arXiv subjects

Xin Liu

Publications and source records attributed to Xin Liu.

At least 865 records · Page 48Linked to original sources

arXiv2020

An orthogonalization-free parallelizable framework for all-electron calculations in density functional theory

All-electron calculations play an important role in density functional theory, in which improving computational efficiency is one of the most needed and challenging tasks. In the model formulations, both nonlinear eigenvalue problem and total energy minimization problem pursue orthogonal solutions. Most existing algorithms for solving these two models invoke orthogonalization process either explicitly or implicitly in each iteration. Their efficiency suffers from this process in view of its cubic complexity and low parallel scalability in terms of the number of electrons for large scale systems. To break through this bottleneck, we propose an orthogonalization-free algorithm framework based on the total energy minimization problem. It is shown that the desired orthogonality can be gradually achieved without invoking orthogonalization in each iteration. Moreover, this framework fully consists of Basic Linear Algebra Subprograms (BLAS) operations and thus can be naturally parallelized. The global convergence of the proposed algorithm is established. We also present a precondition technique which can dramatically accelerate the convergence of the algorithm. The numerical experiments on all-electron calculations show the efficiency and high scalability of the proposed algorithm.

physics.comp-ph↗

arXiv2020

A Chandra X-ray Survey of Optically Selected AGN Pairs

We present a Chandra archival study of optically selected AGN pairs at a median redshift $\bar{z} \sim$ 0.1. Out of 1286 AGN pairs (with projected separations $r_p < 100$ kpc and velocity offsets $Δv < 600$ km s$^{-1}$) optically identified from the Sloan Digital Sky Survey Seventh Data Release, we find 67 systems with archival Chandra observations, which represent the largest sample of optically selected AGN pairs studied in the X-ray. Among the 67 AGN pairs, 21 systems have both nuclei detected in the X-rays, 36 have one nucleus detected in the X-rays, whereas 10 have no X-ray detection. The X-ray detection rate, 78/134 = 58% ($\pm 7$% 1$σ$ Poisson errors), is significantly higher than that (23/134 = $17\% \pm 4\%$) of a comparison sample of star-forming galaxy pairs, lending support to the optical AGN classification. In the conservative case where X-ray contamination from star formation is removed, the X-ray detection rate becomes $27\% \pm 4\%$, consistent with predictions from latest galaxy merger simulations. The 2-10 keV X-ray luminosity $L_{2-10~{\rm keV}}$ increases with decreasing projected separation in AGN pairs for $r_p \gtrsim 15$ kpc, suggesting an enhancement of black hole accretion even in early-stage mergers. On the other hand, $L_{2-10~{\rm keV}}$ appears to decrease with decreasing projected separation at $r_p \lesssim 15$ kpc, which is contradictory to predictions from merger simulations. The apparent decrease in $L_{2-10~{\rm keV}}$ of AGN pairs at $r_p \lesssim 15$ kpc may be caused by: (i) enhanced absorbing columns from merger-induced gas inflows, (ii) feedback effects from early-stage mergers, and/or (iii) small number statistics. Future X-ray studies with larger samples are needed to put our results on firmer statistical ground.

astro-ph.GA↗

arXiv2020

Global Solutions to Compressible Navier-Stokes Equations With Spherically Symmetric Motion and Free Boundary

This work is devoted to study the global existence of strong and classical solutions to compressible Navier-Stokes equations with or without density jump on the moving boundary for spherically symmetric motion. We establish a unified method to track the propagation of regularity of strong and classical solutions which works for the cases when density connects to vacuum continuously and with a jump simultaneously. The result we obtain is able to deal with both strong solutions with physical vacuum for which the sound speed is $1/2$-Hölder continuous across the boundary, and classical solutions with physical vacuum when $ 1 < γ< 3 $. In contrast to the previous results of global weak solutions, we track the regularity globally-in-time up to the symmetric center and the moving boundary. In particular, the free boundary can be traced.

