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Ying Wang

Publications and source records attributed to Ying Wang.

At least 397 records · Page 22Linked to original sources
arXiv2021

An Iterative Heuristic Method to Determine Radial Topology for Distribution System Restoration

Coordinating multiple local power sources can restore critical loads after the major outages caused by extreme events. A radial topology is needed for distribution system restoration, while determining a good topology in real-time for online use is a challenge. In this paper, a graph theory-based heuristic considering power flow state is proposed to fast determine the radial topology. The loops of distribution network are eliminated by iteration. The proposed method is validated by one snapshot and multi-period critical load restoration models on different cases. The case studies indicate that the proposed method can determine radial topology in a few seconds and ensure the restoration capacity.

eess.SY↗
arXiv2021

SWA Object Detection

Do you want to improve 1.0 AP for your object detector without any inference cost and any change to your detector? Let us tell you such a recipe. It is surprisingly simple: train your detector for an extra 12 epochs using cyclical learning rates and then average these 12 checkpoints as your final detection model}. This potent recipe is inspired by Stochastic Weights Averaging (SWA), which is proposed in arXiv:1803.05407 for improving generalization in deep neural networks. We found it also very effective in object detection. In this technique report, we systematically investigate the effects of applying SWA to object detection as well as instance segmentation. Through extensive experiments, we discover the aforementioned workable policy of performing SWA in object detection, and we consistently achieve $\sim$1.0 AP improvement over various popular detectors on the challenging COCO benchmark, including Mask RCNN, Faster RCNN, RetinaNet, FCOS, YOLOv3 and VFNet. We hope this work will make more researchers in object detection know this technique and help them train better object detectors. Code is available at: https://github.com/hyz-xmaster/swa_object_detection .

cs.CV↗
arXiv2021

Electronic structures and topological phases of magnetic layered materials MnBi2Te4, MnBi2Se4 and MnSb2Te4

First-principles calculations are performed to study the electronic structures and topological phases of magnetic layered materials MnBi2Te4, MnBi2Se4 and MnSb2Te4 under different film thicknesses, strains and spin-orbit coupling (SOC) strengths. All these compounds energetically prefer the antiferromagnetic (AFM) state. MnBi2Te4 and MnSb2Te4 bulks are AFM topological insulators (TIs) in the AFM state, while they become Weyl semimetals in the ferromagnetic (FM) state. MnBi2Se4 is trivially insulating in both the AFM and FM states, but it becomes an AFM TI or a Weyl semimetal with increasing SOC strength or applying compressive strains. Under equilibrium lattice constants, the FM MnBi2Te4 slabs thicker than two septuple layers (SLs), the AFM MnBi2Te4 slabs thicker than three SLs and the FM MnSb2Te4 slabs thicker than five SLs are all Chern insulators. In addition, Chern insulators can also be obtained by compressing the in-plane lattice constants of the FM MnBi2Se4 slabs thicker than four SLs and the FM MnSb2Te4 slabs of three or four SLs, but cannot be obtained using the same method in the AFM slabs of these two materials. In-plane tensile strains about 1% to 2% turn the Chern insulators into trivial insulators.

physics.comp-ph↗
arXiv2021

VarifocalNet: An IoU-aware Dense Object Detector

Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by $\sim$2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors.Code is available at https://github.com/hyz-xmaster/VarifocalNet .

cs.CV↗
arXiv2021

Construction of explicit symplectic integrators in general relativity. II. Reissner-Nordstrom black holes

In a previous paper, second- and fourth-order explicit symplectic integrators were designed for a Hamiltonian of the Schwarzschild black hole. Following this work, we continue to trace the possibility of the construction of explicit symplectic integrators for a Hamiltonian of charged particles moving around a Reissner-Nordstrom black hole with an external magnetic field. Such explicit symplectic methods are still available when the Hamiltonian is separated into five independently integrable parts with analytical solutions as explicit functions of proper time. Numerical tests show that the proposed algorithms share the desirable properties in their long-term stability, precision and efficiency for appropriate choices of step sizes. For the applicability of one of the new algorithms, the effects of the black hole's charge, the Coulomb part of the electromagnetic potential and the magnetic parameter on the dynamical behavior are surveyed. Under some circumstances, the extent of chaos gets strong with an increase of the magnetic parameter from a global phase-space structure. No the variation of the black hole's charge but the variation of the Coulomb part is considerably sensitive to affect the regular and chaotic dynamics of particles' orbits. A positive Coulomb part is easier to induce chaos than a negative one.

gr-qc↗
arXiv2021

Damage accumulation during high temperature fatigue of Ti/SiC$_f$ metal matrix composites under different stress amplitudes

