arXiv subjects

Wei Huang

Publications and source records attributed to Wei Huang.

At least 415 records · Page 23Linked to original sources

arXiv2020

Achievable Rate Region of MISO Interference Channel Aided by Intelligent Reflecting Surface

This paper investigates the achievable rate region of the multiple-input single-output (MISO) interference channel aided by intelligent reflecting surfaces (IRSs). We exploit the the additional design degree of freedom provided by the coordinated IRSs to enhance the desired signal and suppress interference so as to enlarge the achievable rate region of the interference channel. To this end, we jointly optimize the active transmit beamforming at the transmitters and passive reflective beamforming at the IRSs, subject to the constant modulus constraints of reflective beamforming vectors. To address the non-convex optimization problem, we propose an iterative algorithm to optimize the transmit beamforming via second-order cone program (SOCP) and the reflective beamforming via the semi-definite relaxation (SDR). Numerical results demonstrate that the performance of the IRS-aided interference channel with the proposed algorithm can significantly outperform the conventional interference channel without IRS.

eess.SP↗

arXiv2020

High-throughput GPU layered decoder of multi-edge type low density parity check codes in continuous-variable quantum key distribution systems

The decoding throughput in the postprocessing is one of the bottlenecks for a continuous-variable quantum key distribution (CV-QKD) system. In this paper, we propose a layered decoder to decode quasi-cyclic multi-edge type LDPC (QC-METLDPC) codes based on graphic processing unit (GPU) in continuous-variable quantum key distribution (CV-QKD) systems. We optimize the storage method of the parity check matrix, merge the sub-matrices which are unrelated, and decode multiple codewords in parallel on GPU. Simulation results demonstrate that the average decoding speed of LDPC codes with three typical code rates, i.e., 0.1, 0.05 and 0.02, is up to 64.11Mbits/s, 48.65Mbits/s and 39.51Mbits/s, respectively, when decoding 128 codewords of length 106 simultaneously without early termination.

quant-ph↗

arXiv2020

Mean field theory for deep dropout networks: digging up gradient backpropagation deeply

In recent years, the mean field theory has been applied to the study of neural networks and has achieved a great deal of success. The theory has been applied to various neural network structures, including CNNs, RNNs, Residual networks, and Batch normalization. Inevitably, recent work has also covered the use of dropout. The mean field theory shows that the existence of depth scales that limit the maximum depth of signal propagation and gradient backpropagation. However, the gradient backpropagation is derived under the gradient independence assumption that weights used during feed forward are drawn independently from the ones used in backpropagation. This is not how neural networks are trained in a real setting. Instead, the same weights used in a feed-forward step needs to be carried over to its corresponding backpropagation. Using this realistic condition, we perform theoretical computation on linear dropout networks and a series of experiments on dropout networks. Our empirical results show an interesting phenomenon that the length gradients can backpropagate for a single input and a pair of inputs are governed by the same depth scale. Besides, we study the relationship between variance and mean of statistical metrics of the gradient and shown an emergence of universality. Finally, we investigate the maximum trainable length for deep dropout networks through a series of experiments using MNIST and CIFAR10 and provide a more precise empirical formula that describes the trainable length than original work.

cs.LG↗

arXiv2020

Spin-Valley Locking Effect in Defect States of Monolayer MoS$_2$

Valley pseudospin in two-dimensional (2D) transition-metal dichalcogenides (TMDs) allows optical control of spin-valley polarization and intervalley quantum coherence. Defect states in TMDs give rise to new exciton features and theoretically exhibit spin-valley polarization; however, experimental achievement of this phenomenon remains challenges. Here, we report unambiguous valley pseudospin of defect-bound localized excitons in CVD-grown monolayer MoS2; enhanced valley Zeeman splitting with an effective g-factor of -6.2 is observed. Our results reveal that all five d-orbitals and the increased effective electron mass contribute to the band shift of defect states, demonstrating a new physics of the magnetic responses of defect-bound localized excitons, strikingly different from that of A excitons. Our work paves the way for the manipulation of the spin-valley degrees of freedom through defects toward valleytronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Enhanced Valley Zeeman Splitting in Fe-Doped Monolayer MoS2

The Zeeman effect offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (g = -6.4) and 10 K (g = -11) in a CVD-grown Fe-doped MoS2 monolayer; the effective g factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization.

cond-mat.mtrl-sci↗

arXiv2020

In-plane terahertz surface plasmon-polaritons coupler based on adiabatic following

