Jun Zhang - Shanghai Jiao Tong U

Jun Zhang

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Jun Zhang
Shanghai Jiao Tong U

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Mathematics - Information Theory (12)
Computer Science - Information Theory (12)
Quantum Physics (8)
Physics - Materials Science (6)
Computer Science - Computation and Language (4)
Cosmology and Nongalactic Astrophysics (4)
Physics - Mesoscopic Systems and Quantum Hall Effect (4)
Computer Science - Computer Vision and Pattern Recognition (3)
Computer Science - Computational Geometry (3)
Physics - Strongly Correlated Electrons (3)
Solar and Stellar Astrophysics (3)
Computer Science - Computational Engineering; Finance; and Science (3)
Physics - Superconductivity (2)
Computer Science - Distributed; Parallel; and Cluster Computing (2)
Computer Science - Cryptography and Security (2)
Physics - Instrumentation and Detectors (2)
Mathematics - Symplectic Geometry (1)
Computer Science - Learning (1)
Computer Science - Neural and Evolutionary Computing (1)
High Energy Physics - Phenomenology (1)
Physics - Optics (1)
Physics - Computational Physics (1)
General Relativity and Quantum Cosmology (1)
Nuclear Experiment (1)
Physics - Soft Condensed Matter (1)
Physics - Fluid Dynamics (1)

Publications Authored By Jun Zhang

Single layer (SL) phosphorus (phosphorene) has drawn considerable research attention recently as a two-dimensional (2D) material for application promises. It is a semiconductor showing superior transport and optical properties. Few-layer or SL black phosphorus has been successfully isolated by exfoliation from bulk crystals and extensively studied thereof for its electronic and optical properties. Read More

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. Read More

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. Read More

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. Read More

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. Read More

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. Read More

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. Read More

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. Read More

At interfaces between oxide materials, lattice and electronic reconstructions always play important roles in exotic phenomena. In this study, the density functional theory and maximally localized Wannier functions are employed to investigate the (LaTiO$_3$)$_n$/(LaVO$_3$)$_n$ magnetic superlattices. The electron transfer from Ti$^{3+}$ to V$^{3+}$ is predicted, which violates the intuitive band alignment based on the electronic structures of LaTiO$_3$ and LaVO$_3$. Read More

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. Read More

In this work, a serial on-line cluster reconstruction technique based on FPGA technology was developed to compress experiment data and reduce the dead time of data transmission and storage. At the same time, X-ray imaging experiment based on a two-dimensional positive sensitive triple GEM detector with an effective readout area of 10 cm*10 cm was done to demonstrate this technique with FPGA development board. The result showed that the reconstruction technology was practicality and efficient. Read More

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. Read More

Recurrent neural networks (RNNs), especially long short-term memory (LSTM) RNNs, are effective network for sequential task like speech recognition. Deeper LSTM models perform well on large vocabulary continuous speech recognition, because of their impressive learning ability. However, it is more difficult to train a deeper network. Read More

Coherence is the most fundamental quantum feature in quantum mechanics. For a bipartite quantum state, if a measurement is performed on one party, the other party, based on the measurement outcomes, will collapse to a corresponding state with some probability and hence gain the average coherence. It is shown that the average coherence is not less than the coherence of its reduced density matrix. Read More

We propose an Analytical method of Blind Separation (ABS) of cosmic magnification from the intrinsic fluctuations of galaxy number density in the observed (lensed) galaxy number density distribution. The ABS method utilizes the different dependences of the signal (cosmic magnification) and contamination (galaxy intrinsic clustering) on galaxy flux, to separate the two. It works directly on the measured cross galaxy angular power spectra between different flux bins. Read More

As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently.We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model. It is a non-interference strategy that the exponential moving average model is not broadcasted to distributed workers to update their local models after model synchronization in the training process, and it is implemented as the final model of the training system. Read More

We consider the reionization process in a cosmological model in which dark matter interacts with dark energy. Using a semi-analytical reionization model, we compute the evolution of the ionized fraction in terms of its spatial average and linear perturbations. We show that certain types of interactions between dark matter and dark energy can significantly affect the reionization history. Read More

Caching at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technique to accommodate the exponentially increasing mobile data traffic. While most previous works ignored user mobility, there are some recent works taking it into account. However, the duration of user contact times has been ignored, making it difficult to explicitly characterize the effect of mobility. Read More

Airborne laser scanning (lidar) point clouds can be process to extract tree-level information over large forested landscapes. Existing procedures typically detect more than 90% of overstory trees, yet they barely detect 60% of understory trees because of reduced number of lidar points penetrating the top canopy layer. Although understory trees provide limited financial value, they offer habitat for numerous wildlife species and are important for stand development. Read More