The damage mechanisms and load redistribution of high strength TC17 titanium alloy/unidirectional SiC fibre composite (fibre diameter = 100 $μ$m) under high temperature (350 °C) fatigue cycling have been investigated in situ using synchrotron X-ray computed tomography (CT) and X-ray diffraction (XRD) for high cycle fatigue (HCF) under different stress amplitudes. The three-dimensional morphology of the crack and fibre fractures has been mapped by CT. During stable growth, matrix cracking dominates with the crack deflecting (by 50-100 $μ$m in height) when bypassing bridging fibres. A small number of bridging fibres have fractured close to the matrix crack plane especially under relatively high stress amplitude cycling. Loading to the peak stress led to rapid crack growth accompanied by a burst of fibre fractures. Many of the fibre fractures occurred 50-300 $μ$m from the matrix crack plane during rapid growth, in contrast to that in the stable growth stage, leading to extensive fibre pull-out on the fracture surface. The changes in fibre loading, interfacial stress, and the extent of fibre-matrix debonding in the vicinity of the crack have been mapped for the fatigue cycle and after the rapid growth by high spatial resolution XRD. The fibre/matrix interfacial sliding extends up to 600 $μ$m (in the stable growth zone) or 700 $μ$m (in the rapid growth zone) either side of the crack plane. The direction of interfacial shear stress reverses with the loading cycle, with the maximum frictional sliding stress reaching ~55 MPa in both the stable growth and rapid growth regimes.

cond-mat.mtrl-sci↗
arXiv2021

Hero: On the Chaos When PATH Meets Modules

Ever since its first release in 2009, the Go programming language (Golang) has been well received by software communities. A major reason for its success is the powerful support of library-based development, where a Golang project can be conveniently built on top of other projects by referencing them as libraries. As Golang evolves, it recommends the use of a new library-referencing mode to overcome the limitations of the original one. While these two library modes are incompatible, both are supported by the Golang ecosystem. The heterogeneous use of library-referencing modes across Golang projects has caused numerous dependency management (DM) issues, incurring reference inconsistencies and even build failures. Motivated by the problem, we conducted an empirical study to characterize the DM issues, understand their root causes, and examine their fixing solutions. Based on our findings, we developed \textsc{Hero}, an automated technique to detect DM issues and suggest proper fixing solutions. We applied \textsc{Hero} to 19,000 popular Golang projects. The results showed that \textsc{Hero} achieved a high detection rate of 98.5\% on a DM issue benchmark and found 2,422 new DM issues in 2,356 popular Golang projects. We reported 280 issues, among which 181 (64.6\%) issues have been confirmed, and 160 of them (88.4\%) have been fixed or are under fixing. Almost all the fixes have adopted our fixing suggestions.

cs.SE↗
arXiv2021

Electroabsorption in gated GaAs nanophotonic waveguides

We report on the analysis of electroabsorption in thin GaAs/Al$_{0.3}$Ga$_{0.7}$As nanophotonic waveguides with an embedded $p$-$i$-$n$ junction. By measuring the transmission through waveguides of different lengths, we derive the propagation loss as a function of electric field, wavelength, and temperature. The results are in good agreement with the Franz-Keldysh model of electroabsorption extending over 200 meV below the GaAs bandgap, i.e. in the 910--970 nm wavelength range. We find a pronounced residual absorption in forward bias, which we attribute to Fermi-level pinning at the waveguide surface, producing over 20 dB/mm loss at room temperature. These results are essential for understanding the origin of loss in nanophotonic devices operating in the emission range of self-assembled InAs semiconductor quantum dots, towards the realization of scalable quantum photonic integrated circuits.

physics.optics↗
arXiv2021

Construction of Explicit Symplectic Integrators in General Relativity. I. Schwarzschild Black Holes

Symplectic integrators that preserve the geometric structure of Hamiltonian flows and do not exhibit secular growth in energy errors are suitable for the long-term integration of N-body Hamiltonian systems in the solar system. However, the construction of explicit symplectic integrators is frequently difficult in general relativity because all variables are inseparable. Moreover, even if two analytically integrable splitting parts exist in a relativistic Hamiltonian, all analytical solutions are not explicit functions of proper time. Naturally, implicit symplectic integrators, such as the midpoint rule, are applicable to this case. In general, these integrators are numerically more expensive to solve than same-order explicit symplectic algorithms. To address this issue, we split the Hamiltonian of Schwarzschild spacetime geometry into four integrable parts with analytical solutions as explicit functions of proper time. In this manner, second- and fourth-order explicit symplectic integrators can be easily made available. The new algorithms are also useful for modeling the chaotic motion of charged particles around a black hole with an external magnetic field. They demonstrate excellent long-term performance in maintaining bounded Hamiltonian errors and saving computational cost when appropriate proper time steps are adopted.

