arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 1,045 records · Page 58Linked to original sources

arXiv2018

Search for Magnetic Monopoles in Magnetic Reconnection Regions

In order to satisfy the symmetry between electric and magnetic fields in the source free Maxwell's equations, electric charges might have magnetic counterparts: magnetic monopoles. Many methods and techniques are proposed to search for the monopoles, but no confirmed results have been obtained. Based on solar observations, we know that magnetic reconnections take place during eruptive solar activities. The magnetic fields can be broken at first and then rejoined, implying that the fields are source-relevant at the broken moment. It is speculated that the magnetic lines undergo outward deflection movement during the broken moment, as the line tying effect disappears and the magnetic tension triggers the movement. The signal of the deflection is detected for the first time by EUV and H$α$ observations in reconnection processes. We propose that the monopoles appear in magnetic reconnection regions at first, and then the annihilation of opposite polarity monopoles releases energy and perhaps also produces particles. To detect the predict monopoles, laboratory plasma experiments can be used to provide some fundamental information.

astro-ph.SR↗

arXiv2018

A Hardware-Efficient Analog Network Structure for Hybrid Precoding in Millimeter Wave Systems

Hybrid precoding has been recently proposed as a cost-effective transceiver solution for millimeter wave (mm-wave) systems. While the number of radio frequency (RF) chains has been effectively reduced in existing works, a large number of high-precision phase shifters are still needed. Practical phase shifters are with coarsely quantized phases, and their number should be reduced to a minimum due to cost and power consideration. In this paper, we propose a novel hardware-efficient implementation for hybrid precoding, called the fixed phase shifter (FPS) implementation. It only requires a small number of phase shifters with quantized and fixed phases. To enhance the spectral efficiency, a switch network is put forward to provide dynamic connections from phase shifters to antennas, which is adaptive to the channel states. An effective alternating minimization (AltMin) algorithm is developed with closed-form solutions in each iteration to determine the hybrid precoder and the states of switches. Moreover, to further reduce the hardware complexity, a group-connected mapping strategy is proposed to reduce the number of switches. Simulation results show that the FPS fully-connected hybrid precoder achieves higher hardware efficiency with much fewer phase shifters than existing proposals. Furthermore, the group-connected mapping achieves a good balance between spectral efficiency and hardware complexity.

cs.IT↗

arXiv2018

Integrating quantum key distribution with classical communications in backbone fiber network

Quantum key distribution (QKD) provides information-theoretic security based on the laws of quantum mechanics. The desire to reduce costs and increase robustness in real-world applications has motivated the study of coexistence between QKD and intense classical data traffic in a single fiber. Previous works on coexistence in metropolitan areas have used wavelength-division multiplexing, however, coexistence in backbone fiber networks remains a great experimental challenge, as Tbps data of up to 20 dBm optical power is transferred, and much more noise is generated for QKD. Here we present for the first time, to the best of our knowledge, the integration of QKD with a commercial backbone network of 3.6 Tbps classical data at 21 dBm launch power over 66 km fiber. With 20 GHz pass-band filtering and large effective core area fibers, real-time secure key rates can reach 4.5 kbps and 5.1 kbps for co-propagation and counter-propagation at the maximum launch power, respectively. This demonstrates feasibility and represents an important step towards building a quantum network that coexists with the current backbone fiber infrastructure of classical communications.

quant-ph↗

arXiv2018

Spin-glass ground state in a triangular-lattice compound YbZnGaO$_4$

We report on comprehensive results identifying the ground state of a triangular-lattice structured YbZnGaO$_4$ to be spin glass, including no long-range magnetic order, prominent broad excitation continua, and absence of magnetic thermal conductivity. More crucially, from the ultralow-temperature a.c. susceptibility measurements, we unambiguously observe frequency-dependent peaks around 0.1 K, indicating the spin-glass ground state. We suggest this conclusion to hold also for its sister compound YbMgGaO$_4$, which is confirmed by the observation of spin freezing at low temperatures. We consider disorder and frustration to be the main driving force for the spin-glass phase.

cond-mat.str-el↗

arXiv2018

Neutron star mergers as a probe of modifications of general relativity with finite-range scalar forces

