arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 415 records · Page 23Linked to original sources

arXiv2020

LiDAR Iris for Loop-Closure Detection

In this paper, a global descriptor for a LiDAR point cloud, called LiDAR Iris, is proposed for fast and accurate loop-closure detection. A binary signature image can be obtained for each point cloud after several LoG-Gabor filtering and thresholding operations on the LiDAR-Iris image representation. Given two point clouds, their similarities can be calculated as the Hamming distance of two corresponding binary signature images extracted from the two point clouds, respectively. Our LiDAR-Iris method can achieve a pose-invariant loop-closure detection at a descriptor level with the Fourier transform of the LiDAR-Iris representation if assuming a 3D (x,y,yaw) pose space, although our method can generally be applied to a 6D pose space by re-aligning point clouds with an additional IMU sensor. Experimental results on five road-scene sequences demonstrate its excellent performance in loop-closure detection.

cs.RO↗

arXiv2020

Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet

The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate. In this paper, we propose a highly efficient multi-level malignant tissue detection through the designed adversarial CAC-UNet. A patch-level model with a pre-prediction strategy and a malignancy area guided label smoothing is adopted to remove the negative WSIs, with which to lower the risk of false positive detection. For the selected key patches by multi-model ensemble, an adversarial context-aware and appearance consistency UNet (CAC-UNet) is designed to achieve robust segmentation. In CAC-UNet, mirror designed discriminators are able to seamlessly fuse the whole feature maps of the skillfully designed powerful backbone network without any information loss. Besides, a mask prior is further added to guide the accurate segmentation mask prediction through an extra mask-domain discriminator. The proposed scheme achieves the best results in MICCAI DigestPath2019 challenge on colonoscopy tissue segmentation and classification task. The full implementation details and the trained models are available at https://github.com/Raykoooo/CAC-UNet.

eess.IV↗

arXiv2020

Berry curvature memory through electrically driven stacking transitions

In two-dimensional layered quantum materials, the stacking order of the layers determines both the crystalline symmetry and electronic properties such as the Berry curvature, topology and electron correlation. Electrical stimuli can influence quasiparticle interactions and the free-energy landscape, making it possible to dynamically modify the stacking order and reveal hidden structures that host different quantum properties. Here we demonstrate electrically driven stacking transitions that can be applied to design nonvolatile memory based on Berry curvature in few-layer WTe$_2$. The interplay of out-of-plane electric fields and electrostatic doping controls in-plane interlayer sliding and creates multiple polar and centrosymmetric stacking orders. In situ nonlinear Hall transport reveals such stacking rearrangements result in a layer-parity-selective Berry curvature memory in momentum space, where the sign reversal of the Berry curvature and its dipole only occurs in odd-layer crystals. Our findings open an avenue towards exploring coupling between topology, electron correlations, and ferroelectricity in hidden stacking orders and demonstrate a new low-energy-cost, electrically controlled topological memory in the atomically thin limit.

cond-mat.mtrl-sci↗

arXiv2020

A coherent spin-photon interface with waveguide induced cycling transitions

Solid-state quantum dots are promising candidates for efficient light-matter interfaces connecting internal spin degrees of freedom to the states of emitted photons. However, selection rules prevent the combination of efficient spin control and optical cyclicity in this platform. By utilizing a photonic crystal waveguide we here experimentally demonstrate optical cyclicity up to $\approx15$ through photonic state engineering while achieving high fidelity spin initialization and coherent optical spin control. These capabilities pave the way towards scalable multi-photon entanglement generation and on-chip spin-photon gates.

quant-ph↗

arXiv2020

Will Dependency Conflicts Affect My Program's Semantics?

Java projects are often built on top of various third-party libraries. If multiple versions of a library exist on the classpath, JVM will only load one version and shadow the others, which we refer to as dependency conflicts. This would give rise to semantic conflict (SC) issues, if the library APIs referenced by a project have identical method signatures but inconsistent semantics across the loaded and shadowed versions of libraries. SC issues are difficult for developers to diagnose in practice, since understanding them typically requires domain knowledge. Although adapting the existing test generation technique for dependency conflict issues, Riddle, to detect SC issues is feasible, its effectiveness is greatly compromised. This is mainly because Riddle randomly generates test inputs, while the SC issues typically require specific arguments in the tests to be exposed. To address that, we conducted an empirical study of 75 real SC issues to understand the characteristics of such specific arguments in the test cases that can capture the SC issues. Inspired by our empirical findings, we propose an automated testing technique Sensor, which synthesizes test cases using ingredients from the project under test to trigger inconsistent behaviors of the APIs with the same signatures in conflicting library versions. Our evaluation results show that \textsc{Sensor} is effective and useful: it achieved a $Precision$ of 0.803 and a $Recall$ of 0.760 on open-source projects and a $Precision$ of 0.821 on industrial projects; it detected 150 semantic conflict issues in 29 projects, 81.8\% of which had been confirmed as real bugs.

