arXiv subjects

Lin Li

Publications and source records attributed to Lin Li.

At least 397 records · Page 22Linked to original sources

arXiv2018

Magnetic-oscillation mechanism for understanding periodic or quasi-periodic modulation of the timing residuals from pulsars

Highly precise pulsar timing is very important for understanding the nature of neutron stars and the implied physics, even being used to detect directly gravitational waves. Unfortunately, the accuracy of the pulsar timing is seriously affected by the spin-down irregularities, such as spin fluctuations with a manifestation of low frequency structures (the so-called red noise processes), and various activities of magnetospheres. Except from the random timing noise, significant periodic or quasi-periodic components could also be found in some timing residuals of pulsars, indicating the presence of unmodelled deterministic effects. The physical origins of these effects still remain unexplained. In this paper, we suggest a new mechanism involving the de Haas-van Alphen magnetic oscillation, which could trigger the observed low frequency structures. We find that the proposed magnetic oscillation period is about 1-$10^{2}$ yr, which is about $10^{-4}$ times as long as the classical characteristic time scale of interior magnetic field evolution for a normal neutron star. Due to the magnetic oscillation, we estimate both the braking index range between $10^{-5}$ and $10^{5}$ and the residuals range between 4 to 906 ms for some specific samples of quasi-periodic pulsars. Those are consistent with the pulsar timing observations. Nonlinear phenomena of de Haas-van Alphen effect and the Condon domain structure are considered. Avalanche-like magnetized process of domain wall motion (rapidly magnetic energy release) could associated with some special emitting manifestations of neuron star radiation.

astro-ph.HE↗

arXiv2018

GPU based Parallel Optimization for Real Time Panoramic Video Stitching

Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional panoramic products depend on post processing, which results in high power consumption, low stability and unsatisfying performance in real time. In order to solve these problems,we propose a real-time panoramic video stitching framework.The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA.The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based on analyzing the GPU resources occupancy rate of each resolution image stitching, we further propose a stream parallel strategy to maximize the utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU resources. The performance of the system accomplished in the paper is 29.2 times than that of the former embedded one, while the power dissipation is reduced to 10W.

cs.CV↗

arXiv2018

Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings

Image-to-video person re-identification identifies a target person by a probe image from quantities of pedestrian videos captured by non-overlapping cameras. Despite the great progress achieved,it's still challenging to match in the multimodal scenario,i.e. between image and video. Currently,state-of-the-art approaches mainly focus on the task-specific data,neglecting the extra information on the different but related tasks. In this paper,we propose an end-to-end neural network framework for image-to-video person reidentification by leveraging cross-modal embeddings learned from extra information.Concretely speaking,cross-modal embeddings from image captioning and video captioning models are reused to help learned features be projected into a coordinated space,where similarity can be directly computed. Besides,training steps from fixed model reuse approach are integrated into our framework,which can incorporate beneficial information and eventually make the target networks independent of existing models. Apart from that,our proposed framework resorts to CNNs and LSTMs for extracting visual and spatiotemporal features,and combines the strengths of identification and verification model to improve the discriminative ability of the learned feature. The experimental results demonstrate the effectiveness of our framework on narrowing down the gap between heterogeneous data and obtaining observable improvement in image-to-video person re-identification.

cs.CV↗

arXiv2018

The combination of context information to enhance simple question answering

With the rapid development of knowledge base,question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity's notable type,outdegree. Experimental results show that context information can improve the result of simple question answering.

cs.AI↗

arXiv2018

An Instance Transfer based Approach Using Enhanced Recurrent Neural Network for Domain Named Entity Recognition

Recently, neural networks have shown promising results for named entity recognition (NER), which needs a number of labeled data to for model training. When meeting a new domain (target domain) for NER, there is no or a few labeled data, which makes domain NER much more difficult. As NER has been researched for a long time, some similar domain already has well labelled data (source domain). Therefore, in this paper, we focus on domain NER by studying how to utilize the labelled data from such similar source domain for the new target domain. We design a kernel function based instance transfer strategy by getting similar labelled sentences from a source domain. Moreover, we propose an enhanced recurrent neural network (ERNN) by adding an additional layer that combines the source domain labelled data into traditional RNN structure. Comprehensive experiments are conducted on two datasets. The comparison results among HMM, CRF and RNN show that RNN performs bette than others. When there is no labelled data in domain target, compared to directly using the source domain labelled data without selecting transferred instances, our enhanced RNN approach gets improvement from 0.8052 to 0.9328 in terms of F1 measure.

