arXiv subjects

Rong Zhang

Publications and source records attributed to Rong Zhang.

At least 127 records · Page 7Linked to original sources

arXiv2020

Anisotropic ultrafast spin/valley dynamics in WTe2 films

WTe2 Weyl semimetal hosts the natural broken inversion symmetry and strong spin orbit coupling, making it promising for exotic spin/valley dynamics within a picosecond timescale. Here, we unveil an anisotropic ultrafast spin/valley dynamics in centimeter-scale, single-crystalline Td-WTe2 films using a femtosecond pump-probe technique at room temperature. We observe a transient (~0.8 ps) intra-valley transition and a subsequent polarization duration (~5 ps) during the whole spin/valley relaxation process. Furthermore, the relaxation exhibits the remarkable anisotropy of approximately six-fold and two-fold symmetries due to the intrinsic anisotropy along the crystalline orientation and the extrinsic matrix element effect, respectively. Our results offer a prospect for the ultrafast manipulation of spin/valleytronics in topological quantum materials for dissipationless high-speed spin/valleytronic devices.

cond-mat.mtrl-sci↗

arXiv2020

2.5-kV AlGaN/GaN Schottky Barrier Diode on Silicon Substrate with Recessed-anode Structure

In this letter, we demonstrate high-performance lateral AlGaN/GaN Schottky barrier diodes (SBD) on Si substrate with a recessed-anode structure. The optimized rapid etch process provides results in improving etching quality with a 0.26-nm roughness of the anode recessed surface. By using the high work function metal Pt as the Schottky electrode, a low Von of 0.71 V is obtained with a high uniformity of 0.023 V for 40 devices. Supported by the flat anode recess surface and related field plate design, the SBD device with the anode-cathode spacing of 15 um show the Ron,sp of 1.53 mOhm.cm2 only, the breakdown voltage can reach 1592 V with a high power FOM (Figure-of-Merit) of 1656 MW/cm2. For the SBD device with the anode-cathode spacing of 30 um, the breakdown voltage can be as high as 2521 V and the power FOM is 1244 MW/cm2.

physics.app-ph↗

arXiv2020

Fate of zero modes in a finite Su-Schrieffer-Heeger Model with $\mathcal{PT}$ Symmetry

Due to the boundary coupling in a finite system, the zero modes of a standard Su-Schrieffer-Heeger (SSH) model may deviate from exact-zero energy. A recent experiment has shown that by increasing the system size or altering gain or loss strength of the SSH model with parity-time ($\mathcal{PT}$) symmetry, the real parts of the energies of the edge modes can be recovered to exact-zero value [Song \emph{et al.} Phys. Rev. Lett. \textbf{123}, 165701 (2019)]. To clarify the effects of $\mathcal{PT}$-symmetric potentials on the recovery of the nontrivial zero modes, we study the SSH model with $\mathcal{PT}$-symmetric potentials of different forms in both infinite and finite systems. Our results indicate that the energies of the edge modes in the infinite size case decide whether or not the success of the recovery of the zero modes by tuning the strength of $\mathcal{PT}$-symmetric potential in a finite system. If the energies of the edge modes amount to zero in the thermodynamic limit under an open boundary condition (OBC), the recovery of the zero modes will break down by increasing the gain or loss strength for a finite system. Our results can be easily examined in different experimental platforms and inspire more insightful understanding on nontrivial edge modes in topologically non-Hermitian systems.

quant-ph↗

arXiv2019

Observation of Shubnikov-de Haas Oscillations in Large-Scale Weyl Semimetal WTe2 Films

Topological Weyl semimetal WTe2 with large-scale film form has a promising prospect for new-generation spintronic devices. However, it remains a hard task to suppress the defect states in large-scale WTe2 films due to the chemical nature. Here, we significantly improve the crystalline quality and remove the Te vacancies in WTe2 films by post annealing. We observe the distinct Shubnikov-de Haas quantum oscillations in WTe2 films. The nontrivial Berry phase can be revealed by Landau fan diagram analysis. The Hall mobility of WTe2 films can reach 1245 cm2V-1s-1 and 1423 cm2V-1s-1 for holes and electrons with the carrier density of 5 * 10^19 cm^-3 and 2 * 10^19 cm^-3, respectively. Our work provides a feasible route to obtain high-quality Weyl semimetal films for the future topological quantum device applications.

