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Lin Li

Publications and source records attributed to Lin Li.

At least 379 records · Page 21Linked to original sources
arXiv2019

Deterministic creation of entangled atom-light Schrödinger-cat states

Quantum physics allows for entanglement between microscopic and macroscopic objects, described by discrete and continuous variables, respectively. As in Schrödinger's famous cat gedanken experiment, a box enclosing the objects can keep the entanglement alive. For applications in quantum information processing, however, it is essential to access the objects and manipulate them with suitable quantum tools. Here we reach this goal and deterministically generate entangled light-matter states by reflecting a coherent light pulse with up to four photons on average from an optical cavity containing one atom. The quantum light propagates freely and reaches a remote receiver for quantum state tomography. We produce a plethora of quantum states and observe negative-valued Wigner functions, a characteristic sign of non-classicality. As a first application, we demonstrate a quantum-logic gate between an atom and a light pulse, with the photonic qubit encoded in the phase of the light field.

quant-ph↗
arXiv2019

An Item Recommendation Approach by Fusing Images based on Neural Networks

There are rich formats of information in the network, such as rating, text, image, and so on, which represent different aspects of user preferences. In the field of recommendation, how to use those data effectively has become a difficult subject. With the rapid development of neural network, researching on multi-modal method for recommendation has become one of the major directions. In the existing recommender systems, numerical rating, item description and review are main information to be considered by researchers. However, the characteristics of the item may affect the user's preferences, which are rarely used for recommendation models. In this work, we propose a novel model to incorporate visual factors into predictors of people's preferences, namely MF-VMLP, based on the recent developments of neural collaborative filtering (NCF). Firstly, we get visual presentation via a pre-trained convolutional neural network (CNN) model. To obtain the nonlinearities interaction of latent vectors and visual vectors, we propose to leverage a multi-layer perceptron (MLP) to learn. Moreover, the combination of MF and MLP has achieved collaborative filtering recommendation between users and items. Our experiments conduct Amazon's public dataset for experimental validation and root-mean-square error (RMSE) as evaluation metrics. To some extent, experimental result on a real-world data set demonstrates that our model can boost the recommendation performance.

cs.IR↗
arXiv2019

Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval

The Q&A community has become an important way for people to access knowledge and information from the Internet. However, the existing translation based on models does not consider the query specific semantics when assigning weights to query terms in question retrieval. So we improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a communitybased question retrieval method that combines question and answer on quality and question relevance (T2LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that Compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91% and 6.31%.

cs.IR↗
arXiv2019

Deep neural network-based classification model for Sentiment Analysis

The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, Bi-LSTM and CNN were established to judge the tendency of the user's implicit sentiment text. Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The Bi-LSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.

cs.CL↗
arXiv2019

Infinitely many sign-changing solutions for Kirchhoff type problems in $\mathbb{R}^3$

In this paper, we consider the following nonlinear Kirchhoff type problem: \[ \left\{\begin{array}{lcl}-\left(a+b\displaystyle\int_{\mathbb{R}^3}|\nabla u|^2\right)Δu+V(x)u=f(u), & \textrm{in}\,\,\mathbb{R}^3,\\ u\in H^1(\mathbb{R}^3), \end{array}\right. \] where $a,b>0$ are constants, the nonlinearity $f$ is superlinear at infinity with subcritical growth and $V$ is continuous and coercive. For the case when $f$ is odd in $u$ we obtain infinitely many sign-changing solutions for the above problem by using a combination of invariant sets method and the Ljusternik-Schnirelman type minimax method. To the best of our knowledge, there are only few existence results for this problem. It is worth mentioning that the nonlinear term may not be 4-superlinear at infinity, in particular, it includes the power-type nonlinearity $|u|^{p-2}u$ with $p\in(2,4]$.

math.AP↗
arXiv2019

Reinforcement Learning for Robotic Time-optimal Path Tracking Using Prior Knowledge

