arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 1,009 records · Page 56Linked to original sources

arXiv2018

Miniaturized high-frequency sine wave gating InGaAs/InP single-photon detector

High-frequency gating InGaAs/InP single-photon detectors (SPDs) are widely used for applications requiring single-photon detection in the near-infrared region such as quantum key distribution. Reducing SPD size is highly desired for practical use, which is favorable to the implementation of further system integration. Here we present, to the best of our knowledge, the most compact high-frequency sine wave gating (SWG) InGaAs/InP SPD. We design and fabricate an InGaAs/InP single-photon avalanche diode (SPAD) with optimized semiconductor structure, and then encapsulate the SPAD chip and a mini-thermoelectric cooler inside a butterfly package with a size of 12.5 mm $\times$ 22 mm $\times$ 10 mm. Moreover, we implement a monolithic readout circuit for the SWG SPD in order to replace the quenching electronics that is previously designed with board-level integration. Finally, the components of SPAD, monolithic readout circuit and the affiliated circuits are integrated into a single module with a size of 13 cm $\times$ 8 cm $\times$ 4 cm. Compared with the 1.25 GHz SWG InGaAs/InP SPD module (25 cm $\times$ 10 cm $\times$ 33 cm) designed in 2012, the volume of our miniaturized SPD is reduced by 95\%. After the characterization, the SPD exhibits excellent performance with a photon detection efficiency of 30\%, a dark count rate of 2.0 kcps and an afterpulse probability of 8.8\% under the conditions of 1.25 GHz gating rate, 100 ns hold-off time and 243 K. Also, we perform the stability test over one week, and the results show the high reliability of the miniaturized SPD module.

physics.ins-det↗

arXiv2018

Three-dimensional Magnetic Reconnection Triggering an X-class Confined Flare in Active Region 12192

We present an extensive analysis of the X2.0-class confined flare on 2014 October 27 in the great active region AR 12192, observed by the \emph{Interface Region Imaging Spectrograph} and the \emph{Solar Dynamics Observatory}. The slipping motion of the substructures within the negative-polarity flare ribbon (R1) and continual reconnection-induced flows during the confined flare are first presented. The substructures within ribbon R1 were observed to slip in opposite directions at apparent speeds of 10-70 km s$^{-1}$. The slipping motion exhibited the quasi-periodic pattern with a period of 80-110 s, which can be observed since the flare start and throughout the impulsive phase of the flare. Simultaneously quasi-periodic flows moved along a reverse-S shaped filament, with an average period of about 90 s. The period of reconnection-induced flows is similar to that of the slippage of ribbon substructures, implying the occurrence of quasi-periodic slipping magnetic reconnection. The spectral observations showed that the Si {\sc iv} line was blueshifted by 50-240 km s$^{-1}$ at the location of the flows. During the process of the flare, the filament did not show the rise phase and was not associated with any failed eruption. The flare mainly consisted of two sets of magnetic systems, with both of their east ends anchoring in ribbon R1. We suggest that the slipping magnetic reconnection between two magnetic systems triggers the confined flare.

astro-ph.SR↗

arXiv2018

Testing the ABS method with the simulated Planck temperature maps

In this study, we apply the Analytical method of Blind Separation (ABS) of the cosmic microwave background (CMB) from foregrounds to estimate the CMB temperature power spectrum from multi-frequency microwave maps. We test the robustness of the ABS estimator and assess the accuracy of the power spectrum recovery by using realistic simulations based on the seven-frequency Planck data, including various frequency-dependent and spatially-varying foreground components (synchrotron, free-free, thermal dust and anomalous microwave emission), as well as an uncorrelated Gaussian-distributed instrumental noise. Considering no prior information about the foregrounds, the ABS estimator can analytically recover the CMB power spectrum over almost all scales with less than $0.5\%$ error for maps where the Galactic plane region ($|b|<10^{\circ}$) is masked out. To further test the flexibility and effectiveness of the ABS approach in a variety of situations, we apply the ABS to the simulated Planck maps in three cases: (1) without any mask, (2) imposing a two-times-stronger synchrotron emission and (3) including only the Galactic plane region ($|b|<10^{\circ}$) in the analysis. In such extreme cases, the ABS approach can still provide an unbiased estimate of band powers at the level of 1 $μ\rm{K}^2$ on average over all $\ell$ range, and the recovered powers are consistent with the input values within 1-$σ$ for most $\ell$ bins.

