March 2020 arXiv papers — page 39

Vishnu Veerathu, Yogesh Tripathi

\textproc{Weighted Vertex Cover} is a variation of an extensively studied NP-complete problem, \textproc{Vertex Cover}, in which we are given a graph, $G = (V,E,w)$, where function $w:V \rightarrow \mathbb{Q}^{+}$ and a parameter $k$. The objective is to determine if there exists a vertex cover, $S$, such that $\sum_{v \in S}w(v) \leq k$. In our work, we fir

Y. Itoh, S. Adachi

We report $^{31}$P NMR studies of an oriented polycrystalline superconductor of Ba$_{0.5}$Sr$_{0.5}$Fe$_{2}$(As$_{1-x}$P$_{x}$)$_{2}$ with $x\sim$ 0.4 ($T_\mathrm{c}$ = 29 K) at $T$ = 14$-$325 K and $B_{0}$ = 1 and 5 T. The $^{31}$P Knight shift $K_{ab}$ at $B_{0}\perp c$ shows a nearly $T$-independent uniform spin susceptibility above $T_\mathrm{c}$ and a s

Xiaofen Huang, Tinggui Zhang, Naihuan Jing

We study the uncertainty relation in the product form of variances and obtain some new uncertainty relations with weight, which are shown to be tighter than those derived from the Cauchy Schwarz inequality.

Ramesh Kolluru, N. Venkata Raghavendra, S. V. Raghurama Rao, G. N. Sekha

The nonlinear convection terms in the governing equations of compressible fluid flows are hyperbolic in nature and are nontrivial for modelling and numerical simulation. Many numerical methods have been developed in the last few decades for this purpose and are typically based on Riemann solvers, which are strongly dependent on the underlying eigen-structure

Pieter Tibboel

We prove that equally spaced choreography solutions of a large class of $n$-body problems including the classical $n$-body problem and a subset of quasi-homogeneous $n$-body problems, have equal masses if the dimension of the space spanned by the point masses is $n-1$, $n-2$, or, if $n$ is odd, if the dimension is $n-3$. If $n$ is even and the dimension is $

Arthur G. Suvorov, Kostas D. Kokkotas

Many gamma-ray bursts are followed by periods of extended emission. At least in some cases, the burst afterglow may be powered by a rapidly rotating, highly-magnetised neutron star, which spins down due to electromagnetic and gravitational wave emission. Such a remnant is likely to strongly precess in the early stages of its life, which would lead to modulat

Trambak Bhattacharyya, Abhik Mukherjee

We study the propagation of energy density perturbation in a hot, ideal quark-gluon medium in which quarks and gluons follow the Tsallis-like momentum distributions. We have observed that a non-extensive MIT bag equation of state obtained with the help of the quantum Tsallis-like distributions gives rise to a breaking wave solution of the equation dictating

Mridusmita Buragohain, Amit Pathak, Itsuki Sakon, Takashi Onaka

Polycyclic Aromatic Hydrocarbon (PAH) molecules have been long adjudged to contribute to the frequently detected distinct emission features at 3.3, 6.2, 7.7, 8.6, 11.2 and 12.7 {\mu}m with weaker and blended features distributed in the 3-20 {\mu}m region. The comparatively weaker 3.4 {\mu}m emission feature has been attributed to have an aliphatic origin as

Chenglong Wang, Masahiro Oda, Kensaku Mori

In this work, we present a memory-efficient fully convolutional network (FCN) incorporated with several memory-optimized techniques to reduce the run-time GPU memory demand during training phase. In medical image segmentation tasks, subvolume cropping has become a common preprocessing. Subvolumes (or small patch volumes) were cropped to reduce GPU memory dem

Ramiro dell'Erba

In a previous work we considered a two-dimensional lattice of particles and calculated its time evolution by using an interaction law based on the spatial position of the particles themselves. The model reproduced the behaviour of deformable bodies both according to the standard Cauchy model and second gradient theory; this success led us to use this method

Hu Liang, Shengrong Zhao

How to extract more and useful information for single image super resolution is an imperative and difficult problem. Learning-based method is a representative method for such task. However, the results are not so stable as there may exist big difference between the training data and the test data. The regularization-based method can effectively utilize the s

Nicolas Weber, Felipe Huici

Modern high performance computing clusters heavily rely on accelerators to overcome the limited compute power of CPUs. These supercomputers run various applications from different domains such as simulations, numerical applications or artificial intelligence (AI). As a result, vendors need to be able to efficiently run a wide variety of workloads on their ha

Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, Guillermo Garrido

We present Felix --- a flexible text-editing approach for generation, designed to derive the maximum benefit from the ideas of decoding with bi-directional contexts and self-supervised pre-training. In contrast to conventional sequence-to-sequence (seq2seq) models, Felix is efficient in low-resource settings and fast at inference time, while being capable of

Shingo Akama, Shin'ichi Hirano, Tsutomu Kobayashi

It has been found that the primordial non-Gaussianity of the curvature perturbation in the case of non-Bunch-Davies initial states can be enhanced compared with those in the case of the Bunch-Davies one due to the interactions among the perturbations on subhorizon scales. The purpose of the present paper is to investigate whether tensor non-Gaussianities can

Min Shi, Jia-Qi Zhang, Shu-Yu Chen, Lin Gao

Coloring line art images based on the colors of reference images is an important stage in animation production, which is time-consuming and tedious. In this paper, we propose a deep architecture to automatically color line art videos with the same color style as the given reference images. Our framework consists of a color transform network and a temporal co

Rababah Baha, Saha Ullash

This paper presents Area coverage of homogenous wireless sensor network using computational geometry. The concepts related to both coverage wireless sensor network and computational geometry have been introduced. Then, the problem has been formulated. After that, current proposed algorithms of hole detection and healing have been investigated and discussed.

Nicholas J. Bianchi, Leonard M. Kahn

Effective medium theory is used to model a one dimensional lossy dielectric quaternary photonic crystal as a homogeneous slab. The unit cell of the original crystal is inversion symmetric with layer sequence ACBCA. The behavior of the branch frequency singularities in the effective refractive index is investigated as parameters in the layered structure chang

Federico Scavia

Let $G$ be a finite group, and let $\{B_{\mathbb{C}}G\}$ the class of its classifying stack $B_{\mathbb{C}}G$ in Ekedahl's Grothendieck ring of algebraic $\mathbb{C}$-stacks $K_0(\operatorname{Stacks}_{\mathbb{C}})$. We show that if $B_{\mathbb{C}}G$ has the mixed Tate property, the invariants $H^i(\{B_{\mathbb{C}}G\})$ defined by T. Ekedahl are zero for all

Ufuk Aydemir, Bob Holdom, Jing Ren

Primordial black holes that survive until the present have been considered as a dark matter candidate. In this paper we argue that primordial 2-2-hole remnants provide a more promising and testable option. 2-2-holes arise in quadratic gravity as a new family of classical solutions for ultracompact matter distributions and they possess the black hole exterior

Karina A. Roundtree, Jason R. Cody, Jennifer Leaf, H. Onan Demirel

Interest in collective robotic systems has increased rapidly due to the potential benefits that can be offered to operators, such as increased safety and support, who perform challenging tasks in high-risk environments. Human-collective transparency research has focused on how the design of the algorithms, visualizations, and control mechanisms influence hum

Hufei Zhu, Yanpeng Wu

In the above paper, the optimal-ordered successive interference cancellation (SIC) detector proposed for multiple input multiple output (MIMO) systems was claimed to require a lower computational complexity than the optimal-ordered SIC detector proposed in the paper "An Improved Square-Root Algorithm for V-BLAST Based on Efficient Inverse Cholesky Factorizat

Wenlong Wang, Li-Chen Zhao, Efstathios G. Charalampidis, Panayotis G. Kevrekidis

In this work, we explore systematically various SO(2)-rotation-induced multiple dark-dark soliton breathing patterns obtained from stationary and spectrally stable multiple dark-bright and dark-dark waveforms in trapped one-dimensional, two-component atomic Bose-Einstein condensates (BECs). The stationary states stem from the associated linear limits (as the

Ly V. Nguyen, A. Lee Swindlehurst, Duy H. N. Nguyen

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much m

Md Shamim Hussain

In this work, we have investigated a 2D model of band-to-band tunneling based on 2-band model and implemented it using 2D NEGF formalism. Being 2D in nature, this model better addresses the variation in the directionality of the tunneling process occurring in most practical TFET device structures. It also works as a compromise between semi-classical and mult

