictive process monitoring aims to predict future characteristics of an ongoing process case, such as case outcome or remaining timestamp. Recently, several predictive process monitoring methods based on deep learning such as Long Short-Term Memory or Convolutional Neural Network have been proposed to address the problem of next event prediction. However,

Nikhil Khatri, Shirshendu Chakrabarti

With low latency, high throughput and enterprise-grade reliability, SSDs have become the de-facto choice for storage in the data center. As a result, SSDs are used in all online data stores in LinkedIn. These apps persist and serve critical user data and have millisecond latencies. For the hosts serving these applications, SSD faults are the single largest c

David Cheban, Zhenxin Liu

In contrast to existing works on stochastic averaging on finite intervals, we establish an averaging principle on the whole real axis, i.e. the so-called second Bogolyubov theorem, for semilinear stochastic ordinary differential equations in Hilbert space with Poisson stable (in particular, periodic, quasi-periodic, almost periodic, almost automorphic etc) c

Jun Yang, Fei Wang

Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. However, it is difficult to collect sufficient accurate and diverse models through once training. This paper proposes Auto-Ensemble (AE) to collect checkpoints of deep learning mode

Ashkan Abbasi, Amirhassan Monadjemi, Leyuan Fang, Hossein Rabbani

The data-driven sparse methods such as synthesis dictionary learning (e.g., K-SVD) and sparsifying transform learning have been proven effective in image denoising. However, they are intrinsically single-scale which can lead to suboptimal results. We propose two methods developed based on wavelet subbands mixing to efficiently combine the merits of both sing

Chien-Yeah Seng, Xu Feng, Mikhail Gorchtein, Lu-Chang Jin

Recently, the first ever lattice computation of the $\gamma W$-box radiative correction to the rate of the semileptonic pion decay allowed for a reduction of the theory uncertainty of that rate by a factor of $\sim3$. A recent dispersion evaluation of the $\gamma W$-box correction on the neutron also led to a significant reduction of the theory uncertainty,

Shanlin Huang, Gengsheng Wang, Ming Wang

We characterize observable sets for 1-dim Schr\"{o}dinger equations in $\mathbb{R}$: $i \partial_t u = (-\partial_x^2+x^{2m})u$ (with $m\in \mathbb{N}:=\{0,1,\dots\}$). More precisely, we obtain what follows: First, when $m=0$, $E\subset\mathbb{R}$ is an observable set at some time if and only if it is thick, namely, there is $\gamma>0$ and $L>0$ so that $$

Chun-Hui Zhang, Yu-Teng Fan, Chun-Mei Zhang, Guang-Can Guo

Digital signature is a key technique in information security, especially for identity authentications. Compared with classical correspondence, quantum digital signatures (QDSs) provide a considerably higher level of security, i.e., information-theoretic security. At present, its performance is limited by key generation protocols (e.g., BB84 or measurement-de

Leonid Positselski, Olaf M. Schnürer

Consider the obvious functor from the unbounded derived category of all finitely generated modules over a left noetherian ring $R$ to the unbounded derived category of all modules. We answer the natural question whether this functor defines an equivalence onto the full subcategory of complexes with finitely generated cohomology modules in two special cases.

Valentin Lychagin, Mikhail Roop

In this paper, we study Schr\"{o}dinger equations on elliptic curves called generalized Lam\'{e} equations. We suggest a method of finding integrable potentials for Schr\"{o}dinger type equations. We apply this method to the Lam\'{e} equations and provide a sequence of integrable potentials for which the eigenvalue problem is solved explicitly.

R. J. Crewther

A genuine dilaton $\sigma$ allows scales to exist even in the limit of exact conformal invariance. In gauge theories, these may occur at an infrared fixed point (IRFP) $\alpha_{\text{IR}}$ through dimensional transmutation. These large scales at $\alpha_{\text{IR}}$ can be separated from small scales produced by $\theta^\mu_\mu$, the trace of the energy-mome

Matías Bravo, Claudia del P. Lagos, Aaron S. G. Robotham, Sabine Bellstedt

Galaxy spectral energy distributions (SEDs) remain among the most challenging yet informative quantities to reproduce in simulations due to the large and complex mixture of physical processes that shape the radiation output of a galaxy. With the increasing number of surveys utilising broadband colours as part of their target selection criteria, the productio

