arXiv subjects

Liang Zhang

Publications and source records attributed to Liang Zhang.

At least 397 records · Page 22Linked to original sources

arXiv2019

Spaceborne low-noise single-photon detection for satellite-based quantum communications

Single-photon detectors (SPDs) play important roles in highly sensitive detection applications, such as fluorescence spectroscopy, remote sensing and ranging, deep space optical communications, elementary particle detection, and quantum communications. However, the adverse conditions in space, such as the increased radiation flux and thermal vacuum, severely limit their noise performances, reliability, and lifetime. Herein, we present the first example of spaceborne, low-noise, high reliability SPDs, based on commercial off-the-shelf (COTS) silicon avalanche photodiodes (APD). Based on the high noise-radiation sensitivity of silicon APD, we have developed special shielding structures, multistage cooling technologies, and configurable driver electronics that significantly improved the COTS APD reliability and mitigated the SPD noise-radiation sensitivity. This led to a reduction of the expected in-orbit radiation-induced dark count rate (DCR) from ~219 counts per second (cps) per day to ~0.76 cps/day. During a continuous period of continuous operations in orbit which spanned of 1029 days, the SPD DCR was maintained below 1000 cps, i.e., the actual in-orbit radiation-induced DCR increment rate was ~0.54 cps/day, i.e., two orders of magnitude lower than those evoked by previous technologies, while its photon detection efficiency was > 45%. Our spaceborne, low-noise SPDs established a feasible satellite-based up-link quantum communication that was validated on the quantum experiment science satellite platform. Moreover, our SPDs open new windows of opportunities for space research and applications in deep-space optical communications, single-photon laser ranging, as well as for testing the fundamental principles of physics in space.

physics.ins-det↗

arXiv2019

Manipulation of a Micro-object Using Topological Hydrodynamic Tweezers

Manipulating micro-scale object plays paramount roles in a wide range of fundamental researches and applications. At micro-scale, various methods have been developed in the past decades, including optical, electric, magnetic, aerodynamic and acoustic methods. However, these non-contact forces are susceptible to external disturbance, and so finding a way to make micro-scale object manipulation immune to external perturbations is challenging and remains elusive. Here we demonstrate a method based on new trapping mechanism to manipulate micro-scale object in a gas flow at ambient conditions. We first show that the micro-droplet is entrapped into a trapping ring constructed by a particular toroidal vortex. The vortex works as tweezers to control the position of the micro-droplet. We then show that the micro-droplet can be transported along the trapping ring. By virtue of the topological character of the gas flow, the transport path is able to bypass external strong perturbations automatically. We further demonstrate a topological transfer process of the micro-droplet between two hydrodynamic tweezers. Our method provides an integrated toolbox to manipulate a micro-scale object, with an intrinsic mechanism that protects the target object from external disturbances.

physics.flu-dyn↗

arXiv2019

Timing analysis of Swift J1658.2-4242's outburst in 2018 with Insight-HXMT, NICER and AstroSat

We present the observational results from a detailed timing analysis of the black hole candidate Swift J1658.2-4242 during its 2018 outburst with the observations of Hard X-ray Modulation Telescope (Insight-HXMT), Neutron Star Interior Composition Explorer (NICER) and AstroSat in 0.1-250keV. The evolution of intensity, hardness and integrated fractional root mean square (rms) observed by Insight-HXMT and NICER are presented in this paper. Type-C quasi-periodic oscillations (QPOs) observed by NICER (0.8-3.5Hz) and Insight-HXMT (1-1.6Hz) are also reported in this work. The features of the QPOs are analysed with an energy range of 0.5-50keV. The relations between QPO frequency and other characteristics such as intensity, hardness and QPO rms are carefully studied. The timing and spectral properties indicate that Swift J1658.2-4242 is a black hole binary system. Besides, the rms spectra of the source calculated from the simultaneous observation of Insight-HXMT, NICER and AstroSat support the Lense-Thirring origin of the QPOs. The relation between QPO phase lag and the centroid frequency of Swift J1658.2-4242 reveals a near zero constant when < 4Hz and a soft phase lag at 6.68 Hz. This independence follows the same trend as the high inclination galactic black hole binaries such as MAXI J1659-152.

