arXiv subjects

Fang Liu

Publications and source records attributed to Fang Liu.

At least 307 records · Page 17Linked to original sources

arXiv2020

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory on nonhuman primates, which are a necessary part of neuroscience research. To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF). The deep Bayesian CNN, Bayesian SegNet, is used as the core segmentation engine. As a probabilistic network, it is not only able to perform accurate high-resolution pixel-wise brain segmentation, but also capable of measuring the model uncertainty by Monte Carlo sampling with dropout in the testing stage. Then, fully connected 3D CRF is used to refine the probability result from Bayesian SegNet in the whole 3D context of the brain volume. The proposed method was evaluated with a manually brain-extracted dataset comprising T1w images of 100 nonhuman primates. Our method outperforms six popular publicly available brain extraction packages and three well-established deep learning based methods with a mean Dice coefficient of 0.985 and a mean average symmetric surface distance of 0.220 mm. A better performance against all the compared methods was verified by statistical tests (all p-values<10-4, two-sided, Bonferroni corrected). The maximum uncertainty of the model on nonhuman primate brain extraction has a mean value of 0.116 across all the 100 subjects...

eess.IV↗

arXiv2020

Programmable coherent linear quantum operations with high-dimensional optical spatial modes

A simple and flexible scheme for high-dimensional linear quantum operations on optical transverse spatial modes is demonstrated. The quantum Fourier transformation (QFT) and quantum state tomography (QST) via symmetric informationally complete positive operator-valued measures (SIC POVMs) are implemented with dimensionality of 15. The matrix fidelity of QFT is 0.85, while the statistical fidelity of SIC POVMs and fidelity of QST are ~0.97 and up to 0.853, respectively. We believe that our device has the potential for further exploration of high-dimensional spatial entanglement provided by spontaneous parametric down conversion in nonlinear crystals.

quant-ph↗

arXiv2020

One-Dimensional Moiré Excitons in Transition-Metal Dichalcogenide Heterobilayers

The formation of interfacial moiré patterns from angular and/or lattice mismatch has become a powerful approach to engineer a range of quantum phenomena in van der Waals heterostructures. For long-lived and valley-polarized interlayer excitons in transition-metal dichalcogenide (TMDC) heterobilayers, signatures of quantum confinement by the moiré landscape have been reported in recent experimental studies. Such moiré confinement has offered the exciting possibility to tailor new excitonic systems, such as ordered arrays of zero-dimensional (0D) quantum emitters and their coupling into topological superlattices. A remarkable nature of the moiré potential is its dramatic response to strain, where a small uniaxial strain can tune the array of quantum-dot-like 0D traps into parallel stripes of one-dimensional (1D) quantum wires. Here, we present direct evidence for the 1D moiré potentials from real space imaging and the corresponding 1D moiré excitons from photoluminescence (PL) emission in MoSe2/WSe2 heterobilayers. Whereas the 0D moiré excitons display quantum emitter-like sharp PL peaks with circular polarization, the PL emission from 1D moiré excitons has linear polarization and two orders of magnitude higher intensity. The results presented here establish strain engineering as a powerful new method to tailor moiré potentials as well as their optical and electronic responses on demand.

cond-mat.mtrl-sci↗

arXiv2020

Modeling Programs Hierarchically with Stack-Augmented LSTM

Programming language modeling has attracted extensive attention in recent years, and it plays an essential role in program processing fields. Statistical language models, which are initially designed for natural languages, have been generally used for modeling programming languages. However, different from natural languages, programming languages contain explicit and hierarchical structure that is hard to learn by traditional statistical language models. To address this challenge, we propose a novel Stack-Augmented LSTM neural network for programming language modeling. Adding a stack memory component into the LSTM network enables our model to capture the hierarchical information of programs through the PUSH and POP operations, which further allows our model capturing the long-term dependency in the programs. We evaluate the proposed model on three program analysis tasks, i.e., code completion, program classification, and code summarization. Evaluation results show that our proposed model outperforms baseline models in all the three tasks, indicating that by capturing the structural information of programs with a stack, our proposed model can represent programs more precisely.

cs.SE↗

arXiv2020

Direct determination of momentum resolved electron transfer in the photo-excited MoS2/WS2 van der Waals heterobilayer by TR-ARPES