astro-ph.CO↗

arXiv2018

Joint Tx-Rx Beamforming and Power Allocation for 5G Millimeter-Wave Non-Orthogonal Multiple Access (MmWave-NOMA) Networks

In this paper, we investigate the combination of non-orthogonal multiple access and millimeter-Wave communications (mmWave-NOMA). A downlink cellular system is considered, where an analog phased array is equipped at both the base station and users. A joint Tx-Rx beamforming and power allocation problem is formulated to maximize the achievable sum rate (ASR) subject to a minimum rate constraint for each user. As the problem is non-convex, we propose a sub-optimal solution with three stages. In the first stage, the optimal power allocation with a closed form is obtained for an arbitrary fixed Tx-Rx beamforming. In the second stage, the optimal Rx beamforming with a closed form is designed for an arbitrary fixed Tx beamforming. In the third stage, the original problem is reduced to a Tx beamforming problem by using the previous results, and a boundary-compressed particle swarm optimization (BC-PSO) algorithm is proposed to obtain a sub-optimal solution. Extensive performance evaluations are conducted to verify the rational of the proposed solution, and the results show that the proposed sub-optimal solution can achieve a near-upper-bound performance in terms of ASR, which is significantly improved compared with those of the state-of-the-art schemes and the conventional mmWave orthogonal multiple access (mmWave-OMA) system.

cs.IT↗

arXiv2018

Casimir Thrust Force on a Rotating Chiral Particle

In the work, the thermal and vacuum fluctuation is predicted capable of generating a Casimir thrust force on a rotating chiral particle, which will push or pull the particle along the rotation axis. The Casimir thrust force comes from two origins: i) the rotation-induced symmetry-breaking in the vacuum and thermal fluctuation and ii) the chiral cross-coupling between electric and magnetic fields and dipoles, which can convert the vacuum spin angular momentum (SAM) to the vacuum force. Using the fluctuation dissipation theorem (FDT), we derive the analytical expressions for the vacuum thrust force in dipolar approximation and the dependences of the force on rotation frequency, temperature and material optical properties are investigated. The work reveals a new mechanism to generate a vacuum force, which opens a new way to exploit zero-point energy of vacuum.

quant-ph↗

arXiv2018

Optimal identification of non-Markovian environments for spin chains

Correlations of an environment are crucial for the dynamics of non-Markovian quantum systems, which may not be known in advance. In this paper, we propose a gradient algorithm for identifying the correlations in terms of time-varying damping rate functions in a time-convolution-less master equation for spin chains. By measuring time trace observables of the system, the identification procedure can be formulated as an optimization problem. The gradient algorithm is designed based on a calculation of the derivative of an objective function with respect to the damping rate functions, whose effectiveness is shown in a comparison to a differential approach for a two-qubit spin chain.

quant-ph↗

arXiv2018

Primordial gravastar from inflation

The dS bubbles can nucleate spontaneously during inflation, and will be stretched by the cosmological expansion to astrophysical scale. We report on a novel phenomenon that such a bubble might develop into a gravastar (an ultra-compact object with a dS core) after inflation, which witnessed the occurrence of inflation and would survive till today. It is pointed out that if a primordial gravastar was involved in one of the LIGO/Virgo gravitational wave (GW) events, the post-merger object could be a gravastar that will eventually collapse into a black hole. As a result, the late-time GW ringdown waveform will exhibit a series of "echoes" with intervals increasing with time.

gr-qc↗

arXiv2018

Compact and lightweight 1.5 μm lidar with a multi-mode fiber coupling free-running InGaAs/InP single-photon detector

