arXiv subjects

Tao Li

Publications and source records attributed to Tao Li.

At least 469 records · Page 26Linked to original sources

arXiv2019

A Pvalue-guided Anomaly Detection Approach Combining Multiple Heterogeneous Log Parser Algorithms on IIoT Systems

Industrial Internet of Things (IIoT) is becoming an attack target of advanced persistent threat (APT). Currently, IIoT logs have not been effectively used for anomaly detection. In this paper, we use blockchain to prevent logs from being tampered with and propose a pvalue-guided anomaly detection approach. This approach uses statistical pvalues to combine multiple heterogeneous log parser algorithms. The weighted edit distance is selected as a score function to calculate the nonconformity score between a log and a predefined event. The pvalue is calculated based on the non-conformity scores which indicate how well a log matches an event. This approach is tested on a large number of real-world HDFS logs and IIoT logs. The experiment results show that abnormal events could be effectively recognized by our pvalue-guided approach.

cs.CR↗

arXiv2019

Distributed Economic Dispatch for Energy Internet Based on Multi-Agent Consensus Control

We consider the economic dispatch (ED) for an Energy Internet composed of energy routers (ERs), interconnected microgrids and main grid. The microgrid consists of several bus nodes associated with distributed generators (DGs) and intelligent control units (ICUs). We propose a distributed ED algorithm for the grid-connected microgrid, where each ICU iterates the estimated electricity price of the distribution system and the estimation for the average power mismatch of the whole microgrid by leader-following and average consensus algorithms, respectively. The ER iterates the incremental power exchanged with the distribution system. By constructing an auxiliary consensus system, we prove that if the communication topology of the Energy Internet contains a spanning tree with the ER as the root and there is a path from each ICU to the ER, then the estimated electricity price of the distribution system converges to its real value, the power supply and demand achieves balance and the ED achieves optimal asymptotically. Furthermore, we propose an autonomous distributed ED algorithm covering both grid-connected and isolated modes of the microgrid by feeding back the estimated average power mismatch for updating the incremental costs with penalty factor. It is proved that if the communication topology of the microgrid is connected and there exists an ICU bi-directionally neighboring the ER, then the microgrid can switches between the two modes reliably. The simulation results demonstrate the effectiveness of the proposed algorithms.

eess.SY↗

arXiv2019

Theory of the giant thermal Hall effect in the high temperature superconductors

Recent transport measurement finds giant negative thermal Hall signal in the pseudo-gap phase of the high temperature superconductors\cite{Taillefer}. Such a signal is found to increase in magnitude with decreasing doping and to reach its maximum in the half-filled antiferromagnetic ordered parent compounds. It is still elusive what is the implication of such a phenomena for the mechanism of the pseudo-gap phase. The observation of such a signal may also challenge our well established understanding of the parent compounds as perfect Heisenberg antiferromagnets. Here we show that such a giant thermal Hall effect can be naturally understood as the orbital magnetic response of a quantum Heisenberg antiferromagnet with sizable multi-spin exchange coupling. We also argue that the observation of the giant thermal Hall effect in the pseudo-gap phase imply that the origin of the pseudo-gap in the high temperature superconductors is closely related to the antiferromagnetic correlation between local spins.

cond-mat.str-el↗

arXiv2019

Beauty Learning and Counterfactual Inference

This work showcases a new approach for causal discovery by leveraging user experiments and recent advances in photo-realistic image editing, demonstrating a potential of identifying causal factors and understanding complex systems counterfactually. We introduce the beauty learning problem as an example, which has been discussed metaphysically for centuries and been proved exists, is quantifiable, and can be learned by deep models in our recent paper, where we utilize a natural image generator coupled with user studies to infer causal effects from facial semantics to beauty outcomes, the results of which also align with existing empirical studies. We expect the proposed framework for a broader application in causal inference.

cs.CV↗

arXiv2019

AnonymousNet: Natural Face De-Identification with Measurable Privacy

With billions of personal images being generated from social media and cameras of all sorts on a daily basis, security and privacy are unprecedentedly challenged. Although extensive attempts have been made, existing face image de-identification techniques are either insufficient in photo-reality or incapable of balancing privacy and usability qualitatively and quantitatively, i.e., they fail to answer counterfactual questions such as "is it private now?", "how private is it?", and "can it be more private?" In this paper, we propose a novel framework called AnonymousNet, with an effort to address these issues systematically, balance usability, and enhance privacy in a natural and measurable manner. The framework encompasses four stages: facial attribute estimation, privacy-metric-oriented face obfuscation, directed natural image synthesis, and adversarial perturbation. Not only do we achieve the state-of-the-arts in terms of image quality and attribute prediction accuracy, we are also the first to show that facial privacy is measurable, can be factorized, and accordingly be manipulated in a photo-realistic fashion to fulfill different requirements and application scenarios. Experiments further demonstrate the effectiveness of the proposed framework.