gr-qc↗
arXiv2021

MPC-CSAS: Multi-Party Computation for Real-time Privacy-preserving Speed Advisory Systems

As a part of Advanced Driver Assistance Systems (ADASs), Consensus-based Speed Advisory Systems (CSAS) have been proposed to recommend a common speed to a group of vehicles for specific application purposes, such as emission control and energy management. With Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I) technologies and advanced control theories in place, state-of-the-art CSAS can be designed to get an optimal speed in a privacy-preserving and decentralized manner. However, the current method only works for specific cost functions of vehicles, and its execution usually involves many algorithm iterations leading long convergence time. Therefore, the state-of-the-art design method is not applicable to a CSAS design which requires real-time decision making. In this paper, we address the problem by introducing MPC-CSAS, a Multi-Party Computation (MPC) based design approach for privacy-preserving CSAS. Our proposed method is simple to implement and applicable to all types of cost functions of vehicles. Moreover, our simulation results show that the proposed MPC-CSAS can achieve very promising system performance in just one algorithm iteration without using extra infrastructure for a typical CSAS.

eess.SY↗
arXiv2021

Transport in helical Luttinger liquids in the fractional quantum Hall regime

Domain walls in fractional quantum Hall ferromagnets are gapless helical one-dimensional channels formed at the boundaries of topologically distinct quantum Hall (QH) liquids. Naïvely, these helical domain walls (hDWs) constitute two counter-propagating chiral states with opposite spins. Coupled to an s-wave superconductor, helical channels are expected to lead to topological superconductivity with high order non-Abelian excitations. Here we investigate transport properties of hDWs in the $ν=2/3$ fractional QH regime. Experimentally we found that current carried by hDWs is substantially smaller than the prediction of the naïve model. Luttinger liquid theory of the system reveals redistribution of currents between quasiparticle charge, spin and neutral modes, and predicts the reduction of the hDW current. Inclusion of spin-non-conserving tunneling processes reconciles theory with experiment. The theory confirms emergence of spin modes required for the formation of fractional topological superconductivity.

cond-mat.mes-hall↗
arXiv2020

An Adaptive Gas Cost Mechanism for Ethereum to Defend Against Under-Priced DoS Attacks

The gas mechanism in Ethereum charges the execution of every operation to ensure that smart contracts running in EVM (Ethereum Virtual Machine) will be eventually terminated. Failing to properly set the gas costs of EVM operations allows attackers to launch DoS attacks on Ethereum. Although Ethereum recently adjusted the gas costs of EVM operations to defend against known DoS attacks, it remains unknown whether the new setting is proper and how to configure it to defend against unknown DoS attacks. In this paper, we make the first step to address this challenging issue by first proposing an emulation-based framework to automatically measure the resource consumptions of EVM operations. The results reveal that Ethereum's new setting is still not proper. Moreover, we obtain an insight that there may always exist exploitable under-priced operations if the cost is fixed. Hence, we propose a novel gas cost mechanism, which dynamically adjusts the costs of EVM operations according to the number of executions, to thwart DoS attacks. This method punishes the operations that are executed much more frequently than before and lead to high gas costs. To make our solution flexible and secure and avoid frequent update of Ethereum client, we design a special smart contract that collaborates with the updated EVM for dynamic parameter adjustment. Experimental results demonstrate that our method can effectively thwart both known and unknown DoS attacks with flexible parameter settings. Moreover, our method only introduces negligible additional gas consumption for benign users.

cs.CR↗
arXiv2020

Contribution of Magnetic Reconnection Events to Energy Dissipation in Magnetosheath Turbulence

By analyzing the magnetosheath measurements from MMS, we obtain the statistical results for the contribution of magnetic reconnection (MR) events at electron scales to the energy dissipation of coherent structures. The Partial Variance of Increments (PVI) method is employed to find coherent structures in the magnetic field data. The current sheet structures with reversal of magnetic field components are further selected. We consider the following criteria to identify the MR events, such as current sheet with magnetic field reversal, significant energy dissipation, and evident electron outflow velocity. Statistically, for most MR events, their PVI values are larger than that of other types of coherent structures, and their energy dissipations are also stronger than that of others. However, due to the relatively small proportion of MR events, their contribution to coherent structures' energy dissipation is relatively trivial. If taken into account the dissipation of non-coherent structures, the MR's contribution to energy dissipation would be less. Hence, we suggest that MR events, though have strong dissipation locally, are not the major contributor to the energy dissipation in the magnetosheath. After analyzing the features of non-MR current sheets, we propose that non-MR current sheets are mainly coherent structures inherent to kinetic Alfvén fluctuations.

