arXiv subjects

Ying Chen

Publications and source records attributed to Ying Chen.

At least 361 records · Page 20Linked to original sources

arXiv2020

Deep SCNN-based Real-time Object Detection for Self-driving Vehicles Using LiDAR Temporal Data

Real-time accurate detection of three-dimensional (3D) objects is a fundamental necessity for self-driving vehicles. Most existing computer vision approaches are based on convolutional neural networks (CNNs). Although the CNN-based approaches can achieve high detection accuracy, their high energy consumption is a severe drawback. To resolve this problem, novel energy efficient approaches should be explored. Spiking neural network (SNN) is a promising candidate because it has orders-of-magnitude lower energy consumption than CNN. Unfortunately, the studying of SNN has been limited in small networks only. The application of SNN for large 3D object detection networks has remain largely open. In this paper, we integrate spiking convolutional neural network (SCNN) with temporal coding into the YOLOv2 architecture for real-time object detection. To take the advantage of spiking signals, we develop a novel data preprocessing layer that translates 3D point-cloud data into spike time data. We propose an analog circuit to implement the non-leaky integrate and fire neuron used in our SCNN, from which the energy consumption of each spike is estimated. Moreover, we present a method to calculate the network sparsity and the energy consumption of the overall network. Extensive experiments have been conducted based on the KITTI dataset, which show that the proposed network can reach competitive detection accuracy as existing approaches, yet with much lower average energy consumption. If implemented in dedicated hardware, our network could have a mean sparsity of 56.24% and extremely low total energy consumption of 0.247mJ only. Implemented in NVIDIA GTX 1080i GPU, we can achieve 35.7 fps frame rate, high enough for real-time object detection.

cs.CV↗

arXiv2020

Roper State from Overlap Fermions

The Roper state is extracted with valence overlap fermions on a $2+1$-flavor domain-wall fermion lattice (spacing $a = 0.114$ fm and $m_π = 330$ MeV) using both the Sequential Empirical Bayes (SEB) method and the variational method. The results are consistent, provided that a large smearing-size interpolation operator is included in the variational calculation to have better overlap with the lowest radial excitation. Similar calculations carried out for an anisotropic clover lattice with similar parameters find the Roper $\approx 280$ MeV higher than that of the overlap fermion. The fact that the prediction of the Roper state by overlap fermions is consistently lower than those of clover fermions, chirally improved fermions, and twisted-mass fermions over a wide range of pion masses has been dubbed a "Roper puzzle." To understand the origin of this difference, we study the hairpin $Z$-diagram in the isovector scalar meson ($a_0$) correlator in the quenched approximation. Comparing the $a_0$ correlators for clover and overlap fermions, at a pion mass of 290 MeV, we find that the spectral weight of the ghost state with clover fermions is smaller than that of the overlap at $a = 0.12$ fm and $0.09$ fm, whereas the whole $a_0$ correlators of clover and overlap at $a = 0.06$ fm coincide within errors. This suggests that chiral symmetry is restored for clover at $a \le 0.06$ fm and that the Roper should come down at and below this $a$. We conclude that this work supports a resolution of the "Roper puzzle" due to $Z$-graph type chiral dynamics. This entails coupling to higher components in the Fock space (e.g. $Nπ$, $Nππ$ states) to induce the effective flavor-spin interaction between quarks as prescribed in the chiral quark model, resulting in the parity-reversal pattern as observed in the experimental excited states of $N, Δ$ and $Λ$.

hep-ph↗

arXiv2020

MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?

Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and allow for efficient inference on mobile or embedded devices with limited power and computational resources. However, the binarization of weights and activations leads to feature maps of lower quality and lower capacity and thus a drop in accuracy compared to traditional networks. Previous work has increased the number of channels or used multiple binary bases to alleviate these problems. In this paper, we instead present an architectural approach: MeliusNet. It consists of alternating a DenseBlock, which increases the feature capacity, and our proposed ImprovementBlock, which increases the feature quality. Experiments on the ImageNet dataset demonstrate the superior performance of our MeliusNet over a variety of popular binary architectures with regards to both computation savings and accuracy. Furthermore, with our method we trained BNN models, which for the first time can match the accuracy of the popular compact network MobileNet-v1 in terms of model size, number of operations and accuracy. Our code is published online at https://github.com/hpi-xnor/BMXNet-v2

