arXiv subjects

Xing He

Publications and source records attributed to Xing He.

At least 37 records · Page 2Linked to original sources

arXiv2024

Impacts of Hot Electron Diffusion, Electron-Phonon Coupling, and Surface Atoms on Metal Surface Dynamics Revealed by Reflection Ultrafast Electron Diffraction

Metals exhibit nonequilibrium electron and lattice subsystems at transient times following femtosecond laser excitation. In the past four decades, various optical spectroscopy and time-resolved diffraction methods have been used to study electron-phonon coupling and the effects of underlying dynamical processes. Here, we take advantage of the surface specificity of reflection ultrafast electron diffraction (UED) to examine the structural dynamics of photoexcited metal surfaces, which are apparently slower in recovery than predicted by thermal diffusion from the profile of absorbed energy. Fast diffusion of hot electrons is found to critically reduce surface excitation and affect the temporal dependence of the increased atomic motions on not only the ultrashort but sub-nanosecond times. Whereas the two-temperature model with the accepted physical constants of platinum can reproduce the observed surface lattice dynamics, gold is found to exhibit appreciably larger-than-expected dynamic vibrational amplitudes of surface atoms while keeping the commonly used electron-phonon coupling constant. Such surface behavioral difference at transient times can be understood in the context of the different strengths of binding to surface atoms for the two metals. In addition, with the quantitative agreements between diffraction and theoretical results, we provide convincing evidence that surface structural dynamics can be reliably obtained by reflection UED even in the presence of laser-induced transient electric fields.

physics.chem-ph↗

arXiv2023

Invisible Units Detection and Estimation Based on Random Matrix Theory

Invisible units mainly refer to small-scale units that are not monitored by, and thus are not visible to utilities. Integration of these invisible units into power systems does significantly affect the way in which a distribution grid is planned and operated. This paper, based on random matrix theory (RMT), proposes a statistical, data-driven framework to handle the massive grid data, in contrast to its deterministic, model-based counterpart. Combining the RMT-based data-mining framework with conventional techniques, some heuristics are derived as the solution to the invisible units detection and estimation task: linear eigenvalue statistic indicators (LESs) are suggested as the main ingredients of the solution; according to the statistical properties of LESs, the hypothesis testing is formulated to conduct change point detection in the high-dimensional space. The proposed method is promising for anomaly detection and pertinent to current distribution networks-it is capable of detecting invisible power usage and fraudulent behavior while even being able to locate the suspect's location. Case studies, using both simulated data and actual data, validate the proposed method.

stat.AP↗

arXiv2023

A Hybrid Framework for Topology Identification of Distribution Grid with Renewables Integration

Topology identification (TI) is a key task for state estimation (SE) in distribution grids, especially the one with high-penetration renewables. The uncertainties, initiated by the time-series behavior of renewables, will almost certainly lead to bad TI results without a proper treatment. These uncertainties are analytically intractable under conventional framework-they are usually jointly spatial-temporal dependent, and hence cannot be simply treated as white noise. For this purpose, a hybrid framework is suggested in this paper to handle these uncertainties in a systematic and theoretical way; in particular, big data analytics are studied to harness the jointly spatial-temporal statistical properties of those uncertainties. With some prior knowledge, a model bank is built first to store the countable typical models of network configurations; therefore, the difference between the SE outputs of each bank model and our observation is capable of being defined as a matrix variate-the so-called random matrix. In order to gain insight into the random matrix, a well-designed metric space is needed. Auto-regression (AR) model, factor analysis (FA), and random matrix theory (RMT) are tied together for the metric space design, followed by jointly temporal-spatial analysis of those matrices which is conducted in a high-dimensional (vector) space. Under the proposed framework, some big data analytics and theoretical results are obtained to improve the TI performance. Our framework is validated using IEEE standard distribution network with some field data in practice.

stat.AP↗

arXiv2023

Exploration of Superposition Theorem in Spectrum Space for Composite Event Analysis in an ADN