cs.CV↗

arXiv2019

Understanding Beauty via Deep Facial Features

The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability. In this paper, we present a novel study on mining beauty semantics of facial attributes based on big data, with an attempt to objectively construct descriptions of beauty in a quantitative manner. We first deploy a deep convolutional neural network (CNN) to extract facial attributes, and then investigate correlations between these features and attractiveness on two large-scale datasets labelled with beauty scores. Not only do we discover the secrets of beauty verified by statistical significance tests, our findings also align perfectly with existing psychological studies that, e.g., small nose, high cheekbones, and femininity contribute to attractiveness. We further leverage these high-level representations to original images by a generative adversarial network (GAN). Beauty enhancements after synthesis are visually compelling and statistically convincing verified by a user survey of 10,000 data points.

cs.CV↗

arXiv2019

Continuous hydrothermal flow synthesis of Gd-doped CeO2 (GDC) nanoparticles for inkjet printing of SOFC electrolytes

GdxCe1-xO2-d (GDC) nanoparticles were synthesized, using continuous hydrothermal flow synthesis. By varying the synthesis conditions, particle size and morphology could be tailored. Here, particle sizes between 6 and 40 nm with polyhedral or octahedral shape could be obtained. Gd0.2Ce0.8O2-d nanoparticles were further processed into inks for inkjet printing. Despite the small particle size/large surface area, inks with excellent printing behavior were formulated. For proof-of-concept, thin GDC layers were printed on a) green NiO-GDC substrates, and on b) presintered NiO-YSZ substrates. While no dense layers could be obtained on the green NiO-GDC substrates, GDC nanoparticles printed on NiO-YSZ substrates formed a dense continuous layer after firing.

cond-mat.mtrl-sci↗

arXiv2019

BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving

In autonomous driving community, numerous benchmarks have been established to assist the tasks of 3D/2D object detection, stereo vision, semantic/instance segmentation. However, the more meaningful dynamic evolution of the surrounding objects of ego-vehicle is rarely exploited, and lacks a large-scale dataset platform. To address this, we introduce BLVD, a large-scale 5D semantics benchmark which does not concentrate on the static detection or semantic/instance segmentation tasks tackled adequately before. Instead, BLVD aims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking, 5D (4D+interactive) interactive event recognition and intention prediction. This benchmark will boost the deeper understanding of traffic scenes than ever before. We totally yield 249,129 3D annotations, 4,902 independent individuals for tracking with the length of overall 214,922 points, 6,004 valid fragments for 5D interactive event recognition, and 4,900 individuals for 5D intention prediction. These tasks are contained in four kinds of scenarios depending on the object density (low and high) and light conditions (daytime and nighttime). The benchmark can be downloaded from our project site https://github.com/VCCIV/BLVD/.

cs.CV↗

arXiv2019

CubemapSLAM: A Piecewise-Pinhole Monocular Fisheye SLAM System

We present a real-time feature-based SLAM (Simultaneous Localization and Mapping) system for fisheye cameras featured by a large field-of-view (FoV). Large FoV cameras are beneficial for large-scale outdoor SLAM applications, because they increase visual overlap between consecutive frames and capture more pixels belonging to the static parts of the environment. However, current feature-based SLAM systems such as PTAM and ORB-SLAM limit their camera model to pinhole only. To compensate for the vacancy, we propose a novel SLAM system with the cubemap model that utilizes the full FoV without introducing distortion from the fisheye lens, which greatly benefits the feature matching pipeline. In the initialization and point triangulation stages, we adopt a unified vector-based representation to efficiently handle matches across multiple faces, and based on this representation we propose and analyze a novel inlier checking metric. In the optimization stage, we design and test a novel multi-pinhole reprojection error metric that outperforms other metrics by a large margin. We evaluate our system comprehensively on a public dataset as well as a self-collected dataset that contains real-world challenging sequences. The results suggest that our system is more robust and accurate than other feature-based fisheye SLAM approaches. The CubemapSLAM system has been released into the public domain.

cs.RO↗

arXiv2019

Detecting the orbital character of the spin fluctuation in the Iron-based superconductors with the resonant inelastic X-ray scattering spectroscopy