Ganesh Ji Omar, Mengsha Li, Xiao Chi, Zhen Huang

Using interlayer interaction to control functional heterostructures with atomic-scale designs has become one of the most effective interface-engineering strategies nowadays. Here, we demonstrate the effect of a crystalline LaFeO3 buffer layer on amorphous and crystalline LaAlO3/SrTiO3 heterostructures. The LaFeO3 buffer layer acts as an energetically favored

Pu Zhao, Meng Zhang, Hui Yu, Hanwen Luo

This paper studies the beamforming design problem of a multi-user downlink network, assuming imperfect channel state information known to the base station. In this scenario, the base station is equipped with multiple antennas, and each user is wiretapped by a specific eavesdropper where each user or eavesdropper is equipped with one antenna. It is supposed t

Kun Xie, Wen Chen, Lili Wei

Secure communication in the Multi-user and Multi-eavesdropper (MUME) scenario is considered in this paper. It has be shown that secrecy can be improved when the transmitter simultaneously transmits information-bearing signal to the intended receivers and artificial noise to confuse the eavesdroppers. Several processing schemes have been proposed to limit the

Kevin Kuo, Daniel Lupton

Machine learning methods have garnered increasing interest among actuaries in recent years. However, their adoption by practitioners has been limited, partly due to the lack of transparency of these methods, as compared to generalized linear models. In this paper, we discuss the need for model interpretability in property & casualty insurance ratemaking, pro

M. Lakshmanan, R. Arun, Avadh Saxena

We study the existence of intrinsic localized one-spin excitation in the Heisenberg one-dimensional ferromagnetic spin chain in the presence of perpendicular and parallel external magnetic fields and current with spin-transfer torque and field-like torque. The Landau-Lifshitz-Gilbert-Slonczewski(LLGS) equation is exactly solved for the one spin excitation in

Rahul Trivedi, Alex White, Shanhui Fan, Jelena Vuckovic

We study the scattering of photons from periodically modulated quantum-optical systems. For excitation-number conserving quantum optical systems, we connect the analytic structure of the frequency-domain N-photon scattering matrix of the system to the Floquet decomposition of its effective Hamiltonian. Furthermore, it is shown that the first order contributi

Yuhang Zhao, Naoki Aritomi, Eleonora Capocasa, Matteo Leonardi

The astrophysical reach of current and future ground-based gravitational-wave detectors is mostly limited by quantum noise, induced by vacuum fluctuations entering the detector output port. The replacement of this ordinary vacuum field with a squeezed vacuum field has proven to be an effective strategy to mitigate such quantum noise and it is currently used

Ziming Zhu, Chunyan Liao, Si Li, Xiaoming Zhang

Magnetic materials with high mobilities are intriguing subject of research from both fundamental and application perspectives. Based on first-principle calculations, we investigate the physical properties of the already synthesized AMnBi(A =K, Rb, Cs)-family materials. We show that these materials are antiferromagnetic (AFM), with Neel temperatures above 300

Xuesong Li, Jose Guivant, Subhan Khan

The task of detecting 3D objects is important to various robotic applications. The existing deep learning-based detection techniques have achieved impressive performance. However, these techniques are limited to run with a graphics processing unit (GPU) in a real-time environment. To achieve real-time 3D object detection with limited computational resources

Jing Liu, Fengnian Xia, Di Xiao, Javier García de Abajo

Semimetals are being explored for their unique advantages in low-energy high-speed photodetection, although they suffer from serious drawbacks such as an intrinsically high dark current. In this perspective, we envision the exploitation of topological effects in the photoresponse of these materials as a promising route to circumvent these problems. We overvi

P. N. Timonin

The percolation of Potts spins with equal values in Potts model on graphs (networks) is considered. The general method for finding the Potts clusters size distributions is developed. It allows for full description of percolation transition when giant cluster of equal-valued Potts spins appears. The method is applied to the short-ranged q-state ferromagnetic

Saumya Shivam, Christopher L. Baldwin, John Barton, Mehran Kardar

We study Eigen's model of quasi-species, characterized by sequences that replicate with a specified fitness and mutate independently at single sites. The evolution of the population vector in time is then closely related to that of quantum spins in imaginary time. We employ multiple perspectives and tools from interacting quantum systems to examine growth an

Naoko Koide-Majima, Kei Majima

The application of near-term quantum devices to machine learning (ML) has attracted much attention. In one such attempt, Mitarai et al. (2018) proposed a framework to use a quantum circuit for supervised ML tasks, which is called quantum circuit learning (QCL). Due to the use of a quantum circuit, QCL can employ an exponentially high-dimensional Hilbert spac