Time-optimal path tracking, as a significant tool for industrial robots, has attracted the attention of numerous researchers. In most time-optimal path tracking problems, the actuator torque constraints are assumed to be conservative, which ignores the motor characteristic; i.e., the actuator torque constraints are velocity-dependent, and the relationship between torque and velocity is piecewise linear. However, considering that the motor characteristics increase the solving difficulty, in this study, an improved Q-learning algorithm for robotic time-optimal path tracking using prior knowledge is proposed. After considering the limitations of the Q-learning algorithm, an improved action-value function is proposed to improve the convergence rate. The proposed algorithms use the idea of reward and penalty, rewarding the actions that satisfy constraint conditions and penalizing the actions that break constraint conditions, to finally obtain a time-optimal trajectory that satisfies the constraint conditions. The effectiveness of the algorithms is verified by experiments.

cs.RO↗
arXiv2019

Evidence for Weyl fermions in the elemental semiconductor tellurium

The recent discovery of Weyl fermions in solids enables exploitation of relativistic physics and development of a spectrum of intriguing physical phenomena. They are constituted of pairs of Weyl points with two-fold band degeneracy, which in principle can be hosted in any materials without inversion or time-reversal symmetry. However, previous studies of Weyl fermions have been limited exclusively to semimetals. Here, by combining magneto-transport measurements, angle-resolved photoemission spectroscopy, and band structure calculations, Weyl fermions are identified in an elemental semiconductor tellurium. This is mainly achieved by direct observation of the representative transport signatures of the chiral anomaly, including the negative longitudinal magnetoresistance and the planar Hall effect. Semiconductor materials are well suited for band engineering, and therefore provide an ideal platform for manipulating the fundamental Weyl fermionic behaviors. Furthermore, introduction of Weyl physics into semiconductors to develop "Weyl semiconductors" also creates a new degree of freedom for the future design of semiconductor electronic and optoelectronic devices.

cond-mat.mtrl-sci↗
arXiv2019

The Formation of Bimodal Dust Species in Nova Ejecta I: Chemical Conditions

The formation of bimodal dust species (that is, both of silicate and amorphous carbon dust grains are observed in a nova eruption) in nova ejecta is still debated. Using the Modules for Experiments in Stellar Astrophysics code and considering the effects of WD's mass, mass-accretion rate and the chemical profiles of WD which are described by new parameter --- mixing depth on the chemical abundances of nova ejecta, we investigate the possibility that bimodal dust species are produced in a nova eruption. We find that $C/O$ (the ratio of carbon number density to oxygen number density) of nova ejecta is affected by the mixing depth. For the model with a small mixing depth, the $C/O$ of nova ejecta can evolute from lager than 1.0 to smaller than 1.0 in a whole eruption, which provides the chemical condition for the formation of bimodal dust species.

astro-ph.SR↗
arXiv2019

Training Multi-Task Adversarial Network for Extracting Noise-Robust Speaker Embedding

Under noisy environments, to achieve the robust performance of speaker recognition is still a challenging task. Motivated by the promising performance of multi-task training in a variety of image processing tasks, we explore the potential of multi-task adversarial training for learning a noise-robust speaker embedding. In this paper we present a novel framework which consists of three components: an encoder that extracts noise-robust speaker embedding; a classifier that classifies the speakers; a discriminator that discriminates the noise type of the speaker embedding. Besides, we propose a training strategy using the training accuracy as an indicator to stabilize the multi-class adversarial optimization process. We conduct our experiments on the English and Mandarin corpus and the experimental results demonstrate that our proposed multi-task adversarial training method could greatly outperform the other methods without adversarial training in noisy environments. Furthermore, experiments indicate that our method is also able to improve the speaker verification performance the clean condition.

cs.SD↗
arXiv2019

Pressure-Induced Modification of Anomalous Hall Effect in Layered Fe$_3$GeTe$_2$

We systematically investigate the influence of high pressure on the electronic transport properties of layered ferromagnetic materials, in particular, those of Fe$_3$GeTe$_2$. Its crystal sustains a hexagonal phase under high pressures up to 25.9 GPa, while the Curie temperature decreases monotonously with the increasing pressure. By applying appropriate pressures, the experimentally measured anomalous Hall conductivity, $σ_{xy}^A$, can be efficiently controlled. Our theoretical study reveals that this finding can be attributed to the shift of the spin--orbit-coupling-induced splitting bands of Fe atoms. With loading compression, $σ_{xy}^A$ reaches its maximal value when the Fermi level lies inside the splitting bands and then attenuates when the splitting bands float above the Fermi level. Further compression leads to a prominent suppression of the magnetic moment, which is another physical cause of the decrease in $σ_{xy}^A$ at high pressure. These results indicate that the application of pressure is an effective approach in controlling the anomalous Hall conductivity of layered magnetic materials, which elucidates the physical mechanism of the large intrinsic anomalous Hall effect.