Photo-induced charge separation in transition metal dichalcogenide heterobilayers is being explored for moiré excitons, spin-valley polarization, and quantum phases of excitons/electrons. While different momentum spaces may be critically involved in charge separation dynamics, little is known directly from experiments. Here we determine momentum-resolved electron dynamics in the WS2/MoS2 heterobilayer using time and angle resolved photoemission spectroscopy (TR-ARPES). Upon photoexcitation in the K valleys, we detect electrons in M/2, M, and Q valleys/points on time scales as short as ~70 fs, followed by dynamic equilibration in K and Q valleys in ~400 fs. The interlayer charge transfer is accompanied by momentum-specific band renormalization. These findings reveal the essential role of phonon scattering, the coexistence of direct and indirect interlayer excitons, and constraints on spin-valley polarization.

cond-mat.mes-hall↗

arXiv2020

An entanglement-based quantum network based on symmetric dispersive optics quantum key distribution

Quantum key distribution (QKD) is a crucial technology for information security in the future. Developing simple and efficient ways to establish QKD among multiple users are important to extend the applications of QKD in communication networks. Herein, we proposed a scheme of symmetric dispersive optics QKD (DO-QKD) and demonstrated an entanglement-based quantum network based on it. In the experiment, a broadband entanglement photon pair source was shared by end users via wavelength and space division multiplexing. The wide spectrum of generated entangled photon pairs was divided into 16 combinations of frequency-conjugate channels. Photon pairs in each channel combination supported a fully-connected subnet with 8 users by a passive beam splitter. Eventually, it showed that an entanglement-based QKD network over 100 users could be supported by one entangled photon pair source in this architecture. It has great potential on applications of local quantum networks with large user number.

quant-ph↗

arXiv2019

Broadband optical parametric amplification by two-dimensional semiconductors

Optical parametric amplification is a second-order nonlinear process whereby an optical signal is amplified by a pump via the generation of an idler field. It is the key ingredient of tunable sources of radiation that play an important role in several photonic applications. This mechanism is inherently related to spontaneous parametric down-conversion that currently constitutes the building block for entangled photon pair generation, which has been exploited in modern quantum technologies ranging from computing to communications and cryptography. Here we demonstrate single-pass optical parametric amplification at the ultimate thickness limit; using semiconducting transition-metal dichalcogenides, we show that amplification can be attained over a propagation through a single atomic layer. Such a second-order nonlinear interaction at the 2D limit bypasses phase-matching requirements and achieves ultrabroad amplification bandwidths. The amplification process is independent on the in-plane polarization of the impinging signal and pump fields. First-principle calculations confirm the observed polarization invariance and linear relationship between idler and pump powers. Our results pave the way for the development of atom-sized tunable sources of radiation with applications in nanophotonics and quantum information technology.

physics.optics↗

arXiv2019

PolSF: PolSAR image dataset on San Francisco

Polarimetric SAR data has the characteristics of all-weather, all-time and so on, which is widely used in many fields. However, the data of annotation is relatively small, which is not conducive to our research. In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area. These five images come from different satellites at different times, which has great scientific research value. We annotate the collected images at the pixel level for image classification and segmentation. For the convenience of researchers, the annotated data is open source https://github.com/liuxuvip/PolSF.

eess.IV↗

arXiv2019

A Survey on Edge Computing Systems and Tools

Driven by the visions of Internet of Things and 5G communications, the edge computing systems integrate computing, storage and network resources at the edge of the network to provide computing infrastructure, enabling developers to quickly develop and deploy edge applications. Nowadays the edge computing systems have received widespread attention in both industry and academia. To explore new research opportunities and assist users in selecting suitable edge computing systems for specific applications, this survey paper provides a comprehensive overview of the existing edge computing systems and introduces representative projects. A comparison of open source tools is presented according to their applicability. Finally, we highlight energy efficiency and deep learning optimization of edge computing systems. Open issues for analyzing and designing an edge computing system are also studied in this survey.

cs.DC↗

arXiv2019

Ab Initio Exact Diagonalization Simulation of the Nagaoka Transition in Quantum Dots