Bing-Zhong Hu, Lei-Lei Nian, Jing-Tao Lü

We show that a current-carrying coherent electron conductor can be treated as effective bosonic energy reservoir involving different types of electron-hole pair excitation. For weak electron-boson coupling, hybrid energy transport between nonequilibrium electrons and bosons can be described by a Landauer-like formula. This allows for unified account of a var

Shijin Ding, Zhijun Ji, Quanrong Li

This paper is concerned with the Rayleigh-Taylor instability for the nonhomogeneous incompressible Navier-Stokes equations with Navier-slip boundary conditions around a steady-state in an infinite slab, where the Navier-slip coefficients do not have defined sign and the slab is horizontally periodic. Motivated by [18], we extend the result from Dirichlet bou

Pradipta Ghosh, Xiaochen Liu, Hang Qiu, Marcos A. M. Vieira

Public cameras often have limited metadata describing their attributes. A key missing attribute is the precise location of the camera, using which it is possible to precisely pinpoint the location of events seen in the camera. In this paper, we explore the following question: under what conditions is it possible to estimate the location of a camera from a si

Zhongguo Li, Fan Lyu, Wei Feng, Song Wang

With the development of Augmented Reality (AR), egocentric action recognition (EAR) plays important role in accurately understanding demands from the user. However, EAR is designed to help recognize human-machine interaction in single egocentric view, thus difficult to capture interactions between two face-to-face AR users. Paired egocentric interaction reco

Piyush Gupta, Demetris Coleman, Joshua E. Siegel

Automated vehicles' neural networks suffer from overfit, poor generalizability, and untrained edge cases due to limited data availability. Researchers synthesize randomized edge-case scenarios to assist in the training process, though simulation introduces potential for overfit to latent rules and features. Automating worst-case scenario generation could yie

Xiaolei Li, Wenhua Song, Dazhi Gao, Wei Gao

A U-Net is trained to recover acoustic interference striations (AISs) from distorted ones. A random mode-coupling matrix model is introduced to generate a large number of training data quickly, which are used to train the U-Net. The performance of AIS recovery of the U-Net is tested in range-dependent waveguides with nonlinear internal waves (NLIWs). Althoug

Taras Goy, Mark Shattuck

In this paper, we evaluate determinants of some families of Toeplitz-Hessenberg matrices having tribonacci number entries. These determinant formulas may also be expressed equivalently as identities that involve sums of products of multinomial coefficients and tribonacci numbers. In particular, we establish a connection between the tribonacci and the Fibonac

Kai Sun, Yan Wang, Zheng-Hao Liu, Xiao-Ye Xu

Remote spatial indistinguishability of identical subsystems as a direct controllable quantum resource at distant sites has not been yet experimentally proven. We design a setup capable to tune the spatial indistinguishability of two photons by independently adjusting their spatial distribution in two distant regions, which leads to polarization entanglement

Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu

Over the past few years, state-of-the-art image segmentation algorithms are based on deep convolutional neural networks. To render a deep network with the ability to understand a concept, humans need to collect a large amount of pixel-level annotated data to train the models, which is time-consuming and tedious. Recently, few-shot segmentation is proposed to

Nikita Evseev, Alexander Menovschikov

We apply the metrical approach to Sobolev spaces, which arise in various evolution PDEs. Functions from those spaces are defined on an interval and take values in a family of Banach spaces. In this case we adapt the definition of Newtonian spaces. For a monotone family, we show the existence of weak derivative, obtain an isomorphism to the standard Sobolev s

Yuliang Guo, Guang Chen, Peitao Zhao, Weide Zhang

We present a generalized and scalable method, called Gen-LaneNet, to detect 3D lanes from a single image. The method, inspired by the latest state-of-the-art 3D-LaneNet, is a unified framework solving image encoding, spatial transform of features and 3D lane prediction in a single network. However, we propose unique designs for Gen-LaneNet in two folds. Firs

Soudeep Deb, Manidipa Majumdar

The ongoing pandemic of Coronavirus disease (COVID-19) emerged in Wuhan, China in the end of 2019. It has already affected more than 300,000 people, with the number of deaths nearing 13000 across the world. As it has been posing a huge threat to global public health, it is of utmost importance to identify the rate at which the disease is spreading. In this s