This study presents a formulation of the Superposition Theorem (ST) in the spectrum space, tailored for the analysis of composite events in an active distribution network (ADN). Our formulated ST enables a quantitative analysis on a composite event, uncovering the property of additivity among independent atom events in the spectrum space. This contribution is a significant addition to the existing literature and has profound implications in various application scenarios. To accomplish this, we leverage random matrix theory (RMT), specifically the asymptotic empirical spectral distribution, Stieltjes transform, and R transform. These mathematical tools establish a nonlinear, model-free, and unsupervised addition operation in the spectrum space. Comprehensive details, including a related roadmap,theorems, deductions, and proofs, are provided in this work. Case studies, utilizing field data, validate our newly derived ST formulation by demonstrating a remarkable performance. Our ST formulation is model-free, non-linear, non-supervised, theory-guided, and uncertainty-insensitive, making it a valuable asset in the realm of composite event analysis in ADN.

stat.AP↗

arXiv2023

FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillation

Although industrial anomaly detection (AD) technology has made significant progress in recent years, generating realistic anomalies and learning priors of normal remain challenging tasks. In this study, we propose an end-to-end industrial anomaly detection method called FractalAD. Training samples are obtained by synthesizing fractal images and patches from normal samples. This fractal anomaly generation method is designed to sample the full morphology of anomalies. Moreover, we designed a backbone knowledge distillation structure to extract prior knowledge contained in normal samples. The differences between a teacher and a student model are converted into anomaly attention using a cosine similarity attention module. The proposed method enables an end-to-end semantic segmentation network to be used for anomaly detection without adding any trainable parameters to the backbone and segmentation head, and has obvious advantages over other methods in training and inference speed.. The results of ablation studies confirmed the effectiveness of fractal anomaly generation and backbone knowledge distillation. The results of performance experiments showed that FractalAD achieved competitive results on the MVTec AD dataset and MVTec 3D-AD dataset compared with other state-of-the-art anomaly detection methods.

cs.CV↗

arXiv2023

HiFi-WaveGAN: Generative Adversarial Network with Auxiliary Spectrogram-Phase Loss for High-Fidelity Singing Voice Generation

Entertainment-oriented singing voice synthesis (SVS) requires a vocoder to generate high-fidelity (e.g. 48kHz) audio. However, most text-to-speech (TTS) vocoders cannot reconstruct the waveform well in this scenario. In this paper, we propose HiFi-WaveGAN to synthesize the 48kHz high-quality singing voices in real-time. Specifically, it consists of an Extended WaveNet served as a generator, a multi-period discriminator proposed in HiFiGAN, and a multi-resolution spectrogram discriminator borrowed from UnivNet. To better reconstruct the high-frequency part from the full-band mel-spectrogram, we incorporate a pulse extractor to generate the constraint for the synthesized waveform. Additionally, an auxiliary spectrogram-phase loss is utilized to approximate the real distribution further. The experimental results show that our proposed HiFi-WaveGAN obtains 4.23 in the mean opinion score (MOS) metric for the 48kHz SVS task, significantly outperforming other neural vocoders.

eess.AS↗

arXiv2023

SVDFormer: Complementing Point Cloud via Self-view Augmentation and Self-structure Dual-generator

In this paper, we propose a novel network, SVDFormer, to tackle two specific challenges in point cloud completion: understanding faithful global shapes from incomplete point clouds and generating high-accuracy local structures. Current methods either perceive shape patterns using only 3D coordinates or import extra images with well-calibrated intrinsic parameters to guide the geometry estimation of the missing parts. However, these approaches do not always fully leverage the cross-modal self-structures available for accurate and high-quality point cloud completion. To this end, we first design a Self-view Fusion Network that leverages multiple-view depth image information to observe incomplete self-shape and generate a compact global shape. To reveal highly detailed structures, we then introduce a refinement module, called Self-structure Dual-generator, in which we incorporate learned shape priors and geometric self-similarities for producing new points. By perceiving the incompleteness of each point, the dual-path design disentangles refinement strategies conditioned on the structural type of each point. SVDFormer absorbs the wisdom of self-structures, avoiding any additional paired information such as color images with precisely calibrated camera intrinsic parameters. Comprehensive experiments indicate that our method achieves state-of-the-art performance on widely-used benchmarks. Code will be available at https://github.com/czvvd/SVDFormer.