astro-ph.HE↗

arXiv2019

Point Attention Network for Semantic Segmentation of 3D Point Clouds

Convolutional Neural Networks (CNNs) have performed extremely well on data represented by regularly arranged grids such as images. However, directly leveraging the classic convolution kernels or parameter sharing mechanisms on sparse 3D point clouds is inefficient due to their irregular and unordered nature. We propose a point attention network that learns rich local shape features and their contextual correlations for 3D point cloud semantic segmentation. Since the geometric distribution of the neighboring points is invariant to the point ordering, we propose a Local Attention-Edge Convolution (LAE Conv) to construct a local graph based on the neighborhood points searched in multi-directions. We assign attention coefficients to each edge and then aggregate the point features as a weighted sum of its neighbors. The learned LAE-Conv layer features are then given to a point-wise spatial attention module to generate an interdependency matrix of all points regardless of their distances, which captures long-range spatial contextual features contributing to more precise semantic information. The proposed point attention network consists of an encoder and decoder which, together with the LAE-Conv layers and the point-wise spatial attention modules, make it an end-to-end trainable network for predicting dense labels for 3D point cloud segmentation. Experiments on challenging benchmarks of 3D point clouds show that our algorithm can perform at par or better than the existing state of the art methods.

cs.CV↗

arXiv2019

Adversarial Training Methods for Network Embedding

Network Embedding is the task of learning continuous node representations for networks, which has been shown effective in a variety of tasks such as link prediction and node classification. Most of existing works aim to preserve different network structures and properties in low-dimensional embedding vectors, while neglecting the existence of noisy information in many real-world networks and the overfitting issue in the embedding learning process. Most recently, generative adversarial networks (GANs) based regularization methods are exploited to regularize embedding learning process, which can encourage a global smoothness of embedding vectors. These methods have very complicated architecture and suffer from the well-recognized non-convergence problem of GANs. In this paper, we aim to introduce a more succinct and effective local regularization method, namely adversarial training, to network embedding so as to achieve model robustness and better generalization performance. Firstly, the adversarial training method is applied by defining adversarial perturbations in the embedding space with an adaptive $L_2$ norm constraint that depends on the connectivity pattern of node pairs. Though effective as a regularizer, it suffers from the interpretability issue which may hinder its application in certain real-world scenarios. To improve this strategy, we further propose an interpretable adversarial training method by enforcing the reconstruction of the adversarial examples in the discrete graph domain. These two regularization methods can be applied to many existing embedding models, and we take DeepWalk as the base model for illustration in the paper. Empirical evaluations in both link prediction and node classification demonstrate the effectiveness of the proposed methods.

cs.LG↗

arXiv2019

Observation of dynamical phase transitions in a topological nanomechanical system

Dynamical phase transitions (DPTs), characterized by non-analytic behaviors in time domain, extend the equilibrium phase transitions to far-from-equilibrium situations. It has been predicted that DPTs can be precisely identified by the discontinuities of the Pancharatnam geometric phase (PGP) during the time evolution. However, PGP always mixes with dynamical phase and the experimental observation of DPTs by PGP is still absent. Here, we theoretically present a novel scheme for eliminating the dynamical phase by taking advantage of chiral symmetry in the Su-Schrieffer-Heeger (SSH) model, and experimentally observe DPTs by directly measuring PGP in a quenched topological nanomechanical lattice. Time-dependent topological structures of the SSH model are configured by eight strong-coupled high-quality-factor nanomechanical oscillators. By measuring the vibration phase and the normalized amplitude of the edge oscillator, we show a direct classical analog of DPTs. Furthermore, we experimentally demonstrate the robustness of DPTs against weak structure disorders, and numerically explore the relation between DPTs and the equilibrium phase boundary. This work not only establishes the quantitative method to identify DPTs, but also opens the door for studying non-equilibrium topological dynamics with a well-controlled nanomechanical system.

quant-ph↗

arXiv2019

Data-driven subtraction of anisotropic flows in jet-like correlation studies in heavy-ion collisions