Recent progress of quantum simulators provides insight into the fundamental problems of strongly correlated systems. To adequately assess the accuracy of these simulators, the precise modeling of the many-body physics, with accurate model parameters, is crucially important. In this paper, we introduce an \emph{ab intio} exact diagonalization framework to compute the correlated physics of a few electrons in artificial potentials. We apply this approach to a quantum-dot system and study the magnetism of the correlated electrons, obtaining good agreement with recent experimental measurements. Through dot separation, potential detuning and control of single tunneling, we examine the Nagaoka transition and determine the robustness of the ferromagnetic state. While the standard Nagaoka theorem considers only a single-band Hubbard model, in this work we perform extensive $ab$ $intio$ calculations that include realistic multi-orbital conditions in which the level splitting is smaller than the interactions. This simulation complements the experiments and provides insight into the formation of ferromagnetism in correlated systems. More generally, our calculation sets the stage for further theoretical analysis of analog quantum simulators at a quantitative level.

cond-mat.str-el↗

arXiv2019

Bayesian Hierarchical Spatial Model for Small Area Estimation with Non-ignorable Nonresponses and Its Applications to the NHANES Dental Caries Assessments

The National Health and Nutrition Examination Survey (NHANES) is a major program of the National Center for Health Statistics, designed to assess the health and nutritional status of adults and children in the United States. The analysis of NHANES dental caries data faces several challenges, including (1) the data were collected using a complex, multistage, stratified, unequal-probability sampling design; (2) the sample size of some primary sampling units (PSU), e.g., counties, is very small; (3) the measures of dental caries have complicated structure and correlation, and (4) there is a substantial percentage of nonresponses, for which the missing data are expected to be not missing at random or non-ignorable. We propose a Bayesian hierarchical spatial model to address these analysis challenges. We develop a two-level Potts model that closely resembles the caries evolution process and captures complicated spatial correlations between teeth and surfaces of the teeth. By adding Bayesian hierarchies to the Potts model, we account for the multistage survey sampling design and also enable information borrowing across PSUs for small area estimation. We incorporate sampling weights by including them as a covariate in the model and adopt flexible B-splines to achieve robust inference. We account for non-ignorable missing outcomes and covariates using the selection model. We use data augmentation coupled with the noisy exchange sampler to obtain the posterior of model parameters that involve doubly-intractable normalizing constants. Our analysis results show strong spatial associations between teeth and tooth surfaces and that dental hygienic factors, fluorosis and sealant reduce the risks of having dental diseases.

stat.AP↗

arXiv2019

A Survey of Deep Learning-based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.

cs.CV↗

arXiv2019

A Dynamic Analysis of Energy Storage with Renewable and Diesel Generation using Volterra Equations

Energy storage systems will play a key role in the power system of the twenty first century considering the large penetrations of variable renewable energy, growth in transport electrification and decentralisation of heating loads. Therefore reliable real time methods to optimise energy storage, demand response and generation are vital for power system operations. This paper presents a concise review of battery energy storage and an example of battery modelling for renewable energy applications and second details an adaptive approach to solve this load levelling problem with storage. A dynamic evolutionary model based on the first kind Volterra integral equation is used in both cases. A direct regularised numerical method is employed to find the least-cost dispatch of the battery in terms of integral equation solution. Validation on real data shows that the proposed evolutionary Volterra model effectively generalises conventional discrete integral model taking into account both state of health and the availability of generation/storage.

math.NA↗

arXiv2019

Phonon and Photon Lasing Dynamics in Optomechanical Cavities

Lasers differ from other light sources in that they are coherent, and their coherence makes them indispensable to both fundamental research and practical application. In optomechanical cavities, phonon and photon lasing is facilitated by the ability of photons and phonons to interact intensively and excite one another coherently. The lasing linewidths of both phonons and photons are critical for practical application. However, thus far, these linewidths have not been explored in detail in cavity optomechanical systems. This study investigates the underlying dynamics of lasing in optomechanical cavities and experimentally demonstrates simultaneous photon and phonon lasing with narrow linewidths in a silicon optomechanical crystal cavity. We find that the linewidths can be accounted for by two distinct physical mechanisms in two regimes, namely the normal regime and the reversed regime, where the intrinsic optical decay rate is either larger or smaller than the intrinsic mechanical decay rate. In the normal regime, an ultra-narrow spectral linewidth of 5.4 kHz for phonon lasing at 6.22 GHz can be achieved regardless of the linewidth of the pump light, while these results are counterintuitively unattainable for photon lasing in the reversed regime. These results pave the way towards harnessing the coherence of both photons and phonons in silicon photonic devices and reshaping their spectra, potentially opening up new technologies in sensing, metrology, spectroscopy, and signal processing, as well as in applications requiring sources that offer an ultra-high degree of coherence.