cond-mat.mtrl-sci↗

arXiv2019

Lauca: Generating Application-Oriented Synthetic Workloads

The synthetic workload is essential and critical to the performance evaluation of database systems. When evaluating the database performance for a specific application, the similarity between synthetic workload and real application workload determines the credibility of evaluation results. However, the workload currently used for performance evaluation is difficult to have the same workload characteristics as the target application, which leads to inaccurate evaluation results. To address this problem, we propose a workload duplicator (Lauca) that can generate synthetic workloads with highly similar performance metrics for specific applications. To the best of our knowledge, Lauca is the first application-oriented transactional workload generator. By carefully studying the application-oriented synthetic workload generation problem, we present the key workload characteristics (transaction logic and data access distribution) of online transaction processing (OLTP) applications, and propose novel workload characterization and generation algorithms, which guarantee the high fidelity of synthetic workloads. We conduct extensive experiments using workloads from TPC-C, SmallBank and micro benchmarks on both MySQL and PostgreSQL databases, and experimental results show that Lauca consistently generates high-quality synthetic workloads.

cs.DB↗

arXiv2019

AutoRemover: Automatic Object Removal for Autonomous Driving Videos

Motivated by the need for photo-realistic simulation in autonomous driving, in this paper we present a video inpainting algorithm \emph{AutoRemover}, designed specifically for generating street-view videos without any moving objects. In our setup we have two challenges: the first is the shadow, shadows are usually unlabeled but tightly coupled with the moving objects. The second is the large ego-motion in the videos. To deal with shadows, we build up an autonomous driving shadow dataset and design a deep neural network to detect shadows automatically. To deal with large ego-motion, we take advantage of the multi-source data, in particular the 3D data, in autonomous driving. More specifically, the geometric relationship between frames is incorporated into an inpainting deep neural network to produce high-quality structurally consistent video output. Experiments show that our method outperforms other state-of-the-art (SOTA) object removal algorithms, reducing the RMSE by over $19\%$.

cs.CV↗

arXiv2019

The TechQA Dataset

We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer support domain. First, it contains actual questions posed by users on a technical forum, rather than questions generated specifically for a competition or a task. Second, it has a real-world size -- 600 training, 310 dev, and 490 evaluation question/answer pairs -- thus reflecting the cost of creating large labeled datasets with actual data. Consequently, TechQA is meant to stimulate research in domain adaptation rather than being a resource to build QA systems from scratch. The dataset was obtained by crawling the IBM Developer and IBM DeveloperWorks forums for questions with accepted answers that appear in a published IBM Technote---a technical document that addresses a specific technical issue. We also release a collection of the 801,998 publicly available Technotes as of April 4, 2019 as a companion resource that might be used for pretraining, to learn representations of the IT domain language.

cs.CL↗

arXiv2019

Visual Analytics of Student Learning Behaviors on K-12 Mathematics E-learning Platforms

With increasing popularity in online learning, a surge of E-learning platforms have emerged to facilitate education opportunities for k-12 (from kindergarten to 12th grade) students and with this, a wealth of information on their learning logs are getting recorded. However, it remains unclear how to make use of these detailed learning behavior data to improve the design of learning materials and gain deeper insight into students' thinking and learning styles. In this work, we propose a visual analytics system to analyze student learning behaviors on a K-12 mathematics E-learning platform. It supports both correlation analysis between different attributes and a detailed visualization of user mouse-movement logs. Our case studies on a real dataset show that our system can better guide the design of learning resources (e.g., math questions) and facilitate quick interpretation of students' problem-solving and learning styles.

cs.HC↗

arXiv2019

Sequential Wnt Agonist then Antagonist Treatment Accelerates Tissue Repair and Minimizes Fibrosis

Tissue fibrosis compromises organ function and occurs as a potential long-term outcome in response to acute tissue injuries. Currently, lack of mechanistic understanding prevents effective prevention and treatment of the progression from acute injury to fibrosis. Here, we combined quantitative experimental studies with a mouse kidney injury model and a computational approach to determine how the physiological consequences are determined by the severity of ischemia injury, and to identify how to manipulate Wnt signaling to accelerate repair of ischemic tissue damage while minimizing fibrosis. The study reveals that Wnt-mediated memory of prior injury contributes to fibrosis progression, and ischemic preconditioning reduces the risk of death but increases the risk of fibrosis. Furthermore, we validated the prediction that sequential combination therapy of initial treatment with a Wnt agonist followed by treatment with a Wnt antagonist can reduce both the risk of death and fibrosis in response to acute injuries.