math.AP↗

arXiv2020

Dust Reverberation Mapping in Distant Quasars from Optical and Mid-Infrared Imaging Surveys

The size of the dust torus in Active Galactic Nuclei (AGN) and their high-luminosity counterparts, quasars, can be inferred from the time delay between UV/optical accretion disk continuum variability and the response in the mid-infrared (MIR) torus emission. This dust reverberation mapping (RM) technique has been successfully applied to $\sim 70$ $z\lesssim 0.3$ AGN and quasars. Here we present first results of our dust RM program for distant quasars covered in the SDSS Stripe 82 region combining $\sim 20$-yr ground-based optical light curves with 10-yr MIR light curves from the WISE satellite. We measure a high-fidelity lag between W1-band (3.4 $μ$m) and $g$ band for 587 quasars over $0.3\lesssim z\lesssim 2$ ($\left \sim 0.8$) and two orders of magnitude in quasar luminosity. They tightly follow (intrinsic scatter $\sim 0.17$ dex in lag) the IR lag-luminosity relation observed for $z<0.3$ AGN, revealing a remarkable size-luminosity relation for the dust torus over more than four decades in AGN luminosity, with little dependence on additional quasar properties such as Eddington ratio and variability amplitude. This study motivates further investigations in the utility of dust RM for cosmology, and strongly endorses a compelling science case for the combined 10-yr Vera C. Rubin Observatory Legacy Survey of Space and Time (optical) and 5-yr Nancy Grace Roman Space Telescope 2$μ$m light curves in a deep survey for low-redshift AGN dust RM with much lower luminosities and shorter, measurable IR lags. The compiled optical and MIR light curves for 7,384 quasars in our parent sample are made public with this work.

astro-ph.GA↗

arXiv2020

Decomposing Word Embedding with the Capsule Network

Word sense disambiguation tries to learn the appropriate sense of an ambiguous word in a given context. The existing pre-trained language methods and the methods based on multi-embeddings of word did not explore the power of the unsupervised word embedding sufficiently. In this paper, we discuss a capsule network-based approach, taking advantage of capsule's potential for recognizing highly overlapping features and dealing with segmentation. We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S. In this approach, the unsupervised ambiguous embedding is fed into capsule network to produce its multiple morpheme-like vectors, which are defined as the basic semantic language units of meaning. With attention operations, CapsDecE2S integrates the word context to reconstruct the multiple morpheme-like vectors into the context-specific sense embedding. To train CapsDecE2S, we propose a sense matching training method. In this method, we convert the sense learning into a binary classification that explicitly learns the relation between senses by the label of matching and non-matching. The CapsDecE2S was experimentally evaluated on two sense learning tasks, i.e., word in context and word sense disambiguation. Results on two public corpora Word-in-Context and English all-words Word Sense Disambiguation show that, the CapsDecE2S model achieves the new state-of-the-art for the word in context and word sense disambiguation tasks.

cs.CL↗

arXiv2020

Knot theory for two-band model of two-dimensional square lattice with high topological numbers

A knot theory for two-dimensional square lattice is proposed, which sheds light on design of new two-dimensional material with high topological numbers. We consider a two-band model, focusing on the Hall conductance σxy = e^2/hbar*P, where P is a topological number, the so-called Pontrjagin index. By re-interpreting the periodic momentum components kx and ky as the string parameters of two entangled knots, we discover that P becomes the Gauss linking number between the knots. This leads to a successful re-derivation of the typical P-evaluations in literature: P = 0;{\pm}1. Furthermore, with the aid of this explicit knot theoretical picture we modify the two-band model to achieve higher topological numbers, P = 0;{\pm}1;{\pm}2.