We present a compact and lightweight 1.5 μm lidar using a free-running single-photon detector (SPD) based on a multi-mode fiber (MMF) coupling InGaAs/InP negative feedback avalanche diode. The ultimate light detection sensitivity of SPD highly reduces the power requirement of laser, whilst the enhanced collection efficiency due to MMF coupling significantly reduces the volume and weight of telescopes. We develop a specific algorithm for the corrections of errors caused by the SPD and erbium-doped fiber amplifier to extract accurate backscattering signals. We also perform a comparison between single-mode fiber (SMF) coupling and MMF coupling in the lidar receiver, and the results show that the collection efficiency with MMF coupling is five times higher than SMF coupling. In order to validate the functionality, we use the lidar system for the application of cloud detection. The lidar system exhibits the ability to detect both the cloud base height and the thickness of multi-layer clouds to an altitude of 12 km with a temporal resolution of 1 s and a spatial resolution of 15 m. Due to the advantages of compactness and lightweight, our lidar system can be installed on unmanned aerial vehicles for wide applications in practice.

physics.app-ph↗

arXiv2018

A statistical normalization method and differential expression analysis for RNA-seq data between different species

Background: High-throughput techniques bring novel tools but also statistical challenges to genomic research. Identifying genes with differential expression between different species is an effective way to discover evolutionarily conserved transcriptional responses. To remove systematic variation between different species for a fair comparison, the normalization procedure serves as a crucial pre-processing step that adjusts for the varying sample sequencing depths and other confounding technical effects. Results: In this paper, we propose a scale based normalization (SCBN) method by taking into account the available knowledge of conserved orthologous genes and hypothesis testing framework. Considering the different gene lengths and unmapped genes between different species, we formulate the problem from the perspective of hypothesis testing and search for the optimal scaling factor that minimizes the deviation between the empirical and nominal type I errors. Conclusions: Simulation studies show that the proposed method performs significantly better than the existing competitor in a wide range of settings. An RNA-seq dataset of different species is also analyzed and it coincides with the conclusion that the proposed method outperforms the existing method. For practical applications, we have also developed an R package named "SCBN" and the software is available at http://www.bioconductor.org/packages/devel/bioc/html/SCBN.html.

stat.ME↗

arXiv2018

Joint Activity Detection and Channel Estimation for IoT Networks: Phase Transition and Computation-Estimation Tradeoff

Massive device connectivity is a crucial communication challenge for Internet of Things (IoT) networks, which consist of a large number of devices with sporadic traffic. In each coherence block, the serving base station needs to identify the active devices and estimate their channel state information for effective communication. By exploiting the sparsity pattern of data transmission, we develop a structured group sparsity estimation method to simultaneously detect the active devices and estimate the corresponding channels. This method significantly reduces the signature sequence length while supporting massive IoT access. To determine the optimal signature sequence length, we study \emph{the phase transition behavior} of the group sparsity estimation problem. Specifically, user activity can be successfully estimated with a high probability when the signature sequence length exceeds a threshold; otherwise, it fails with a high probability. The location and width of the phase transition region are characterized via the theory of conic integral geometry. We further develop a smoothing method to solve the high-dimensional structured estimation problem with a given limited time budget. This is achieved by sharply characterizing the convergence rate in terms of the smoothing parameter, signature sequence length and estimation accuracy, yielding a trade-off between the estimation accuracy and computational cost. Numerical results are provided to illustrate the accuracy of our theoretical results and the benefits of smoothing techniques.

cs.IT↗

arXiv2018

A Unified Framework for the Tractable Analysis of Multi-Antenna Wireless Networks

Densifying networks and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, the complicated distributions of the signal power and the accumulated interference power, largely induced by various space-time processing techniques, make it highly challenging to quantitatively characterize the performance of multi-antenna networks. In this paper, using tools from stochastic geometry, a unified framework is developed for the analysis of such networks. The major results are two innovative representations of the coverage probability, which make the analysis of multi-antenna networks almost as tractable as the single-antenna case. One is expressed as an $\ell_1$-induced norm of a Toeplitz matrix, and the other is given in a finite sum form. With a compact representation, the former incorporates many existing analytical results on single- and multi-antenna networks as special cases, and leads to tractable expressions for evaluating the coverage probability in both ad hoc and cellular networks. While the latter is more complicated for numerical evaluation, it helps analytically gain key design insights. In particular, it helps prove that the coverage probability of ad hoc networks is a monotonically decreasing convex function of the transmitter density and that there exists a peak value of the coverage improvement when increasing the number of transmit antennas. On the other hand, in multi-antenna cellular networks, it is shown that the coverage probability is independent of the transmitter density and that the outage probability decreases exponentially as the number of transmit antennas increases.

