arXiv subjects

Yuan Wang

Publications and source records attributed to Yuan Wang.

At least 253 records · Page 14Linked to original sources

arXiv2019

Electron-hole Hybridization in Bilayer Graphene

Band structure determines the motion of electrons in a solid, giving rise to exotic phenomena when properly engineered. Drawing an analogy between electrons and photons, artificially designed optical lattices indicate the possibility of a similar band modulation effect in graphene systems. Yet due to the fermionic nature of electrons, modulated electronic systems promise far richer categories of behaviors than those found in optical lattices. Here, we uncovered a strong modulation of electronic states in bilayer graphene subject to periodic potentials. We observed for the first time the hybridization of electron and hole sub-bands, resulting in local band gaps at both primary and secondary charge neutrality points. Such hybridization leads to the formation of flat bands, enabling the study of correlated effects in graphene systems. This work may also offer a viable platform to form and continuously tune Majorana zero modes, which is important to the realization of topological quantum computation.

cond-mat.mes-hall↗

arXiv2019

PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation

Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optimizing the v-disparity map using dynamic programming. This linear model is then utilized to reduce the redundant information in the left and right road images. The right image is also transformed into the left perspective view, which greatly enhances the road surface similarity between the two images. Finally, the processed stereo images and their disparity maps are concatenated to create a set of 3D images, which are then utilized to train our neural network. The experimental results illustrate that our network achieves a maximum F1-measure of approximately 91.19% when analyzing the images from the KITTI road dataset.

cs.CV↗

arXiv2019

Gapless surface Dirac cone in antiferromagnetic topological insulator MnBi$_2$Te$_4$

The recent discovered antiferromagnetic topological insulators in Mn-Bi-Te family with intrinsic magnetic ordering have rapidly drawn broad interest since its cleaved surface state is believed to be gapped, hosting the unprecedented axion states with half-integer quantum Hall effect. Here, however, we show unambiguously by using high-resolution angle-resolved photoemission spectroscopy that a gapless Dirac cone at the (0001) surface of MnBi$_2$Te$_4$ exists between the bulk band gap. Such unexpected surface state remains unchanged across the bulk Néel temperature, and is even robust against severe surface degradation, indicating additional topological protection. Through symmetry analysis and $\textit{ab}$-$\textit{initio}$ calculations we consider different types of surface reconstruction of the magnetic moments as possible origins giving rise to such linear dispersion. Our results reveal that the intrinsic magnetic topological insulator hosts a rich platform to realize various topological phases such as topological crystalline insulator and time-reversal-preserved topological insulator, by tuning the magnetic configurations.

cond-mat.mtrl-sci↗

arXiv2019

Observation of Rydberg exciton polaritons and their condensate in a perovskite cavity

The condensation of half-light half-matter exciton polaritons in semiconductor optical cavities is a striking example of macroscopic quantum coherence in a solid state platform. Quantum coherence is possible only when there are strong interactions between the exciton polaritons provided by their excitonic constituents. Rydberg excitons with high principle value exhibit strong dipole-dipole interactions in cold atoms. However, polaritons with the excitonic constituent that is an excited state, namely Rydberg exciton polaritons (REPs), have not yet been experimentally observed. Here, for the first time, we observe the formation of REPs in a single crystal CsPbBr3 perovskite cavity without any external fields. These polaritons exhibit strong nonlinear behavior that leads to a coherent polariton condensate with a prominent blue shift. Furthermore, the REPs in CsPbBr3 are highly anisotropic and have a large extinction ratio, arising from the perovskite's orthorhombic crystal structure. Our observation not only sheds light on the importance of many-body physics in coherent polariton systems involving higher-order excited states, but also paves the way for exploring these coherent interactions for solid state quantum optical information processing.

cond-mat.mtrl-sci↗

arXiv2019

G-flocking: Flocking Model Optimization based on Genetic Framework

Flocking model has been widely used to control robotic swarm. However, with the increasing scalability, there exist complex conflicts for robotic swarm in autonomous navigation, brought by internal pattern maintenance, external environment changes, and target area orientation, which results in poor stability and adaptability. Hence, optimizing the flocking model for robotic swarm in autonomous navigation is an important and meaningful research domain.

