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Dan Li

Publications and source records attributed to Dan Li.

At least 217 records · Page 12Linked to original sources
arXiv2019

A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection

It is important to identify the change point of a system's health status, which usually signifies an incipient fault under development. The One-Class Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly detection and hence could be used for identifying change points; however, it is sometimes difficult to obtain a good OC-SVM model that can be used on sensor measurement time series to identify the change points in system health status. In this paper, we propose a novel approach for calibrating OC-SVM models. The approach uses a heuristic search method to find a good set of input data and hyperparameters that yield a well-performing model. Our results on the C-MAPSS dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in detecting change point in time series with fewer training data, compared to state-of-the-art deep learning approaches. In our case study, the OC-SVM calibrated by the proposed model is shown to be useful especially in scenarios with limited amount of training data.

cs.LG↗
arXiv2019

Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is known that the performance of supervised learning approaches highly relies on the availability and quality of labeled training data. In Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient fault data has posed a major challenge to applying these supervised learning techniques to commercial buildings. To overcome this challenge, this paper proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised learning pipeline, so that the resulting neural network is able to detect and diagnose unseen incipient fault examples. We also examine the proposed MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in indicating the most likely incipient fault types.

cs.LG↗
arXiv2019

The relative efficiency of time-to-progression and continuous measures of cognition in pre-symptomatic Alzheimer's

Pre-symptomatic (or Preclinical) Alzheimer's Disease is defined by biomarker evidence of fibrillar amyloid beta pathology in the absence of clinical symptoms. Clinical trials in this early phase of disease are challenging due to the slow rate of disease progression as measured by periodic cognitive performance tests or by transition to a diagnosis of Mild Cognitive Impairment. In a multisite study, experts provide diagnoses by central chart review without the benefit of in-person assessment. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently compared to models of time-to-progression to an endpoint such as change in diagnosis. Multivariate continuous data are simulated from a Bayesian joint mixed effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data. We find that power is approximately doubled with models of repeated continuous outcomes compared to the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias which inflates treatment effects, yet 5% Type I error is maintained.

stat.AP↗
arXiv2019

Efficient quantum circuit for singular value thresholding

Singular value thresholding (SVT) operation is a fundamental core module in many mathematical models in computer vision and machine learning, particularly for many nuclear norm minimizing-based problems. We presented a quantum SVT (QSVT) algorithm which was used as a subroutine to address an image classification problem. This algorithm runs in $O\left[\log\left(pq\right)\right]$, an exponential speed improvement over the classical algorithm which runs in $O\left[poly\left(pq\right)\right]$. In this study, we investigate this algorithm and design a scalable quantum circuit for QSVT. In the circuit design, we introduce an adjustable parameter $α$ to ensure the high probability of obtaining the final result and the high fidelity of actual and ideal final states. We also show that the value of $α$ can be computed ahead of implementing the quantum circuit when the inputs of the QSVT algorithm, i.e. matrix ${\bf{A}}$ and constant $τ$, are given. In addition, we propose a small-scale quantum circuit for QSVT. We numerically simulate and demonstrate the performance of this circuit, verifying its capability to solve the intended SVT. The quantum circuit for QSVT implies a tempting possibility for experimental realization on a quantum computer.

quant-ph↗
arXiv2019

Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series

Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are unable to deal with the increasingly dynamic and complex nature of the CPSs. On the other hand, the networked sensors and actuators generate large amounts of data streams that can be continuously monitored for intrusion events. Unsupervised machine learning techniques can be used to model the system behaviour and classify deviant behaviours as possible attacks. In this work, we proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method for such complex networked CPSs. We used LSTM-RNN in our GAN to capture the distribution of the multivariate time series of the sensors and actuators under normal working conditions of a CPS. Instead of treating each sensor's and actuator's time series independently, we model the time series of multiple sensors and actuators in the CPS concurrently to take into account of potential latent interactions between them. To exploit both the generator and the discriminator of our GAN, we deployed the GAN-trained discriminator together with the residuals between generator-reconstructed data and the actual samples to detect possible anomalies in the complex CPS. We used our GAN-AD to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system. Experimental results showed that the proposed strategy is effective in identifying anomalies caused by various attacks with high detection rate and low false positive rate as compared to existing methods.