cs.IT↗

arXiv2018

Scalable network adaptation for Cloud-RANs: An imitation learning approach

Network adaptation is essential for the efficient operation of Cloud-RANs. Unfortunately, it leads to highly intractable mixed-integer nonlinear programming problems. Existing solutions typically rely on convex relaxation, which yield performance gaps that are difficult to quantify. Meanwhile, global optimization algorithms such as branch-and-bound can find optimal solutions but with prohibitive computational complexity. In this paper, to obtain near-optimal solutions at affordable complexity, we propose to approximate the branch-and-bound algorithm via machine learning. Specifically, the pruning procedure in branch-and-bound is formulated as a sequential decision problem, followed by learning the oracle's action via imitation learning. A unique advantage of this framework is that the training process only requires a small dataset, and it is scalable to problem instances with larger dimensions than the training setting. This is achieved by identifying and leveraging the problem-size independent features. Numerical simulations demonstrate that the learning based framework significantly outperforms competing methods, with computational complexity much lower than the traditional branch-and-bound algorithm.

eess.SP↗

arXiv2018

Inverse-Consistent Deep Networks for Unsupervised Deformable Image Registration

Deformable image registration is a fundamental task in medical image analysis, aiming to establish a dense and non-linear correspondence between a pair of images. Previous deep-learning studies usually employ supervised neural networks to directly learn the spatial transformation from one image to another, requiring task-specific ground-truth registration for model training. Due to the difficulty in collecting precise ground-truth registration, implementation of these supervised methods is practically challenging. Although several unsupervised networks have been recently developed, these methods usually ignore the inherent inverse-consistent property (essential for diffeomorphic mapping) of transformations between a pair of images. Also, existing approaches usually encourage the to-be-estimated transformation to be locally smooth via a smoothness constraint only, which could not completely avoid folding in the resulting transformation. To this end, we propose an Inverse-Consistent deep Network (ICNet) for unsupervised deformable image registration. Specifically, we develop an inverse-consistent constraint to encourage that a pair of images are symmetrically deformed toward one another, until both warped images are matched. Besides using the conventional smoothness constraint, we also propose an anti-folding constraint to further avoid folding in the transformation. The proposed method does not require any supervision information, while encouraging the diffeomoprhic property of the transformation via the proposed inverse-consistent and anti-folding constraints. We evaluate our method on T1-weighted brain magnetic resonance imaging (MRI) scans for tissue segmentation and anatomical landmark detection, with results demonstrating the superior performance of our ICNet over several state-of-the-art approaches for deformable image registration. Our code will be made publicly available.

cs.CV↗

arXiv2018

Towards an Intelligent Edge: Wireless Communication Meets Machine Learning

The recent revival of artificial intelligence (AI) is revolutionizing almost every branch of science and technology. Given the ubiquitous smart mobile gadgets and Internet of Things (IoT) devices, it is expected that a majority of intelligent applications will be deployed at the edge of wireless networks. This trend has generated strong interests in realizing an "intelligent edge" to support AI-enabled applications at various edge devices. Accordingly, a new research area, called edge learning, emerges, which crosses and revolutionizes two disciplines: wireless communication and machine learning. A major theme in edge learning is to overcome the limited computing power, as well as limited data, at each edge device. This is accomplished by leveraging the mobile edge computing (MEC) platform and exploiting the massive data distributed over a large number of edge devices. In such systems, learning from distributed data and communicating between the edge server and devices are two critical and coupled aspects, and their fusion poses many new research challenges. This article advocates a new set of design principles for wireless communication in edge learning, collectively called learning-driven communication. Illustrative examples are provided to demonstrate the effectiveness of these design principles, and unique research opportunities are identified.

cs.IT↗

arXiv2018

Coronal condensations caused by magnetic reconnection between solar coronal loops