physics.optics↗

arXiv2019

Phonon-laser sensing in a hetero optomechanical crystal cavity

Micro- and nanomechanical resonators have emerged as promising platforms for sensing a broad range of physical properties such as mass, force, torque, magnetic field, and acceleration. The sensing performance relies critically on the motional mass, the mechanical frequency, and the linewidth of the mechanical resonator. Here, we demonstrate a hetero optomechanical crystal (OMC) cavity based on a silicon nanobeam structure. The cavity supports phonon lasing in a fundamental mechanical mode with a frequency of 5.91 GHz, an effective mass of 116 fg, and a mechanical linewidth narrowing from 3.3 MHz to 5.2 kHz, while the optomechanical coupling rate of is as high as 1.9 MHz. With this phonon laser, the on-chip sensing with a resolution of $δ$$λ$/$λ$ = 1.0*10-8 can be attained, which is at least two orders of magnitude larger than that obtained with conventional silicon-based sensors. The use of a silicon-based hetero OMC cavity that harnesses phonon lasing could pave the way towards exciting, high-precision sensors that lend themselves to silicon monolithic integration and offer unprecedented sensitivity for broad physical sensing applications.

physics.optics↗

arXiv2019

PolSAR Image Classification based on Polarimetric Scattering Coding and Sparse Support Matrix Machine

POLSAR image has an advantage over optical image because it can be acquired independently of cloud cover and solar illumination. PolSAR image classification is a hot and valuable topic for the interpretation of POLSAR image. In this paper, a novel POLSAR image classification method is proposed based on polarimetric scattering coding and sparse support matrix machine. First, we transform the original POLSAR data to get a real value matrix by the polarimetric scattering coding, which is called polarimetric scattering matrix and is a sparse matrix. Second, the sparse support matrix machine is used to classify the sparse polarimetric scattering matrix and get the classification map. The combination of these two steps takes full account of the characteristics of POLSAR. The experimental results show that the proposed method can get better results and is an effective classification method.

cs.CV↗

arXiv2019

Semi-supervised Complex-valued GAN for Polarimetric SAR Image Classification

Polarimetric synthetic aperture radar (PolSAR) images are widely used in disaster detection and military reconnaissance and so on. However, their interpretation faces some challenges, e.g., deficiency of labeled data, inadequate utilization of data information and so on. In this paper, a complex-valued generative adversarial network (GAN) is proposed for the first time to address these issues. The complex number form of model complies with the physical mechanism of PolSAR data and in favor of utilizing and retaining amplitude and phase information of PolSAR data. GAN architecture and semi-supervised learning are combined to handle deficiency of labeled data. GAN expands training data and semi-supervised learning is used to train network with generated, labeled and unlabeled data. Experimental results on two benchmark data sets show that our model outperforms existing state-of-the-art models, especially for conditions with fewer labeled data.

eess.IV↗

arXiv2019

Pixel DAG-Recurrent Neural Network for Spectral-Spatial Hyperspectral Image Classification

Exploiting rich spatial and spectral features contributes to improve the classification accuracy of hyperspectral images (HSIs). In this paper, based on the mechanism of the population receptive field (pRF) in human visual cortex, we further utilize the spatial correlation of pixels in images and propose pixel directed acyclic graph recurrent neural network (Pixel DAG-RNN) to extract and apply spectral-spatial features for HSIs classification. In our model, an undirected cyclic graph (UCG) is used to represent the relevance connectivity of pixels in an image patch, and four DAGs are used to approximate the spatial relationship of UCGs. In order to avoid overfitting, weight sharing and dropout are adopted. The higher classification performance of our model on HSIs classification has been verified by experiments on three benchmark data sets.

cs.CV↗