cs.LG↗

arXiv2020

On the Capacity of Fractal D2D Social Networks with Hierarchical Communications

The maximum capacity of fractal D2D (device-to-device) social networks with both direct and hierarchical communications is studied in this paper. Specifically, the fractal networks are characterized by the direct social connection and the self-similarity. Firstly, for a fractal D2D social network with direct social communications, it is proved that the maximum capacity is $ Θ\left(\frac{1}{\sqrt{n\log n}}\right) $ if a user communicates with one of his/her direct contacts randomly, where $ n $ denotes the total number of users in the network, and it can reach up to $ Θ\left(\frac{1}{\log n}\right) $ if any pair of social contacts with distance $ d $ communicate according to the probability in proportion to $ d^{-β} $. Secondly, since users might get in touch with others without direct social connections through the inter-connected multiple users, the fractal D2D social network with these hierarchical communications is studied as well, and the related capacity is further derived. Our results show that this capacity is mainly affected by the correlation exponent $ε$ of the fractal structure. The capacity is reduced in proportional to $ \frac{1}{\log n} $ if $ 2<ε<3 $, while the reduction coefficient is $ \frac{1}{n} $ if $ ε>3 $.

cs.IT↗

arXiv2020

The Luneburg-Lissajous lens

We design a new absolute optical instrument by composing Luneburg lens and Lissajous lens, and analyze its imaging mechanism from the perspective of simple harmonic oscillations. The imaging positions are determined by the periods of motions in x and y directions. Besides, instruments composed with multi parts are also devised, which can form imaging or self-imaging as long as the motion periods of x and y directions are satisfied to similar conditions. Our work provides a new way to analyze the imaging of different lens by simply dissociating the equations of motions, and reveal the internal mechanism of some absolute optical instruments.

physics.optics↗

arXiv2020

Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement

Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.

cs.CV↗

arXiv2020

MixTConv: Mixed Temporal Convolutional Kernels for Efficient Action Recogntion

To efficiently extract spatiotemporal features of video for action recognition, most state-of-the-art methods integrate 1D temporal convolution into a conventional 2D CNN backbone. However, they all exploit 1D temporal convolution of fixed kernel size (i.e., 3) in the network building block, thus have suboptimal temporal modeling capability to handle both long-term and short-term actions. To address this problem, we first investigate the impacts of different kernel sizes for the 1D temporal convolutional filters. Then, we propose a simple yet efficient operation called Mixed Temporal Convolution (MixTConv), which consists of multiple depthwise 1D convolutional filters with different kernel sizes. By plugging MixTConv into the conventional 2D CNN backbone ResNet-50, we further propose an efficient and effective network architecture named MSTNet for action recognition, and achieve state-of-the-art results on multiple benchmarks.

cs.CV↗

arXiv2020

A General Initialization Scheme for Electromagnetic Transient Simulation: Towards Large-Scale Hybrid AC-DC Grids

With the large-scale hybrid AC-DC grids coming into being, electromagnetic transient (EMT) simulation is required to accurately describe the dynamics of systems. However, the EMT steady-state initialization for hybrid AC-DC system is difficult and time-consuming when the system scale is huge. In order to provide a stable snapshot for EMT simulation with nonlinear components and black-box components, this paper proposes a general initialization scheme for EMT simulation (EMT-GIS) which can be implemented in the electromagnetic transient program (EMTP)-type simulators. First, an integrated power flow (IPF) algorithm is introduced to provide the steady-state results. Then, an initialized snapshot calculation-and-splicing mechanism is designed for EMT-GIS. The proposed EMT-GIS is tested using a hybrid AC-DC system in China on the CloudPSS simulation platform. Test results verify the effectiveness of the proposed EMT-GIS.

eess.SY↗

arXiv2019

Portfolio liquidation under transient price impact -- theoretical solution and implementation with 100 NASDAQ stocks

We derive an explicit solution for deterministic market impact parameters in the Graewe and Horst (2017) portfolio liquidation model. The model allows to combine various forms of market impact, namely instantaneous, permanent and temporary. We show that the solutions to the two benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013) are obtained as special cases. We relate the different forms of market impact to the microstructure of limit order book markets and show how the impact parameters can be estimated from public market data. We investigate the numerical performance of the derived optimal trading strategy based on high frequency limit order books of 100 NASDAQ stocks that represent a range of market impact profiles. It shows the strategy achieves significant cost savings compared to the benchmark models of Almgren and Chris (2001) and of Obizhaeva and Wang (2013).

q-fin.MF↗

arXiv2019

Plasmon-polaritonic quadrupole topological insulators

Quadrupole topological insulator is a symmetry-protected higher-order topological phase with intriguing topology of Wannier bands, which, however, has not yet been realized in plasmonic metamaterials. Here, we propose a lattice of plasmon-polaritonic nanocavities which can realize quadrupole topological insulators by exploiting the geometry-dependent sign-reversal of the couplings between the daisy-like nanocavities. The designed system exhibits various topological and trivial phases as characterized by the nested Wannier bands and the topological quadrupole moment which can be controlled by the distances between the nanocavities. Our study opens a pathway toward plasmonic topological metamaterials with quadrupole topology.