We propose a robust and broadband integrated terahertz (THz) coupler based on the in-plane surface plasmon polaritons (SPPs) waveguides, conducted with the quantum coherent control -- Stimulated Raman Adiabatic Passage (STIRAP). Our coupler consists of two asymmetric specific curved corrugated metallic structures working as the input and output SPPs waveguides, and one straight corrugated metallic structure functioning as the middle SPPs waveguide. From the theoretical and simulated results, we demonstrate that the SPPs can be efficiently transfered from the input to the output waveguides. Our device is robust against the perturbations of geometric parameters, and meanwhile it manifests broadband performance (from 0.3 THz to 0.8 THz) with the high transmission rate over 70$\%$. The in-plane THz coupler can largely simplify the fabrication process, which will make contribution to develop compact and robust integrated THz devices and promote the future applications in all optical network and THz communications.

physics.optics↗

arXiv2020

Population transfer via a dissipative structural continuum

We propose a model to study quantum population transfer via a structural continuum. The model is composed of two spins which are coupled to two bosonic modes separately by two control pulses, and the two bosonic modes are coupled to a common structural continuum. We show that efficient population transfer can be achieved between the two spins by using a multi-level stimulated Raman adiabatic passage (STIRAP) across the continuum, which we refer to as straddle STIRAP via continuum. We also consider the stability of this model against different control parameters and show that efficient population transfer can be achieved even in presence a moderate dissipation.

quant-ph↗

arXiv2019

Cache-Enabled Coordinated Mobile Edge Network: Opportunities and Challenges

Cache-enabled coordinated mobile edge network is an emerging network architecture, wherein serving nodes located at the network edge have the capabilities of baseband signal processing and caching files at their local cache. The main goals of such an emerging network architecture are to alleviate the burden on fronthaul links, while achieving low-latency high rate content delivery, say on the order of the millisecond level end-to-end content delivery latency. Consequently, the application of delaysensitive and content-aware has been executed in close proximity to end users. In this article, an outlook of research directions, challenges, and opportunities is provided and discussed in depth. We first introduce the cache-enabled coordinated mobile edge network architecture, and then discuss the key technical challenges and opening research issues that need to be addressed for this architecture. Then, to reduce the delivery latency and the burden on fronthaul links, new cache-enabled physical layer transmission schemes are discussed. Finally, artificial intelligence based cache-enabled communications are discussed as future research directions. Numerical studies show that several gains are achieved by caching popular content at the network edge with proper coordinated transmission schemes.

eess.SP↗

arXiv2019

Gaussian Process Latent Variable Model Factorization for Context-aware Recommender Systems

Context-aware recommender systems (CARS) have gained increasing attention due to their ability to utilize contextual information. Compared to traditional recommender systems, CARS are, in general, able to generate more accurate recommendations. Latent factors approach accounts for a large proportion of CARS. Recently, a non-linear Gaussian Process (GP) based factorization method was proven to outperform the state-of-the-art methods in CARS. Despite its effectiveness, GP model-based methods can suffer from over-fitting and may not be able to determine the impact of each context automatically. In order to address such shortcomings, we propose a Gaussian Process Latent Variable Model Factorization (GPLVMF) method, where we apply an appropriate prior to the original GP model. Our work is primarily inspired by the Gaussian Process Latent Variable Model (GPLVM), which was a non-linear dimensionality reduction method. As a result, we improve the performance on the real datasets significantly as well as capturing the importance of each context. In addition to the general advantages, our method provides two main contributions regarding recommender system settings: (1) addressing the influence of bias by setting a non-zero mean function, and (2) utilizing real-valued contexts by fixing the latent space with real values.

cs.LG↗

arXiv2019

One-Stage Inpainting with Bilateral Attention and Pyramid Filling Block

Recent deep learning based image inpainting methods which utilize contextual information and two-stage architecture have exhibited remarkable performance. However, the two-stage architecture is time-consuming, the contextual information lack high-level semantics and ignores both the semantic relevance and distance information of hole's feature patches, these limitations result in blurry textures and distorted structures of final result. Motivated by these observations, we propose a new deep generative model-based approach, which trains a shared network twice with different targets and utilizes a single network during the testing phase, so that we can effectively save inference time. Specifically, the targets of two training steps are structure reconstruction and texture generation respectively. During the second training, we first propose a Pyramid Filling Block (PF-block) to utilize the high-level features that the hole regions has been filled to guide the filling process of low-level features progressively, the missing content can be filled from deep to shallow in a pyramid fashion. Then, inspired by the classical bilateral filter [30], we propose the Bilateral Attention layer (BA-layer) to optimize filled feature map, which synthesizes feature patches at each position by computing weighted sums of the surrounding feature patches, these weights are derived by considering both distance and value relationships between feature patches, thus making the visually plausible inpainting results. Finally, experiments on multiple publicly available datasets show the superior performance of our approach.