cs.LG↗
arXiv2019

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems. The rich sensor data can be continuously monitored for intrusion events through anomaly detection. However, conventional threshold-based anomaly detection methods are inadequate due to the dynamic complexities of these systems, while supervised machine learning methods are unable to exploit the large amounts of data due to the lack of labeled data. On the other hand, current unsupervised machine learning approaches have not fully exploited the spatial-temporal correlation and other dependencies amongst the multiple variables (sensors/actuators) in the system for detecting anomalies. In this work, we propose an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs). Instead of treating each data stream independently, our proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables. We also fully exploit both the generator and discriminator produced by the GAN, using a novel anomaly score called DR-score to detect anomalies by discrimination and reconstruction. We have tested our proposed MAD-GAN using two recent datasets collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water Distribution (WADI) datasets. Our experimental results showed that the proposed MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.

cs.LG↗
arXiv2018

Topological insulators and K-theory

We analyze the topological $\mathbb{Z}_2$ invariant, which characterizes time reversal invariant topological insulators, in the framework of index theory and K-theory. The topological $\mathbb{Z}_2$ invariant counts the parity of generalized Majorana zero modes, which can be interpreted as an analytical index. As we show, it fits perfectly into a mod 2 index theorem, and the topological index provides an efficient way to compute the topological $\mathbb{Z}_2$ invariant. Finally, we give a new version of the bulk-boundary correspondence which yields an alternative explanation of the index theorem and the topological $\mathbb{Z}_2$ invariant. Here the boundary is not the geometric boundary of a probe, but an effective boundary in the momentum space.

math-ph↗
arXiv2018

Gemini, SOFIA, and ATCA Reveal Very Young, Massive Protostars in the Collapsing Molecular Cloud BYF 73

We present multi-wavelength data on the globally infalling molecular cloud/protostellar cluster BYF 73. These include new far-IR spectral line and continuum data from SOFIA's Far Infrared Field-Imaging Line Spectrometer (FIFI-LS), mid-infrared (MIR) observations with the Thermal-Region Camera Spectrograph (T-ReCS) on Gemini-South, and 3 mm continuum data from the Australia Telescope Compact Array (ATCA), plus archival data from Spitzer/IRAC, and Herschel/PACS and SPIRE. The FIFI-LS spectroscopy in [OI]$\lambda63 μ$m, [OIII]$\lambda88 μ$m, [OI]$\lambda145 μ$m, and [CII]$\lambda158 μ$m highlights different gas environments in and between the dense molecular cloud and HII region. The photo-dissociation region (PDR) between the cloud and HII region is best traced by [OI]$\lambda145 μ$m and may have density $>$10$^{10}$ m$^{-3}$, but the observed $\lambda145μ$m/$\lambda63μ$m and $\lambda63μ$m/$\lambda158μ$m line ratios in the densest gas are well outside model values. The HII region is well-traced by [CII], with the $\lambda158μ$m/$\lambda145μ$m line ratio indicating a density of 10$^{8.5}$ m$^{-3}$ and a relatively weak ionizing radiation field, 1.5 $\lesssim$ log$(G/G_0)\lesssim$ 2. The T-ReCS data reveal eight protostellar objects in the cloud, of which six appear deeply embedded ($A_V$ $>$ 30$^m$ or more) near the cloud's center. MIR 2 has the most massive core at $\sim$240 M$_{\odot}$, more massive than all the others combined by up to tenfold, with no obvious gas outflow, negligible cooling line emission, and $\sim3-8$% of its 4.7$\times$10$^3$ L$_{\odot}$ luminosity originating from the release of gravitational potential energy. MIR 2's dynamical age may be as little as 7000 yr. This fact, and the cloud's total embedded stellar mass being far less than its gas mass, confirm BYF 73's relatively early stage of evolution.