Observations of gravitational radiation from compact binary systems provide an unprecedented opportunity to test General Relativity in the strong field dynamical regime. In this paper, we investigate how future observations of gravitational radiation from binary neutron star mergers might provide constraints on finite-range forces from a universally coupled massive scalar field. Such scalar degrees of freedom are a characteristic feature of many extensions of General Relativity. For concreteness, we work in the context of metric $f(R)$ gravity, which is equivalent to General Relativity and a universally coupled scalar field with a non-linear potential whose form is fixed by the choice of $f(R)$. In theories where neutron stars (or other compact objects) obtain a significant scalar charge, the resulting attractive finite-range scalar force has implications for both the inspiral and merger phases of binary systems. We first present an analysis of the inspiral dynamics in Newtonian limit, and forecast the constraints on the mass of the scalar and charge of the compact objects for the Advanced LIGO gravitational wave observatory. We then perform a comparative study of binary neutron star mergers in General Relativity with those of a one-parameter model of $f(R)$ gravity using fully relativistic hydrodynamical simulations. These simulations elucidate the effects of the scalar on the merger and post-merger dynamics. We comment on the utility of the full waveform (inspiral, merger, post-merger) to probe different regions of parameter space for both the particular model of $f(R)$ gravity studied here and for finite-range scalar forces more generally.

gr-qc↗

arXiv2018

18-qubit entanglement with photon's three degrees of freedom

A central theme in quantum information science is to coherently control an increasing number of quantum particles as well as their internal and external degrees of freedom (DoFs), meanwhile maintaining a high level of coherence. The ability to create and verify multiparticle entanglement with individual control and measurement of each qubit serves as an important benchmark for quantum technologies. To this end, genuine multipartite entanglement have been reported up to 14 trapped ions, 10 photons, and 10 superconducting qubits. Here, we experimentally demonstrate an 18-qubit Greenberger-Horne-Zeilinger (GHZ) entanglement by simultaneous exploiting three different DoFs of six photons, including their paths, polarization, and orbital angular momentum (OAM). We develop high-stability interferometers for reversible quantum logic operations between the photon's different DoFs with precision and efficiencies close to unity, enabling simultaneous readout of 262,144 outcome combinations of the 18-qubit state. A state fidelity of 0.708(16) is measured, confirming the genuine entanglement of all the 18 qubits.

quant-ph↗

arXiv2017

A non-biased trust model for wireless mesh networks

Trust models that rely on recommendation trusts are vulnerable to badmouthing and ballot-stuffing attacks. To cope with these attacks, existing trust models use different trust aggregation techniques to process the recommendation trusts and combine them with the direct trust values to form a combined trust value. However, these trust models are biased as recommendation trusts that deviate too much from one's own opinion are discarded. In this paper, we propose a non-biased trust model that considers every recommendation trusts available regardless they are good or bad. Our trust model is based on a combination of 2 techniques: the dissimilarity test and the Dempster-Shafer Theory. The dissimilarity test determines the amount of conflict between 2 trust records, whereas the Dempster-Shafer Theory assigns belief functions based on the results of the dissimilarity test. Numerical results show that our trust model is robust against reputation-based attacks when compared to trust aggregation techniques such as the linear opinion pooling, subjective logic model, entropy-based probability model, and regression analysis. In addition, our model has been extensively tested using network simulator NS-3 in an Infrastructure-based wireless mesh networks and a Hybrid-based wireless mesh networks to demonstrate that it can mitigate blackhole and grayhole attacks.

cs.CR↗

arXiv2017

A Secure and Authenticated Key Management Protocol (SA-KMP) for Vehicular Networks

Public key infrastructure (PKI) is the most widely used security mechanism for securing communications over the network. However, there are known performance issues, making it unsuitable for use in vehicular networks. In this paper, we propose a secure and authenticated key management protocol (SA-KMP) to overcome the shortcomings of the PKI. The SA-KMP scheme distributes repository containing the bindings of the en-tity's identity and its corresponding public key to each vehicle and road side unit. By doing so, certificate exchanges and certificate revocation lists are eliminated. Furthermore, the SA-KMP scheme uses symmetric keys derived based on a 3-D-matrix-based key agreement scheme to reduce the high computational costs of using asymmetric cryptography. We demonstrate the efficiency of the SA-KMP through performance evaluations in terms of transmission and storage overhead, network latency, and key generation time. Analytical results show that the SA-KMP is more scalable and outperforms the certificate-based PKI. Simulation results indicate that the key generation time of the SA-KMP scheme is less than that of the existing Elliptic Curve Diffie--Hellman and Diffie--Hellman protocols. In addition, we use Proverif to prove that the SA-KMP scheme is secure against an active attacker under the Dolev and Yao model and further show that the SA-KMP scheme is secure against denial of service, collusion attacks, and a wide range of other malicious attacks.

