arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 1,081 records · Page 60Linked to original sources

arXiv2017

A Survey on Mobile Edge Computing: The Communication Perspective

Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.

cs.IT↗

arXiv2017

Fully integrated free-running InGaAs/InP single-photon detector for accurate lidar applications

We present a fully integrated InGaAs/InP negative feedback avalanche diode (NFAD) based free-running single-photon detector (SPD) designed for accurate lidar applications. A free-piston Stirling cooler is used to cool down the NFAD with a large temperature range, and an active hold-off circuit implemented in a field programmable gate array is applied to further suppress the afterpulsing contribution. The key parameters of the free-running SPD including photon detection efficiency (PDE), dark count rate (DCR), afterpulse probability, and maximum count rate (MCR) are dedicatedly optimized for lidar application in practice. We then perform a field experiment using a Mie lidar system with 20 kHz pulse repetition frequency to compare the performance between the free-running InGaAs/InP SPD and a commercial superconducting nanowire single-photon detector (SNSPD). Our detector exhibits good performance with 1.6 Mcps MCR (0.6 μs hold-off time), 10% PDE, 950 cps DCR, and 18% afterpulse probability over 50 μs period. Such performance is worse than the SNSPD with 60% PDE and 300 cps DCR. However, after performing a specific algorithm that we have developed for afterpulse and count rate corrections, the lidar system performance in terms of range-corrected signal (Pr2) distribution using our SPD agrees very well with the result using the SNSPD, with only a relative error of ~2%. Due to the advantages of low-cost and small size of InGaAs/InP NFADs, such detector provides a practical solution for accurate lidar applications.

quant-ph↗

arXiv2017

Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM

In this paper, for the purpose of data centre energy consumption monitoring and analysis, we propose to detect the running programs in a server by classifying the observed power consumption series. Time series classification problem has been extensively studied with various distance measurements developed; also recently the deep learning based sequence models have been proved to be promising. In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbour and long short term memory (LSTM) neural network. More specifically, first we propose a new distance measurement termed as Local Time Warping (LTW), which utilizes a user-specified set for local warping, and is designed to be non-commutative and non-dynamic programming. Second we hybridize the 1NN-LTW and LSTM together. In particular, we combine the prediction probability vector of 1NN-LTW and LSTM to determine the label of the test cases. Finally, using the power consumption data from a real data center, we show that the proposed LTW can improve the classification accuracy of DTW from about 84% to 90%. Our experimental results prove that the proposed LTW is competitive on our data set compared with existed DTW variants and its non-commutative feature is indeed beneficial. We also test a linear version of LTW and it can significantly outperform existed linear runtime lower bound methods like LB_Keogh. Furthermore, with the hybrid algorithm, for the power series classification task we achieve an accuracy up to about 93%. Our research can inspire more studies on time series distance measurement and the hybrid of the deep learning models with other traditional models.

cs.NE↗

arXiv2017

Frame Stacking and Retaining for Recurrent Neural Network Acoustic Model

Frame stacking is broadly applied in end-to-end neural network training like connectionist temporal classification (CTC), and it leads to more accurate models and faster decoding. However, it is not well-suited to conventional neural network based on context-dependent state acoustic model, if the decoder is unchanged. In this paper, we propose a novel frame retaining method which is applied in decoding. The system which combined frame retaining with frame stacking could reduces the time consumption of both training and decoding. Long short-term memory (LSTM) recurrent neural networks (RNNs) using it achieve almost linear training speedup and reduces relative 41\% real time factor (RTF). At the same time, recognition performance is no degradation or improves sightly on Shenma voice search dataset in Mandarin.

cs.CL↗

arXiv2017

Incentive Mechanism Design for Cache-Assisted D2D Communications: A Mobility-Aware Approach