cond-mat.str-el↗

arXiv2020

An Opportunistic Bandit Approach for User Interface Experimentation

Facing growing competition from online rivals, the retail industry is increasingly investing in their online shopping platforms to win the high-stake battle of customer' loyalty. User experience is playing an essential role in this competition, and retailers are continuously experimenting and optimizing their user interface for better user experience. The cost of experimentation is dominated by the opportunity cost of providing a suboptimal service to the customers. Through this paper, we demonstrate the effectiveness of opportunistic bandits to make the experiments as inexpensive as possible using real online retail data. In fact, we model user interface experimentation as an opportunistic bandit problem, in which the cost of exploration varies under a factor extracted from customer features. We achieve significant regret reduction by mitigating costly exploration and providing extra contextual information that helps to guide the testing process. Moreover, we analyze the advantages and challenges of using opportunistic bandits for online retail experimentation.

cs.LG↗

arXiv2020

Neural Subgraph Isomorphism Counting

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires more global inference to oversee the whole graph. To make it scalable for large-scale graphs and patterns, we propose a learning framework which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize subgraph isomorphisms for the global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models. A mutagenic compound dataset, MUTAG, is also used to evaluate neural models and demonstrate the success of transfer learning. While the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in linear time compared to the exponential time of the original NP-complete problem. Experimental results show that learning based subgraph isomorphism counting can speed up the traditional algorithm, VF2, 10-1,000 times with acceptable errors. Domain adaptation based on fine-tuning also shows the usefulness of our approach in real-world applications.

cs.LG↗

arXiv2020

Positive Contrast Susceptibility MR Imaging Using GPU-based Primal-Dual Algorithm

The susceptibility-based positive contrast MR technique was applied to estimate arbitrary magnetic susceptibility distributions of the metallic devices using a kernel deconvolution algorithm with a regularized L-1 minimization.Previously, the first-order primal-dual (PD) algorithm could provide a faster reconstruction time to solve the L-1 minimization, compared with other methods. Here, we propose to accelerate the PD algorithm of the positive contrast image using the multi-core multi-thread feature of graphics processor units (GPUs). The some experimental results showed that the GPU-based PD algorithm could achieve comparable accuracy of the metallic interventional devices in positive contrast imaging with less computational time. And the GPU-based PD approach was 4~15 times faster than the previous CPU-based scheme.

physics.med-ph↗

arXiv2020

Dark Energy Survey Identification of A Low-Mass Active Galactic Nucleus at Redshift 0.823 from Optical Variability

We report the identification of a low-mass AGN, DES J0218$-$0430, in a redshift $z = 0.823$ galaxy in the Dark Energy Survey (DES) Supernova field. We select DES J0218$-$0430 as an AGN candidate by characterizing its long-term optical variability alone based on DES optical broad-band light curves spanning over 6 years. An archival optical spectrum from the fourth phase of the Sloan Digital Sky Survey shows both broad Mg II and broad H$β$ lines, confirming its nature as a broad-line AGN. Archival XMM-Newton X-ray observations suggest an intrinsic hard X-ray luminosity of $L_{\rm 2-12\,keV}\sim7.6\pm0.4\times10^{43}$ erg s$^{-1}$, which exceeds those of the most X-ray luminous starburst galaxies, in support of an AGN driving the optical variability. Based on the broad H$β$ from SDSS spectrum, we estimate a virial BH mass of $M_{\bullet}\approx10^{6.43}$-$10^{6.72}M_{\odot}$ (with the error denoting 1$σ$ statistical uncertainties only), consistent with the estimation from OzDES, making it the lowest mass AGN with redshift $>$ 0.4 detected in optical. We estimate the host galaxy stellar mass to be $M_{\ast}\sim10^{10.5\pm0.3}M_{\odot}$ based on modeling the multi-wavelength spectral energy distribution. DES J0218$-$0430 extends the $M_{\bullet}$-$M_{\ast}$ relation observed in luminous AGNs at $z\sim1$ to masses lower than being probed by previous work. Our work demonstrates the feasibility of using optical variability to identify low-mass AGNs at higher redshift in deeper synoptic surveys with direct implications for the upcoming Legacy Survey of Space and Time at Vera C. Rubin Observatory.