cond-mat.mtrl-sci↗
arXiv2019

Using Context Information to Enhance Simple Question Answering

With the rapid development of knowledge bases(KBs),question answering(QA)based on KBs has become a hot research issue. In this paper,we propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus answering single-relation factoid question. In both of two frameworks,we study the effect of context information on the quality of QA,such as the entity's notable type,out-degree. In the end-to-end framework,we combine char-level encoding and self-attention mechanisms,using weight sharing and multi-task strategies to enhance the accuracy of QA. Experimental results show that context information can get better results of simple QA whether it is the pipeline framework or the end-to-end framework. In addition,we find that the end-to-end framework achieves results competitive with state-of-the-art approaches in terms of accuracy and take much shorter time than them.

cs.CL↗
arXiv2019

Relational Network for Skeleton-Based Action Recognition

With the fast development of effective and low-cost human skeleton capture systems, skeleton-based action recognition has attracted much attention recently. Most existing methods use Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) to extract spatio-temporal information embedded in the skeleton sequences for action recognition. However, these approaches are limited in the ability of relational modeling in a single skeleton, due to the loss of important structural information when converting the raw skeleton data to adapt to the input format of CNN or RNN. In this paper, we propose an Attentional Recurrent Relational Network-LSTM (ARRN-LSTM) to simultaneously model spatial configurations and temporal dynamics in skeletons for action recognition. We introduce the Recurrent Relational Network to learn the spatial features in a single skeleton, followed by a multi-layer LSTM to learn the temporal features in the skeleton sequences. Between the two modules, we design an adaptive attentional module to focus attention on the most discriminative parts in the single skeleton. To exploit the complementarity from different geometries in the skeleton for sufficient relational modeling, we design a two-stream architecture to learn the structural features among joints and lines simultaneously. Extensive experiments are conducted on several popular skeleton datasets and the results show that the proposed approach achieves better results than most mainstream methods.

cs.CV↗
arXiv2019

Spatial anisotropy of Kondo screening cloud in a type-II Weyl semimetal

We theoretically study the Kondo screening of a spin-1/2 magnetic impurity in the bulk of a type-II Weyl semimetal (WSM) by use of the variational wave function method. We consider a type-II WSM model with two Weyl nodes located on the $k_z$-axis, and the tilting of the Weyl cones are along the $k_x$ direction. Due to co-existing electron and hole pockets, the density of states at the Fermi energy becomes finite, leading to a significant enhancement of Kondo effect. Consequently, the magnetic impurity and the conduction electrons always form a bound state, this behavior is distinct from that in the type-I WSMs, where the bound state is only formed when the hybridization exceeds a critical value. Meanwhile, the spin-orbit coupling and unique geometry of the Fermi surface lead to strongly anisotropic Kondo screening cloud in coordinate space. The tilting terms break the rotational symmetry of the type-II WSM about the $k_z$-axis, but the system remains invariant under a combined transformation $\mathcal{T}R^{y}(π)$, where $\mathcal{T}$ is the time-reversal operation and $R^{y}(π)$ is the rotation about the $y$-axis by $π$. Largely modified diagonal and off-diagonal components of the spin-spin correlation function on three principal planes reflect this change in band symmetry. Most saliently, the tilting terms trigger the emergence of non-zero off-diagonal components of spin-spin correlation function on the $x$-$z$ principal plane.