q-bio.TO↗

arXiv2019

Global existence of weak solutions to the compressible quantum Navier-Stokes equations with degenerate viscosity

We study the compressible quantum Navier-Stokes (QNS) equations with degenerate viscosity in the three dimensional periodic domains. On the one hand, we consider QNS with additional damping terms. Motivated by the recent works [Li-Xin, arXiv:1504.06826] and [Antonelli-Spirito, Arch. Ration. Mech. Anal., 203(2012), 499--527], we construct a suitable approximate system which has smooth solutions satisfying the energy inequality and the BD entropy estimate. Using this system, we obtain the global existence of weak solutions to the compressible QNS equations with damping terms for large initial data. Moreover, we obtain some new a priori estimates, which can avoid using the assumption that the gradient of the velocity is a well-defined function, which is indeed used directly in [Vasseur-Yu, SIAM J. Math. Anal., 48 (2016), 1489--1511; Invent. Math., 206 (2016), 935--974]. On the other hand, in the absence of damping terms, we also prove the global existence of weak solutions to the compressible QNS equations without the lower bound assumption on the dispersive coefficient, which improves the previous result due to [Antonelli-Spirito, Arch. Ration. Mech. Anal., 203(2012), 499--527].

math.AP↗

arXiv2019

Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network

High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutional-neural-network, which promote the efficiency for creating models. Unsupervised solutions such as feature average pooling, as a simple label-independent parameter-free based method, has limited ability to represent the video. While the supervised methods, like RNN, can greatly improve the recognition accuracy. However, the video length is usually long, and there are hierarchical relationships between frames across events in the video, the performance of RNN based models are decreased. In this paper, we proposes a novel video classification method based on a deep convolutional graph neural network(DCGN). The proposed method utilize the characteristics of the hierarchical structure of the video, and performed multi-level feature extraction on the video frame sequence through the graph network, obtained a video representation re ecting the event semantics hierarchically. We test our model on YouTube-8M Large-Scale Video Understanding dataset, and the result outperforms RNN based benchmarks.

cs.CV↗

arXiv2019

Feature Fusion Encoder Decoder Network For Automatic Liver Lesion Segmentation

Liver lesion segmentation is a difficult yet critical task for medical image analysis. Recently, deep learning based image segmentation methods have achieved promising performance, which can be divided into three categories: 2D, 2.5D and 3D, based on the dimensionality of the models. However, 2.5D and 3D methods can have very high complexity and 2D methods may not perform satisfactorily. To obtain competitive performance with low complexity, in this paper, we propose a Feature-fusion Encoder-Decoder Network (FED-Net) based 2D segmentation model to tackle the challenging problem of liver lesion segmentation from CT images. Our feature fusion method is based on the attention mechanism, which fuses high-level features carrying semantic information with low-level features having image details. Additionally, to compensate for the information loss during the upsampling process, a dense upsampling convolution and a residual convolutional structure are proposed. We tested our method on the dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge and achieved competitive results compared with other state-of-the-art methods.

cs.CV↗

arXiv2019

Deep Features Analysis with Attention Networks

Deep neural network models have recently draw lots of attention, as it consistently produce impressive results in many computer vision tasks such as image classification, object detection, etc. However, interpreting such model and show the reason why it performs quite well becomes a challenging question. In this paper, we propose a novel method to interpret the neural network models with attention mechanism. Inspired by the heatmap visualization, we analyze the relation between classification accuracy with the attention based heatmap. An improved attention based method is also included and illustrate that a better classifier can be interpreted by the attention based heatmap.

cs.LG↗

arXiv2018

Strong mechanical squeezing in an unresolved-sideband optomechanical system

We study how strong mechanical squeezing (beyond 3 dB) can be achieved through reservoir engineering in an optomechanical system which is far from the resolved-sideband regime. In our proposed setup, the effect of unwanted counter-rotating terms is suppressed by quantum interference from two auxiliary cavities. In the weak coupling regime we develop an analytical treatment based on the effective master equation approach, which allows us to obtain explicitly the condition of maximum squeezing.

