arXiv subjects

Ning Wang

Publications and source records attributed to Ning Wang.

At least 307 records · Page 17Linked to original sources

arXiv2020

Negative compressibility in MoS2 capacitance

Large capacitance enhancement is useful for increasing the gate capacitance of field-effect transistors (FETs) to produce low-energy-consuming devices with improved gate controllability. We report strong capacitance enhancement effects in a newly emerged two-dimensional channel material, molybdenum disulfide (MoS2). The enhancement effects are due to strong electron-electron interaction at the low carrier density regime in MoS2. We achieve about 50% capacitance enhancement in monolayer devices and 10% capacitance enhancement in bilayer devices. However, the enhancement effect is not obvious in multilayer (layer number >3) devices. Using the Hartree-Fock approximation, we illustrate the same trend in our inverse compressibility data.

physics.app-ph↗

arXiv2019

Machine Learning-Based Delay-Aware UAV Detection and Operation Mode Identification over Encrypted Wi-Fi Traffic

The consumer UAV (unmanned aerial vehicle) market has grown significantly over the past few years. Despite its huge potential in spurring economic growth by supporting various applications, the increase of consumer UAVs poses potential risks to public security and personal privacy. To minimize the risks, efficiently detecting and identifying invading UAVs is in urgent need for both invasion detection and forensics purposes. Given the fact that consumer UAVs are usually used in a civilian environment, existing physical detection methods (such as radar, vision, and sound) may become ineffective in many scenarios. Aiming to complement the existing physical detection mechanisms, we propose a machine learning-based framework for fast UAV identification over encrypted Wi-Fi traffic. It is motivated by the observation that many consumer UAVs use Wi-Fi links for control and video streaming. The proposed framework extracts features derived only from packet size and inter-arrival time of encrypted Wi-Fi traffic, and can efficiently detect UAVs and identify their operation modes. In order to reduce the online identification time, our framework adopts a re-weighted $\ell_1$-norm regularization, which considers the number of samples and computation cost of different features. This framework jointly optimizes feature selection and prediction performance in a unified objective function. To tackle the packet inter-arrival time uncertainty when optimizing the trade-off between the detection accuracy and delay, we utilize Maximum Likelihood Estimation (MLE) method to estimate the packet inter-arrival time. We collect a large number of real-world Wi-Fi data traffic of eight types of consumer UAVs and conduct extensive evaluation on the performance of our proposed method.

cs.CR↗

arXiv2019

Nuclear mass parabola and its applications

We propose a method to extract the properties of the isobaric mass parabola based on the total double $β$ decay energies of isobaric nuclei. Two important parameters of the mass parabola, the location of the most $β$-stable nuclei $Z_{A}$ and the curvature parameter $b_{A}$, are obtained for 251 A values based on the total double $β$ decay energies of nuclei compiled in AME2016 database.The advantage of this approach is that we can remove the pairing energy term $P_{A}$ caused by odd-even variation, and the mass excess $M(A,Z_{A})$ of the most stable nuclide for mass number $A$ in the performance process, which are used in the mass parabolic fitting method. The Coulomb energy coefficient $a_{c}=0.6910$ MeV is determined by the mass difference relation of mirror nuclei, and the symmetry energy coefficient is also studied by the relation $a_{\rm sym}(A)=0.25b_{A}Z_{A}$.

nucl-th↗

arXiv2019

Few-Shot Sequence Labeling with Label Dependency Transfer and Pair-wise Embedding

While few-shot classification has been widely explored with similarity based methods, few-shot sequence labeling poses a unique challenge as it also calls for modeling the label dependencies. To consider both the item similarity and label dependency, we propose to leverage the conditional random fields (CRFs) in few-shot sequence labeling. It calculates emission score with similarity based methods and obtains transition score with a specially designed transfer mechanism. When applying CRF in the few-shot scenarios, the discrepancy of label sets among different domains makes it hard to use the label dependency learned in prior domains. To tackle this, we introduce the dependency transfer mechanism that transfers abstract label transition patterns. In addition, the similarity methods rely on the high quality sample representation, which is challenging for sequence labeling, because sense of a word is different when measuring its similarity to words in different sentences. To remedy this, we take advantage of recent contextual embedding technique, and further propose a pair-wise embedder. It provides additional certainty for word sense by embedding query and support sentence pairwisely. Experimental results on slot tagging and named entity recognition show that our model significantly outperforms the strongest few-shot learning baseline by 11.76 (21.2%) and 12.18 (97.7%) F1 scores respectively in the one-shot setting.