cs.CV↗

arXiv2023

Multiple lattice instabilities and complex ground state in Cs$_2$AgBiBr$_6$

Metal halides perovskites (MHPs) are attracting considerable interest for optoelectronic applications, with Cs$_2$AgBiBr$_6$ one of the main contenders among lead-free systems. Cs$_2$AgBiBr$_6$ crystallizes in a nominally double-perovskite structure, but exhibits a soft lattice with large atomic fluctuations characteristic of MHPs. While crucial to understand electron-phonon and phonon-phonon couplings, the spatio-temporal correlations of these fluctuations remain largely unknown. Here, we reveal these correlations using comprehensive neutron and x-ray scattering measurements on Cs$_2$AgBiBr$_6$ single-crystals, complemented with first-principles simulations augmented with machine-learned neural-network potentials. We report the discovery of an unexpected complex modulated ground state structure containing several hundred atoms, arising from a soft-phonon instability of the low-temperature tetragonal phase. Further, our experiments and simulations both reveal extensive correlated 2D fluctuations of Br octahedra at finite temperature, arising from soft anharmonic optic phonons, reflecting very shallow potential wells. These results provide new insights into the atomic structure and fluctuations in MHPs, critical to understand and control their thermal and optoelectronic properties.

cond-mat.mtrl-sci↗

arXiv2023

Cross-Examination of Photoinitiated Carrier and Structural Dynamics of Black Phosphorus at Elevated Fluences

Revived attention in black phosphorus (bP) has been tremendous in the past decade. While many photoinitiated experiments have been conducted, a cross-examination of bP's photocarrier and structural dynamics is still lacking. In this report, we provide such analysis by examining time-resolved data acquired using optical transient reflectivity and reflection ultrafast electron diffraction, two complementary methods under the same experimental conditions. At elevated excitation fluences, we find that more than 90% of the photoinjected carriers are annihilated within the first picosecond (ps) and transfer their energy to phonons in a nonthermal, anisotropic fashion. Electronically, the remaining carrier density around the band edges induces a significant interaction that leads to an interlayer lattice contraction in a few ps but soon diminishes as a result of the continuing loss of carriers. Structurally, phonon-phonon scattering redistributes the energy in the lattice and results in the generation of out-of-plane coherent acoustic phonons and thermal lattice expansion. Their onset times at ~6 ps are found to be in good agreement. Later, a thermalized quasi-equilibrium state is reached following a period of about 40-50 ps. Hence, we propose a picture with five temporal regimes for bP's photodynamics.

cond-mat.mtrl-sci↗

arXiv2022

Xiaoicesing 2: A High-Fidelity Singing Voice Synthesizer Based on Generative Adversarial Network

XiaoiceSing is a singing voice synthesis (SVS) system that aims at generating 48kHz singing voices. However, the mel-spectrogram generated by it is over-smoothing in middle- and high-frequency areas due to no special design for modeling the details of these parts. In this paper, we propose XiaoiceSing2, which can generate the details of middle- and high-frequency parts to better construct the full-band mel-spectrogram. Specifically, in order to alleviate this problem, XiaoiceSing2 adopts a generative adversarial network (GAN), which consists of a FastSpeech-based generator and a multi-band discriminator. We improve the feed-forward Transformer (FFT) block by adding multiple residual convolutional blocks in parallel with the self-attention block to balance the local and global features. The multi-band discriminator contains three sub-discriminators responsible for low-, middle-, and high-frequency parts of the mel-spectrogram, respectively. Each sub-discriminator is composed of several segment discriminators (SD) and detail discriminators (DD) to distinguish the audio from different aspects. The experiment on our internal 48kHz singing voice dataset shows XiaoiceSing2 significantly improves the quality of the singing voice over XiaoiceSing.

eess.AS↗

arXiv2022

Strong Coupling of Self-Trapped Excitons to Acoustic Phonons in Bismuth Perovskite $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$