cs.CL↗

arXiv2018

Using Sentiment Representation Learning to Enhance Gender Classification for User Profiling

User profiling means exploiting the technology of machine learning to predict attributes of users, such as demographic attributes, hobby attributes, preference attributes, etc. It's a powerful data support of precision marketing. Existing methods mainly study network behavior, personal preferences, post texts to build user profile. Through our data analysis of micro-blog, we find that females show more positive and have richer emotions than males in online social platform. This difference is very conducive to the distinction between genders. Therefore, we argue that sentiment context is important as well for user profiling.This paper focuses on exploiting microblog user posts to predict one of the demographic labels: gender. We propose a Sentiment Representation Learning based Multi-Layer Perceptron(SRL-MLP) model to classify gender. First we build a sentiment polarity classifier in advance by training Long Short-Term Memory(LSTM) model on e-commerce review corpus. Next we transfer sentiment representation to a basic MLP network. Last we conduct experiments on gender classification by sentiment representation. Experimental results show that our approach can improve gender classification accuracy by 5.53\%, from 84.20\% to 89.73\%.

cs.CL↗

arXiv2018

Proposal for detecting nodal-line semimetal surface-states with resonant spin-flipped reflection

Topological nodal-line semimetals are predicted to exhibit unique drumhead-like surface states (DSS). Yet, a direct detection of such states remains a challenge. Here, we propose spin-resolved transport in a junction between a normal metal and a spin-orbit coupled nodal-line semimetal as the mechanism for their detection. Specifically, we find that in such a device, the DSS induce resonant spin-flipped reflection. This effect can be probed by both vertical spin transport and lateral charge transport between anti-parallel magnetic terminals. In the tunneling limit of the junction, both spin and charge conductances exhibit a resonant peak around zero energy, providing a unique evidence of the DSS. This signature is robust to both dispersive DSS and interface disorder. Based on numerical calculations, we show that the scheme can be implemented in the topological semimetal HgCr$_2$Se$_4$.

cond-mat.mes-hall↗

arXiv2018

ReLU Deep Neural Networks and Linear Finite Elements

In this paper, we investigate the relationship between deep neural networks (DNN) with rectified linear unit (ReLU) function as the activation function and continuous piecewise linear (CPWL) functions, especially CPWL functions from the simplicial linear finite element method (FEM). We first consider the special case of FEM. By exploring the DNN representation of its nodal basis functions, we present a ReLU DNN representation of CPWL in FEM. We theoretically establish that at least $2$ hidden layers are needed in a ReLU DNN to represent any linear finite element functions in $Ω\subseteq \mathbb{R}^d$ when $d\ge2$. Consequently, for $d=2,3$ which are often encountered in scientific and engineering computing, the minimal number of two hidden layers are necessary and sufficient for any CPWL function to be represented by a ReLU DNN. Then we include a detailed account on how a general CPWL in $\mathbb R^d$ can be represented by a ReLU DNN with at most $\lceil\log_2(d+1)\rceil$ hidden layers and we also give an estimation of the number of neurons in DNN that are needed in such a representation. Furthermore, using the relationship between DNN and FEM, we theoretically argue that a special class of DNN models with low bit-width are still expected to have an adequate representation power in applications. Finally, as a proof of concept, we present some numerical results for using ReLU DNNs to solve a two point boundary problem to demonstrate the potential of applying DNN for numerical solution of partial differential equations.

math.NA↗

arXiv2018

An Alternative Channel For High-Mass Binary Black Holes-----Dark Matter Accretion Onto Black Hole

By a method of population synthesis we construct a model of dark matter (DM) accretion onto binary black holes (BHs), and investigate the merger rate of the binary BHs. We find that the merger rate can weakly increase (less than 10\%). However, the DM accretion can efficiently enhance the masses of binary BHs. In our model, the result for $Z = 0.01$ without the DM accretion can not explain the GW170104, GW170814, and GW150914, while with the DM accretion, it can cover all observations well. For the higher metallicity ($Z = 0.02$), our model can not explain the mergers of high-mass binary BHs like GW170104, GW170814, and GW150914. We estimate that the merger rate of binary BHs lies between $\rm55Gpc^{-3}yr^{-1}$ to $\rm197Gpc^{-3}yr^{-1}$.