cs.SE↗

arXiv2020

You Only Search Once: A Fast Automation Framework for Single-Stage DNN/Accelerator Co-design

DNN/Accelerator co-design has shown great potential in improving QoR and performance. Typical approaches separate the design flow into two-stage: (1) designing an application-specific DNN model with high accuracy; (2) building an accelerator considering the DNN specific characteristics. However, it may fail in promising the highest composite score which combines the goals of accuracy and other hardware-related constraints (e.g., latency, energy efficiency) when building a specific neural-network-based system. In this work, we present a single-stage automated framework, YOSO, aiming to generate the optimal solution of software-and-hardware that flexibly balances between the goal of accuracy, power, and QoS. Compared with the two-stage method on the baseline systolic array accelerator and Cifar10 dataset, we achieve 1.42x~2.29x energy or 1.79x~3.07x latency reduction at the same level of precision, for different user-specified energy and latency optimization constraints, respectively.

eess.SP↗

arXiv2020

Combining GHOST and Casper

We present "Gasper," a proof-of-stake-based consensus protocol, which is an idealized version of the proposed Ethereum 2.0 beacon chain. The protocol combines Casper FFG, a finality tool, with LMD GHOST, a fork-choice rule. We prove safety, plausible liveness, and probabilistic liveness under different sets of assumptions.

cs.CR↗

arXiv2020

Near Transform-limited Quantum Dot Linewidths in a Broadband Photonic Crystal Waveguide

Planar nanophotonic structures enable broadband, near-unity coupling of emission from quantum dots embedded within, thereby realizing ideal singe-photon sources. The efficiency and coherence of the single-photon source is limited by charge noise, which results in the broadening of the emission spectrum.We report suppression of the noise by fabricating photonic crystal waveguides in a gallium arsenide membrane containing quantum dots embedded in a $p$-$i$-$n$ diode. Local electrical contacts in the vicinity of the waveguides minimize the leakage current and allow fast electrical control ($\approx$4 MHz bandwidth) of the quantum dot resonances. Resonant linewidth measurements of $79$ quantum dots coupled to the photonic crystal waveguides exhibit near transform-limited emission over a 6 nm wide range of emission wavelengths. Importantly, the local electrical contacts allow independent tuning of multiple quantum dots on the same chip, which together with the transform-limited emission are key components in realizing multiemitter-based quantum information processing.

quant-ph↗

arXiv2020

Accelerating Generative Neural Networks on Unmodified Deep Learning Processors -- A Software Approach

Generative neural network is a new category of neural networks and it has been widely utilized in applications such as content generation, unsupervised learning, segmentation and pose estimation. It typically involves massive computing-intensive deconvolution operations that cannot be fitted to conventional neural network processors directly. However, prior works mainly investigated specialized hardware architectures through intensive hardware modifications to the existing deep learning processors to accelerate deconvolution together with the convolution. In contrast, this work proposes a novel deconvolution implementation with a software approach and enables fast and efficient deconvolution execution on the legacy deep learning processors. Our proposed method reorganizes the computation of deconvolution and allows the deep learning processors to treat it as the standard convolution by splitting the original deconvolution filters into multiple small filters. Compared to prior acceleration schemes, the implemented acceleration scheme achieves 2.41x - 4.34x performance speedup and reduces the energy consumption by 27.7% - 54.5% on a set of realistic benchmarks. In addition, we also applied the deconvolution computing approach to the off-the-shelf commodity deep learning processors. The performance of deconvolution also exhibits significant performance speedup over prior deconvolution implementations.

cs.LG↗

arXiv2020

EnGN: A High-Throughput and Energy-Efficient Accelerator for Large Graph Neural Networks