cs.CR↗

arXiv2017

Removing the Impact of Correlated PSF Uncertainties in Weak Lensing

Accurate reconstruction of the spatial distributions of the Point Spread Function (PSF) is crucial for high precision cosmic shear measurements. Nevertheless, current methods are not good at recovering the PSF fluctuations of high spatial frequencies. In general, the residual PSF fluctuations are spatially correlated, therefore can significantly contaminate the correlation functions of the weak lensing signals. We propose a method to correct for this contamination statistically, without any assumptions on the PSF and galaxy morphologies or their spatial distribution. We demonstrate our idea with the data from the W2 field of CFHTLenS.

astro-ph.CO↗

arXiv2017

An accurate centroid algorithm for PSF reconstruction

In this work, we present a novel centroiding method based on Fourier space Phase Fitting(FPF) for Point Spread Function(PSF) reconstruction. We generate two sets of simulations to test our method. The first set is generated by GalSim with elliptical Moffat profile and strong anisotropy which shifts the center of the PSF. The second set of simulation is drawn from CFHT i band stellar imaging data. We find non-negligible anisotropy from CFHT stellar images, which leads to $\sim$0.08 scatter in unit of pixels using polynomial fitting method Vakili and Hogg (2016). And we apply FPF method to estimate the centroid in real space, this scatter reduces to $\sim$0.04 in SNR=200 CFHT like sample. In low SNR (50 and 100) CFHT like samples, the background noise dominates the shifting of the centroid, therefore the scatter estimated from different methods are similar. We compare polynomial fitting and FPF using GalSim simulation with optical anisotropy. We find that in all SNR$\sim$50, 100 and 200) samples, FPF performs better than polynomial fitting by a factor of $\sim$3. In general, we suggest that in real observations there are anisotropy which shift the centroid, and FPF method is a better way to accurately locate it.

astro-ph.IM↗

arXiv2017

Galaxy-galaxy weak-lensing measurement from SDSS: II. host halo properties of galaxy groups

As the second paper of a series on studying galaxy-galaxy lensing signals using the Sloan Digital Sky Survey Data Release 7 (SDSS DR7), we present our measurement and modelling of the lensing signals around groups of galaxies. We divide the groups into four halo mass bins, and measure the signals around four different halo-center tracers: brightest central galaxy (BCG), luminosity-weighted center, number-weighted center and X-ray peak position. For X-ray and SDSS DR7 cross identified groups, we further split the groups into low and high X-ray emission subsamples, both of which are assigned with two halo-center tracers, BCGs and X-ray peak positions. The galaxy-galaxy lensing signals show that BCGs, among the four candidates, are the best halo-center tracers. We model the lensing signals using a combination of four contributions: off-centered NFW host halo profile, sub-halo contribution, stellar contribution, and projected 2-halo term. We sample the posterior of 5 parameters i.e., halo mass, concentration, off-centering distance, sub halo mass, and fraction of subhalos via a MCMC package using the galaxy-galaxy lensing signals. After taking into account the sampling effects (e.g. Eddington bias), we found the best fit halo masses obtained from lensing signals are quite consistent with those obtained in the group catalog based on an abundance matching method, except in the lowest mass bin. Subject headings: (cosmology:) gravitational lensing, galaxies: clusters: general