Measurements of two-particle azimuthal angle correlations are a useful tool to study the distribution of jet energy loss, however, they are complicated because of the significant anisotropic flow background. We devise a data-driven method for subtracting anisotropic flow background in jet-like correlation analysis. We first require a large recoil momentum ($P_x$) within a given pseudo-rapidity ($η$) range from a high-transverse momentum particle to enhance in-acceptance population of away-side jet-like correlations. Then we take the difference of two-particle correlations in the close-region and far-region with respect to the $η$ region of $P_x$ to subtract the anisotropic flow background. We use a toy model which contains only anisotropic flow and PYTHIA8 which have jets to demonstrate the validity of our data-driven method. The results indicate that the data-driven method can subtract anisotropic flow effectively.

nucl-ex↗

arXiv2019

Deep Reinforcement Learning for List-wise Recommendations

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedbacks. In particular, we introduce an online user-agent interacting environment simulator, which can pre-train and evaluate model parameters offline before applying the model online. Moreover, we validate the importance of list-wise recommendations during the interactions between users and agent, and develop a novel approach to incorporate them into the proposed framework LIRD for list-wide recommendations. The experimental results based on a real-world e-commerce dataset demonstrate the effectiveness of the proposed framework.

cs.LG↗

arXiv2019

Unveiling delay-time-resolved phase noise dynamics of narrow-linewidth laser via coherent optical time domain reflectometry

Laser with high spectral purity plays a crucial role in high-precision optical metrology and coherent communication. Thanks to the rapid development of laser frequency stabilization, the laser phase noise can be remarkably compensated, allowing its ultra-narrow linewidth subject to mostly quantum limit. Nevertheless, the accurate characterization of phase noise dynamics and its intrinsic linewidth of a highly coherent laser remains ambiguous and challenging. Here, we present an approach capable of revealing delay-time-resolved phase noise dynamics of a coherent laser based on coherent optical time domain reflectometry (COTDR), in which distributed Rayleigh scattering along a delay fibre essentially allows a time-of-flight mapping of a heterodyne beating signal associated with delay-time-dependent phase information from a single laser source. Ultimately, this novel technique facilitates a precise measurement of ultra-narrow laser linewidth by exploiting its delay-time-resolved phase jitter statistics, confirmed with the analytical modelling and numerical simulations.

physics.ins-det↗

arXiv2019

GCN-LASE: Towards Adequately Incorporating Link Attributes in Graph Convolutional Networks

Graph Convolutional Networks (GCNs) have proved to be a most powerful architecture in aggregating local neighborhood information for individual graph nodes. Low-rank proximities and node features are successfully leveraged in existing GCNs, however, attributes that graph links may carry are commonly ignored, as almost all of these models simplify graph links into binary or scalar values describing node connectedness. In our paper instead, links are reverted to hypostatic relationships between entities with descriptional attributes. We propose GCN-LASE (GCN with Link Attributes and Sampling Estimation), a novel GCN model taking both node and link attributes as inputs. To adequately captures the interactions between link and node attributes, their tensor product is used as neighbor features, based on which we define several graph kernels and further develop according architectures for LASE. Besides, to accelerate the training process, the sum of features in entire neighborhoods are estimated through Monte Carlo method, with novel sampling strategies designed for LASE to minimize the estimation variance. Our experiments show that LASE outperforms strong baselines over various graph datasets, and further experiments corroborate the informativeness of link attributes and our model's ability of adequately leveraging them.

cs.LG↗

arXiv2019

Long term variability of Swift J1753.5-0127]{Long term variability of Swift J1753.5-0127: X-ray spectral-temporal correlations during state transitions

We studied the long-term evolution of the spectral-temporal correlated properties of the black hole candidate Swift J1753.5-0127 from the onset of its outburst until 2011 with the Rossi X-ray Timing Explorer (RXTE). The source stayed most of its lifetime during hard state, with occasionally transitioned to the hard intermediate state. Similar to typical black hole transients, Swift J1753.5-0127 traces a clear hard line in absolute rms - intensity diagram during the low hard state, with expected highest absolute rms, while shows a clear turn during the hard intermediate state, accompanied by lower absolute rms. Different from Cyg X-1, we found that frequency-dependent time lag increased significantly in the 0.02 -- 3.2 Hz band during state transition in this source. The X-ray time lags in 0.02 -- 3.2 Hz can therefore be used as indicators of state transition in this source. Type-C QPO frequency is positively related with its fractional rms and X-ray photon index, suggesting a moving inwards disk/corona scenario. We discussed the physical interpretation of our results in our paper.