Employing Solar Dynamics Observatory (SDO)/Atmospheric Imaging Assembly (AIA) multi-wavelength images, we report the coronal condensation during the magnetic reconnection (MR) between a system of open and closed coronal loops. Higher-lying magnetically open structures, observed in AIA 171 A images above the solar limb, move downward and interact with the lower-lying closed loops, resulting in the formation of dips in the former. An X-type structure forms at the interface. The interacting loops reconnect and disappear. Two sets of newly-reconnected loops then form and recede from the MR region. During the MR process, bright emission appears sequentially in the AIA 131 A and 304 A channels repeatedly in the dips of higher-lying open structures. This indicates the cooling and condensation process of hotter plasma from ~0.9 MK down to ~0.6 MK, and then to ~0.05 MK, also supported by the light curves of the AIA 171 A, 131 A, and 304 A channels. The part of higher-lying open structures supporting the condensations participate in the successive MR. The condensations without support by underlying loops then rain back to the solar surface along the newly-reconnected loops. Our results suggest that the MR between coronal loops leads to the condensation of hotter coronal plasma and its downflows. MR thus plays an active role in the mass cycle of coronal plasma because it can initiate the catastrophic cooling and condensation. This underlines that the magnetic and thermal evolution has to be treated together and cannot be separated, even in the case of catastrophic cooling.

astro-ph.SR↗

arXiv2018

Testing Local Realism into the Past without Detection and Locality Loopholes

Inspired by the recent remarkable progress in the experimental test of local realism, we report here such a test that achieves an efficiency greater than (78%)^2 for entangled photon pairs separated by 183 m. Further utilizing the randomness in cosmic photons from pairs of stars on the opposite sides of the sky for the measurement setting choices, we not only close the locality and detection loopholes simultaneously, but also test the null hypothesis against local hidden variable mechanisms for events that took place 11 years ago (13 orders of magnitude longer than previous experiments). After considering the bias in measurement setting choices, we obtain an upper bound on the p value of 7.87 * 10^-4, which clearly indicates the rejection with high confidence of potential local hidden variable models. One may further push the time constraint on local hidden variable mechanisms deep into the cosmic history by taking advantage of the randomness in photon emissions from quasars with large aperture telescopes.

quant-ph↗

arXiv2018

Hybrid entanglement of three quantum memories with three photons

Quantum network has significant applications both practically and fundamentally. A hybrid architecture with photons and stationary nodes is highly promising. So far, experimental realizations are limited to two nodes with two photons. Going beyond state of the art by entangling many photons with many quantum nodes is highly appreciated. Here, we report an experiment realizing hybrid entanglement between three photons and three atomic-ensemble quantum memories. We make use of three similar setups, in each of which one pair of photon-memory entanglement with high overall efficiency is created via cavity enhancement. Through three-photon interference, the three quantum memories get entangled with the three photons. Via measuring the photons and applying feedforward, we heraldedly entangle the three memories. Our work demonstrates the largest size of hybrid memory-photon entanglement, which may be employed as a build block to construct larger and complex quantum network.

quant-ph↗

arXiv2018

A Unified Stochastic Particle Bhatnagar-Gross-Krook Method for Multiscale Gas Flows

The stochastic particle method based on Bhatnagar-Gross-Krook (BGK) or ellipsoidal statistical BGK (ESBGK) model approximates the pairwise collisions in the Boltzmann equation using a relaxation process. Therefore, it is more efficient to simulate gas flows at small Knudsen numbers than the counterparts based on the original Boltzmann equation, such as the Direct Simulation Monte Carlo (DSMC) method. However, the traditional stochastic particle BGK method decouples the molecular motions and collisions in analogy to the DSMC method, and hence its transport properties deviate from physical values as the time step increases. This defect significantly affects its computational accuracy and efficiency for the simulation of multiscale flows, especially when the transport processes in the continuum regime is important. In the present paper, we propose a unified stochastic particle ESBGK (USP-ESBGK) method by combining the molecular convection and collision effects. In the continuum regime, the proposed method can be applied using large temporal-spatial discretization and approaches to the Navier-Stokes solutions accurately. Furthermore, it is capable to simulate both the small scale non-equilibrium flows and large scale continuum flows within a unified framework efficiently and accurately. The applications of USP-ESBGK method to a variety of benchmark problems, including Couette flow, thermal Couette flow, Poiseuille flow, Sod tube flow, cavity flow, and flow through a slit, demonstrated that it is a promising tool to simulate multiscale gas flows ranging from rarefied to continuum regime.

physics.comp-ph↗