To assess the potential optoelectronic applications of metal-halide perovskites, it is critical to have a detailed understanding of the nature, strength, and dynamics of the interactions between carriers and the polar lattices. Here, we report the electronic and structural dynamics of bismuth-based perovskite $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$ revealed by transient reflectivity and ultrafast electron diffraction. A cross-examination of these experimental results combined with theoretical analyses allows the identification of the major carrier-phonon coupling mechanism and the associated time scales. It is found that carriers photoinjected into $\textrm{Cs}_{3}\textrm{Bi}_{2}\textrm{I}_{9}$ form self-trapped excitons on an ultrafast time scale. However, they retain most of their energy and their coupling to Fröhlich-type optical phonons is limited at early times. Instead, the long-lived excitons exert an electronic stress via deformation potential and develop a prominent, sustaining strain field as coherent acoustic phonons in 10 ps. From sub-ps to ns and beyond, a similar extent of the atomic displacements is found throughout the different stages of structural distortions, from limited local modulations to a coherent strain field to the Debye-Waller random atomic motions on longer times. The current results suggest the potential use of bismuth-based perovskites for applications other than photovoltaics to take advantage of carriers' stronger self-trapping and long lifetime.

cond-mat.mtrl-sci↗

arXiv2022

Accelerated Primal-Dual Mirror Dynamics for Centrailized and Distributed Constrained Convex Optimization Problems

This paper investigates two accelerated primal-dual mirror dynamical approaches for smooth and nonsmooth convex optimization problems with affine and closed, convex set constraints. In the smooth case, an accelerated primal-dual mirror dynamical approach (APDMD) based on accelerated mirror descent and primal-dual framework is proposed and accelerated convergence properties of primal-dual gap, feasibility measure and the objective function value along with trajectories of APDMD are derived by the Lyapunov analysis method. Then, we extend APDMD into two distributed dynamical approaches to deal with two types of distributed smooth optimization problems, i.e., distributed constrained consensus problem (DCCP) and distributed extended monotropic optimization (DEMO) with accelerated convergence guarantees. Moreover, in the nonsmooth case, we propose a smoothing accelerated primal-dual mirror dynamical approach (SAPDMD) with the help of smoothing approximation technique and the above APDMD. We further also prove that primal-dual gap, objective function value and feasibility measure of SAPDMD have the same accelerated convergence properties as APDMD by choosing the appropriate smooth approximation parameters. Later, we propose two smoothing accelerated distributed dynamical approaches to deal with nonsmooth DEMO and DCCP to obtain accelerated and efficient solutions. Finally, numerical experiments are given to demonstrate the effectiveness of the proposed accelerated mirror dynamical approaches.

math.OC↗

arXiv2022

Ultrafast carrier-coupled interlayer contraction, coherent intralayer motions, and phonon thermalization dynamics of black phosphorus

Black phosphorus (BP) exhibits highly anisotropic properties and dynamical behavior that are unique even among two-dimensional and van der Waals (vdW) layered materials. Here, we show that an interlayer lattice contraction and concerted, symmetric intralayer vibrations take place concurrently within few picoseconds following the photoinjection and relaxation of carriers, using ultrafast electron diffraction in the reflection geometry to probe the out-of-plane motions. A strong coupling between the photocarriers and BP's puckered structure, with the alignment of the electronic band structure, is at work for such directional atomic motions without a photoinduced phase transition. Three temporal regimes can be identified for the phonon thermalization dynamics where a quasi-equilibrium without anisotropy is reached in about 50 ps, followed by propagation of coherent acoustic phonons and heat diffusion into the bulk. The early-time out-of-plane dynamics reported here have important implications for single- and few-layer BP and other vdW materials with strong electronic-lattice correlations.

cond-mat.mes-hall↗

arXiv2021

Discriminative-Generative Representation Learning for One-Class Anomaly Detection

As a kind of generative self-supervised learning methods, generative adversarial nets have been widely studied in the field of anomaly detection. However, the representation learning ability of the generator is limited since it pays too much attention to pixel-level details, and generator is difficult to learn abstract semantic representations from label prediction pretext tasks as effective as discriminator. In order to improve the representation learning ability of generator, we propose a self-supervised learning framework combining generative methods and discriminative methods. The generator no longer learns representation by reconstruction error, but the guidance of discriminator, and could benefit from pretext tasks designed for discriminative methods. Our discriminative-generative representation learning method has performance close to discriminative methods and has a great advantage in speed. Our method used in one-class anomaly detection task significantly outperforms several state-of-the-arts on multiple benchmark data sets, increases the performance of the top-performing GAN-based baseline by 6% on CIFAR-10 and 2% on MVTAD.