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. Read More

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. Read More

We report a new kagome quantum spin liquid candidate Cu$_3$Zn(OH)$_6$FBr, which does not experience any phase transition down to 50 mK, more than three orders lower than the antiferromagnetic Curie-Weiss temperature ($\sim$ 200 K). A clear spin gap opening at low temperature is observed in the uniform spin susceptibility obtained from $^{19}$F nuclear magnetic resonance measurements. We observe, for the first time, the characteristic magnetic field dependence of the gap as expected for fractionalized spin-1/2 spinon excitations. Read More

Mobile-edge computing (MEC) has recently emerged as a prominent technology to liberate mobile devices from computationally intensive workloads, by offloading them to the proximate MEC server. To make offloading effective, the radio and computational resources need to be dynamically managed, to cope with the time-varying computation demands and wireless fading channels. In this paper, we develop an online joint radio and computational resource management algorithm for multi-user MEC systems, with the objective as minimizing the long-term average weighted sum power consumption of the mobile devices and the MEC server, subject to a task buffer stability constraint. Read More

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. Read More

Oxides with $4d$/$5d$ transition metal ions are physically interesting for their particular crystalline structures as well as the spin-orbit coupled electronic structures. Recent experiments revealed a series of $4d$/$5d$ transition metal oxides $R_3M$O$_7$ ($R$: rare earth; $M$: $4d$/$5d$ transition metal) with unique quasi-one-dimensional $M$ chains. Here first-principles calculations have been performed to study the electronic structures of La$_3$OsO$_7$ and La$_3$RuO$_7$. Read More

Mobile-edge computing (MEC) has emerged as a prominent technique to provide mobile services with high computation requirement, by migrating the computation-intensive tasks from the mobile devices to the nearby MEC servers. To reduce the execution latency and device energy consumption, in this paper, we jointly optimize task offloading scheduling and transmit power allocation for MEC systems with multiple independent tasks. A low-complexity sub-optimal algorithm is proposed to minimize the weighted sum of the execution delay and device energy consumption based on alternating minimization. Read More

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter wave (mmWave) communications. While existing works on hybrid precoding mainly focus on single-user single-carrier transmission, in practice multicarrier transmission is needed to combat the much increased bandwidth, and multiuser MIMO can provide additional spatial multiplexing gains. In this paper, we propose a new hybrid precoding structure for multiuser OFDM mmWave systems, which greatly simplifies the hybrid precoder design and is able to approach the performance of the fully digital precoder. Read More

Monolayer Mo$_2$C is a new member of two-dimensional materials. Here the electronic structure and lattice dynamics of monolayer Mo$_2$C are calculated. According to the electron-phonon interaction, it is predicted that monolayer Mo$_2$C could be a quasi-two-dimensional superconductor and the effects of functional-groups are crucially important considering its unsaturated surface. Read More

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. Read More

Airborne LiDAR point cloud representing a forest can be processed to derive vertical stand structure. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an over-story and multiple under-story tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. Read More

This paper presents a distributed approach that scales up to segment tree crowns within a LiDAR point cloud representing an arbitrarily large forested area. The approach uses a single-processor tree segmentation algorithm as a building block in order to process the data delivered in the shape of tiles in parallel. The distributed processing is performed in a master-slave manner, in which the master maintains the global map of the tiles and coordinates the slaves that segment tree crowns within and across the boundaries of the tiles. Read More

This paper presents a non-parametric approach for segmenting trees from airborne LiDAR data in deciduous forests. Based on the LiDAR point cloud, the approach collects crown information such as steepness and height on-the-fly to delineate crown boundaries, and most importantly, does not require a priori assumptions of crown shape and size. The approach segments trees iteratively starting from the tallest within a given area to the smallest until all trees have been segmented. Read More

An iterative scheme can be used to find a steady-state solution to the Boltzmann equation, however, it is very slow to converge in the near-continuum flow regime. In this paper, a synthetic iterative scheme is developed to speed up the solution of the linearized Boltzmann equation. The velocity distribution function is first solved by the conventional iterative scheme, then it is corrected such that the macroscopic flow velocity is governed by a diffusion equation which is asymptotic-preserving in the Navier-Stokes limit. Read More

The randomness from a quantum random number generator (QRNG) relies on the accurate characterization of its devices. However, device imperfections and inaccurate characterizations can result in wrong entropy estimation and bias in practice, which highly affects the genuine randomness generation and may even induce the disappearance of quantum randomness in an extreme case. Here we experimentally demonstrate a measurement-device-independent (MDI) QRNG based on time-bin encoding to achieve certified quantum randomness even when the measurement devices are uncharacterized and untrusted. Read More

Based on the \emph{Interface Region Imaging Spectrograph} observations, we study the response of a solar sunspot light wall to external disturbances. A flare occurrence near the light wall caused material to erupt from the lower solar atmosphere into the corona. Some material falls back to the solar surface, and hits the light bridge (i. Read More