astro-ph.HE↗

arXiv2018

Graph Model Selection via Random Walks

In this paper, we present a novel approach based on the random walk process for finding meaningful representations of a graph model. Our approach leverages the transient behavior of many short random walks with novel initialization mechanisms to generate model discriminative features. These features are able to capture a more comprehensive structural signature of the underlying graph model. The resulting representation is invariant to both node permutation and the size of the graph, allowing direct comparison between large classes of graphs. We test our approach on two challenging model selection problems: the discrimination in the sparse regime of an Erdös-Renyi model from a stochastic block model and the planted clique problem. Our representation approach achieves performance that closely matches known theoretical limits in addition to being computationally simple and scalable to large graphs.

cs.SI↗

arXiv2018

Rashba induced Kondo screening of a magnetic impurity in two-dimensional superconductor

We study the Kondo screening of a magnetic impurity in a two-dimensional superconductor with Rashba spin-orbit coupling (SOC). It is found that the Rashba interaction generates a novel Kondo screening channel, in which the local moment is screened by the exchange coupling with conduction electrons in different spin and orbital states. The Kondo temperature associated with this process is determined by the interplay between the Rashba SOC and superconducting energy gap. As a result, the quantum phase transition between the magnetic doublet and Kondo singlet ground states is significantly affected by increasing Rashba SOC in such a system. This result uncovers that the Rashba SOC plays an instructive role and provides a novel screening channel for the Kondo effect, which is expected to be observed in future experiments.

cond-mat.str-el↗

arXiv2017

Single Magnetic Impurity in Tilted Dirac Surface States

We utilize variational method to investigate the Kondo screening of a spin-1/2 magnetic impurity in tilted Dirac surface states with the Dirac cone tilted along the $k_y$-axis. We mainly study about the effect of the tilting term on the binding energy and the spin-spin correlation between magnetic impurity and conduction electrons, and compare the results with the counterparts in a two dimensional helical metal. The binding energy has a critical value while the Dirac cone is slightly tilted. However, as the tilting term increases, the density of states around the Fermi surface becomes significant, such that the impurity and the host material always favor a bound state. The diagonal and the off-diagonal terms of the spin-spin correlation between the magnetic impurity and conduction electrons are also studied. Due to the spin-orbit coupling and the tilting of the spectra, various components of spin-spin correlation show very strong anisotropy in coordinate space, and are of power-law decay with respect to the spatial displacements.

cond-mat.str-el↗

arXiv2017

Magnetoresistance and robust resistivity plateau in MoAs2

We have grown the MoAs$_2$ single crystal which crystallizes in a monoclinic structure with C2/m space group. Transport measurements show that MoAs$_2$ displays a metallic behavior at zero field and undergoes a metal-to-semiconductor crossover at low temperatures when the applied magnetic field is over 5 T. A robust resistivity plateau appears below 18 K and persists for the field up to 9 T. A large positive magnetoresistance (MR), reaching about 2600\% at 2 K and 9 T, is observed when the field is perpendicular to the current.The MR becomes negative below 40 K when the field is rotated to be parallel to the current. The Hall resistivity shows the non-linear field-dependence below 70 K. The analysis using two-band model indicates a compensated electron-hole carrier density at low temperatures. A combination of the breakdown of Kohler's rule, the abnormal drop and the cross point in Hall data implies that a possible Lifshitz transition has occurred between 30 K and 60 K, likely driving the compensated electron-hole density, the large MR as well as the metal-semiconductor transition in MoAs$_2$. Our results indicate that the family of centrosymmetric transition-metal dipnictides has rich transport behavior which can in general exhibit variable metallic and topological features.

cond-mat.mtrl-sci↗

arXiv2017

Tests of Cold Atom Clock in Orbit

Since the atomic clock was invented, its performance has been improved for one digit every decade until 90s of last century when the traditional atomic clock almost reached its limit. With laser cooled atoms, the performance can be further improved, and nowadays the cold atom based clocks are widely used as primary frequency standards. Such a kind of cold atom clocks has great applications in space. This paper presents the design and tests of a cold atom clock (CAC) operating in space. In microgravity, the atoms are cooled, trapped, launched and finally detected after being interrogated by microwave field with Ramsey method. The results of laser cooling of atoms in microgravity in orbit are presented and compared with that on ground for the first time. That the full width at half maximum (FWHM) of obtained central Ramsey fringes varies linearly with launching velocity of cold atoms shows the effects of microgravity. With appropriate parameters, a closed-loop locking of the CAC is realized in orbit and the estimated short term frequency stability of $3.0\times 10 ^{-13}/\sqrt{τ}$ has been reached.