A. Mandilara, Y. Balkybek, V. M. Akulin

We design a scheme for detecting a single photon loss from multi-modal quantum signals transmitted via a fiber or in free space. This consists of a special type of unitary coding transformation, the controlled-squeezing, applied prior to the transmission on the signal composed by information and ancilla modes. At the receiver, the inverse unitary transformat

Naghmeh Akhavan, Dawoud Ahmadi Dastjerdi

In 1988 Boyle and Krieger defined sub-matrices for representation matrices of sofic shift. This paper presents some details of relations between integral sub-matrices and representation matrices. Besides, we express a new version of the Decomposition Theorem by sub-matrices. Generally, strong shift equivalence (conjugacy) of sub-matrices does not apply to re

Masanori Asakura

The Ross symbol is defined to be an element {1-z,1-w\} in K_2 of a Fermat curve z^n+w^m=1. Ross showed that it is non-torsion by computing the Beilinson regulator. In this paper, we introduce a generalization of the Ross symbols in K_{d+1} of a variety (1-x_0^{n_0})\cdots(1-x_d^{n_d})=t. The main result is that the Beilinson regulator is described by the hyp

Xue Liang, Xichao Zhang, Jing Xia, Motohiko Ezawa

Magnetic skyrmions are particle-like topological spin configurations, which can carry binary information and thus are promising building blocks for future spintronic devices. In this work, we investigate the relationship between the skyrmion dynamics and the characteristics of injected current in a skyrmion-based spin torque nano-oscillator, where the excita

Rui Qiao, Ngoc Hieu Tran, Baozhen Shan, Ali Ghodsi

Traditional vaccines against viruses are designed to target their surface proteins, i.e., antigens, which can trigger the immune system to produce specific antibodies to capture and neutralize the viruses. However, viruses often evolve quickly, and their antigens are prone to mutations to avoid recognition by the antibodies (antigenic drift). This limitation

Yijie Lou, Yisheng Fang, Zhichao Ruan

Topological physics desires stable methods to measure the polarization singularities in optical vector fields. Here a periodic plasmonic metasurface is proposed to perform divergence computation of vectorial paraxial beams. We design such an optical device to compute spatial differentiation along two directions, parallel and perpendicular to the incident pla

Chao Zhang, Xian-Yong Ding, Li-Yong Gan, Yu Cao

The exploration of topological phases in carbon allotropes offers a fascinating avenue to realize topological devices based on carbon materials. Here, using first-principles calculations, we propose a novel metastable carbon allotrope, which possesses exotically helical carbon chains bridged by quadrangle-rings. This unique structure with sp2-sp3 bonding net

Jiaming Song, Lunjia Hu, Michael Auli, Yann Dauphin

Memorization in over-parameterized neural networks could severely hurt generalization in the presence of mislabeled examples. However, mislabeled examples are hard to avoid in extremely large datasets collected with weak supervision. We address this problem by reasoning counterfactually about the loss distribution of examples with uniform random labels had t

Shengqi Yang, Xiaolong Du, Andrew J. Benson, Anthony R. Pullen

Understanding the non-linear dynamics of satellite halos (a.k.a. "sub-halos") is important for predicting the abundance and distribution of dark matter substructures and satellite galaxies, and for distinguishing among microphysical dark matter models using observations. Typically, modeling these dynamics requires large N-body simulations with high resolutio

Keisuke Teramoto

We show relation between sign of Gaussian curvature of cuspidal edge and geometric invariants through types of singularities of Gauss map. Moreover, we define and characterize positivity/negativity of cusps of Gauss maps by geometric invariants of cuspidal edges, and show relation between sign of cusps and of the Gaussian curvature.

John Aiken, Fabio L. Traversa

In this work, we introduce the concept of an entirely new circuit architecture based on the novel, physics-inspired computing paradigm: Memcomputing. In particular, we focus on digital memcomputing machines (DMMs) that can be designed leveraging properties of non-linear dynamical systems; ultimate descriptors of electronic circuits. The working principle of

Yoichi Matsuo, Tatsuaki Kimura, Ken Nishimatsu

When a failure occurs in a network, network operators need to recognize service impact, since service impact is essential information for handling failures. In this paper, we propose Deep learning based Service Impact Prediction (DeepSIP), a system to predict the time to recovery from the failure and the loss of traffic volume due to the failure in a network

