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Yang Li

Publications and source records attributed to Yang Li.

At least 1,243 records · Page 69Linked to original sources
arXiv2021

Effect of acetylene links on electronic and optical properties of semiconducting graphynes

The family of graphynes, novel two-dimensional semiconductors with various and fascinating chemical and physical properties, has attracted great interest from both science and industry. Currently, the focus of graphynes is on graphdiyne, or graphyne-2. In this work, we systematically study the effect of acetylene, i.e., carbon-carbon triple bond, links on the electronic and optical properties of a series of graphynes (graphyne-n, where n = 1-5, the number of acetylene bonds) using the ab initio calculations. We find an even-odd pattern, i.e., n = 1, 3, 5 and n = 2, 4 having different features, which has not be discovered in studying graphyne or graphdyine only. It is found that as the number of acetylene bonds increases, the electron effective mass increases continuously in the low energy range because of the flatter conduction band induced by the longer acetylene links. Meanwhile, longer acetylene links result in larger redshift of the imaginary part of the dielectric function, loss function, and extinction coefficient. In this work, we propose an effective method to tune and manipulate both the electronic and optical properties of graphynes for the applications in optoelectronic devices and photo-chemical catalysis.

cond-mat.mtrl-sci↗
arXiv2021

Universal characteristics of one-dimensional non-Hermitian superconductors

We establish a non-Bloch band theory for one-dimensional(1D) non-Hermitian topological superconductors. The universal physical properties of non-Hermitian topological superconductors are revealed based on the theory. According to the particle-hole symmetry, there exist reciprocal particle and hole loops of generalized Brillouin zone (GBZ). The critical point of quantum phase transition, where the energy gap closes, appears when the particle and hole loops intersect and their values of GBZ satisfy |β| = 1. If the non-Hermitian system has skin modes, these modes should be Z2 style, i.e., the corresponding eigenstates of particle and hole localize at opposite ends of an open chain, respectively. The non-Bloch band theory is applied to two examples, non-Hermitian p- and s-wave topological superconductors. Topological phase transitions occur at β_{c}= \pm 1 in the two systems. In terms of Majorana Pfaffian, a Z2 non-Bloch topological invariant is defined to establish the non-Hermitian bulk-boundary correspondence in non-Hermitian superconductors.

cond-mat.supr-con↗
arXiv2021

Qualification tests of 997 8-inch photomultiplier tubes for the water Cherenkov detector array of the LHAASO experiment

The Large High-Altitude Air Shower Observatory (LHAASO) is being built at Haizi Mountain, Sichuan province of China at an altitude of 4410 meters. One of its main goals is to survey the northern sky for very-high-energy gamma ray sources via its ground-based water Cherenkov detector array (WCDA). 900 8-inch photomultiplier tubes (PMTs) CR365-02-1 from Beijing Hamamatsu Photon Techniques INC. (BHP) are installed in the WCDA, collecting Cherenkov photons produced by air shower particles crossing water. The design of the PMT base with a high dynamic range for CR365-02-1, the PMT batch test system, and the test results of 997 PMTs are presented in this paper.

physics.ins-det↗
arXiv2021

SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature Extraction

Robotic automation in surgery requires precise tracking of surgical tools and mapping of deformable tissue. Previous works on surgical perception frameworks require significant effort in developing features for surgical tool and tissue tracking. In this work, we overcome the challenge by exploiting deep learning methods for surgical perception. We integrated deep neural networks, capable of efficient feature extraction, into the tissue reconstruction and instrument pose estimation processes. By leveraging transfer learning, the deep learning based approach requires minimal training data and reduced feature engineering efforts to fully perceive a surgical scene. The framework was tested on three publicly available datasets, which use the da Vinci Surgical System, for comprehensive analysis. Experimental results show that our framework achieves state-of-the-art tracking performance in a surgical environment by utilizing deep learning for feature extraction.