astro-ph.GA↗

arXiv2020

Giant enhancement in the thermal responsivity of microelectromechanical resonators by internal mode coupling

We report on a giant enhancement in the thermal responsivity of the doubly-clamped GaAs microelectromechanical (MEMS) beam resonators by using the internal mode coupling effect. This is achieved by coupling the fundamental bending mode with the fundamental torsional mode of the MEMS beam resonators through the cubic Duffing nonlinearity. In the mode coupling regime, we have found that, when the input heat to the MEMS resonators is modulated at a particular frequency, the resonance frequency shift caused by heating can be enhanced by almost two orders of magnitude. The observed effect is promising for realizing high-sensitivity thermal sensing by using MEMS resonators, such as ultrasensitive terahertz detection at room temperature.

cond-mat.mes-hall↗

arXiv2020

BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease of the brain and the spinal cord, which leads to paralysis of motor functions. Patients retain their ability to blink, which can be used for communication. Here, We present an Artificial Intelligence (AI) system that uses eye-blinks to communicate with the outside world, running on real-time Internet-of-Things (IoT) devices. The system uses a Convolutional Neural Network (CNN) to find the blinking pattern, which is defined as a series of Open and Closed states. Each pattern is mapped to a collection of words that manifest the patient's intent. To investigate the best trade-off between accuracy and latency, we investigated several Convolutional Network architectures, such as ResNet, SqueezeNet, DenseNet, and InceptionV3, and evaluated their performance. We found that the InceptionV3 architecture, after hyper-parameter fine-tuning on the specific task led to the best performance with an accuracy of 99.20% and 94ms latency. This work demonstrates how the latest advances in deep learning architectures can be adapted for clinical systems that ameliorate the patient's quality of life regardless of the point-of-care.

cs.CV↗

arXiv2020

MMFashion: An Open-Source Toolbox for Visual Fashion Analysis

We present MMFashion, a comprehensive, flexible and user-friendly open-source visual fashion analysis toolbox based on PyTorch. This toolbox supports a wide spectrum of fashion analysis tasks, including Fashion Attribute Prediction, Fashion Recognition and Retrieval, Fashion Landmark Detection, Fashion Parsing and Segmentation and Fashion Compatibility and Recommendation. It covers almost all the mainstream tasks in fashion analysis community. MMFashion has several appealing properties. Firstly, MMFashion follows the principle of modular design. The framework is decomposed into different components so that it is easily extensible for diverse customized modules. In addition, detailed documentations, demo scripts and off-the-shelf models are available, which ease the burden of layman users to leverage the recent advances in deep learning-based fashion analysis. Our proposed MMFashion is currently the most complete platform for visual fashion analysis in deep learning era, with more functionalities to be added. This toolbox and the benchmark could serve the flourishing research community by providing a flexible toolkit to deploy existing models and develop new ideas and approaches. We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.

cs.CV↗

arXiv2020

How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets

Network traffic classification, which has numerous applications from security to billing and network provisioning, has become a cornerstone of today's computer networks. Previous studies have developed traffic classification techniques using classical machine learning algorithms and deep learning methods when large quantities of labeled data are available. However, capturing large labeled datasets is a cumbersome and time-consuming process. In this paper, we propose a semi-supervised approach that obviates the need for large labeled datasets. We first pre-train a model on a large unlabeled dataset where the input is the time series features of a few sampled packets. Then the learned weights are transferred to a new model that is re-trained on a small labeled dataset. We show that our semi-supervised approach achieves almost the same accuracy as a fully-supervised method with a large labeled dataset, though we use only 20 samples per class. In tests based on a dataset generated from the more challenging QUIC protocol, our approach yields 98% accuracy. To show its efficacy, we also test our approach on two public datasets. Moreover, we study three different sampling techniques and demonstrate that sampling packets from an arbitrary portion of a flow is sufficient for classification.