Ambrish Kumar Srivastava, Abhishek Kumar, Gargi Tiwari, Ratnesh Kumar

A novel strain of coronavirus, namely, COVID-19 has been identified in Wuhan city of China in December 2019. There are no specific therapies available and investigations regarding the treatment of the COVID-19 are still lacking. This prompted us to perform a preliminary in silico study on the COVID-19 protease with anti-malarial compounds in the search of po

Mingjie Jian, Daisuke Taniguchi, Noriyuki Matsunaga, Naoto Kobayashi

A line-depth ratio (LDR) of two spectral lines with different excitation potentials is expected to be correlated with the effective temperature ($T_\mathrm{eff}$). It is possible to determine $T_\mathrm{eff}$ of a star with a precision of tens of Kelvin if dozens or hundreds of tight LDR-$T_\mathrm{eff}$ relations can be used. Most of the previous studies on

Miklos Bona, Elijah DeJonge

We investigate permutations and involutions that avoid a pattern of length three and have a {\em unique} longest increasing subsequence.

I-Ta Lee, Manish Marwah, Martin Arlitt

While applications of machine learning in cyber-security have grown rapidly, most models use manually constructed features. This manual approach is error-prone and requires domain expertise. In this paper, we design a self-supervised sequence-to-sequence model with attention to learn an embedding for data routinely used in cyber-security applications. The me

Yan-Jun Zhao, Xun-Wei Xu, Hui Wang, Yu-xi Liu

We propose to periodically modulate the onsite energy via two-tone drives, which can be furthermore used to engineer artificial gauge potential. As an example, we show that the fermionic ladder model penetrated with effective magnetic flux can be constructed by superconducting flux qubits using such two-tone-drive-engineered artificial gauge potential. In th

Ruixuan Liu, Yang Cao, Masatoshi Yoshikawa, Hong Chen

As massive data are produced from small gadgets, federated learning on mobile devices has become an emerging trend. In the federated setting, Stochastic Gradient Descent (SGD) has been widely used in federated learning for various machine learning models. To prevent privacy leakages from gradients that are calculated on users' sensitive data, local different

Shuchi Chawla, Yifeng Teng, Christos Tzamos

We study the multi-item mechanism design problem where a monopolist sells $n$ heterogeneous items to a single buyer. We focus on buy-many mechanisms, a natural class of mechanisms frequently used in practice. The buy-many property allows the buyer to interact with the mechanism multiple times instead of once as in the more commonly studied buy-one mechanisms

Yoshiki Matsushita, Takuya Nakashima, Keisuke Teramoto

We investigate geometric properties of surfaces given by certain formulae. In particular, we calculate the singular curvature and the limiting normal curvature of such surfaces along the set of singular points consisting of singular points of the first kind. Moreover, we study fold singular points of smooth maps.

V. G. Bagrov, D. M. Gitman, A. A. Shishmarev, A. J. D. Farias

In the beginning, the synchrotron radiation (SR) was studied by classical methods using the Li\'{e}nard-Wiechert potentials of electric currents. Subsequently, quantum corrections to the obtained classical formulas were studied, considering the emission of photons arising from electronic transitions between spectral levels, described in terms of the Dirac eq

Dong Yan, Yijie Zeng, Yishi Lin, Lingyong Zeng

The interplay between superconductivity and charge density wave (CDW)/metal-to-insulator transition (MIT) has long been interested and studied in condensed matter physics. Here we study systematically the charge density wave and superconductivity properties in the solid solutions Zn1-xCuxIr2-yN(N = Al, Ti, Rh)yTe4. Resistivity, magnetic susceptibility and sp

Takeshi Chiba, Fabio Chibana, Masahide Yamaguchi

We study the frame dependence/independence of cosmological observables under disformal transformations, extending the previous results regarding conformal transformations, and provide the correspondence between Jordan-frame and Einstein-frame variables. We consider quantities such as the gravitational constant in the Newtonian limit, redshift, luminosity and

Liuxian Zhao, Miao Yu

In this paper, we explore the concept of structural Luneburg Lens (SLL) as a design framework for performing dynamic structural tailoring to obtain a structural wave cloak and a structural waveguide. The SLL is a graded refractive index lens, which is realized by using a variable thickness structure defined in a thin plate. Due to the thickness variation of

Zhi-Gang Wang

In this article, we construct the color-singlet-color-singlet type currents and the color-singlet-color-singlet-color-singlet type currents to study the scalar $D^*\bar{D}^*$, $D^*D^*$ tetraquark molecular states and the vector $D^*D^*\bar{D}^*$, $D^*D^*D^*$ hexaquark molecular states with the QCD sum rules in details. In calculations, we choose the pertinen