astro-ph.CO↗

arXiv2017

Deep Learning for identifying radiogenomic associations in breast cancer

Purpose: To determine whether deep learning models can distinguish between breast cancer molecular subtypes based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Materials and methods: In this institutional review board-approved single-center study, we analyzed DCE-MR images of 270 patients at our institution. Lesions of interest were identified by radiologists. The task was to automatically determine whether the tumor is of the Luminal A subtype or of another subtype based on the MR image patches representing the tumor. Three different deep learning approaches were used to classify the tumor according to their molecular subtypes: learning from scratch where only tumor patches were used for training, transfer learning where networks pre-trained on natural images were fine-tuned using tumor patches, and off-the-shelf deep features where the features extracted by neural networks trained on natural images were used for classification with a support vector machine. Network architectures utilized in our experiments were GoogleNet, VGG, and CIFAR. We used 10-fold crossvalidation method for validation and area under the receiver operating characteristic (AUC) as the measure of performance. Results: The best AUC performance for distinguishing molecular subtypes was 0.65 (95% CI:[0.57,0.71]) and was achieved by the off-the-shelf deep features approach. The highest AUC performance for training from scratch was 0.58 (95% CI:[0.51,0.64]) and the best AUC performance for transfer learning was 0.60 (95% CI:[0.52,0.65]) respectively. For the off-the-shelf approach, the features extracted from the fully connected layer performed the best. Conclusion: Deep learning may play a role in discovering radiogenomic associations in breast cancer.

cs.CV↗

arXiv2017

Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ

Purpose: To determine whether deep learning-based algorithms applied to breast MR images can aid in the prediction of occult invasive disease following the di- agnosis of ductal carcinoma in situ (DCIS) by core needle biopsy. Material and Methods: In this institutional review board-approved study, we analyzed dynamic contrast-enhanced fat-saturated T1-weighted MRI sequences of 131 patients at our institution with a core needle biopsy-confirmed diagnosis of DCIS. The patients had no preoperative therapy before breast MRI and no prior history of breast cancer. We explored two different deep learning approaches to predict whether there was a hidden (occult) invasive component in the analyzed tumors that was ultimately detected at surgical excision. In the first approach, we adopted the transfer learning strategy, in which a network pre-trained on a large dataset of natural images is fine-tuned with our DCIS images. Specifically, we used the GoogleNet model pre-trained on the ImageNet dataset. In the second approach, we used a pre-trained network to extract deep features, and a support vector machine (SVM) that utilizes these features to predict the upstaging of the DCIS. We used 10-fold cross validation and the area under the ROC curve (AUC) to estimate the performance of the predictive models. Results: The best classification performance was obtained using the deep features approach with GoogleNet model pre-trained on ImageNet as the feature extractor and a polynomial kernel SVM used as the classifier (AUC = 0.70, 95% CI: 0.58- 0.79). For the transfer learning based approach, the highest AUC obtained was 0.53 (95% CI: 0.41-0.62). Conclusion: Convolutional neural networks could potentially be used to identify occult invasive disease in patients diagnosed with DCIS at the initial core needle biopsy.

cs.CV↗

arXiv2017

Revisiting the electronic phase diagram of YBa2Cu3Oy via temperature derivative of in-plane resistivity

We have re-examined the temperature-doping (T-p) phase diagram of YBa2Cu3Oy (YBCO) to address several issues therein by using the temperature derivative of in-plane resistivity, d\{rho}ab/dT. For p less than about 0.15, a temperature Tf has been defined to mark the onset of an upturn in d\{rho}ab/dT at T>Tc, which, in light of prior studies on another cuprate La2-xSrxCuO4, is attributed to the onset of superconducting fluctuations in normal state. The Tf exhibits a doping dependence similar to Tc, and the interval between Tf and Tc is about 5-30 K across the underdoped regime, showing agreement with a variety of other measurements to probe a restricted temperature range of superconducting fluctuations. Above Tf, the d\{rho}ab/dT increases linearly as T increases up to a value denoted as T2, which is about half the T* and falls roughly in parallel with T* as p rises up to about 0.13, indicating a prominent T^2-dependent \{rho}ab in this T-p region. The d\{rho}ab/dT helps reveal that, at Tf 0.10-0.11, thereby leaving a narrow window of doping for \{rho}ab to show a pure T^2 dependence. As T increases further, the d\{rho}ab/dT reaches a local maximum at a temperature T_R, signifying the known curvature change (inflection point) in \{rho}ab. With the derivatives, it is illustrated that, in the vicinity of T_R, the in-plane Hall coefficient R_H and thermopower Sab of YBCO display curvature changes as well, suggesting a correlation of the three transport properties. It is also found that the dSab/dT shows the onset of an upturn at a temperature coinciding with the Tf, which, backing the identification of Tf with the onset of superconducting fluctuations, demonstrates further the virtue of using the temperature derivative to unveil information helpful for the study of high-Tc cuprates.