cs.CV↗

arXiv2019

Hybrid Organic-Metal Oxide Multilayer Channel Transistors with Record Operational Stability

Metal oxide thin-film transistors are fast becoming a ubiquitous technology for application in driving backplanes of organic light-emitting diode displays. Currently all commercial products rely on metal oxides processed via physical vapor deposition methods. Transition to simpler, higher throughput manufacturing methods such as solution-based processes, are currently been explored as cost-effective alternatives. However, developing printable oxide transistors with high carrier mobility and bias-stable operation has proved challenging. Here we show that hybrid multilayer channels composed of alternating ultra-thin layers ($\leq$4 nm) of indium oxide, zinc oxide nanoparticles, ozone-treated polystyrene and a compact zinc oxide layer, all solution-processed in ambient atmosphere, can be used to create TFTs with remarkably high electron mobility (50 cm$^{2}$/Vs) and record operational stability. Insertion of the ozone-treated polystyrene interlayer is shown to reduce the concentration of electron traps at the metal oxide surfaces and heterointerfaces. The resulting transistors exhibit dramatically enhanced bias stability over 24 h continuous operation and while subjected to large electric field flux density (2.1$\times$10$^{-6}$ C/cm$^{2}$) with no adverse effects on the electron mobility. Density functional theory calculations identify the origin of this enhanced stability as the passivation of the oxygen vacancy-related gap states due to interaction between ozonolyzed styrene moieties and the oxides. Our results sets new design guidelines for bias-stress resilient metal oxide transistors.

physics.app-ph↗

arXiv2019

SoK: Hardware Security Support for Trustworthy Execution

In recent years, there have emerged many new hardware mechanisms for improving the security of our computer systems. Hardware offers many advantages over pure software approaches: immutability of mechanisms to software attacks, better execution and power efficiency and a smaller interface allowing it to better maintain secrets. This has given birth to a plethora of hardware mechanisms providing trusted execution environments (TEEs), support for integrity checking and memory safety and widespread uses of hardware roots of trust. In this paper, we systematize these approaches through the lens of abstraction. Abstraction is key to computing systems, and the interface between hardware and software contains many abstractions. We find that these abstractions, when poorly designed, can both obscure information that is needed for security enforcement, as well as reveal information that needs to be kept secret, leading to vulnerabilities. We summarize such vulnerabilities and discuss several research trends of this area.

cs.CR↗

arXiv2019

Multiobjective Optimization Differential Evolution Enhanced with Principle Component Analysis for Constrained Optimization

Multiobjective evolutionary algorithms (MOEAs) have been successfully applied to a number of constrained optimization problems. Many of them adopt mutation and crossover operators from differential evolution. However, these operators do not explicitly utilise features of fitness landscapes. To improve the performance of algorithms, this paper aims at designing a search operator adapting to fitness landscapes. Through an observation, we find that principle component analysis (PCA) can be used to characterise fitness landscapes. Based on this finding, a new search operator, called PCA-projection, is proposed. In order to verify the effectiveness of PCA-projection, we design two algorithms enhanced with PCA-projection for solving constrained optimization problems, called PMODE and HECO-PDE, respectively. Experiments have been conducted on the IEEE CEC 2017 competition benchmark suite in constrained optimisation. PMODE and HECO-PDE are compared with the algorithms from the IEEE CEC 2018 competition and another recent MOEA for constrained optimisation. Experimental results show that an algorithm enhanced with PCA-projection performs better than its corresponding opponent without this operator. Furthermore, HECO-PDE is ranked first on all dimensions according to the competition rules. This study reveals that decomposition-based MOEAs, such as HECO-PDE, are competitive with best single-objective and multiobjective evolutionary algorithms for constrained optimisation, but MOEAs based on non-dominance, such as PMODE, may not.

cs.NE↗

arXiv2019

Device-Independent Quantum Private Query Protocol without the Assumption of Perfect Detectors