cs.CL↗

arXiv2019

Physical Layer Key Generation in 5G Wireless Networks

The bloom of the fifth generation (5G) communication and beyond serves as a catalyst for physical layer key generation techniques. In 5G communications systems, many challenges in traditional physical layer key generation schemes, such as co-located eavesdroppers, the high bit disagreement ratio, and high temporal correlation, could be overcome. This paper lists the key-enabler techniques in 5G wireless networks, which offer opportunities to address existing issues in physical layer key generation. We survey the existing key generation methods and introduce possible solutions for the existing issues. The new solutions include applying the high signal directionality in beamforming to resist co-located eavesdroppers, utilizing the sparsity of millimeter wave (mmWave) channel to achieve a low bit disagreement ratio under low signal-to-noise-ratio (SNR), and exploiting hybrid precoding to reduce the temporal correlation among measured samples. Finally, the future trends of physical layer key generation in 5G and beyond communications are discussed.

eess.SP↗

arXiv2019

Generating Multi-Scroll Chua's Attractors via Simplified Piecewise-Linear Chua's Diode

High implementation complexity of multi-scroll circuit is a bottleneck problem in real chaos-based communication. Especially, in multi-scroll Chua's circuit, the simplified implementation of piecewise-linear resistors with multiple segments is difficult due to their intricate irregular breakpoints and slopes. To solve the challenge, this paper presents a systematic scheme for synthesizing a Chua's diode with multi-segment piecewise-linearity, which is achieved by cascading even-numbered passive nonlinear resistors with odd-numbered ones via a negative impedance converter. The traditional voltage mode op-amps are used to implement nonlinear resistors. As no extra DC bias voltage is employed, the scheme can be implemented by much simpler circuits. The voltage-current characteristics of the obtained Chua's diode are analyzed theoretically and verified by numerical simulations. Using the Chua's diode and a second-order active Sallen-Key high-pass filter, a new inductor-free Chua's circuit is then constructed to generate multi-scroll chaotic attractors. Different number of scrolls can be generated by changing the number of passive nonlinear resistor cells or adjusting two coupling parameters. Besides, the system can be scaled by using different power supplies, satisfying the low-voltage low-power requirement of integrated circuit design. The circuit simulations and hardware experiments both confirmed the feasibility of the designed system.

nlin.CD↗

arXiv2019

The Design and Implementation of a Real Time Visual Search System on JD E-commerce Platform

We present the design and implementation of a visual search system for real time image retrieval on JD.com, the world's third largest and China's largest e-commerce site. We demonstrate that our system can support real time visual search with hundreds of billions of product images at sub-second timescales and handle frequent image updates through distributed hierarchical architecture and efficient indexing methods. We hope that sharing our practice with our real production system will inspire the middleware community's interest and appreciation for building practical large scale systems for emerging applications, such as ecommerce visual search.

cs.IR↗

arXiv2019

Plasmonic Heterodyne Spectrometry for Resolving the Spectral Signatures of Ammonia over a 1-5 THz Frequency Range

We present a heterodyne terahertz spectrometry platform based on plasmonic photomixing, which enables the resolution of narrow spectral signatures of gases over a broad terahertz frequency range. This plasmonic heterodyne spectrometer replaces the terahertz mixer and local oscillator of conventional heterodyne spectrometers with a plasmonic photomixer and a heterodyning optical pump beam, respectively. The heterodyning optical pump beam is formed by two continuous-wave, wavelength-tunable lasers with a broadly tunable terahertz beat frequency. This broadly tunable terahertz beat frequency enables spectrometry over a broad bandwidth, which is not restricted by the bandwidth limitations of conventional terahertz mixers and local oscillators. We use this plasmonic heterodyne spectrometry platform to resolve the spectral signatures of ammonia over a 1-5 THz frequency range.

physics.optics↗

arXiv2019

Real-Time Correlation Tracking via Joint Model Compression and Transfer

Correlation filters (CF) have received considerable attention in visual tracking because of their computational efficiency. Leveraging deep features via off-the-shelf CNN models (e.g., VGG), CF trackers achieve state-of-the-art performance while consuming a large number of computing resources. This limits deep CF trackers to be deployed to many mobile platforms on which only a single-core CPU is available. In this paper, we propose to jointly compress and transfer off-the-shelf CNN models within a knowledge distillation framework. We formulate a CNN model pretrained from the image classification task as a teacher network, and distill this teacher network into a lightweight student network as the feature extractor to speed up CF trackers. In the distillation process, we propose a fidelity loss to enable the student network to maintain the representation capability of the teacher network. Meanwhile, we design a tracking loss to adapt the objective of the student network from object recognition to visual tracking. The distillation process is performed offline on multiple layers and adaptively updates the student network using a background-aware online learning scheme. Extensive experiments on five challenging datasets demonstrate that the lightweight student network accelerates the speed of state-of-the-art deep CF trackers to real-time on a single-core CPU while maintaining almost the same tracking accuracy.