physics.optics↗

arXiv2019

Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss

Single image super-resolution (SISR) is the task of inferring a high-resolution image from a single low-resolution image. Recent research on super-resolution has achieved great progress due to the development of deep convolutional neural networks in the field of computer vision. Existing super-resolution reconstruction methods have high performances in the criterion of Mean Square Error (MSE) but most methods fail to reconstruct an image with shape edges. To solve this problem, the mixed gradient error, which is composed by MSE and a weighted mean gradient error, is proposed in this work and applied to a modified U-net network as the loss function. The modified U-net removes all batch normalization layers and one of the convolution layers in each block. The operation reduces the number of parameters, and therefore accelerates the reconstruction. Compared with the existing image super-resolution algorithms, the proposed reconstruction method has better performance and time consumption. The experiments demonstrate that modified U-net network architecture with mixed gradient loss yields high-level results on three image datasets: SET14, BSD300, ICDAR2003. Code is available online.

eess.IV↗

arXiv2019

Study of the pseudoscalar glueball in $J/ψ$ radiative decays

We aim to explore the production rate of the pseudoscalar glueball in $J/ψ$ radiative decay by lattice QCD in quenched approximation. The calculation is performed on three anisotropic lattices with the spatial lattice spacing ranging from 0.222(2) fm to 0.110(1) fm. As a calibration of some systematical uncertainties, we first extract the $M1$ form factor $\hat{V}(0)$ of the process $J/ψ\toγη_{c}$ and get the result $\hat{V}(0)=1.933(41)$ in the continuum limit, which gives the partial width $Γ(J/ψ\toγη_{c})=2.47(11)$ keV. These results are in agreement with that of previous lattice studies. As for the pseudoscalar glueball $G_{0^{-+}}$, its mass is derived to be $2.395(14)$ GeV, and the form factor $\hat{V}(0)$ of the process $J/ψ\toγG_{0^{-+}}$ is determined to be $\hat{V}(0)=0.0246(43)$ after continuum extrapolation. Finally, the production rate of the pseudoscalar glueball is predicted to be $2.31(90)\times10^{-4}$, which is much smaller than that of conventional light $q\bar{q}$ $η$ states. After the subtraction of the phase space factor, the couplings of $J/ψXγ$ are similar where $X$ stands for $η$ states and the pseudoscalar glueball. Possibly, the $U_{A}(1)$ anomaly plays an important role for the large couplings of gluons to the flavor singlet $η$ states in $J/ψ$ radiative decays.

hep-lat↗

arXiv2019

Image Reconstruction with Low-rankness and Self-consistency of k-space Data in Parallel MRI

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging.

physics.med-ph↗

arXiv2019

A coupled-channel lattice study on the resonance-like structure $Z_c(3900)$

In this exploratory study, near-threshold scattering of $D$ and $\bar{D}^*$ meson is investigated using lattice QCD with $N_f=2+1+1$ twisted mass fermion configurations. The calculation is performed within the coupled-channel Lüscher's finite-size formalism. The study focuses on the channel with $I^G(J^{PC})=1^+(1^{+-})$ where the resonance-like structure $Z_c(3900)$ was discovered. We first identify the most relevant two channels of the problem and the lattice study is performed within the two-channel scattering model. Combined with a two-channel Ross-Shaw theory, scattering parameters are extracted from the energy levels by solving the generalized eigenvalue problem. Our results on the scattering length parameters suggest that, at the particular lattice parameters that we studied, the best fitted parameters do not correspond to a peak behavior in the elastic scattering cross section near the threshold. Furthermore, within the zero-range Ross-Shaw theory, the scenario of a narrow resonance close to the threshold is disfavored beyond $3σ$ level.

hep-lat↗

arXiv2019

Bee$^+$: A 95-mg Four-Winged Insect-Scale Flying Robot Driven by Twinned Unimorph Actuators