astro-ph.HE↗

arXiv2019

Prediction on Elastic Properties of Nb-doped Ni Systems

On the basis of the first principles simulation, the structure, formation enthalpy, and mechanical properties (elastic constant, bulk, and shear modulus and hardness) of five Nb-doped Ni systems are systematically studied. The calculated equilibrium volume increases with the Nb concentration increasing. The computational elastic constants and formation enthalpy indicate that all Nb-doped Ni systems are mechanically and thermodynamically stable in our research. The hardness of these systems also be predicted after the bulk modulus and shear modulus have been accurately calculated. The results show that the hardness increases with the Nb concentration increasing when the Nb concentration below 4.9%, beyond which the hardness will decrease within the scope of our study.

cond-mat.mtrl-sci↗

arXiv2019

Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction

The recommender system is an important form of intelligent application, which assists users to alleviate from information redundancy. Among the metrics used to evaluate a recommender system, the metric of conversion has become more and more important. The majority of existing recommender systems perform poorly on the metric of conversion due to its extremely sparse feedback signal. To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i.e., high-level agent and low-level agent. The high-level agent catches long-term sparse conversion signals, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and interacts with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel deep hierarchical reinforcement learning algorithm via multi-goals abstraction (HRL-MG). Our proposed algorithm contains three characteristics: 1) the high-level agent generates multiple goals to guide the low-level agent in different stages, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciate benefit assignment function is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.

cs.LG↗

arXiv2019

SepNE: Bringing Separability to Network Embedding

Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on super-large or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to super-large networks, automatic implementation in distributed learning and further adaptations. We demonstrate the effectiveness of this approach on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.

cs.LG↗

arXiv2019

Event-plane dependent away-side jet-like correlation shape in Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV from STAR

We employ a data-driven method to subtract the flow background of all harmonics by calculating the difference of the two-particle correlations between the close-region and far-region, determined depending on the pseudo-rapidity ($η$) distance from the region where an enhanced recoil transverse momentum ($P_x$) from a high-$p_T$ trigger particle is selected. We analyze the correlation shape as a function of the trigger particle azimuthal angle relative to the event-plane (EP) reconstructed from the beam-beam counters (BBCs) which are displaced by several units in $η$ from the mid-rapidity region. The large $η$ gap can effectively eliminate the auto-correlation between trigger particles and EP. We correct for the relatively large resolution effect from the BBC EP determination via an unfolding procedure. The width of unfolded away-side jet-like correlation increases with longer path-length, which is an indication of jet-medium interactions.

nucl-ex↗

arXiv2018

Observation of Mollow quintuplet in F=3/2 hyperfine structure state of 3He atomic cell

We experimentally observed the Mollow quintuplet (MQ) in F=3/2 hyperfine structure state of 3He atoms. The metastability-exchange collisions (MECs) transfer the Mollow Triplet (MT) from the ground states of 3He atoms to the metastable states, and the MQ is demonstrated by four Zeeman levels of F=3/2 hyperfine states with linearly polarized light. The similar effect also achieves in the mixture cell of 3He and 4He.

quant-ph↗

arXiv2018

Observability on lattice points for heat equations and applications

Observability inequalities on lattice points are established for non-negative solutions of the heat equation with potentials in the whole space. As applications, some controllability results of heat equations are derived by the above-mentioned observability inequalities.

math.OC↗

arXiv2018

Real-time Power System State Estimation and Forecasting via Deep Neural Networks

Contemporary power grids are being challenged by rapid voltage fluctuations that are caused by large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity however, the existing power system state estimation (PSSE) schemes become computationally expensive or yield suboptimal performance. To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring. By unrolling an iterative physics-based prox-linear solver, a novel model-specific DNN is developed for real-time PSSE with affordable training and minimal tuning effort. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are also pursued for power system state forecasting. Deep RNNs leverage the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Numerical tests showcase improved performance of the proposed DNN-based estimation and forecasting approaches compared with existing alternatives. In real load data experiments on the IEEE 118-bus benchmark system, the novel model-specific DNN-based PSSE scheme outperforms nearly by an order-of-magnitude the competing alternatives, including the widely adopted Gauss-Newton PSSE solver.

cs.LG↗