cs.MA↗

arXiv2019

An Energy-Efficient Mixed-Signal Parallel Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

Convolutional neural networks (CNN) have achieved excellent performance on various tasks, but deploying CNN to edge is constrained by the high energy consumption of convolution operation. Stochastic computing (SC) is an attractive paradigm which performs arithmetic operations with simple logic gates and low hardware cost. This paper presents an energy-efficient mixed-signal multiply-accumulate (MAC) engine based on SC. A parallel architecture is adopted in this work to solve the latency problem of SC. The simulation results show that the overall energy consumption of our design is 5.03pJ per 26-input MAC operation under 28nm CMOS technology.

eess.SP↗

arXiv2019

Resilient Consensus Through Event-based Communication

We consider resilient versions of discrete-time multi-agent consensus in the presence of faulty or even malicious agents in the network. In particular, we develop event-triggered update rules which can mitigate the influence of the malicious agents and at the same time reduce the necessary communication. Each regular agent updates its state based on a given rule using its neighbors' information. Only when the triggering condition is satisfied, the regular agents send their current states to their neighbors. Otherwise, the neighbors will continue to use the state received the last time. Assuming that a bound on the number of malicious nodes is known, we propose two update rules with event-triggered communication. They follow the so-called mean subsequence reduced (MSR) type algorithms and ignore values received from potentially malicious neighbors. We provide full characterizations for the necessary connectivity in the network for the algorithms to perform correctly, which are stated in terms of the notion of graph robustness. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

eess.SY↗

arXiv2019

Evidence of charge density wave with anisotropic gap in monolayer VTe$_2$ film

We report experimental evidence of charge density wave (CDW) transition in monolayer 1T-VTe$_2$ film. 4$\times$4 reconstruction peaks are observed by low energy electron diffraction below the transition temperature $T_{CDW}$ = 186 K. Angle-resolved photoemission spectroscopy measurements reveal arc-like pockets with anisotropic CDW gaps up to 50 meV. The anisotropic CDW gap is attributed to the imperfect nesting of the CDW wave vector, and first-principles calculations reveal phonon softening at the same vector, suggesting the important roles of Fermi surface nesting and electron-phonon interaction in the CDW mechanism.

cond-mat.mtrl-sci↗

arXiv2019

Memory System Designed for Multiply-Accumulate (MAC) Engine Based on Stochastic Computing

Convolutional neural network (CNN) achieves excellent performance on fascinating tasks such as image recognition and natural language processing at the cost of high power consumption. Stochastic computing (SC) is an attractive paradigm implemented in low power applications which performs arithmetic operations with simple logic and low hardware cost. However, conventional memory structure designed and optimized for binary computing leads to extra data conversion costs, which significantly decreases the energy efficiency. Therefore, a new memory system designed for SC-based multiply-accumulate (MAC) engine applied in CNN which is compatible with conventional memory system is proposed in this paper. As a result, the overall energy consumption of our new computing structure is 0.91pJ, which is reduced by 82.1% compared with the conventional structure, and the energy efficiency achieves 164.8 TOPS/W.

eess.SP↗

arXiv2019

A Parallel Bitstream Generator for Stochastic Computing

Stochastic computing (SC) presents high error tolerance and low hardware cost, and has great potential in applications such as neural networks and image processing. However, the bitstream generator, which converts a binary number to bitstreams, occupies a large area and energy consumption, thus weakening the superiority of SC. In this paper, we propose a novel technique for generating bitstreams in parallel, which needs only one clock for conversion and significantly reduces the hardware cost. Synthesis results demonstrate that the proposed parallel bitstream generator improves 2.5x area and 712x energy consumption.

cs.ET↗

arXiv2019

Room-temperature giant Stark effect of single photon emitter in van der Waals material