Caching popular contents at mobile devices, assisted by device-to-device (D2D) communications, is considered as a promising technique for mobile content delivery. It can effectively reduce backhaul traffic and service cost, as well as improving the spectrum efficiency. However, due to the selfishness of mobile users, incentive mechanisms will be needed to motivate device caching. In this paper, we investigate incentive mechanism design in cache-assisted D2D networks, taking advantage of the user mobility information. An inter-contact model is adopted to capture the average time between two consecutive contacts of each device pair. A Stackelberg game is formulated, where each user plays as a follower aiming at maximizing its own utility and the mobile network operator (MNO) plays as a leader aiming at minimizing the cost. Assuming that user responses can be predicted by the MNO, a cost minimization problem is formulated. Since this problem is NP-hard, we reformulate it as a non-negative submodular maximization problem and develop $(\frac{1}{4+ε})$-approximation local search algorithm to solve it. In the simulation, we demonstrate that the local search algorithm provides near optimal performance. By comparing with other caching strategies, we validate the effectiveness of the proposed incentive-based mobility-aware caching strategy.

cs.IT↗

arXiv2017

A Tractable Framework for Performance Analysis of Dense Multi-Antenna Networks

Densifying the network and deploying more antennas at each access point are two principal ways to boost the capacity of wireless networks. However, due to the complicated distributions of random signal and interference channel gains, largely induced by various space-time processing techniques, it is highly challenging to quantitatively characterize the performance of dense multi-antenna networks. In this paper, using tools from stochastic geometry, a tractable framework is proposed for the analytical evaluation of such networks. The major result is an innovative representation of the coverage probability, as an induced $\ell_1$-norm of a Toeplitz matrix. This compact representation incorporates lots of existing analytical results on single- and multi-antenna networks as special cases, and its evaluation is almost as simple as the single-antenna case with Rayleigh fading. To illustrate its effectiveness, we apply the proposed framework to investigate two kinds of prevalent dense wireless networks, i.e., physical layer security aware networks and millimeter-wave networks. In both examples, in addition to tractable analytical results of relevant performance metrics, insightful design guidelines are also analytically obtained.

cs.IT↗

arXiv2017

Partially-Connected Hybrid Precoding in mm-Wave Systems With Dynamic Phase Shifter Networks

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter wave (mm-wave) communications, and its design challenge mainly lies in the analog component which consists of a network of phase shifters. The partially-connected structure employs a small number of phase shifters and therefore serves as an energy efficient solution for hybrid precoding. In this paper, we propose a double phase shifter (DPS) implementation for the phase shifter network in the partially-connected structure, which allows more tractable and flexible hybrid precoder design. In particular, the hybrid precoder design is identified as an eigenvalue problem. To further enhance the performance, dynamic mapping from radio frequency (RF) chains to antennas is proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms with the DPS implementation over existing ones. Given its low hardware complexity and high spectral efficiency, the proposed structure is a promising candidate for 5G mm-wave systems.

cs.IT↗

arXiv2017

Testing PSF Interpolation In Weak Lensing With Real Data

Reconstruction of the point spread function (PSF) is a critical process in weak lensing measurement. We develop a real-data based and galaxy-oriented pipeline to compare the performances of various PSF reconstruction schemes. Making use of a large amount of the CFHTLenS data, the performances of three classes of interpolating schemes - polynomial, Kriging, and Shepard - are evaluated. We find that polynomial interpolations with optimal orders and domains perform the best. We quantify the effect of the residual PSF reconstruction error on shear recovery in terms of the multiplicative and additive biases, and their spatial correlations using the shear measurement method of Zhang et al. (2015). We find that the impact of PSF reconstruction uncertainty on the shear-shear correlation can be significantly reduced by cross correlating the shear estimators from different exposures. It takes only 0.2 stars (SNR > 100) per square arcmin on each exposure to reach the best performance of PSF interpolation, a requirement that is generally satisfied in the CFHTlenS data.

astro-ph.CO↗

arXiv2017

Approaching the Cramer-Rao Bound in Weak Lensing with PDF Symmetrization

Weak lensing statistics is typically measured as weighted sum of shear estimators or their products (shear-shear correlation). The weighting schemes are designed in the hope of minimizing the statistical error without introducing systematic errors. It would be ideal to approach the Cramer-Rao bound (the lower bound of the statistical uncertainty) in shear statistics, though it is generally difficult to do so in practice. The reasons may include: difficulties in galaxy shape measurement, inaccurate knowledge of the probability-distribution-function (PDF) of the shear estimator, misidentification of point sources as galaxies, etc.. Using the shear estimators defined in Zhang et al. (2015), we show that one can overcome all these problems, and allow shear measurement accuracy to approach the Cramer-Rao bound. This can be achieved by symmetrizing the PDF of the shear estimator, or the joint PDF of shear estimator pairs (for shear-shear correlation), without any prior knowledge of the PDF. Using simulated galaxy images, we demonstrate that under general observing conditions, this idea works as expected: it minimizes the statistical uncertainty without introducing systematic error.