astro-ph.GA↗
arXiv2018

Data-driven Variable Speed Limit Design for Highways via Distributionally Robust Optimization

This paper introduces an optimization problem (P) and a solution strategy to design variable-speed-limit controls for a highway that is subject to traffic congestion and uncertain vehicle arrival and departure. By employing a finite data-set of samples of the uncertain variables, we aim to find a data-driven solution that has a guaranteed out-of-sample performance. In principle, such formulation leads to an intractable problem (P) as the distribution of the uncertainty variable is unknown. By adopting a distributionally robust optimization approach, this work presents a tractable reformulation of (P) and an efficient algorithm that provides a suboptimal solution that retains the out-of-sample performance guarantee. A simulation illustrates the effectiveness of this method.

math.OC↗
arXiv2018

Superconvergence of Numerical Gradient for Weak Galerkin Finite Element Methods on Nonuniform Cartesian Partitions in Three Dimensions

A superconvergence error estimate for the gradient approximation of the second order elliptic problem in three dimensions is analyzed by using weak Galerkin finite element scheme on the uniform and non-uniform cubic partitions. Due to the loss of the symmetric property from two dimensions to three dimensions, this superconvergence result in three dimensions is not a trivial extension of the recent superconvergence result in two dimensions \cite{sup_LWW2018} from rectangular partitions to cubic partitions. The error estimate for the numerical gradient in the $L^{2}$-norm arrives at a superconvergence order of ${\cal O}(h^r) (1.5 \leq r\leq 2)$ when the lowest order weak Galerkin finite elements consisting of piecewise linear polynomials in the interior of the elements and piecewise constants on the faces of the elements are employed. A series of numerical experiments are illustrated to confirm the established superconvergence theory in three dimensions.

math.NA↗
arXiv2018

Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers

The goal of a technology-assisted review is to achieve high recall with low human effort. Continuous active learning algorithms have demonstrated good performance in locating the majority of relevant documents in a collection, however their performance is reaching a plateau when 80\%-90\% of them has been found. Finding the last few relevant documents typically requires exhaustively reviewing the collection. In this paper, we propose a novel method to identify these last few, but significant, documents efficiently. Our method makes the hypothesis that entities carry vital information in documents, and that reviewers can answer questions about the presence or absence of an entity in the missing relevance documents. Based on this we devise a sequential Bayesian search method that selects the optimal sequence of questions to ask. The experimental results show that our proposed method can greatly improve performance requiring less reviewing effort.

cs.IR↗
arXiv2018

Discrete-time Quantum Walk on the Cayley Graph of the Dihedral Group

The finite dihedral group generated by one rotation and one flip is the simplest case of the non-abelian group. Cayley graphs are diagrammatic counterparts of groups. In this paper, much attention is given to the Cayley graph of the dihedral group. Considering the characteristics of the elements in the dihedral group, we conduct the model of discrete-time quantum walk on the Cayley graph of the dihedral group by special coding mode. This construction makes Fourier transformation can be used to carry out spectral analysis of the dihedral quantum walk, i.e. the non-abelian case. Furthermore, the relation between quantum walk without memory on the Cayley graph of the dihedral group and quantum walk with memory on a cycle is discussed, so that we can explore the potential of quantum walks without and with memory. Here, the numerical simulation is carried out to verify the theoretical analysis results and other properties of the proposed model are further studied.

quant-ph↗
arXiv2018

Model updating using sum of squares (SOS) optimization to minimize modal dynamic residuals