Single photon emitters (SPEs) are critical building blocks needed for quantum science and technology. For practical applications, large-scale room-temperature solid-state platforms are required. Color centers in layered hexagonal boron nitride (hBN) have recently been found to be ultra-bright and stable SPEs at room temperature. Yet, to scale up solid-state quantum information processing, large tuning range of single photon energy is demanded for wavelength division multiplexing quantum key distribution, where indistinguishability is not required, and for indistinguishable single-photon production from multi-emitters. Stark effect can tune the single photon energy by an electric field, which however, has been achieved only at cryogenic temperature so far. Here we report the first room-temperature Stark effect of SPEs by exploiting hBN color centers. Surprisingly, we observe a giant Stark shift of single photon more than 30 meV, about one order of magnitude greater than previously reported in color center emitters. Moreover, for the first time, the orientation of the electric permanent dipole moment in the solid-state SPE is determined via angle-resolved Stark effect, revealing the intrinsic broken symmetries at such a color center. The remarkable Stark shift discovered here and the significant advance in understanding its atomic structure pave a way towards the scalable solid-state on-chip quantum communication and computation at room temperature.

cond-mat.mes-hall↗

arXiv2019

PointIT: A Fast Tracking Framework Based on 3D Instance Segmentation

Recently most popular tracking frameworks focus on 2D image sequences. They seldom track the 3D object in point clouds. In this paper, we propose PointIT, a fast, simple tracking method based on 3D on-road instance segmentation. Firstly, we transform 3D LiDAR data into the spherical image with the size of 64 x 512 x 4 and feed it into instance segment model to get the predicted instance mask for each class. Then we use MobileNet as our primary encoder instead of the original ResNet to reduce the computational complexity. Finally, we extend the Sort algorithm with this instance framework to realize tracking in the 3D LiDAR point cloud data. The model is trained on the spherical images dataset with the corresponding instance label masks which are provided by KITTI 3D Object Track dataset. According to the experiment results, our network can achieve on Average Precision (AP) of 0.617 and the performance of multi-tracking task has also been improved.

cs.CV↗

arXiv2019

Focal Loss in 3D Object Detection

3D object detection is still an open problem in autonomous driving scenes. When recognizing and localizing key objects from sparse 3D inputs, autonomous vehicles suffer from a larger continuous searching space and higher fore-background imbalance compared to image-based object detection. In this paper, we aim to solve this fore-background imbalance in 3D object detection. Inspired by the recent use of focal loss in image-based object detection, we extend this hard-mining improvement of binary cross entropy to point-cloud-based object detection and conduct experiments to show its performance based on two different 3D detectors: 3D-FCN and VoxelNet. The evaluation results show up to 11.2AP gains through the focal loss in a wide range of hyperparameters for 3D object detection.

cs.CV↗

arXiv2018

MALT90 molecular content on high-mass IR-dark clumps

High mass stars form in groups or clusters within massive cores in dense molecular clumps with sizes of 1pc and masses of 200Msun which are important laboratories for high-mass star formation in order to study the initial conditions. We investigate the physical and chemical properties of high-mass clumps in order to better understand the early evolutionary stages and find targets that show star formation signs. We selected the high-mass clumps from ATLASGAL survey that were identified as dark at 8/24$μ$m wavelengths and used MALT90 data which provides a molecular line set to investigate the physical and chemical conditions in early evolutionary stages. Eleven sources have significant SiO detection (over 3$σ$) which usually indicates outflow activities. Thirteen sources are found with blue profiles in both or either HCO$^+$ and/or HNC lines and clump mass infall rates are estimated to be in the range of 0.2E+3 Msunyr$^{-1}$ $-$ 1.8E-2 Msunyr$^{-1}$. The excitation temperature is obtained as <24K for all sources. The column densities for optically thin lines of H$^{13}$CO$^{+}$ and HN$^{13}$C are in the range of 0.4-8.8(E+12) cm$^{-2}$, and 0.9-11.9(E+12) cm$^{-2}$, respectively, while it is in the range of 0.1-7.5(E+14) cm$^{-2}$ for HCO$^{+}$ and HNC lines. The column densities for N$_{2}$H$^{+}$ were ranging between 4.4-275.7(E+12) cm$^{-2}$ as expected from cold dense regions. Large line widths of N$_{2}$H$^{+}$ might indicate turbulence and large line widths of HCO$^{+}$, HNC, and SiO indicate outflow activities. Mean optical depths are 20.32, and 23.19 for optically thick HCO$^{+}$ and HCN lines, and 0.39 and 0.45 for their optically thin isotopologues H$^{13}$CO$^{+}$ and HN$^{13}$C, respectively. This study reveals the physical and chemical properties of 30 high-mass IR-dark clumps and the interesting targets among them based on their emission line morphology and kinematics.