A challenge in precision measurement with squeezed spin state arises from the spin dephasing due to stray magnetic fields. To suppress such environmental noises, we employ a continuous driving protocol, rotary echo, to enhance the spin coherence of a spin-1 Bose-Einstein condensate in stray magnetic fields. Our analytical and numerical results show that the coherent and the squeezed spin states are preserved for a significantly long time, compared to the free induction decay time, if the condition $h\tau = m\pi$ is met with $h$ the pulse amplitude and $\tau$ pulse width. Read More

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. Read More

Affiliations: 1Shanghai Jiao Tong University, 2Shanghai Jiao Tong University, 3Shanghai Jiao Tong University, 4Shanghai Jiao Tong University, 5Peking University, 6Shanghai Normal University, 7Purple Mountain Observatory, 8Peking University

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. Read More

Quantum Hamiltonian identification is important for characterizing the dynamics of quantum systems, calibrating quantum devices and achieving precise quantum control. In this paper, an effective two-step optimization (TSO) quantum Hamiltonian identification algorithm is developed within the framework of quantum process tomography. In the identification method, different probe states are inputted into quantum systems and the output states are estimated using the quantum state tomography protocol via linear regression estimation. Read More

This paper studies how some symplectic invariants which are born from Hamiltonian Floer theory (e.g. spectral invariant, boundary depth, (partial) symplectic quasi-state) change with respect to symplectic structure perturbations, i. Read More

Mobile-edge computing (MEC) has recently emerged as a promising paradigm to liberate mobile devices from increasingly intensive computation workloads, as well as to improve the quality of computation experience. In this paper, we investigate the tradeoff between two critical but conflicting objectives in multi-user MEC systems, namely, the power consumption of mobile devices and the execution delay of computation tasks. A power consumption minimization problem with task buffer stability constraints is formulated to investigate the tradeoff, and an online algorithm that decides the local execution and computation offloading policy is developed based on Lyapunov optimization. Read More

With the high-resolution data from the Interface Region Imaging Spectrograph, we detect a light wall above a sunspot light bridge in the NOAA active region (AR) 12403. In the 1330 A slit-jaw images, the light wall is brighter than the ambient areas while the wall top and base are much brighter than the wall body, and it keeps oscillating above the light bridge. A C8. Read More

In this article, we study the ground states and the first radial excited states of the tensor-tensor type scalar hidden-charm tetraquark states with the QCD sum rules. We separate the ground state contributions from the first radial excited state contributions unambiguously, and obtain the QCD sum rules for the ground states and the first radial excited states, respectively. Then we search for the Borel parameters and continuum threshold parameters according to four criteria and obtain the masses of the tensor-tensor type scalar hidden-charm tetraquark states, which can be confronted to the experimental data in the future. Read More

In this paper, we propose effective channel assignment algorithms for network utility maximization in a cellular network with underlaying device-to-device (D2D) communications. A major innovation is the consideration of partial channel state information (CSI), i.e. Read More

In mobile edge computing systems, mobile devices can offload compute-intensive tasks to a nearby cloudlet,so as to save energy and extend battery life. Unlike a fully-fledged cloud, a cloudlet is a small-scale datacenter deployed at a wireless access point, and thus is highly constrained by both radio and compute resources. We show in this paper that separately optimizing the allocation of either compute or radio resource, as most existing works did, is highly suboptimal: the congestion of compute resource leads to the waste of radio resource, and vice versa. Read More

InGaAs/InP single-photon avalanche diodes (SPADs) are widely used in practical applications requiring near-infrared photon counting such as quantum key distribution (QKD). Photon detection efficiency and dark count rate are the intrinsic parameters of InGaAs/InP SPADs, due to the fact that their performances cannot be improved using different quenching electronics given the same operation conditions. After modeling these parameters and developing a simulation platform for InGaAs/InP SPADs, we investigate the semiconductor structure design and optimization. Read More

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. Read More

Extracting CMB B-mode polarization from complicated foregrounds is a challenging task in searching for inflationary gravitational waves. We propose an analytical solution to the B-mode power spectrum measurement directly from post-processing the cross bandpower between different frequency bands, without free parameters or fitting procedures, or any assumptions on foregrounds. Testing against a variety of foregrounds, survey frequency configurations and instrument noise, we verify its applicability and numerical stability. Read More

We present the quasi-periodic slipping motion of flux rope structures prior to the onset of an eruptive X-class flare on 2015 March 11, obtained by the \emph{Interface Region Imaging Spectrograph} (\emph{IRIS}) and the \emph{Solar Dynamics Observatory} (\emph{SDO}). The slipping motion occurred at the north part of the flux rope and seemed to successively peel off the flux rope. The speed of the slippage was 30$-$40 km s$^{-1}$, with an average period of 130$\pm$30 s. Read More