This research studies finite element (FE) model updating through sum of squares (SOS) optimization to minimize modal dynamic residuals. In the past few decades, many FE model updating algorithms have been studied to improve the similitude between a numerical model and the as-built structure. FE model updating usually requires solving nonconvex optimization problems, while most off-the-shelf optimization solvers can only find local optima. To improve the model updating performance, this paper proposes the SOS global optimization method for minimizing modal dynamic residuals of the generalized eigenvalue equations in structural dynamics. The proposed method is validated through both numerical simulation and experimental study of a four-story shear frame structure.

math.OC↗
arXiv2018

Performance Analysis of Indoor THz Communications with One-Bit Precoding

In this paper, the performance of indoor Terahertz (THz) communication systems with one-bit digital-to-analog converters (DACs) is investigated. Array-of-subarrays architecture is assumed for the antennas at the access points, where each RF chain uniquely activates a disjoint subset of antennas, each of which is connected to an exclusive phase shifter. Hybrid precoding, including maximum ratio transmission (MRT) and zero-forcing (ZF) precoding, is considered. The best beamsteering direction for the phase shifter in the large subarray antenna regime is first proved to be the direction of the line-of-sight (LOS) path. Subsequently, the closed-form expression of the lower-bound of the achievable rate in the large subarray antenna regime is derived, which is the same for both MRT and ZF and is independent of the transmit power. Numerical results validating the analysis are provided as well.

cs.IT↗
arXiv2018

EAST Real-Time VOD System Based on MDSplus

As with EAST (Experimental Advanced Superconducting Tokamak) experimental data analyzed by more and more collaborators, the experimental videos which directly reflect the real status of vacuum attract more and more researchers' attention. The real time VOD (Video On Demand) system based on MDSplus allows users reading the video frames in real time as same as the signal data which is also stored in the MDSplus database. User can display the plasma discharge videos and analyze videos frame by frame through jScope or our VOD web station. The system mainly includes the frames storing and frames displaying. The frames storing application accepts shot information by using socket TCP communication firstly, then reads video frames through disk mapping, finally stores them into MDSplus. The displaying process is implemented through B/S (Browser/Server) framework, it uses PHP and JavaScript to realize VOD function and read frames information from MDSplus. The system offers a unit way to access and backup experimental data and video during the EAST experiment, which is of great benefit to EAST experimenter than the formal VOD system in VOD function and real time performance.

cs.MM↗
arXiv2018

Superconvergence of the Gradient Approximation for Weak Galerkin Finite Element Methods on Nonuniform Rectangular Partitions

This article presents a superconvergence for the gradient approximation of the second order elliptic equation discretized by the weak Galerkin finite element methods on nonuniform rectangular partitions. The result shows a convergence of ${\cal O}(h^r)$, $1.5\leq r \leq 2$, for the numerical gradient obtained from the lowest order weak Galerkin element consisting of piecewise linear and constant functions. For this numerical scheme, the optimal order of error estimate is ${\cal O}(h)$ for the gradient approximation. The superconvergence reveals a superior performance of the weak Galerkin finite element methods. Some computational results are included to numerically validate the superconvergence theory.

math.NA↗
arXiv2018

The $A_α$-spectral radius of graphs with given degree sequence

Let $G$ be a graph with adjacency matrix $A(G)$, and let $D(G)$ be the diagonal matrix of the degrees of $G$. For any real $α\in[0,1]$, write $A_α(G)$ for the matrix $$A_α(G)=αD(G)+(1-α)A(G).$$ This paper presents some extremal results about the spectral radius $ρ(A_α(G))$ of $A_α(G)$ that generalize previous results about $ρ(A_0(G))$ and $ρ(A_{\frac{1}{2}}(G))$. In this paper, we give some results on graph perturbation for $A_α$-matrix with $α\in [0,1)$. As applications, we characterize all extremal trees with the maximum $A_α$-spectral radius in the set of all trees with prescribed degree sequence firstly. Furthermore, we characterize the unicyclic graphs that have the largest $A_α$-spectral radius for a given unicycilc degree sequence.

math.CO↗