cs.NI↗

arXiv2020

Multitask Learning for Network Traffic Classification

Traffic classification has various applications in today's Internet, from resource allocation, billing and QoS purposes in ISPs to firewall and malware detection in clients. Classical machine learning algorithms and deep learning models have been widely used to solve the traffic classification task. However, training such models requires a large amount of labeled data. Labeling data is often the most difficult and time-consuming process in building a classifier. To solve this challenge, we reformulate the traffic classification into a multi-task learning framework where bandwidth requirement and duration of a flow are predicted along with the traffic class. The motivation of this approach is twofold: First, bandwidth requirement and duration are useful in many applications, including routing, resource allocation, and QoS provisioning. Second, these two values can be obtained from each flow easily without the need for human labeling or capturing flows in a controlled and isolated environment. We show that with a large amount of easily obtainable data samples for bandwidth and duration prediction tasks, and only a few data samples for the traffic classification task, one can achieve high accuracy. We conduct two experiment with ISCX and QUIC public datasets and show the efficacy of our approach.

cs.LG↗

arXiv2020

Branching Fractions and CP Asymmetries of the Quasi-Two-Body Decays in $B_{s} \to K^0(\overline K^0)K^\pm π^\mp$ within PQCD Approach

Motivated by the first untagged decay-time-integrated amplitude analysis of $B_s \to K_SK^{\mp}π^{\pm}$ decays performed by LHCb collaboration, where the decay amplitudes are modeled to contain the resonant contributions from intermediate resonances $K^*(892)$, $K_0^*(1430)$ and $K_2^*(1430)$, we comprehensively investigate the quasi-two-body $B_{s} \to K^0/\overline{\kern -0.2em K}^0 K^{\pm}π^{\mp}$ decays, and calculate the branching fractions and the time-dependent $CP$ asymmetries within the perturbative QCD approach based on the $k_T$ factorization. In the quasi-two-body space region the calculated branching fractions with the considered intermediate resonances are in good agreement with the experimental results of LHCb by adopting proper $Kπ$ pair wave function, describing the interaction between the kaon and pion in the $Kπ$ pair. Furthermore,within the obtained branching fractions of the quasi-two-body decays, we also calculate the branching fractions of corresponding two-body decays, and the results consist with the LHCb measurements and the earlier studies with errors. For these considered decays, since the final states are not flavour-specific, the time-dependent $CP$ could be measured. We calculate six $CP$-violation observables, which can be tested in the ongoing LHCb experiment.

hep-ph↗

arXiv2020

Superconducting proximity effect in a transparent van der Waals superconductor-metal junction

We report on Andreev reflections at clean NbSe2-bilayer graphene junctions. The high transparency of the junction, which manifests as a large conductance enhancement of up to 1.8, enables us to see clear evidence of a proximity-induced superconducting gap in bilayer graphene and two Andreev reflections through a vertical NbSe2-graphene and a lateral graphene-graphene junction respectively. Quantum transport simulations capture the complexity of the experimental data and illuminate the impact of various microscopic parameters on the transmission of the junction. Our work establishes the practice and understanding of an all-van-der-Waals, high-performance superconducting junction. The realization of a highly transparent proximized graphene-graphene junction opens up possibilities to engineer emergent quantum phenomena.

cond-mat.mes-hall↗

arXiv2020

On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification

Implicit discourse relation classification is one of the most difficult parts in shallow discourse parsing as the relation prediction without explicit connectives requires the language understanding at both the text span level and the sentence level. Previous studies mainly focus on the interactions between two arguments. We argue that a powerful contextualized representation module, a bilateral multi-perspective matching module, and a global information fusion module are all important to implicit discourse analysis. We propose a novel model to combine these modules together. Extensive experiments show that our proposed model outperforms BERT and other state-of-the-art systems on the PDTB dataset by around 8% and CoNLL 2016 datasets around 16%. We also analyze the effectiveness of different modules in the implicit discourse relation classification task and demonstrate how different levels of representation learning can affect the results.

cs.CL↗