cond-mat.supr-con↗

arXiv2017

Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach

Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks (Cloud-RANs), which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each transmission block, which will help to determine the active RRHs and the associated fronthaul links. In this paper, we propose innovative approaches to reduce the complexity of this key step in group sparse beamforming. Specifically, we first develop a smoothed $\ell_p$-minimization approach with the iterative reweighted-$\ell_2$ algorithm to return a Karush-Kuhn-Tucker (KKT) point solution, as well as enhancing the capability of inducing group sparsity in the beamforming vectors. By leveraging the Lagrangian duality theory, we obtain closed-form solutions at each iteration to reduce the computational complexity. The well-structured solutions provide the opportunities to apply the large-dimensional random matrix theory to derive deterministic approximations for the RRH ordering criterion. Such an approach helps to guide the RRH selection only based on the statistical channel state information (CSI), which does not require frequent update, thereby significantly reducing the computation overhead. Simulation results shall demonstrate the performance gains of the proposed $\ell_p$-minimization approach, as well as the effectiveness of the large system analysis based framework for computing RRH ordering criterion.

cs.IT↗

arXiv2017

Joint Power Control and Beamforming for Uplink Non-Orthogonal Multiple Access in 5G Millimeter-Wave Communications

In this paper, we investigate the combination of two key enabling technologies for the fifth generation (5G) wireless mobile communication, namely millimeter-wave (mmWave) communications and non-orthogonal multiple access (NOMA). In particular, we consider a typical 2-user uplink mmWave-NOMA system, where the base station (BS) equips an analog beamforming structure with a single RF chain and serves 2 NOMA users. An optimization problem is formulated to maximize the achievable sum rate of the 2 users while ensuring a minimal rate constraint for each user. The problem turns to be a joint power control and beamforming problem, i.e., we need to find the beamforming vectors to steer to the two users simultaneously subject to an analog beamforming structure, and meanwhile control appropriate power on them. As direct search for the optimal solution of the non-convex problem is too complicated, we propose to decompose the original problem into two sub-problems that are relatively easy to solve: one is a power control and beam gain allocation problem, and the other is an analog beamforming problem under a constant-modulus constraint. The rational of the proposed solution is verified by extensive simulations, and the performance evaluation results show that the proposed sub-optimal solution achieve a close-to-bound uplink sum-rate performance.

cs.IT↗

arXiv2017

1.25 GHz sine wave gating InGaAs/InP single-photon detector with monolithically integrated readout circuit

InGaAs/InP single-photon detectors (SPDs) are the key devices for applications requiring near-infrared single-photon detection. Gating mode is an effective approach to synchronous single-photon detection. Increasing gating frequency and reducing module size are important challenges for the design of such detector system. Here we present for the first time an InGaAs/InP SPD with 1.25 GHz sine wave gating using a monolithically integrated readout circuit (MIRC). The MIRC has a size of 15 mm * 15 mm and implements the miniaturization of avalanche extraction for high-frequency sine wave gating. In the MIRC, low-pass filters and a low-noise radio frequency amplifier are integrated based on the technique of low temperature co-fired ceramic, which can effectively reduce the parasitic capacitance and extract weak avalanche signals. We then characterize the InGaAs/InP SPD to verify the functionality and reliability of MIRC, and the SPD exhibits excellent performance with 27.5 % photon detection efficiency, 1.2 kcps dark count rate, and 9.1 % afterpulse probability at 223 K and 100 ns hold-off time. With this MIRC, one can further design miniaturized high-frequency SPD modules that are highly required for practical applications.

quant-ph↗

arXiv2017

Explicit Deep Holes of Reed-Solomon Codes

In this paper, deep holes of Reed-Solomon (RS) codes are studied. A new class of deep holes for generalized affine RS codes is given if the evaluation set satisfies certain combinatorial structure. Three classes of deep holes for projective Reed-Solomon (PRS) codes are constructed explicitly. In particular, deep holes of PRS codes with redundancy three are completely obtained when the characteristic of the finite field is odd. Most (asymptotically of ratio $1$) of the deep holes of PRS codes with redundancy four are also obtained.

cs.IT↗