COHERENT Collaboration, D. Akimov, J. B. Albert, P. An

We report the first measurement of coherent elastic neutrino-nucleus scattering (\cevns) on argon using a liquid argon detector at the Oak Ridge National Laboratory Spallation Neutron Source. Two independent analyses prefer \cevns over the background-only null hypothesis with greater than $3\sigma$ significance. The measured cross section, averaged over the

Qing-Ming Cheng, Zhi Li, Guoxin Wei

In this paper, we classify $3$-dimensional complete self-shrinkers in Euclidean space $\mathbb R^{4}$ with constant squared norm of the second fundamental form $S$ and constant $f_{4}$.

Lei Zhou, Zixin Luo, Tianwei Shen, Jiahui Zhang

Temporal camera relocalization estimates the pose with respect to each video frame in sequence, as opposed to one-shot relocalization which focuses on a still image. Even though the time dependency has been taken into account, current temporal relocalization methods still generally underperform the state-of-the-art one-shot approaches in terms of accuracy. I

Suat Gumussoy, Wim Michiels

H-infinity controllers are frequently used in control theory due to their robust performance and stabilization. Classical H-infinity controller synthesis methods for finite dimensional LTI MIMO plants result in high-order controllers for high-order plants whereas low-order controllers are desired in practice. We design fixed-order H-infinity controllers for

Meiyu Cui, Jingmei Chang, Ming-Jing Zhao, Xiaofen Huang

We study the equivalence of mixed states under local unitary transformations. First we express quantum states in Bloch representation. Then based on the coefficient matrices, some invariants are constructed. This method and results can be extended to multipartite high dimensional system.

Oleksii Sidorov, Ronghang Hu, Marcus Rohrbach, Amanpreet Singh

Image descriptions can help visually impaired people to quickly understand the image content. While we made significant progress in automatically describing images and optical character recognition, current approaches are unable to include written text in their descriptions, although text is omnipresent in human environments and frequently critical to unders

Meiyu Cui, Xiaofen Huang, Tinggui Zhang

We study the trade-off relations on the maximal violation of CHSH tests for the multi-qubit pure states. Firstly, according to the classification of 3-qubit pure states under stochastic local operations and classical communication, four trade-off relations on CHSH tests are provided. The process of proof also provides the method of calculating its exact valu

Xiantao Jiang, Xuemei Zhao, Wenli Bao, Rongchao Shi

By breaking the restriction of mirrors, random lasers from a disordered medium have found unique applications spanning from displays, spectroscopy, biomedical treatments, to Li-Fi.Gain media in the form of two-dimension with distinct physical and chemical properties may lead to the next-generation of random lasers. Graphdiyne, a 2D graphene allotrope with in

Xiwang Cao

Perfect (quantum) state transfer has been proved to be an effective model for quantum information processing. In this paper, we give a characterization of cubelike graphs having perfect edge state transfer. By using a lifting technique, we show that every bent function, and some semi-bent functions as well, can produce some graphs having PEST. Some concrete

Daisuke Ishii, Tomohito Yabu

Interval arithmetic libraries provide the four elementary arithmetic operators for operand intervals bounded by floating-point numbers. Actual implementations need to make a large case analysis that considers, e.g., magnitude relations between all pairs of argument bounds, positional relations between the arguments and zero, and handling of the special value

Shimin Wang, Hongwei Zhang, Zhiyong Chen

This paper studies cooperative tracking problem of heterogeneous Euler-Lagrange systems with an uncertain leader. Different from most existing works, system dynamic knowledge of the leader node is unaccessible to any follower node in our paper. Distributed adaptive observers are designed for all follower nodes, simultaneously estimate the state and parameter

Kei Nemoto, Shweta Jain

Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through crowdsourcing services like Amazon Mechanical Turk. With the advent of the Internet, data has become abundant and a large number of machine learning based systems started being train

Zhipeng Liu, Yinhui Han, Jianwei Fan, Lin Zhang

Cellular vehicle-to-everything (C-V2X) communication, as a part of 5G wireless communication, has been considered one of the most significant techniques for Smart City. Vehicles platooning is an application of Smart City that improves traffic capacity and safety by C-V2X. However, different from vehicles platooning travelling on highways, C-V2X could be more