Graph neural networks (GNNs) emerge as a powerful approach to process non-euclidean data structures and have been proved powerful in various application domains such as social networks and e-commerce. While such graph data maintained in real-world systems can be extremely large and sparse, thus employing GNNs to deal with them requires substantial computational and memory overhead, which induces considerable energy and resource cost on CPUs and GPUs. In this work, we present a specialized accelerator architecture, EnGN, to enable high-throughput and energy-efficient processing of large-scale GNNs. The proposed EnGN is designed to accelerate the three key stages of GNN propagation, which is abstracted as common computing patterns shared by typical GNNs. To support the key stages simultaneously, we propose the ring-edge-reduce(RER) dataflow that tames the poor locality of sparsely-and-randomly connected vertices, and the RER PE-array to practice RER dataflow. In addition, we utilize a graph tiling strategy to fit large graphs into EnGN and make good use of the hierarchical on-chip buffers through adaptive computation reordering and tile scheduling. Overall, EnGN achieves performance speedup by 1802.9X, 19.75X, and 2.97X and energy efficiency by 1326.35X, 304.43X, and 6.2X on average compared to CPU, GPU, and a state-of-the-art GCN accelerator HyGCN, respectively.

cs.DC↗

arXiv2020

Scalable integrated single-photon source

Photonic qubits are key enablers for quantum-information processing deployable across a distributed quantum network. An on-demand and truly scalable source of indistinguishable single photons is the essential component enabling high-fidelity photonic quantum operations. A main challenge is to overcome noise and decoherence processes in order to reach the steep benchmarks on generation efficiency and photon indistinguishability required for scaling up the source. We report on the realization of a deterministic single-photon source featuring near-unity indistinguishability using a quantum dot in an 'on-chip' planar nanophotonic waveguide circuit. The device produces long strings of $>100$ single photons without any observable decrease in the mutual indistinguishability between photons. A total generation rate of $122$ million photons per second is achieved corresponding to an 'on-chip' source efficiency of $84 \%$. These specifications of the single-photon source are benchmarked for boson sampling and found to enable scaling into the regime of quantum advantage.

quant-ph↗

arXiv2020

On the Radiality Constraints for Distribution System Restoration and Reconfiguration Problems

Radiality constraints are involved in both distribution system restoration and reconfiguration problems. However, a set of widely used radiality constraints, i.e., the spanning tree (ST) constraints, has its limitations which have not been well recognized. In this letter, the limitation of the ST constraints is analyzed and an effective set of constraints, referred to as the single-commodity flow constraints, is presented. Furthermore, a combined set of constraints is proposed and case studies indicate that the combined constraints can gain computational efficiency in the reconfiguration problem. Recommendations on the use of radiality constraints are also provided.

eess.SY↗

arXiv2020

An Empirical Study of Usages, Updates and Risks of Third-Party Libraries in Java Projects

Third-party libraries are a central building block to develop software systems. However, outdated third-party libraries are commonly used, and developers are usually less aware of the potential risks. Therefore, a quantitative and holistic study on usages, updates and risks of third-party libraries can provide practical insights to improve the ecosystem sustainably. In this paper, we conduct such a study in the Java ecosystem. Specifically, we conduct a library usage analysis (e.g., usage intensity and outdatedness) and a library update analysis (e.g., update intensity and delay) using 806 open-source projects. The two analyses aim to quantify usage and update practices holistically from the perspective of both open-source projects and third-party libraries. Then, we conduct a library risk analysis (e.g., potential risk and developer response) in terms of bugs with 15 popularly-used third-party libraries. This analysis aims to quantify the potential risk of using outdated libraries and the developer response to the risk. Our findings from the three analyses provide practical insights to developers and researchers on problems and potential solutions in maintaining third-party libraries (e.g., smart alerting and automated updating of outdated libraries). To demonstrate the usefulness of our findings, we propose a bug-driven alerting system for assisting developers to make confident decisions in updating third-party library versions. We have released our dataset to foster valuable applications and improve the ecosystem.