We introduce Bee$^+$, a 95-mg four-winged microrobot with improved controllability and open-loop-response characteristics with respect to those exhibited by state-of-the-art two-winged microrobots with the same size and similar weight (i.e., the 75-mg Harvard RoboBee). The key innovation that made possible the development of Bee$^+$ is the introduction of an extremely light (28-mg) pair of twinned unimorph actuators, which enabled the design of a new microrobotic mechanism that flaps four wings independently. A first main advantage of the proposed design, compared to those of two-winged flyers, is that by increasing the number of actuators from two to four, the number of direct control inputs increases from three to four when simple sinusoidal excitations are employed. A second advantage of Bee$^+$ is that its four-wing configuration and flapping mode naturally damp the rotational disturbances that commonly affect the yaw degree of freedom of two-winged microrobots. In addition, the proposed design greatly reduces the complexity of the associated fabrication process compared to those of other microrobots, as the unimorph actuators are fairly easy to build. Lastly, we hypothesize that given the relatively low wing-loading affecting their flapping mechanisms, the life expectancy of Bee$^+$s must be considerably higher than those of the two-winged counterparts. The functionality and basic capabilities of the robot are demonstrated through a set of simple control experiments.

cs.RO↗

arXiv2019

Where does active travel fit within local community narratives of mobility space and place?

Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private motorized vehicles towards active modes. The current literature on `soft' policy emphasizes the importance of tailoring behavior change campaigns to individual or geographic context. Yet, there is a lack of insight and appropriate tools to promote active mobility and overcome transport disadvantage from the local community perspective. The current study investigates the promotion of walking and cycling adoption using a series of focus groups with local residents in two geographic communities, namely Chicago's (1) Humboldt Park neighborhood and (2) suburb of Evanston. The research approach combines traditional qualitative discourse analysis with quantitative text-mining tools, namely topic modeling and sentiment analysis. The analysis uncovers the local mobility culture, embedded norms and values associated with acceptance of active travel modes in different communities. We observe that underserved populations within diverse communities view active mobility simultaneously as a necessity and as a symbol of privilege that is sometimes at odds with the local culture. The mixed methods approach to analyzing community member discourses is translated into policy findings that are either tailored to local context or broadly applicable to curbing automobile dominance. Overall, residents of both Humboldt Park and Evanston envision a society in which multimodalism replaces car-centrism, but differences in the local physical and social environments would and should influence the manner in which overarching policy objectives are met.

econ.GN↗

arXiv2019

High thermal conductivity of high-quality monolayer boron nitride and its thermal expansion

Heat management becomes more and more critical, especially in miniaturized modern devices, so the exploration of highly thermally conductive materials with electrical insulation and favorable mechanical properties is of great importance. Here, we report that high-quality monolayer boron nitride (BN) has a thermal conductivity (\k{appa}) of 751 W/mK at room temperature. Though smaller than that of graphene, this value is larger than that of cubic boron nitride (cBN) and only second to those of diamond and lately discovered cubic boron arsenide (BAs). Monolayer BN has the second largest \k{appa} per unit weight among all semiconductors and insulators, just behind diamond, if density is considered. The \k{appa} of atomically thin BN decreases with increased thickness. Our large-scale molecular dynamic simulations using Green-Kubo formalism accurately reproduce this trend, and the density functional theory (DFT) calculations reveal the main scattering mechanism. The thermal expansion coefficients (TECs) of monolayer to trilayer BN at 300-400 K are also experimentally measured, and the results are comparable to atomistic ab initio DFT calculations in a wider range of temperatures. Thanks to its wide bandgap, high thermal conductivity, outstanding strength, good flexibility, and excellent thermal and chemical stability, atomically thin BN is a strong candidate for heat dissipation applications, especially in the next generation of flexible electronic devices.

cond-mat.mtrl-sci↗

arXiv2019

Segmentation of Levator Hiatus Using Multi-Scale Local Region Active contours and Boundary Shape Similarity Constraint

In this paper, a multi-scale framework with local region based active contour and boundary shape similarity constraint is proposed for the segmentation of levator hiatus in ultrasound images. In this paper, we proposed a multiscale active contour framework to segment levator hiatus ultrasound images by combining the local region information and boundary shape similarity constraint. In order to get more precisely initializations and reduce the computational cost, Gaussian pyramid method is used to decompose the image into coarse-to-fine scales. A localized region active contour model is firstly performed on the coarsest scale image to get a rough contour of the levator hiatus, then the segmentation result on the coarse scale is interpolated into the finer scale image as the initialization. The boundary shape similarity between different scales is incorporate into the local region based active contour model so that the result from coarse scale can guide the contour evolution at finer scale. By incorporating the multi-scale and boundary shape similarity, the proposed method can precisely locate the levator hiatus boundaries despite various ultrasound image artifacts. With a data set of 90 levator hiatus ultrasound images, the efficiency and accuracy of the proposed method are validated by quantitative and qualitative evaluations (TP, FP, Js) and comparison with other two state-of-art active contour segmentation methods (C-V model, DRLSE model).

cs.CV↗