quant-ph↗

arXiv2018

Arterial Blood Pressure Feature Estimation Using Photoplethysmography

Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the estimates, as measured by the root mean square of the model residuals, was less than 5 mmHg during normal breathing and less than 8 mmHg during the breath-hold maneuver. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.

eess.SP↗

arXiv2018

Mathematical Modeling of Arterial Blood Pressure Using Photo-Plethysmography Signal in Breath-hold Maneuver

Recent research has shown that each apnea episode results in a significant rise in the beat-to-beat blood pressure and by a drop to the pre-episode levels when patient resumes normal breathing. While the physiological implications of these repetitive and significant oscillations are still unknown, it is of interest to quantify them. Since current array of instruments deployed for polysomnography studies does not include beat-to-beat measurement of blood pressure, but includes oximetry, it is both of clinical interest to estimate the magnitude of BP oscillations from the photoplethysmography (PPG) signal that is readily available from sleep lab oximeters. We have investigated a new method for continuous estimation of systolic (SBP), diastolic (DBP), and mean (MBP) blood pressure waveforms from PPG. Peaks and troughs of PPG waveform are used as input to a 5th order autoregressive moving average model to construct estimates of SBP, DBP, and MBP waveforms. Since breath hold maneuvers are shown to simulate apnea episodes faithfully, we evaluated the performance of the proposed method in 7 subjects (4 F; 32+-4 yrs., BMI 24.57+-3.87 kg/m2) in supine position doing 5 breath maneuvers with 90s of normal breathing between them. The modeling error ranges were (all units are in mmHg) -0.88+-4.87 to -2.19+-5.73 (SBP); 0.29+-2.39 to -0.97+-3.83 (DBP); and -0.42+-2.64 to -1.17+-3.82 (MBP). The cross validation error ranges were 0.28+-6.45 to -1.74+-6.55 (SBP); 0.09+-3.37 to -0.97+-3.67 (DBP); and 0.33+-4.34 to -0.87+-4.42 (MBP). The level of estimation error in, as measured by the root mean squared of the model residuals, was less than 7 mmHg

eess.SP↗

arXiv2018

Ultrafast photocarrier recombination dynamics in black phosphorus-molybdenum disulfide (BP/MoS2) heterostructure

Van der Waals (vdW) heterostructures constructed with two-dimensional (2D) materials have attracted great interests, due to their fascinating properties and potential for novel applications. While earlier efforts have advanced the understanding of the ultrafast cross-layer charge transfer process in 2D heterostructures, mechanisms for the interfacial photocarrier recombination remain, to a large extent, unclear. Here, we investigate a heterostructure comprised of black phosphorus (BP) and molybdenum disulfide (MoS2), with a type-II band alignment. Interestingly, it is found that the photo-generated electrons in MoS2 (transferred from BP) exhibit an ultrafast lifetime of about 5 ps, significantly shorter than those of the constituent materials. By corroborating with the relaxation of photo-excited holes in BP, it is revealed that the ultrafast time constant is as a result of efficient Langevin recombination, where the high hole mobility of BP facilitates a large recombination coefficient (approximately 2x10^-10 m^2/s). In addition, broadband transient absorption spectroscopy confirms that the hot electrons transferred to MoS2 distribute over a broad energy range following an ultrafast thermalization. The rate of the interlayer Langevin recombination is found to exhibit no energy level dependence. Our findings provide essential insights into the fundamental photo-physics in type-II 2D heterostructures, and also provide useful guidelines for customizing photocarrier lifetimes of BP for high-speed photo-sensitive devices.

cond-mat.mes-hall↗

arXiv2018

Review of Deep Learning

In recent years, China, the United States and other countries, Google and other high-tech companies have increased investment in artificial intelligence. Deep learning is one of the current artificial intelligence research's key areas. This paper analyzes and summarizes the latest progress and future research directions of deep learning. Firstly, three basic models of deep learning are outlined, including multilayer perceptrons, convolutional neural networks, and recurrent neural networks. On this basis, we further analyze the emerging new models of convolution neural networks and recurrent neural networks. This paper then summarizes deep learning's applications in many areas of artificial intelligence, including speech processing, computer vision, natural language processing and so on. Finally, this paper discusses the existing problems of deep learning and gives the corresponding possible solutions.

cs.LG↗