cs.SE↗

arXiv2020

Interactive, Effort-Aware Library Version Harmonization

As a mixed result of intensive dependency on third-party libraries, flexible mechanism to declare dependencies, and increased number of modules in a project, multiple versions of the same third-party library are directly depended in different modules of a project. Such library version inconsistencies can increase dependency maintenance cost, or even lead to dependency conflicts when modules are inter-dependent. Although automated build tools (e.g., Maven's enforcer plugin) provide partial support to detect library version inconsistencies, they do not provide any support to harmonize inconsistent library versions. We first conduct a survey with 131 Java developers from GitHub to retrieve first-hand information about the root causes, detection methods, reasons for fixing or not fixing, fixing strategies, fixing efforts, and tool expectations on library version inconsistencies. Then, based on the insights from our survey, we propose LibHarmo, an interactive, effort-aware library version harmonization technique, to detect library version inconsistencies, interactively suggest a harmonized version with the least harmonization efforts based on library API usage analysis, and refactor build configuration files. LibHarmo is currently developed for Java Maven projects. Our experimental study on 443 highly-starred Java Maven projects from GitHub indicates that i) LibHarmo identifies 621 library version inconsistencies covering 152 (34.3%) of projects, and ii) the average harmonization efforts are that 1 and 12 library API calls are affected, respectively due to the deleted and changed library APIs in the harmonized version. 5 library version inconsistencies have been confirmed, and 1 of them has been already harmonized by developers.

cs.SE↗

arXiv2020

On-chip deterministic operation of quantum dots in dual-mode waveguides for a plug-and-play single-photon source

A deterministic source of coherent single photons is an enabling device of quantum-information processing for quantum simulators, and ultimately a full-fledged quantum internet. Quantum dots (QDs) in nanophotonic structures have been employed as excellent sources of single photons, and planar waveguides are well suited for scaling up to multiple photons and emitters exploring near-unity photon-emitter coupling and advanced active on-chip functionalities. An ideal single-photon source requires suppressing noise and decoherence, which notably has been demonstrated in electrically-contacted heterostructures. It remains a challenge to implement deterministic resonant excitation of the QD required for generating coherent single photons, since residual light from the excitation laser should be suppressed without compromising source efficiency and scalability. Here, we present the design and realization of a novel planar nanophotonic device that enables deterministic pulsed resonant excitation of QDs through the waveguide. Through nanostructure engineering, the excitation light and collected photons are guided in two orthogonal waveguide modes enabling deterministic operation. We demonstrate a coherent single-photon source that simultaneously achieves high-purity ($g^{(2)}(0)$ = 0.020 $\pm$ 0.005), high-indistinguishability ($V$ = 96 $\pm$ 2 %), and $>$80 % coupling efficiency into the waveguide. The novel `plug-and-play' coherent single-photon source could be operated unmanned for several days and will find immediate applications, e.g., for constructing heralded multi-photon entanglement sources for photonic quantum computing or sensing.

quant-ph↗

arXiv2019

Optical needles with arbitrary homogeneous three-dimensional polarization

We propose a new method to generate optical needles by focusing vector beams comprised of radially polarized component and azimuthally polarized vortex components. The radial part can generate longitudinal polarization, while the azimuthal parts can generate left- and right-handed polarization. Hence, an arbitrary 3D polarization can be obtained. To our knoeledge, it may be the first time that arbitrarily polarized optical needles whose transverse sizes are under 0.5$λ$ have been achieved. And their polarized homogeneity is beyond 0.97.

physics.optics↗

arXiv2019

Breast Anatomy Enriched Tumor Saliency Estimation

Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is a challenging task for breast ultrasound due to the complicated breast structure and poor quality of the images. In this paper, we propose a novel tumor saliency estimation model guided by enriched breast anatomy knowledge to localize the tumor. Firstly, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with increasing 10% of F_meansure on the BUS public dataset.

cs.CV↗

arXiv2019

Inpatient2Vec: Medical Representation Learning for Inpatients

Representation learning (RL) plays an important role in extracting proper representations from complex medical data for various analyzing tasks, such as patient grouping, clinical endpoint prediction and medication recommendation. Medical data can be divided into two typical categories, outpatient and inpatient, that have different data characteristics. However, few of existing RL methods are specially designed for inpatients data, which have strong temporal relations and consistent diagnosis. In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning. In this work, weproposeInpatient2Vec, anovelmodel for learning three kinds of representations for inpatient, including medical activity, hospital day and diagnosis. A multi-layer self-attention mechanism with two training tasks is designed to capture the inpatient data characteristics and process the unordered set. Using a real-world dataset, we demonstrate that the proposed approach outperforms the competitive baselines on semantic similarity measurement and clinical events prediction tasks.

cs.LG↗