K. Muhammed Shafi, Kazunori Iida, Emi Tsutsumi, Akiharu Miyanaga

We discuss photo-luminescence characteristics of CdSe core/shell quantum dots at cryogenic temperatures using a hybrid system of a single quantum dot and an optical nanofiber. The key point is to control the emission species of quantum dot to charged excitons, known as trions, which have superior characteristics to neutral excitons. We investigate the photoc

S. S. Huh, Y. S. Kim, W. S. Kyung, J. K. Jung

We performed angle-resolved photoemission spectroscopy (ARPES) studies of the electronic structure of the nematic phase in LaFeAsO. Degeneracy breaking between the dxz and dyz hole bands near the {\Gamma} and M point is observed in the nematic phase. Different temperature dependent band splitting behaviors are observed at the {\Gamma} and M points. The energ

Sebastián Torres

A smooth projective variety $Y$ is said to satisfy Bott vanishing if $\Omega_Y^j\otimes L$ has no higher cohomology for every $j$ and every ample line bundle $L$. Few examples are known to satisfy this property. Among them are toric varieties, as well as the quintic del Pezzo surface, recently shown by Totaro. Here we present a new class of varieties satisfy

Timothy Ferguson

Define the monomials $e_n(x) := x^n$ and let $L$ be a linear functional. In this paper we describe a method which, under specified conditions, produces approximations for the value $L(e_0 )$ in terms of Hankel determinants constructed from the values $L(e_1 )$, $L(e_2 )$, . . . . Many constants of mathematical interest can be expressed as the values of integ

Yu Ye, Hao Chen, Ming Xiao, Mikael Skoglund

The alternating direction method of multipliers (ADMM) has been recently recognized as a promising optimizer for large-scale machine learning models. However, there are very few results studying ADMM from the aspect of communication costs, especially jointly with privacy preservation, which are critical for distributed learning. We investigate the communicat

Andrey Sarantsev

A powerful tool for studying long-term convergence of a Markov process to its stationary distribution is a Lyapunov function. In some sense, this is a substitute for eigenfunctions. For a stochastically ordered Markov process on the half-line, Lyapunov functions can be used to easily find explicit rates of convergence. Our earlier research focused on exponen

Soham Gadgil, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan

The Blood-Oxygen-Level-Dependent (BOLD) signal of resting-state fMRI (rs-fMRI) records the temporal dynamics of intrinsic functional networks in the brain. However, existing deep learning methods applied to rs-fMRI either neglect the functional dependency between different brain regions in a network or discard the information in the temporal dynamics of brai

Fabricio Mendoza-Granada, Marcos Villagra

Spectral sparsification is a technique that is used to reduce the number of non-zero entries in a positive semidefinite matrix with little changes to its spectrum. In particular, the main application of spectral sparsification is to construct sparse graphs whose spectra are close to a given dense graph. We study spectral sparsification under the assumption t

Seyon Sivarajah, Silas Dilkes, Alexander Cowtan, Will Simmons

We present t$|$ket$\rangle$, a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t$|$ket$\rangle$ is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively ben

Li-Hua Song, Shang-Fei Xin, Yin-Jie Zhang

At a next-to-leading order calculation, the initial-state energy loss of the quark is investigated by means of the Drell-Yan experimental data including the new E906 measurements. Furthermore, the incoming gluon energy loss effect embedded in the Compton scattering subprocess of the nuclear Drell-Yan process is also examined. The next-to-leading order comput

Cheng Meng, Xinlian Zhang, Jingyi Zhang, Wenxuan Zhong

We consider the problem of approximating smoothing spline estimators in a nonparametric regression model. When applied to a sample of size $n$, the smoothing spline estimator can be expressed as a linear combination of $n$ basis functions, requiring $O(n^3)$ computational time when the number of predictors $d\geq 2$. Such a sizable computational cost hinders

Shangbang Long, Cong Yao

Synthetic data has been a critical tool for training scene text detection and recognition models. On the one hand, synthetic word images have proven to be a successful substitute for real images in training scene text recognizers. On the other hand, however, scene text detectors still heavily rely on a large amount of manually annotated real-world images, wh

Lie Ju, Xin Wang, Xin Zhao, Huimin Lu

The need for comprehensive and automated screening methods for retinal image classification has long been recognized. Well-qualified doctors annotated images are very expensive and only a limited amount of data is available for various retinal diseases such as age-related macular degeneration (AMD) and diabetic retinopathy (DR). Some studies show that AMD an