Shi Jin, Lei Li, Jian-Guo Liu

We consider in this work the convergence of Random Batch Method proposed in our previous work [Jin et al., J. Comput. Phys., 400(1), 2020] for interacting particles to the case of disparate species and weights. We show that the strong error is of $O(\sqrt{\tau})$ while the weak error is of $O(\tau)$ where $\tau$ is the time step between two random divisions

Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo, Irem Boybat

Deep neural networks (DNNs) have surpassed human-level accuracy in a variety of cognitive tasks but at the cost of significant memory/time requirements in DNN training. This limits their deployment in energy and memory limited applications that require real-time learning. Matrix-vector multiplications (MVM) and vector-vector outer product (VVOP) are the two

Christian Baer, Rafe Mazzeo

The Rarita-Schwinger operator is the twisted Dirac operator restricted to 3/2-spinors. Rarita-Schwinger fields are solutions of this operator which are in addition divergence-free. This is an overdetermined problem and solutions are rare; it is even more unexpected for there to be large dimensional spaces of solutions. In this paper we prove the existence of

Phung Huynh The

A closed convex subset of a normed linear space is said to have the strong separation property if it can be strongly separated from every other disjoint closed and convex set by a closed hyperplane. In this paper we give some results on the separation of convex sets with noticing the role of barrier cones, develop some characterizations of subsets having the

Yoeri E. Boink, Markus Haltmeier, Sean Holman, Johannes Schwab

Data assisted reconstruction algorithms, incorporating trained neural networks, are a novel paradigm for solving inverse problems. One approach is to first apply a classical reconstruction method and then apply a neural network to improve its solution. Empirical evidence shows that such two-step methods provide high-quality reconstructions, but they lack a c

N. Fraija, P. Veres, P. Beniamini, A. Galvan-Gamez

Very-high-energy (VHE) emission is usually interpreted in the synchrotron-self Compton (SSC) scenario, and expected from the low-redshift and high-luminosity gamma-ray bursts (GRBs), as GRB 180720B and GRB 190114C. Recently, VHE emission was detected by the H.E.S.S. telescopes from one of the closest burst GRB 190829A which was associated with the supernova

J. Lim, Y. S. Ang, F. J. G. de Abajó, I. Kaminer

Frequency multiplication of terahertz signals on a solid state platform is highly sought-after for the next generation of high-speed electronics and the creation of frequency combs. Solutions to efficiently generate extreme harmonics (up to the $31^{\rm{st}}$ harmonic and beyond) of a terahertz signal with modest input intensities, however, remain elusive. U

Makoto Naruse, Martin Berthel, Hirokazu Hori, Aurelien Drezet

In our former work (Sci. Rep. 4: 6039, 2014), we theoretically and numerically demonstrated that chaotic oscillation can be induced in a nanoscale system consisting of quantum dots between which energy transfer occurs via optical near-field interactions. Furthermore, in addition to the nanoscale implementation of oscillators, it is intriguing that the chaoti

Jongwon Choi, Kwang Moo Yi, Jihoon Kim, Jinho Choo

Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard to deal with. In this work, we show that this is harmful. We propose a method based on the Bayes' rule, that can naturally incorporate class imbalance into the Active Learning fra

J. Atalaya, S. Zhang, M. Y. Niu, A. Babakhani

We develop a protocol for continuous operation of a quantum error correcting code for protection of coherent evolution due to an encoded Hamiltonian against environmental errors, using the three qubit bit flip code and bit flip errors as a canonical example. To detect errors in real time, we filter the output signals from continuous measurement of the error

Bithika Karmakar, Najmul Haque, Munshi G Mustafa

Considering the strong field approximation we compute the hard thermal loop pressure at finite temperature and chemical potential of hot and dense deconfined QCD matter in lowest Landau level in one-loop order. We consider the anisotropic pressure in the presence of the strong magnetic field i.e., longitudinal and transverse pressure along parallel and perpe

Renjie Wu, Eamonn J. Keogh

Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Bec

Deepak S. Kathyat, Arnob Mukherjee, Sanjeev Kumar

We derive a new microscopic spin Hamiltonian for Rashba-coupled double exchange metals. The Hamiltonian consists of anisotropic interactions of the Dzyaloshinskii-Moriya (DM) and Kitaev form, in addition to the standard isotropic term. We validate the spin Hamiltonian by comparing results with those on the exact spin-fermion model, and present its phase diag