astro-ph.GA↗

arXiv2018

Topological Inference of Manifolds with Boundary

Given a set of data points sampled from some underlying space, there are two important challenges in geometric and topological data analysis when dealing with sampled data: reconstruction -- how to assemble discrete samples into global structures, and inference -- how to extract geometric and topological information from data that are high-dimensional, incomplete and noisy. Niyogi et al. (2008) have shown that by constructing an offset of the samples using a suitable offset parameter could provide reconstructions that preserve homotopy types therefore homology for densely sampled smooth submanifolds of Euclidean space without boundary. Chazal et al. (2009) and Attali et al. (2013) have introduced a parameterized set of sampling conditions that extend the results of Niyogi et al. to a large class of compact subsets of Euclidean space. Our work tackles data problems that fill a gap between the work of Niyogi et al. and Chazal et al. In particular, we give a probabilistic notion of sampling conditions for manifolds with boundary that could not be handled by existing theories. We also give stronger results that relate topological equivalence between the offset and the manifold as a deformation retract.

stat.CO↗

arXiv2018

PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud

In this paper, we propose PointSeg, a real-time end-to-end semantic segmentation method for road-objects based on spherical images. We take the spherical image, which is transformed from the 3D LiDAR point clouds, as input of the convolutional neural networks (CNNs) to predict the point-wise semantic map. To make PointSeg applicable on a mobile system, we build the model based on the light-weight network, SqueezeNet, with several improvements. It maintains a good balance between memory cost and prediction performance. Our model is trained on spherical images and label masks projected from the KITTI 3D object detection dataset. Experiments show that PointSeg can achieve competitive accuracy with 90fps on a single GPU 1080ti. which makes it quite compatible for autonomous driving applications.

cs.CV↗

arXiv2018

A Time Series Graph Cut Image Segmentation Scheme for Liver Tumors

Tumor detection in biomedical imaging is a time-consuming process for medical professionals and is not without errors. Thus in recent decades, researchers have developed algorithmic techniques for image processing using a wide variety of mathematical methods, such as statistical modeling, variational techniques, and machine learning. In this paper, we propose a semi-automatic method for liver segmentation of 2D CT scans into three labels denoting healthy, vessel, or tumor tissue based on graph cuts. First, we create a feature vector for each pixel in a novel way that consists of the 59 intensity values in the time series data and propose a simplified perimeter cost term in the energy functional. We normalize the data and perimeter terms in the functional to expedite the graph cut without having to optimize the scaling parameter $λ$. In place of a training process, predetermined tissue means are computed based on sample regions identified by expert radiologists. The proposed method also has the advantage of being relatively simple to implement computationally. It was evaluated against the ground truth on a clinical CT dataset of 10 tumors and yielded segmentations with a mean Dice similarity coefficient (DSC) of .77 and mean volume overlap error (VOE) of 36.7%. The average processing time was 1.25 minutes per slice.

cs.CV↗

arXiv2018

Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

In this study, we evaluated the performance of the MODIS C6.1 AOD products and compared them with the C6 products over regions in China with multifarious underlying surfaces during 2001 to 2016. The AOD retrievals were validated against 20 AERONET sites, and the results show that the R for DT retrievals in C6.1 is 0.946, while the fraction within the EE can be considered relatively low at only 54.03%. DB retrievals in C6.1 have a slightly lower R value, but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the LEV areas. For the HEV areas, DB performs synthetically better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the different land cover types. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DB tends to be high in areas where the main land cover type is bare soil and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals in C6.1 increases slightly compared to C6, and the most remarkable improvement is observed for the coarse aerosol particles.

physics.ao-ph↗