The first device-independent quantum private query protocol (MRT17) which is proposed by Maitra \emph{et al.} [Phys. Rev. A 95, 042344 (2017)] to enhance the security through the certification of the states and measurements. However, the MRT17 protocol works under an assumption of perfect detectors, which increases difficulty in the implementations. Therefore, it is crucial to investigate what would affect the security of this protocol if the detectors were imperfect. Meanwhile, Maitra \emph{et al.} also pointed out that this problem remains open. In this paper, we analyze the security of MRT17 protocol when the detectors are imperfect and then find that this protocol is under attack in the aforementioned case. Furthermore, we propose device-independent QPQ protocol without the assumption of perfect detectors. Compared with MRT17 protocol, our protocol is more practical without relaxing the security in the device-independent framework.

quant-ph↗

arXiv2019

testRNN: Coverage-guided Testing on Recurrent Neural Networks

Recurrent neural networks (RNNs) have been widely applied to various sequential tasks such as text processing, video recognition, and molecular property prediction. We introduce the first coverage-guided testing tool, coined testRNN, for the verification and validation of a major class of RNNs, long short-term memory networks (LSTMs). The tool implements a generic mutation-based test case generation method, and it empirically evaluates the robustness of a network using three novel LSTM structural test coverage metrics. Moreover, it is able to help the model designer go through the internal data flow processing of the LSTM layer. The tool is available through: https://github.com/TrustAI/testRNN under the BSD 3-Clause licence.

cs.NE↗

arXiv2019

High Speed Continuous Variable Source-Independent Quantum Random Number Generation

As a fundamental phenomenon in nature, randomness has a wide range of applications in the fields of science and engineering. Among different types of random number generators (RNG), quantum random number generator (QRNG) is a kind of promising RNG as it can provide provable true random numbers based on the inherent randomness of fundamental quantum processes. Nevertheless, the randomness from a QRNG can be diminished (or even destroyed) if the devices (especially the entropy source devices) are not perfect or ill-characterized. To eliminate the practical security loopholes from the source, source-independent QRNGs, which allow the source to have arbitrary and unknown dimensions, have been introduced and become one of the most important semi-device-independent QRNGs. Herein a method that enables ultra-fast unpredictable quantum random number generation from quadrature fluctuations of quantum optical field without any assumptions on the input states is proposed. Particularly, to estimate a lower bound on the extractable randomness that is independent from side information held by an eavesdropper, a new security analysis framework is established based on the extremality of Gaussian states, which can be easily extended to design and analyze new semi-device-independent continuous variable QRNG protocols. Moreover, the practical imperfections of the QRNG including the effects of excess noise, finite sampling range, finite resolution and asymmetric conjugate quadratures are taken into account and quantitatively analyzed. Finally, the proposed method is experimentally demonstrated to obtain high secure random number generation rates of 15.07 Gbits/s in off-line configuration and can potentially achieve 6 Gbits/s by real-time post-processing.

quant-ph↗

arXiv2019

Isomorphism in Wavelets

Two scaling functions $φ_A$ and $φ_B$ for Parseval frame wavelets are algebraically isomorphic, $φ_A \simeq φ_B$, if they have matching solutions to their (reduced) isomorphic systems of equations. Let $A$ and $B$ be $d\times d$ and $s\times s$ \thematrix matrices with $d, s\geq 1$ respectively and let $φ_A$ be a scaling function associated with matrix $A$ and generated by a finite solution. There always exists a scaling function $φ_B$ associated with matrix $B$ such that \begin{equation*} φ_B \simeq φ_A. \end{equation*} An example shows that the assumption on the finiteness of the solutions can not be removed.

math.FA↗

arXiv2019

A new coupling mechanism between two graphene electron waveguides for ultrafast switching

We propose a novel ultrafast electronic switching device based on dual-graphene electron waveguides, in analogy to the optical dual-channel waveguide device. The design utilizes the principle of coherent quantum mechanical tunneling of Rabi oscillations between the two graphene electron waveguides. Based on a modified coupled mode theory, we construct a theoretical model to analyse the device characteristics, and predict that the swtiching speed is faster than 1 ps. Due to the long mean free path of electrons in graphene at room temperature, the proposed design avoids the limitation of low temperature operation required in the normal semiconductor quantum-well structure. The layout of the our design is similar to that of a standard CMOS transistor that should be readily fabricated with current state-of-art nanotechnology.

quant-ph↗