Jozef Genzor, Tomotoshi Nishino, Andrej Gendiar

One of the challenging problems in the condensed matter physics is to understand the quantum many-body systems, especially, their physical mechanisms behind. Since there are only a few complete analytical solutions of these systems, several numerical simulation methods have been proposed in recent years. Amongst all of them, the Tensor Network algorithms hav

Liu Ziyin, Zihao Wang, Makoto Yamada, Masahito Ueda

We propose a novel regularization method, called \textit{volumization}, for neural networks. Inspired by physics, we define a physical volume for the weight parameters in neural networks, and we show that this method is an effective way of regularizing neural networks. Intuitively, this method interpolates between an $L_2$ and $L_\infty$ regularization. Ther

Chih-Hong Cheng

We study how state-of-the-art neural networks for 3D object detection using a single-stage pipeline can be made safety aware. We start with the safety specification (reflecting the capability of other components) that partitions the 3D input space by criticality, where the critical area employs a separate criterion on robustness under perturbation, quality o

Qilong Wang, Li Zhang, Banggu Wu, Dongwei Ren

Recent works have demonstrated that global covariance pooling (GCP) has the ability to improve performance of deep convolutional neural networks (CNNs) on visual classification task. Despite considerable advance, the reasons on effectiveness of GCP on deep CNNs have not been well studied. In this paper, we make an attempt to understand what deep CNNs benefit

Masahiro Sato, Yoshitaka Morisaku

We theoretically study the nonlinear magnetic resonance driven by intense laser or electromagnetic wave in a fully polarized frustrated magnet near a less-visible spin-nematic ordered phase. In general, both magnons and magnon pairs (two-magnon bound state) appear as the low-energy excitation in the saturated state of spin-nematic magnets. Their excitation e

Nitin Singh, Martin van Hecke

Mechanism - collections of rigid elements coupled by perfect hinges which exhibit a zero-energy motion -- motivate the design of a variety of mechanical metamaterials. We significantly enlarge this design space by considering pseudo-mechanisms, collections of elastically coupled elements that exhibit motions with very low energy costs. We show that their geo

Jiaxiang Li, Krishnakumar Balasubramanian, Shiqian Ma

We consider stochastic zeroth-order optimization over Riemannian submanifolds embedded in Euclidean space, where the task is to solve Riemannian optimization problem with only noisy objective function evaluations. Towards this, our main contribution is to propose estimators of the Riemannian gradient and Hessian from noisy objective function evaluations, bas

Cong Nhan Le, Xuan Truong Le

In this paper, we study the global regularity estimates in Lorentz spaces for gradients of solutions to quasilinear elliptic equations with measure data of the form \begin{eqnarray*} \left\{ \begin{array}{rcl} -{\rm div}(\mathcal{A}(x, \nabla u))&=& \mu \quad \text{in} ~\Omega, u&=&0 \quad \text{on}~ \partial \Omega, \end{array}\right. \end{eqnarray*} where

Shan You, Tao Huang, Mingmin Yang, Fei Wang

Training a supernet matters for one-shot neural architecture search (NAS) methods since it serves as a basic performance estimator for different architectures (paths). Current methods mainly hold the assumption that a supernet should give a reasonable ranking over all paths. They thus treat all paths equally, and spare much effort to train paths. However, it

Bin Liu, Chenxu Zhu, Guilin Li, Weinan Zhang

Learning feature interactions is crucial for click-through rate (CTR) prediction in recommender systems. In most existing deep learning models, feature interactions are either manually designed or simply enumerated. However, enumerating all feature interactions brings large memory and computation cost. Even worse, useless interactions may introduce noise and

Yuejun Wei, Yuhang Yang, Ming Jiang, Wen Chen

The demand for flexible broadband wireless services makes the pruning technique, including both shortening and puncturing, an indispensable component of error correcting codes. The analysis of the pruning process for structured lowdensity parity-check (LDPC) codes can be considerably simplified with their equivalent representations through base-matrices or p

Yuejun Wei, Ming Jiang, Wen Chen, Yuhang Yang

Turbo codes and CRC codes are usually decoded separately according to the serially concatenated inner codes and outer codes respectively. In this letter, we propose a hybrid decoding algorithm of turbo-CRC codes, where the outer codes, CRC codes, are not used for error detection but as an assistance to improve the error correction performance. Two independen