cs.RO↗
arXiv2021

OTCE: A Transferability Metric for Cross-Domain Cross-Task Representations

Transfer learning across heterogeneous data distributions (a.k.a. domains) and distinct tasks is a more general and challenging problem than conventional transfer learning, where either domains or tasks are assumed to be the same. While neural network based feature transfer is widely used in transfer learning applications, finding the optimal transfer strategy still requires time-consuming experiments and domain knowledge. We propose a transferability metric called Optimal Transport based Conditional Entropy (OTCE), to analytically predict the transfer performance for supervised classification tasks in such cross-domain and cross-task feature transfer settings. Our OTCE score characterizes transferability as a combination of domain difference and task difference, and explicitly evaluates them from data in a unified framework. Specifically, we use optimal transport to estimate domain difference and the optimal coupling between source and target distributions, which is then used to derive the conditional entropy of the target task (task difference). Experiments on the largest cross-domain dataset DomainNet and Office31 demonstrate that OTCE shows an average of 21% gain in the correlation with the ground truth transfer accuracy compared to state-of-the-art methods. We also investigate two applications of the OTCE score including source model selection and multi-source feature fusion.

cs.LG↗
arXiv2021

Dynamic Feature Acquisition with Arbitrary Conditional Flows

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we propose models that dynamically acquire new features to further improve the prediction assessment. To trade off the improvement with the cost of acquisition, we leverage an information theoretic metric, conditional mutual information, to select the most informative feature to acquire. We leverage a generative model, arbitrary conditional flow (ACFlow), to learn the arbitrary conditional distributions required for estimating the information metric. We also learn a Bayesian network to accelerate the acquisition process. Our model demonstrates superior performance over baselines evaluated in multiple settings.

cs.LG↗
arXiv2021

Optimal Scheduling of Integrated Demand Response-Enabled Integrated Energy Systems with Uncertain Renewable Generations: A Stackelberg Game Approach

In order to balance the interests of integrated energy operator (IEO) and users, a novel Stackelberg game-based optimization framework is proposed for the optimal scheduling of integrated demand response (IDR)-enabled integrated energy systems with uncertain renewable generations, where the IEO acts as the leader who pursues the maximization of his profits by setting energy prices, while the users are the follower who adjusts energy consumption plans to minimize their energy costs. Taking into account the inherent uncertainty of renewable generations, the probabilistic spinning reserve is written in the form of a chance constraint; in addition, a district heating network model is built considering the characteristics of time delay and thermal attenuation by fully exploiting its potential, and the flexible thermal comfort requirements of users in IDR are considered by introducing a predicted mean vote (PMV) index. To solve the raised model, sequence operation theory is introduced to convert the chance constraint into its deterministic equivalent form, and thereby, the leader-follower Stackelberg game is tackled into a mixed-integer quadratic programming formulation through Karush-Kuhn-Tucker optimality conditions and is finally solved by the CPLEX optimizer. The results of two case studies demonstrate that the proposed Stackelberg game-based approach manages to achieve the Stackelberg equilibrium between IEO and users by the coordination of renewable generations and IDR. Furthermore, the study on a real integrated energy system in China verifies the applicability of the proposed approach for real-world applications.

eess.SP↗
arXiv2021

A new tool for two-dimensional field-reversed configuration equilibrium study

A new tool (GSEQ-FRC) for solving two-dimensional (2D) equilibrium of field-reversed configuration (FRC) based on fixed boundary and free boundary conditions with external coils included is developed. Benefiting from the two-parameter modified rigid rotor (MRR) radial equilibrium model and the numerical approaches presented by [Ma et al, Nucl. Fusion, 61, 036046, 2021], GSEQ-FRC are used to study the equilibrium properties of FRC quantitatively and will be used for fast FRC equilibrium reconstruction. In GSEQ-FRC, the FRC equilibrium can be conveniently determined by two parameters, i.e., the ratio between thermal pressure and magnetic pressure at the seperatrix $β_s$, and the normalized scrape of layer (SOL) width $δ_s$. Examples with fixed and free boundary conditions are given to demonstrate the capability of GSEQ-FRC in the equilibrium calculations. This new tool is used to quantitatively study the factors affecting the shape of the FRC separatrix, revealing how the FRC changes from racetrack-like to ellipse-like.

physics.plasm-ph↗
arXiv2021

Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber-Physical Power System (CPPS). Two-step principal component analysis (PCA) is used for classifying the system's operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories' samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

eess.SP↗
arXiv2021

Robust Kalman filter-based dynamic state estimation of natural gas pipeline networks