cs.CV↗

arXiv2019

Interaction effects and superconductivity signatures in twisted double-bilayer WSe$_2$

Twisted bilayer graphene provides a new two-dimensional platform for studying electron interaction phenomena and flat band properties such as correlated insulator transition, superconductivity and ferromagnetism at certain magic angles. Here, we present strong evidence of correlated insulator states and superconductivity signatures in p-type twisted double-bilayer WSe$_2$. Enhanced interlayer interactions are observed when the twist angle decreases to a few degrees as reflected by the high-order satellites in the electron diffraction patterns taken from the 2H/3R-stacked domains reconstructed from a conventional Moiré superlattice. In contrast to twisted bilayer graphene, there is no specific magic angle for twisted WSe$_2$. The flat band properties are observed at twist angles ranging from 1 to 4 degrees. The highest superconducting transition temperature observed by transport measurement is 6 K. Our work has facilitated future study in the area of flat band related properties in twisted transition metal dichalcogenide layered structures.

cond-mat.mes-hall↗

arXiv2019

Collecting and Analyzing Multidimensional Data with Local Differential Privacy

Local differential privacy (LDP) is a recently proposed privacy standard for collecting and analyzing data, which has been used, e.g., in the Chrome browser, iOS and macOS. In LDP, each user perturbs her information locally, and only sends the randomized version to an aggregator who performs analyses, which protects both the users and the aggregator against private information leaks. Although LDP has attracted much research attention in recent years, the majority of existing work focuses on applying LDP to complex data and/or analysis tasks. In this paper, we point out that the fundamental problem of collecting multidimensional data under LDP has not been addressed sufficiently, and there remains much room for improvement even for basic tasks such as computing the mean value over a single numeric attribute under LDP. Motivated by this, we first propose novel LDP mechanisms for collecting a numeric attribute, whose accuracy is at least no worse (and usually better) than existing solutions in terms of worst-case noise variance. Then, we extend these mechanisms to multidimensional data that can contain both numeric and categorical attributes, where our mechanisms always outperform existing solutions regarding worst-case noise variance. As a case study, we apply our solutions to build an LDP-compliant stochastic gradient descent algorithm (SGD), which powers many important machine learning tasks. Experiments using real datasets confirm the effectiveness of our methods, and their advantages over existing solutions.

cs.CR↗

arXiv2019

Unsupervised Classification of Street Architectures Based on InfoGAN

Street architectures play an essential role in city image and streetscape analysing. However, existing approaches are all supervised which require costly labeled data. To solve this, we propose a street architectural unsupervised classification framework based on Information maximizing Generative Adversarial Nets (InfoGAN), in which we utilize the auxiliary distribution $Q$ of InfoGAN as an unsupervised classifier. Experiments on database of true street view images in Nanjing, China validate the practicality and accuracy of our framework. Furthermore, we draw a series of heuristic conclusions from the intrinsic information hidden in true images. These conclusions will assist planners to know the architectural categories better.

cs.CV↗

arXiv2019

Effects of Hexagonal Boron Nitride Encapsulation on the Electronic Structure of Few-layer MoS$_2$

The hexagonal boron nitride (hBN) encapsulation has been widely used in the electronics applications of 2D materials to improve device performance by protecting 2D materials against contamination and degradation. It is often assumed that hBN layers as a dielectric would not affect the electronic structure of encapsulated 2D materials. Here we studied few-layer MoS$_2$ encapsulated in hBN flakes by using a combination of theoretical and experimental Raman spectroscopy. We found that after the encapsulation the out-of-plane A$_{1g}$ mode is upshifted, while the in-plane E$_{2g}^1$ mode is downshifted. The measured downshift of the E$_{2g}^1$ mode does not decrease with increasing the thickness of MoS$_2$, which can be attributed to tensile strains in bilayer and trilayer MoS$_2$ caused by the typical experimental process of the hBN encapsulation. We estimated the strain magnitude and found that the induced strain may cause the K-Q crossover in the conduction band of few-layer MoS$_2$, so greatly modifies its electronic properties as an n-type semiconductor. Our study suggests that the hBN encapsulation should be used with caution, as it may affect the electronic properties of encapsulated few-layer 2D materials.