Meng Zhang, Jian Huang, Hui Yu, Hanwen Luo

In this letter, we study relay-aided networks with presence of single eavesdropper. We provide joint beamforming design of the source and relay that can minimize the overall power consumption while satisfying our predefined quality-of-service (QoS) requirements. Additionally, we investigate the case that the channel between relay and eavesdropper suffers fro

Mahmood Yousefi-Azar, Mohamed-Ali Kaafar, Andy Walker

Micro-segmentation is a network security technique that requires delivering services for each unique segment. To do so, the first stage is defining these unique segments (a.k.a security groups) and then initializing policy-driven security controls. In this paper, we propose an unsupervised learning technique that covers both the security grouping and policy

Carlos Armando De Castro

In this paper we develop a SIR epidemiological model with parameters calculated according to existing data at the time of writing (24/03/2020); the data is from Italy, South Korea and Colombia, the model is then used to project the evolution of the COVID-19 epidemic in Colombia for different scenarios using the data of population for the country and known in

Himadri Pathak, Takeshi Sato, Kenichi L. Ishikawa

We report successful implementation of the time-dependent second-order many-body perturbation theory using optimized orthonormal orbital functions called time-dependent optimized second-order many-body perturbation theory [TD-OMP2] to reach out to relatively larger chemical systems for the study of intense-laser-driven multielectron dynamics. We apply this m

Bin Zhang, Jian Li, Yabiao Wang, Ying Tai

In this paper, we propose a novel Automatic and Scalable Face Detector (ASFD), which is based on a combination of neural architecture search techniques as well as a new loss design. First, we propose an automatic feature enhance module named Auto-FEM by improved differential architecture search, which allows efficient multi-scale feature fusion and context e

Sahin Lale, Kamyar Azizzadenesheli, Babak Hassibi, Anima Anandkumar

We study the problem of system identification and adaptive control in partially observable linear dynamical systems. Adaptive and closed-loop system identification is a challenging problem due to correlations introduced in data collection. In this paper, we present the first model estimation method with finite-time guarantees in both open and closed-loop sys

Takashi Aoki, Ryuichi Ishimura, Yasunori Okada

In this paper, we consider the locally convex spaces of entire functions with growth given by proximate orders, and study the representation as a differential operator of a continuous homomorphism from such a space to another one. As a corollary, we give a characterization of continuous endomorphisms of such spaces.

Seung-Il Choi, Young-Hun Kim, Sun-Young Nam, Young-Tak Oh

We study the $H_n(0)$-module $\mathbf{S}^\sigma_\alpha$ due to Tewari and van Willigenburg, which was constructed using new combinatorial objects called standard permuted composition tableaux and decomposed into cyclic submodules. First, we show that every direct summand appearing in their decomposition is indecomposable and characterize when $\mathbf{S}^\si

Kishor Bharti, Tobias Haug, Vlatko Vedral, Leong-Chuan Kwek

The goal of machine learning is to facilitate a computer to execute a specific task without explicit instruction by an external party. Quantum foundations seeks to explain the conceptual and mathematical edifice of quantum theory. Recently, ideas from machine learning have successfully been applied to different problems in quantum foundations. Here, we compi

Weizhang Huang, Weishi Liu, Yufei Yu

Ionic flow carries electrical signals for cells to communicate with each other. The permanent charge of an ion channel is a crucial protein structure for flow properties while boundary conditions play a role of the driving force. Their effects on flow properties have been analyzed via a quasi-one-dimensional Poisson-Nernst-Planck model for small and relative

Qingyong Ren, Chenguang Fu, Qinyi Qiu, Shengnan Dai

Chemical doping is one of the most important strategies for tuning electrical properties of semiconductors, particularly thermoelectric materials. Generally, the main role of chemical doping lies in optimizing the carrier concentration, but there can potentially be other important effects. Here, we show that chemical doping plays multiple roles for both elec

Alexis Akira Toda

I estimate the Susceptible-Infected-Recovered (SIR) epidemic model for Coronavirus Disease 2019 (COVID-19). The transmission rate is heterogeneous across countries and far exceeds the recovery rate, which enables a fast spread. In the benchmark model, 28% of the population may be simultaneously infected at the peak, potentially overwhelming the healthcare sy