To obtain the accurate transient states of the big scale natural gas pipeline networks under the bad data and non-zero mean noises conditions, a robust Kalman filter-based dynamic state estimation method is proposed using the linearized gas pipeline transient flow equations in this paper. Firstly, the dynamic state estimation model is built. Since the gas pipeline transient flow equations are less than the states, the boundary conditions are used as supplementary constraints to predict the transient states. To increase the measurement redundancy, the zero mass flow rate constraints at the sink nodes are taken as virtual measurements. Secondly, to ensure the stability under bad data condition, the robust Kalman filter algorithm is proposed by introducing a time-varying scalar matrix to regulate the measurement error variances correctly according to the innovation vector at every time step. At last, the proposed method is applied to a 30-node gas pipeline networks in several kinds of measurement conditions. The simulation shows that the proposed robust dynamic state estimation can decrease the effects of bad data and achieve better estimating results.

eess.SP↗
arXiv2021

Relaxed phase-matching constraints in zero-index waveguides

The nonlinear optical response of materials is the foundation upon which applications such as frequency conversion, all-optical signal processing, molecular spectroscopy, and nonlinear microscopy are built. However, the utility of all such parametric nonlinear optical processes is hampered by phase-matching requirements. Quasi-phase-matching, birefringent phase matching, and higher-order-mode phase matching have all been developed to address this constraint, but the methods demonstrated to date suffer from the inconvenience of only being phase-matched for a single, specific arrangement of beams, typically co-propagating, resulting in cumbersome experimental configurations and large footprints for integrated devices. Here, we experimentally demonstrate that these phase-matching requirements may be satisfied in a parametric nonlinear optical process for multiple, if not all, configurations of input and output beams when using low-index media. Our measurement constitutes the first experimental observation of direction-independent phase matching for a medium sufficiently long for phase matching concerns to be relevant. We demonstrate four-wave mixing from spectrally distinct co- and counter-propagating pump and probe beams, the backward-generation of a nonlinear signal, and excitation by an out-of-plane probe beam. These results explicitly show that the unique properties of low-index media relax traditional phase-matching constraints, which can be exploited to facilitate nonlinear interactions and miniaturize nonlinear devices, thus adding to the established exceptional properties of low-index materials.

physics.optics↗
arXiv2021

TapNet: The Design, Training, Implementation, and Applications of a Multi-Task Learning CNN for Off-Screen Mobile Input

To make off-screen interaction without specialized hardware practical, we investigate using deep learning methods to process the common built-in IMU sensor (accelerometers and gyroscopes) on mobile phones into a useful set of one-handed interaction events. We present the design, training, implementation and applications of TapNet, a multi-task network that detects tapping on the smartphone. With phone form factor as auxiliary information, TapNet can jointly learn from data across devices and simultaneously recognize multiple tap properties, including tap direction and tap location. We developed two datasets consisting of over 135K training samples, 38K testing samples, and 32 participants in total. Experimental evaluation demonstrated the effectiveness of the TapNet design and its significant improvement over the state of the art. Along with the datasets, (https://sites.google.com/site/michaelxlhuang/datasets/tapnet-dataset), and extensive experiments, TapNet establishes a new technical foundation for off-screen mobile input.

cs.HC↗
arXiv2021

Spacewalker: Rapid UI Design Exploration Using Lightweight Markup Enhancement and Crowd Genetic Programming

User interface design is a complex task that involves designers examining a wide range of options. We present Spacewalker, a tool that allows designers to rapidly search a large design space for an optimal web UI with integrated support. Designers first annotate each attribute they want to explore in a typical HTML page, using a simple markup extension we designed. Spacewalker then parses the annotated HTML specification, and intelligently generates and distributes various configurations of the web UI to crowd workers for evaluation. We enhanced a genetic algorithm to accommodate crowd worker responses from pairwise comparison of UI designs, which is crucial for obtaining reliable feedback. Based on our experiments, Spacewalker allows designers to effectively search a large design space of a UI, using the language they are familiar with, and improve their design rapidly at a minimal cost.