physics.plasm-ph↗
arXiv2020

Bayesian Bits: Unifying Quantization and Pruning

We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition of the quantization operation, which sequentially considers doubling the bit width. At each new bit width, the residual error between the full precision value and the previously rounded value is quantized. We then decide whether or not to add this quantized residual error for a higher effective bit width and lower quantization noise. By starting with a power-of-two bit width, this decomposition will always produce hardware-friendly configurations, and through an additional 0-bit option, serves as a unified view of pruning and quantization. Bayesian Bits then introduces learnable stochastic gates, which collectively control the bit width of the given tensor. As a result, we can obtain low bit solutions by performing approximate inference over the gates, with prior distributions that encourage most of them to be switched off. We experimentally validate our proposed method on several benchmark datasets and show that we can learn pruned, mixed precision networks that provide a better trade-off between accuracy and efficiency than their static bit width equivalents.

cs.LG↗
arXiv2020

Characterizing and Detecting Freezing of Gait using Multi-modal Physiological Signals

Freezing-of-gait a mysterious symptom of Parkinsons disease and defined as a sudden loss of ability to move forward. Common treatments of freezing episodes are currently of moderate efficacy and can likely be improved through a reliable freezing evaluation. Basic-science studies about the characterization of freezing episodes and a 24/7 evidence-support freezing detection system can contribute to the reliability of the evaluation in daily life. In this study, we analyzed multi-modal features from brain, eye, heart, motion, and gait activity from 15 participants with idiopathic Parkinsons disease and 551 freezing episodes induced by turning in place. Statistical analysis was first applied on 248 of the 551 to determine which multi-modal features were associated with freezing episodes. Features significantly associated with freezing episodes were ranked and used for the freezing detection. We found that eye-stabilization speed during turning and lower-body trembling measure significantly associated with freezing episodes and used for freezing detection. Using a leave-one-subject-out cross-validation, we obtained a sensitivity of 97%+/-3%, a specificity of 96%+/-7%, a precision of 73%+/-21%, a Matthews correlation coefficient of 0.82+/-0.15, and an area under the Precision-Recall curve of 0.94+/-0.05. According to the Precision-Recall curves, the proposed freezing detection method using the multi-modal features performed better than using single-modal features.

eess.SP↗
arXiv2020

VC-Net: Deep Volume-Composition Networks for Segmentation and Visualization of Highly Sparse and Noisy Image Data

The motivation of our work is to present a new visualization-guided computing paradigm to combine direct 3D volume processing and volume rendered clues for effective 3D exploration such as extracting and visualizing microstructures in-vivo. However, it is still challenging to extract and visualize high fidelity 3D vessel structure due to its high sparseness, noisiness, and complex topology variations. In this paper, we present an end-to-end deep learning method, VC-Net, for robust extraction of 3D microvasculature through embedding the image composition, generated by maximum intensity projection (MIP), into 3D volume image learning to enhance the performance. The core novelty is to automatically leverage the volume visualization technique (MIP) to enhance the 3D data exploration at deep learning level. The MIP embedding features can enhance the local vessel signal and are adaptive to the geometric variability and scalability of vessels, which is crucial in microvascular tracking. A multi-stream convolutional neural network is proposed to learn the 3D volume and 2D MIP features respectively and then explore their inter-dependencies in a joint volume-composition embedding space by unprojecting the MIP features into 3D volume embedding space. The proposed framework can better capture small / micro vessels and improve vessel connectivity. To our knowledge, this is the first deep learning framework to construct a joint convolutional embedding space, where the computed vessel probabilities from volume rendering based 2D projection and 3D volume can be explored and integrated synergistically. Experimental results are compared with the traditional 3D vessel segmentation methods and the deep learning state-of-the-art on public and real patient (micro-)cerebrovascular image datasets. Our method demonstrates the potential in a powerful MR arteriogram and venogram diagnosis of vascular diseases.

cs.GR↗
arXiv2020

Automatic Speech Summarisation: A Scoping Review

Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literature, with no restrictions on time frame, language summarised, research method, or paper type. We reviewed a total of 110 papers out of a set of 153 found through a literature search and extracted speech features used, methods, scope, and training corpora. Most studies employ one of four speech summarisation architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarisation; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. Overall, supervised methods (e.g. Hidden Markov support vector machines, Ranking support vector machines, Conditional random fields) performed better than unsupervised methods. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods. Recent research into unsupervised methods focusses on extending language modelling, for example by combining Uni-gram modelling with deep neural networks. Protocol registration: The protocol for this scoping review is registered at https://osf.io.

cs.CL↗