Oleg Evnin

We consider a Gaussian rotationally invariant ensemble of random real totally symmetric tensors with independent normally distributed entries, and estimate the largest eigenvalue of a typical tensor in this ensemble by examining the rate of growth of a random initial vector under successive applications of a nonlinear map defined by the random tensor. In the

Simon Kitson

Motivated by Wigner's theorem, a canonical construction is described that produces an Atiyah-Singer Dirac operator with both unitary and anti-unitary symmetries. This Dirac operator includes the Dirac operator for KR-theory as a special case, filling a long-standing gap in the literature. In order to make the construction, orientifold Spin-c-structures are d

Jian Wang

We prove sharp radial estimates using Besov spaces. We also prove the propagation of singularities in Besov spaces.

H. Itoyama, Sota Nakajima

We investigate the structure of the moduli space of 9D heterotic interpolating models with a complete set of Wilson line backgrounds and a radius parameter by computing the one-loop partition functions and the cosmological constants and by deriving the massless spectra, paying attention to the region $a \approx 0$ where supersymmetry is asymptotically restor

Bin Cheng, Xiangke Wang, Zhongkui Li

This paper investigates the distributed event-based consensus problem of switching networks satisfying the jointly connected condition. Both the state consensus of homogeneous linear networks and output consensus of heterogeneous networks are studied. Two kinds of event-based protocols based on local sampled information are designed, without the need to solv

Dominik Adolf

Consider $(\kappa^{+++},\kappa^{++}) \twoheadrightarrow (\kappa^+,\kappa)$ where $\kappa$ is an uncountable regular cardinal. By a result of Shelah's we have $\operatorname{cof}(X \cap \kappa^{++}) = \kappa$ for almost all $X \subset \kappa^{+++}$ witnessing this. Here we consider the question if there could be a similar result for $X \cap \kappa^+$. We use

I. Keramidas Charidakos, J. R. Myra, S. Ku, R. M. Churchill

The physical processes taking place at the edge region are crucial for the operation of tokamaks as they govern the interaction of hot plasma with the vessel walls. Numerical modeling of the edge with state-of-the-art codes attempts to elucidate interactions between neoclassical drifts, turbulence, poloidal and parallel flows that control the physical set-up

Hongfeng You, Shengwei Tian, Long Yu, Xiang Ma

To better retain the deep features of an image and solve the sparsity problem of the end-to-end segmentation model, we propose a new deep convolutional network model for medical image pixel segmentation, called MC-Net. The core of this network model consists of four parts, namely, an encoder network, a multiple max-pooling integration module, a cross multisc

Ingrid Irmer

A topology is defined on the mapping class group of a compact connected orientable surface. It is shown that a notion of "genericity" on subsets of the mapping class group arises from this definition. Many plausible results follow from this notion easily; for example, the set of pseudo-Anosov maps is shown to be generic, and can be assumed to have arbitrary

Shuhei Yokoo, Kohei Ozaki, Edgar Simo-Serra, Satoshi Iizuka

We propose an efficient pipeline for large-scale landmark image retrieval that addresses the diversity of the dataset through two-stage discriminative re-ranking. Our approach is based on embedding the images in a feature-space using a convolutional neural network trained with a cosine softmax loss. Due to the variance of the images, which include extreme vi

Yiding Yu, Soung Chang Liew, Taotao Wang

This paper investigates a futuristic spectrum sharing paradigm for heterogeneous wireless networks with imperfect channels. In the heterogeneous networks, multiple wireless networks adopt different medium access control (MAC) protocols to share a common wireless spectrum and each network is unaware of the MACs of others. This paper aims to design a distribut

Ya Zhou, Jianfeng Xu, Kazuyuki Tasaka, Zhibo Chen

Various blur distortions in video will cause negative impact on both human viewing and video-based applications, which makes motion-robust deblurring methods urgently needed. Most existing works have strong dataset dependency and limited generalization ability in handling challenging scenarios, like blur in low contrast or severe motion areas, and non-unifor

Michele Peruzzi, Sudipto Banerjee, Andrew O. Finley

We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioning the spatial domain and modeling the regions in the partition using a sparsity-inducing directed acyclic graph (DAG). We extend the model over the DAG to a well-