astro-ph.CO↗

arXiv2017

Coverage Analysis for Millimeter Wave Networks: The Impact of Directional Antenna Arrays

Millimeter wave (mm-wave) communications is considered a promising technology for 5G networks. Exploiting beamforming gains with large-scale antenna arrays to combat the increased path loss at mm-wave bands is one of its defining features. However, previous works on mm-wave network analysis usually adopted oversimplified antenna patterns for tractability, which can lead to significant deviation from the performance with actual antenna patterns. In this paper, using tools from stochastic geometry, we carry out a comprehensive investigation on the impact of directional antenna arrays in mm-wave networks. We first present a general and tractable framework for coverage analysis with arbitrary distributions for interference power and arbitrary antenna patterns. It is then applied to mm-wave ad hoc and cellular networks, where two sophisticated antenna patterns with desirable accuracy and analytical tractability are proposed to approximate the actual antenna pattern. Compared with previous works, the proposed approximate antenna patterns help to obtain more insights on the role of directional antenna arrays in mm-wave networks. In particular, it is shown that the coverage probabilities of both types of networks increase as a non-decreasing concave function with the antenna array size. The analytical results are verified to be effective and reliable through simulations, and numerical results also show that large-scale antenna arrays are required for satisfactory coverage in mm-wave networks.

cs.IT↗

arXiv2017

Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection

The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as \textit{early rumor detection}, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model on the basis of recurrent neural networks (RNN) to learn \textit{selectively} temporal hidden representations of sequential posts for identifying rumors. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features; (2) the introduction of soft attention mechanism can effectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.

cs.CL↗

arXiv2017

The Measurement-Disturbance Relation and the Disturbance Trade-off Relation in Terms of Relative Entropy

We employ quantum relative entropy to establish the relation between the measurement uncertainty and its disturbance on a state in the presence (and absence) of quantum memory. For two incompatible observables, we present the measurement-disturbance relation and the disturbance trade-off relation. We find that without quantum memory the disturbance induced by the measurement is never less than the measurement uncertainty and with quantum memory they depend on the conditional entropy of the measured state. We also generalize these relations to the case with multiple measurements. These relations are demonstrated by two examples.

quant-ph↗

arXiv2017

Optimal Photon blockade on the maximal atomic coherence

There is generally no obvious evidence in any direct relation between photon blockade and atomic coherence. Here instead of only illustrating the photon statistics, we show an interesting relation between the steady-state photon blockade and the atomic coherence by designing a weakly driven cavity QED system with a two-level atom trapped. It is shown for the first time that the maximal atomic coherence has a perfect correspondence with the optimal photon blockade. The negative effects of the strong dissipations on photon statistics, atomic coherence and their correspondence are also addressed. The numerical simulation is also given to support all of our results.

quant-ph↗

arXiv2017

Guiding microscale swimmers using teardrop-shaped posts

The swimming direction of biological or artificial microscale swimmers tends to be randomised over long time-scales by thermal fluctuations. Bacteria use various strategies to bias swimming behaviour and achieve directed motion against a flow, maintain alignment with gravity or travel up a chemical gradient. Herein, we explore a purely geometric means of biasing the motion of artificial nanorod swimmers. These artificial swimmers are bimetallic rods, powered by a chemical fuel, which swim on a substrate printed with teardrop-shaped posts. The artificial swimmers are hydrodynamically attracted to the posts, swimming alongside the post perimeter for long times before leaving. The rods experience a higher rate of departure from the higher curvature end of the teardrop shape, thereby introducing a bias into their motion. This bias increases with swimming speed and can be translated into a macroscopic directional motion over long times by using arrays of teardrop-shaped posts aligned along a single direction. This method provides a protocol for concentrating swimmers, sorting swimmers according to different speeds, and could enable artificial swimmers to transport cargo to desired locations.

cond-mat.soft↗

arXiv2017

Observation of forbidden phonons and dark excitons by resonance Raman scattering in few-layer WS$_2$

