arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 991 records · Page 55Linked to original sources

arXiv2018

Solar jet-like features rooted in flare ribbons

Employing the high spatio-temporal Interface Region Imaging Spectrograph 1330 Å observations, we investigated the jet-like features that occurred during the X8.2 class flare in NOAA active region (AR) 12673 on 2017 September 10. These jet-like features were rooted in the flare ribbons. We examined 15 features, and the mean values of the lifetimes, projected widths, lengths and velocities of these features were 87 s, 890 km, 2.7 Mm and 70 km s$^{-1}$, respectively. We also observed many jet-like features which happened during the X1.0 class flare on 2014 October 25. We studied the spectra at the base of a jet-like feature during its development. The Fe XXI 1354.08 Å line in the corona displays blueshift, while the Si IV 1402.77 Å line in the transition region exhibits redshift, which indicates the chromospheric evaporation. This is the first time that the jet-like features are reported to be rooted in the flare ribbons, and we suggest that these jet-like features were driven by the mechanism of chromospheric evaporation.

astro-ph.SR↗

arXiv2018

A secondary fan-spine magnetic structure in active region 11897

Fan-spine is a special topology in solar atmosphere and is closely related to magnetic null point as well as circular-ribbon flares, which can provide important information for understanding the intrinsic three-dimensional (3D) nature of solar flares. However, the fine structure within the fan has rarely been investigated. In present paper, we investigate a secondary fan-spine (SFS) structure within the fan of a larger fan-spine topology. On 2013 November 18, this large fan-spine structure was traced out due to the partial eruption of a filament, which caused a circular-ribbon flare in active region 11897. The extrapolated 3D magnetic fields and squashing factor Q maps depict distinctly this fan-spine topology, its surrounding quasi-separatrix layer (QSL) halo, and a smaller quasi-circular ribbon with high Q located in the center, which implies the existence of fine structure within the fan. The imaging observations, extrapolated 3D fields, and Q maps on November 17 show that there indeed exists an SFS surrounded by a QSL, which is enveloped by another QSL-halo corresponding to the overlying larger domeshaped fan. Moreover, the material flows caused by the null-point reconnection are also detected along this SFS. After checking the evolution of the underneath magnetic fields, we suggest that the continuous emergence of magnetic flux within the central parasitic region encompassed by the opposite-polarity fields results in the formation of the SFS under the large fan.

astro-ph.SR↗

arXiv2018

Uniaxial dynamical decoupling for an open quantum system

Dynamical decoupling (DD) is an active and effective method for suppressing decoherence of a quantum system from its environment. In contrast to the nominal biaxial DD,this work presents a uniaxial decoupling protocol that requires a significantly reduced number of pulses and a much lower bias field satisfying the "magic" condition.We show this uniaxial DD protocol works effectively in a number of model systems of practical interests, e.g., a spinor atomic Bose-Einstein condensate in stray magnetic fields (classical noise), or an electron spin coupled to nuclear spins (quantum noise) in a semiconductor quantum dot. It requires only half the number of control pulses and a 10-100 times lower bias field for decoupling as normally employed in the above mentioned illustrative examples,and the overall efficacy is robust against rotation errors of the control pulses. The uniaxial DD protocol we propose shines new light on coherent controls in quantum computing and quantum information processing, quantum metrology, and low field nuclear magnetic resonance.

quant-ph↗

arXiv2018

A giant dark channel across the solar equator consisting of two filament channels with different chiralities

Solar filaments are the largest magnetic structure that can be physically traced to the chromosphere. The structure and evolution of solar filaments are important for our understanding of solar atmosphere physics. In this work, we investigate a giant dark channel crossing the solar equator, which consists of two filament channels with different chiralities. From 2016 April 22 to April 27, this giant dark channel occupied the solar disk. Within this giant channel, a filament channel with dextral chirality was detected in the northern hemisphere, and another filament channel with sinistral chirality was observed in the southern hemisphere. At the junction of the two filament channels, a cusp structure was observed associated with active region (AR) 12532 near the solar equator. The extrapolated three-dimensional magnetic fields reveal that this cusp structure was composed of two sets of field lines belonging to two different filament channels and was rooted on the AR positive-polarity fields. In addition, dark material flows from filaments in the two channels to the cusp structure were detected as well as the flux emergence and cancellation around the cusp footpoints. On 2016 May 21, after a solar rotation, the cusp structure had disappeared, and the giant dark channel broke in the middle where another AR, 12546, had emerged completely. We propose that the magnetic flux emergence and cancellation around the cusp region resulted in the disappearance of the cusp structure and the break of the giant dark channel.

astro-ph.SR↗

arXiv2018

Current sheets in the wake of an eruption of two crossing filaments

Employing Solar Dynamic Observatory/Atmosphertic Imaging Assembly (AIA) multi-wavelength images, we study an eruption of two crossing filaments, and firstly report the current sheets (CSs) connecting the lower flare ribbons and the upper erupting filaments. On July 8, 2014, two crossing filaments are observed in the NOAA active region (AR) 12113. The lower-lying filament rises first, and then meets the higher-lying one. Thereafter, both of them erupt together. The filament eruption draws the overlying magnetic field lines upward, leading to the approach of two legs, with opposite magnetic polarities, of the overlying field lines. Two sets of bright CSs form at the interface of these two legs, and magnetic reconnection takes place in the CSs producing the underneath flare ribbons and post-flare loops. Several bright plasmoids appear in the CSs, and propagate along the CSs bi-directionally. The CSs and plasmoids are observed in AIA multi-wavelength channels, indicating that both of them have been heated during the reconnection process, with hot and warm components. Employing the differential emission measure (EM) analysis, we find that both the temperature and EM of the CSs decrease from the flare arcades outward to the erupting filaments, and those of the plasmoids are significantly larger than the regions where no plasmoid is detected.

astro-ph.SR↗

arXiv2018

Transfer Learning for Mixed-Integer Resource Allocation Problems in Wireless Networks

Effective resource allocation plays a pivotal role for performance optimization in wireless networks. Unfortunately, typical resource allocation problems are mixed-integer nonlinear programming (MINLP) problems, which are NP-hard. Machine learning based methods recently emerge as a disruptive way to obtain near-optimal performance for MINLP problems with affordable computational complexity. However, they suffer from severe performance deterioration when the network parameters change, which commonly happens in practice and can be characterized as the task mismatch issue. In this paper, we propose a transfer learning method via self-imitation, to address this issue for effective resource allocation in wireless networks. It is based on a general "learning to optimize" framework for solving MINLP problems. A unique advantage of the proposed method is that it can tackle the task mismatch issue with a few additional unlabeled training samples, which is especially important when transferring to large-size problems. Numerical experiments demonstrate that with much less training time, the proposed method achieves comparable performance with the model trained from scratch with sufficient amount of labeled samples. To the best of our knowledge, this is the first work that applies transfer learning for resource allocation in wireless networks.

eess.SP↗

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↗