cs.HC↗
arXiv2021

Active Feature Acquisition with Generative Surrogate Models

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches either require all features to be acquired beforehand or regard part of them as missing data that cannot be acquired. In this work, we consider models that perform active feature acquisition (AFA) and query the environment for unobserved features to improve the prediction assessments at evaluation time. Our work reformulates the Markov decision process (MDP) that underlies the AFA problem as a generative modeling task and optimizes a policy via a novel model-based approach. We propose learning a generative surrogate model (GSM) that captures the dependencies among input features to assess potential information gain from acquisitions. The GSM is leveraged to provide intermediate rewards and auxiliary information to aid the agent navigate a complicated high-dimensional action space and sparse rewards. Furthermore, we extend AFA in a task we coin active instance recognition (AIR) for the unsupervised case where the target variables are the unobserved features themselves and the goal is to collect information for a particular instance in a cost-efficient way. Empirical results demonstrate that our approach achieves considerably better performance than previous state of the art methods on both supervised and unsupervised tasks.

cs.LG↗
arXiv2021

Partially Observed Exchangeable Modeling

Modeling dependencies among features is fundamental for many machine learning tasks. Although there are often multiple related instances that may be leveraged to inform conditional dependencies, typical approaches only model conditional dependencies over individual instances. In this work, we propose a novel framework, partially observed exchangeable modeling (POEx) that takes in a set of related partially observed instances and infers the conditional distribution for the unobserved dimensions over multiple elements. Our approach jointly models the intra-instance (among features in a point) and inter-instance (among multiple points in a set) dependencies in data. POEx is a general framework that encompasses many existing tasks such as point cloud expansion and few-shot generation, as well as new tasks like few-shot imputation. Despite its generality, extensive empirical evaluations show that our model achieves state-of-the-art performance across a range of applications.

cs.LG↗
arXiv2021

Bayesian Bi-clustering Methods with Applications in Computational Biology

Bi-clustering is a useful approach in analyzing biological data when observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions, and propose three Bayesian bi-clustering models on categorical data, which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: from situations where data are cluster-distinguishable only among a small subset of features but masked by a large amount of noise, to situations where different groups of data are identified by different sets of features or data exhibit hierarchical structures. Through simulation studies, we show that our methods outperform existing (bi-)clustering methods in both identifying clusters and recovering feature distributional patterns across bi-clusters. We apply our methods to two genetic datasets, though the area of application of our methods is even broader.

stat.AP↗
arXiv2021

Deep Learning for Short-Term Voltage Stability Assessment of Power Systems

To fully learn the latent temporal dependencies from post-disturbance system dynamic trajectories, deep learning is utilized for short-term voltage stability (STVS) assessment of power systems in this paper. First of all, a semi-supervised cluster algorithm is performed to obtain class labels of STVS instances due to the unavailability of reliable quantitative criteria. Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics. Finally, the trained assessment model is employed to determine the systems stability status in real time. The test results on the IEEE 39-bus system suggest that the proposed approach manages to assess the stability status of the system accurately and timely. Furthermore, the superiority of the proposed method over traditional shallow learning-based assessment methods has also been proved.

eess.SP↗
arXiv2021

Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events

The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for these models to accurately describe spatiotemporal deduction processes, such as microgrid CPS attack identification, risk propagation, safety assessment, defense control, and cascading failure. To address this problem, a modeling method for the coupling relations of microgrid CPS driven by hybrid spatiotemporal events is proposed in the present work. First, according to the topological correlation and coupling logic of the microgrid CPS, the cyclical conversion mechanism of information flow and energy flow is analyzed, and a microgrid CPS architecture with multi-agents as the core is constructed. Next, the spatiotemporal evolution characteristic of the CPS is described by hybrid automata, and the task coordination mechanism of the multi-agent CPS terminal is designed. On this basis, a discrete-continuous correlation and terminal structure characteristic representation method of the CPS based on heterogeneous multi-groups are then proposed. Finally, four spatiotemporal events, namely state perception, network communication, intelligent decision-making, and action control, are defined. Considering the constraints of the temporal conversion of information flow and energy flow, a microgrid CPS coupling model is established, the effectiveness of which is verified by simulating false data injection attack (FDIA) scenarios.

eess.SP↗