physics.atom-ph↗

arXiv2017

Global strong solution to the two-dimensional density-dependent nematic liquid crystal flows with vacuum

We are concerned with the Cauchy problem of the two-dimensional (2D) nonhomogeneous incompressible nematic liquid crystal flows on the whole space $\mathbb{R}^{2}$ with vacuum as far field density. It is proved that the 2D nonhomogeneous incompressible nematic liquid crystal flows admits a unique global strong solution provided the initial data density and the gradient of orientation decay not too slow at infinity, and the basic energy $\|\sqrt{ρ_0}\mathbf{u}_0\|_{L^2}^2+\|\nabla\mathbf{d}_0\|_{L^2}^2$ is small. In particular, the initial density may contain vacuum states and even have compact support. Moreover, the large time behavior of the solution is also obtained.

math.AP↗

arXiv2017

Making Sense of Unstructured Text Data

Many network analysis tasks in social sciences rely on pre-existing data sources that were created with explicit relations or interactions between entities under consideration. Examples include email logs, friends and followers networks on social media, communication networks, etc. In these data, it is relatively easy to identify who is connected to whom and how they are connected. However, most of the data that we encounter on a daily basis are unstructured free-text data, e.g., forums, online marketplaces, etc. It is considerably more difficult to extract network data from unstructured text. In this work, we present an end-to-end system for analyzing unstructured text data and transforming the data into structured graphs that are directly applicable to a downstream application. Specifically, we look at social media data and attempt to predict the most indicative words from users' posts. The resulting keywords can be used to construct a context+content network for downstream processing such as graph-based analysis and learning. With that goal in mind, we apply our methods to the application of cross-domain entity resolution. The performance of the resulting system with automatic keywords shows improvement over the system with user-annotated hashtags.

cs.SI↗

arXiv2017

Discovery of intrinsic ferromagnetism in 2D van der Waals crystals

It has been long hoped that the realization of long-range ferromagnetic order in two-dimensional (2D) van der Waals (vdW) crystals, combined with their rich electronic and optical properties, would open up new possibilities for magnetic, magnetoelectric and magneto-optic applications. However, in 2D systems, the long-range magnetic order is strongly hampered by thermal fluctuations which may be counteracted by magnetic anisotropy, according to the Mermin-Wagner theorem. Prior efforts via defect and composition engineering, and proximity effect only locally or extrinsically introduce magnetic responses. Here we report the first experimental discovery of intrinsic long-range ferromagnetic order in pristine Cr2Ge2Te6 atomic layers by scanning magneto-optic Kerr microscopy. In such a 2D vdW soft ferromagnet, for the first time, an unprecedented control of transition temperature of ~ 35% - 57% enhancement is realized via surprisingly small fields (<= 0.3 Tesla in this work), in stark contrast to the stiffness of the transition temperature to magnetic fields in the three-dimensional regime. We found that the small applied field enables an effective anisotropy far surpassing the tiny magnetocrystalline anisotropy, opening up a sizable spin wave excitation gap. Confirmed by renormalized spin wave theory, we explain the phenomenon and conclude that the unusual field dependence of transition temperature constitutes a hallmark of 2D soft ferromagnetic vdW crystals. Our discovery of 2D soft ferromagnetic Cr2Ge2Te6 presents a close-to-ideal 2D Heisenberg ferromagnet for studying fundamental spin behaviors, and opens the door for exploring new applications such as ultra-compact spintronics.

cond-mat.mes-hall↗

arXiv2017

Thickness dependent properties in oxide heterostructures driven by structurally induced metal-oxygen hybridization variations

Thickness driven electronic phase transitions are broadly observed in different types of functional perovskite heterostructures. However, uncertainty remains whether these effects are solely due to spatial confinement, broken symmetry or rather to a change of structure with varying film thickness. Here, we present direct evidence for the relaxation of oxygen 2p and Mn 3d orbital (p-d) hybridization coupled to the layer dependent octahedral tilts within a La2/3Sr1/3MnO3 film driven by interfacial octahedral coupling. An enhanced Curie temperature is achieved by reducing the octahedral tilting via interface structure engineering. Atomically resolved lattice, electronic and magnetic structures together with X-ray absorption spectroscopy demonstrate the central role of thickness dependent p-d hybridization in the widely observed dimensionality effects present in correlated oxide heterostructures.

cond-mat.mtrl-sci↗