The optical properties of the two-dimensional (2D) crystals are dominated by tightly bound electron-hole pairs (excitons) and lattice vibration modes (phonons). The exciton-phonon interaction is fundamentally important to understand the optical properties of 2D materials and thus help develop emerging 2D crystal based optoelectronic devices. Here, we presented the excitonic resonant Raman scattering (RRS) spectra of few-layer WS$_2$ excited by 11 lasers lines covered all of A, B and C exciton transition energies at different sample temperatures from 4 to 300 K. As a result, we are not only able to probe the forbidden phonon modes unobserved in ordinary Raman scattering, but also can determine the bright and dark state fine structures of 1s A exciton. In particular, we also observed the quantum interference between low-energy discrete phonon and exciton continuum under resonant excitation. Our works pave a way to understand the exciton-phonon coupling and many-body effects in 2D materials.

cond-mat.mtrl-sci↗

arXiv2017

Logic Locking for Secure Outsourced Chip Fabrication: A New Attack and Provably Secure Defense Mechanism

Chip designers outsource chip fabrication to external foundries, but at the risk of IP theft. Logic locking, a promising solution to mitigate this threat, adds extra logic gates (key gates) and inputs (key bits) to the chip so that it functions correctly only when the correct key, known only to the designer but not the foundry, is applied. In this paper, we identify a new vulnerability in all existing logic locking schemes. Prior attacks on logic locking have assumed that, in addition to the design of the locked chip, the attacker has access to a working copy of the chip. Our attack does not require a working copy and yet we successfully recover a significant fraction of key bits from the design of the locked chip only. Empirically, we demonstrate the success of our attack on eight large benchmark circuits from a benchmark suite that has been tailored specifically for logic synthesis research, for two different logic locking schemes. Then, to address this vulnerability, we initiate the study of provably secure logic locking mechanisms. We formalize, for the first time to our knowledge, a precise notion of security for logic locking. We establish that any locking procedure that is secure under our definition is guaranteed to counter our desynthesis attack, and all other such known attacks. We then devise a new logic locking procedure, Meerkat, that guarantees that the locked chip reveals no information about the key or the designer's intended functionality. A main insight behind Meerkat is that canonical representations of boolean functionality via Reduced Ordered Binary Decision Diagrams (ROBDDs) can be leveraged effectively to provide security. We analyze Meerkat with regards to its security properties and the overhead it incurs. As such, our work is a contribution to both the foundations and practice of securing digital ICs.

cs.CR↗

arXiv2017

LRC: Dependency-Aware Cache Management for Data Analytics Clusters

Memory caches are being aggressively used in today's data-parallel systems such as Spark, Tez, and Piccolo. However, prevalent systems employ rather simple cache management policies--notably the Least Recently Used (LRU) policy--that are oblivious to the application semantics of data dependency, expressed as a directed acyclic graph (DAG). Without this knowledge, memory caching can at best be performed by "guessing" the future data access patterns based on historical information (e.g., the access recency and/or frequency), which frequently results in inefficient, erroneous caching with low hit ratio and a long response time. In this paper, we propose a novel cache replacement policy, Least Reference Count (LRC), which exploits the application-specific DAG information to optimize the cache management. LRC evicts the cached data blocks whose reference count is the smallest. The reference count is defined, for each data block, as the number of dependent child blocks that have not been computed yet. We demonstrate the efficacy of LRC through both empirical analysis and cluster deployments against popular benchmarking workloads. Our Spark implementation shows that, compared with LRU, LRC speeds up typical applications by 60%.

cs.DC↗

arXiv2017

Random number generation with cosmic photons

Random numbers are indispensable for a variety of applications ranging from testing physics foundation to information encryption. In particular, nonlocality tests provide a strong evidence to our current understanding of nature -- quantum mechanics. All the random number generators (RNG) used for the existing tests are constructed locally, making the test results vulnerable to the freedom-of-choice loophole. We report an experimental realization of RNGs based on the arrival time of cosmic photons. The measurement outcomes (raw data) pass the standard NIST statistical test suite. We present a realistic design to employ these RNGs in a Bell test experiment, which addresses the freedom-of-choice loophole.

quant-ph↗