cond-mat.str-el↗
arXiv2019

Single-Photon Distillation via a Photonic Parity Measurement Using Cavity QED

Single photons with tailored temporal profiles are a vital resource for future quantum networks. Here we distill them out of custom-shaped laser pulses that reflect from a single atom strongly coupled to an optical resonator. A subsequent measurement on the atom is employed to herald a successful distillation. Out of vacuum-dominated light pulses, we create single photons with fidelity $66(1)\%$, two-and-more-photon suppression $95.5(6)\%$, and a Wigner function with negative value $-0.125(6)$. Our scheme applied to state-of-the-art fiber resonators could boost the single-photon fidelity to up to $96\%$.

quant-ph↗
arXiv2018

Analysis of Adaptive Short-time Fourier Transform-based Synchrosqueezing Transform

Recently the study of modeling a non-stationary signal as a superposition of amplitude and frequency-modulated Fourier-like oscillatory modes has been a very active research area. The synchrosqueezing transform (SST) is a powerful method for instantaneous frequency estimation and component separation of non-stationary multicomponent signals. The short-time Fourier transform-based SST (FSST for short) reassigns the frequency variable to sharpen the time-frequency representation and to separate the components of a multicomponent non-stationary signal. Very recently the FSST with a time-varying parameter, called the adaptive FSST, was introduced. The simulation experiments show that the adaptive FSST is very promising in instantaneous frequency estimation of the component of a multicomponent signal, and in accurate component recovery. However the theoretical analysis of the adaptive FSST has not been carried out. In this paper, we study the theoretical analysis of the adaptive FSST and obtain the error bounds for the instantaneous frequency estimation and component recovery with the adaptive FSST and the 2nd-order adaptive FSST.

math.NA↗
arXiv2018

On Isolated Millisecond Pulsars Formed by the Coalescence of Neutron Stars and Massive White Dwarfs

This paper uses population synthesis to investigate the possible origin of isolated millisecond pulsars (IMSPs) as born from the coalescence of a neutron star (NS) and a white dwarf (WD). Results show that the galactic birth - rate of IMSPs is likely to lie between $5.8\times10^{-5}$ $\rm yr^{-1}$ and $2.0\times10^{-4}$ $\rm yr^{-1}$, depending on critical variables, such as the stability of mass transfer via the Roche lobe and the value of kick velocity. In addition to this, this paper estimates that the solar mass of IMSPs can range from $1.5$ and 2.0 $M_{\odot}$, making them more massive than other 'normal' pulsars. Finally, the majority of IMSPs in our simulations have spin periods ranging from several to 20 milliseconds, which is consistent with previous observations.

astro-ph.HE↗
arXiv2018

Superconductivity, Electronic Phase Diagram and Pressure Effect in Sr_{1-x}Pr_{x}FBiS_{2}

Based on a combination of X-ray diffraction, electrical transports, magnetic susceptibility, specific heat and pressure effect measurements, we report a series of BiS$_{2}$-based Sr$_{1-x}$Pr$_{x}$FBiS$_{2}$ superconductors with the maximum $T_c$ of 2.7 K for $x =$ 0.5 at ambient pressure. Superconductivity can be only observed at 0.4 $\leq$ x $\leq$ 0.7 while the normal state resistivity shows the semiconducting-like behaviors. $χ(T)$ displays the low superconducting shielding volume fractions and $C(T)$ does not exhibit distinguishable anomaly around $T_c$, suggesting a filamentary superconductivity in the Pr-doped polycrystalline samples. By varying doping concentrations, an electrnoic phase diagram can thus be established. Upon applying pressure on the optimal doping Sr$_{0.5}$Pr$_{0.5}$FBiS$_{2}$ system, $T_c$ is abruptly enhanced, reaches 8.5 K at a critical pressure of $P_c =$ 1.5 GPa and remains a slight increase up to 9.7 K up to 2.5 GPa. Accompanied with the enhancement of superconductivity from the low-$T_c$ phase to the high-$T_c$ phase, the normal state undergoes a semiconductor-to-metal transition under pressure. The scenario can be linked to the enhancement of orbital overlap of Bi-6p and S-p, contributing to the bulk superconductivity above $P_c$. Phase diagram for Sr$_{0.5}$Pr$_{0.5}$FBiS$_{2}$ is also obtained.

cond-mat.supr-con↗