cond-mat.mtrl-sci↗

arXiv2019

Controllable Defect Driven Symmetry Change and Domain Structure Evolution in BiFeO3 with Enhanced Tetragonality

Defect engineering has been a powerful tool to enable the creation of exotic phases and the discovery of intriguing phenomena in ferroelectric oxides. However, accurate control the concentration of defects remains a big challenge. In this work, ion implantation, that can provide controllable point defects, allows us the ability to produce a controlled defect-driven true super-tetragonal (T) phase with enhanced tetragonality in ferroelectric BiFeO3 thin films. This point defect engineering is found to drive the phase transition from the as-grown mixed rhombohedral-like (R) and tetragonal-like (MC) phase to true tetragonal (T) symmetry. By further increasing the injected dose of He ion, we demonstrate an enhanced tetragonality super-tetragonal (super-T) phase with the largest c/a ratio (~ 1.3) that has ever been experimentally achieved in BiFeO3. A combination of morphology change and domain evolution further confirm that the mixed R/MC phase structure transforms to the single-domain-state true tetragonal phase. Moreover, the re-emergence of R phase and in-plane stripe nanodomains after heat treatment reveal the memory effect and reversible phase transition. Our findings demonstrate the control of R-Mc-T-super T symmetry changes and the creation of true T phase BiFeO3 with enhanced tetragonality through controllable defect engineering. This work also provides a pathway to generate large tetragonality (or c/a ratio) that could be extended to other ferroelectric material systems (such as PbTiO3, BaTiO3 and HfO2) which may lead to strong polarization enhancement.

cond-mat.mtrl-sci↗

arXiv2019

Unsupervised Deep Tracking

We propose an unsupervised visual tracking method in this paper. Different from existing approaches using extensive annotated data for supervised learning, our CNN model is trained on large-scale unlabeled videos in an unsupervised manner. Our motivation is that a robust tracker should be effective in both the forward and backward predictions (i.e., the tracker can forward localize the target object in successive frames and backtrace to its initial position in the first frame). We build our framework on a Siamese correlation filter network, which is trained using unlabeled raw videos. Meanwhile, we propose a multiple-frame validation method and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy of fully supervised trackers, which require complete and accurate labels during training. Furthermore, unsupervised framework exhibits a potential in leveraging unlabeled or weakly labeled data to further improve the tracking accuracy.

cs.CV↗

arXiv2019

Maximizing spreading influence via measuring influence overlap for social networks

Influence overlap is a universal phenomenon in influence spreading for social networks. In this paper, we argue that the redundant influence generated by influence overlap cause negative effect for maximizing spreading influence. Firstly, we present a theoretical method to calculate the influence overlap and record the redundant influence. Then in term of eliminating redundant influence, we present two algorithms, namely, Degree-Redundant-Influence (DRS) and Degree-Second-Neighborhood (DSN) for multiple spreaders identification. The experiments for four empirical social networks successfully verify the methods, and the spreaders selected by the DSN algorithm show smaller degree and k-core values.

cs.SI↗

arXiv2019

Accelerating spin-space sampling by auxiliary spin-dynamics and temperature-dependent spin-cluster expansion

Atomistic simulations of thermodynamic properties of magnetic materials rely on an accurate modelling of magnetic interactions and an efficient sampling of the high-dimensional spin space. Recent years have seen significant progress with a clear trend from model systems to material specific simulations that are usually based on electronic-structure methods. Here we develop a Hamiltonian Monte Carlo framework that makes use of auxiliary spin-dynamics and an auxiliary effective model, the temperature-dependent spin-cluster expansion, in order to efficiently sample the spin space. Our method does not require a specific form of the model and is suitable for simulations based on electronic-structure methods. We demonstrate fast warm-up and a reasonably small dynamical critical exponent of our sampler for the classical Heisenberg model. We further present an application of our method to the magnetic phase transition in bcc iron using magnetic bond-order potentials.

cond-mat.stat-mech↗

arXiv2018

Quantum coherence control at near 1000 K

Quantum coherence control usually requires extremely low temperature environments. Even for spins in diamond, a remarkable exception, the coherence signal is lost as temperature approaches 700 K. Here we demonstrate quantum coherence control of the electron spins of nitrogen-vacancy centers in nanodiamonds at temperatures near 1000 K. The scheme is based on initialization and readout of the spins at room temperature and control at high temperature, which is enabled by pulse laser heating and rapid diffusion cooling of nanodiamonds on amorphous carbon films. Using high-temperature spin control, we observe the magnetic phase transition of a single nickel nanoparticle at about 615 K. This work enables nano-thermometry and nano-magnetometry in the high-temperature regime.

quant-ph↗