cs.CV↗

arXiv2021

Ghost Handwritten Digit Recognition based on Deep Learning

We present a ghost handwritten digit recognition method for the unknown handwritten digits based on ghost imaging (GI) with deep neural network, where a few detection signals from the bucket detector, generated by the Cosine Transform speckle, are used as the characteristic information and the input of the designed deep neural network (DNN), and the classification is designed as the output of the DNN. The results show that the proposed scheme has a higher recognition accuracy (as high as 98.14% for the simulations, and 92.9% for the experiments ) with a smaller sampling ratio (say 12.76%). With the increase of the sampling ratio, the recognition accuracy is enhanced greatly. Compared with the traditional recognition scheme using the same DNN structure, the proposed scheme has a little better performance with a lower complexity and non-locality property. The proposed scheme provides a promising way for remote sensing.

eess.IV↗

arXiv2021

When Text Simplification Is Not Enough: Could a Graph-Based Visualization Facilitate Consumers' Comprehension of Dietary Supplement Information?

Dietary supplements are widely used but not always safe. With the rapid development of the Internet, consumers usually seek health information including dietary supplement information online. To help consumers access quality online dietary supplement information, we have identified trustworthy dietary supplement information sources and built an evidence-based knowledge base of dietary supplement information-the integrated DIetary Supplement Knowledge base (iDISK) that integrates and standardizes dietary supplement related information across these different sources. However, as information in iDISK was collected from scientific sources, the complex medical jargon is a barrier for consumers' comprehension. To assess how different approaches to simplify and represent dietary supplement information from iDISK will affect lay consumers' comprehension, using a crowdsourcing platform, we recruited participants to read dietary supplement information in four different representations from iDISK: original text, syntactic and lexical text simplification, manual text simplification, and a graph-based visualization. We then assessed how the different simplification and representation strategies affected consumers' comprehension of dietary supplement information in terms of accuracy and response time to a set of comprehension questions. With responses from 690 qualified participants, our experiments confirmed that the manual approach had the best performance for both accuracy and response time to the comprehension questions, while the graph-based approach ranked the second outperforming other representations. In some cases, the graph-based representation outperformed the manual approach in terms of response time. A hybrid approach that combines text and graph-based representations might be needed to accommodate consumers' different information needs and information seeking behavior.

cs.CY↗

arXiv2020

Unsupervised Feature Learning for Online Voltage Stability Evaluation and Monitoring Based on Variational Autoencoder

With the increase of uncertain elements in power systems and extensive deployment of online monitoring devices, it is necessary to search a more real-time and robust voltage stability assessment method. This study, using PMU monitoring data, explores a novel data-driven approach for long-term voltage stability assessment based on variational autoencoder (VAE). Our method is capable of extracting the most representative features by an unsupervised data mining method in a probabilistic learning way. Different from most of familiar feature extraction methods, it regularizes latent features in an expected stochastic distribution. Furthermore, a statistical indicator by sampling latent features after variance reduction is proposed to assess long-term voltage stability. Our approach is tested in various simulated power systems with different load increment models. Other cases show the accuracy and speed of our approach for estimating voltage collapse point. These testing cases successfully demonstrate the accuracy and effectiveness of our approach.

eess.SP↗

arXiv2020

Early Anomaly Detection and Location in Distribution Network: A Data-Driven Approach

The measurement data collected from the supervisory control and data acquisition (SCADA) system installed in distribution network can reflect the operational state of the network effectively. In this paper, a random matrix theory (RMT) based approach is developed for early anomaly detection and localization by using the data. For every feeder in the distribution network, a corresponding data matrix is formed. Based on the Marchenko-Pastur Law for the empirical spectral analysis of covariance `signal+noise' matrix, the linear eigenvalue statistics are introduced to indicate the anomaly, and the outliers and their corresponding eigenvectors are analyzed for locating the anomaly. As for the low observability feeders in the distribution network, an increasing data dimension algorithm is designed for the formulated low-dimensional matrices being more accurately analyzed. The developed approach can detect and localize the anomaly at an early stage, and it is robust to random disturbance and measurement error. Cases on Matpower simulation data and real SCADA data corroborate the feasibility of the approach.

stat.AP↗