The orbital distribution of the spin fluctuation in the iron-based superconductors(IBSs) is the key information needed to understand the magnetism, superconductivity and electronic nematicity in these multi-orbital systems. In this work, we propose that the resonant inelastic X-ray scattering(RIXS) technique can be used to probe selectively the spin fluctuation on different Fe $3d$ orbitals. In particular, the spin fluctuation on the three $t_{2g}$ orbitals, namely, the $3d_{xz}$, $3d_{yz}$ and the $3d_{xy}$ orbital, can be selectively probed in the $σ\rightarrowπ'$ scattering geometry by aligning the direction of the outgoing photon in the $y$, $x$ and $z$ direction. Such orbital-resolved information on the spin fluctuation is invaluable for the study of the orbital-selective physics in the IBSs and can greatly advance our understanding on the relation between orbital ordering and spin nematicity in the IBSs and the orbital-selective pairing mechanism in these multi-orbital systems.

cond-mat.supr-con↗

arXiv2019

Multiclass Information Flow Propagation Control under Vehicle-to-Vehicle Communication Environments

Most existing models for information flow propagation in a vehicle-to-vehicle (V2V) communications environment are descriptive. They lack capabilities to control information flow, which may preclude their ability to meet application needs, including the need to propagate different information types simultaneously to different target locations within corresponding time delay bounds. This study proposes a queuing-based modeling approach to control the propagation of information flow of multiple classes. Two control parameters associated with a vehicle are leveraged to control the propagation performance of different information classes. A two-layer model is developed to characterize the information flow propagation wave (IFPW) under the designed queuing strategy. The upper layer is formulated as integro-differential equations to characterize the spatiotemporal information dissemination due to V2V communication. The lower layer characterizes the traffic flow dynamics using the Lighthill-Whitham-Richards model. The analytical solution of the asymptotic density of informed vehicles and the necessary condition for existence of the IFPW are derived for homogeneous traffic conditions. Numerical experiments provide insights on the impact of the mean communication service rate on information spread and its spatial coverage. Further, a numerical solution method is developed to solve the two-layer model, which aids in estimating the impacts of the control parameters in the queuing strategy on the IFPW speed under homogeneous and heterogeneous conditions. The proposed modeling approach enables controlling the propagation of information of different information classes to meet application needs, which can assist traffic managers to design effective and efficient traffic management and control strategies under V2V communications.

eess.SY↗

arXiv2018

Possible origin of the pseudogap end point in the high-T$_{c}$ cuprates

Recent experiments find that the pseudogap phase of the high-T$_{c}$ cuprates ends suddenly at an electron doping $x^{*}$ when the Fermi surface change its shape from hole-like to electron-like. In this short note, we argue that the antiferromagnetic(AF) spin correlation of the system should drop abruptly at the same doping. At the same time, we argue that the critical behavior observed at $x^{*}$ in the specific heat measurement should be attributed to the strong renormalization of the quasiparticle excitation in the anti-nodal region by the critical AF spin fluctuation. This picture also predicts that any pseudogap-like spectral feature related to AF scattering in the electron-doped cuprates should terminate at the doping level when the Fermi surface becomes tangent to the boundary of AF Brillouin zone. Recent ARPES measurement on the electron-doped cuprate Nd$_{2-x}$Ce$_{x}$CuO$_{4}$ seems to be consistent with such a prediction.

cond-mat.supr-con↗

arXiv2018

Faithful entanglement purification for high-capacity quantum communication with two-photon four-qubit systems

As the hyperentanglement of photon systems presents lots of unique opportunities in high-capacity quantum networking, the hyperentanglement purification protocol (hyper-EPP) becomes a vital project work and the quality of its accomplishment attracts much attention recently. Here we present the first theoretical scheme of faithful hyper-EPP for nonlocal two-photon systems in two degrees of freedom (DOFs) by constructing several fidelity-robust quantum circuits for hyper-encoded photons. With this faithful hyper-EPP, the bit-flip errors in both the polarization and spatial-mode DOFs can be efficiently corrected and the maximal hyperentanglement in two DOFs could be in principle achieved by performing the hyper-EPP multiple rounds. Moreover, the fidelity-robust quantum circuits, parity-check quantum nondemolition detectors, and SWAP gates make this hyper-EPP works faithfully as the errors coming from practical scattering, in these quantum circuits, are converted into a detectable failure rather than infidelity. Furthermore, this hyper-EPP can be directly extended to purify photon systems entangled in single polarization or spatial-mode DOF and that hyperentangled in polarization and multiple-spatial-mode DOFs.

quant-ph↗

arXiv2018

Semi-supervised Text Regression with Conditional Generative Adversarial Networks

Enormous online textual information provides intriguing opportunities for understandings of social and economic semantics. In this paper, we propose a novel text regression model based on a conditional generative adversarial network (GAN), with an attempt to associate textual data and social outcomes in a semi-supervised manner. Besides promising potential of predicting capabilities, our superiorities are twofold: (i) the model works with unbalanced datasets of limited labelled data, which align with real-world scenarios; and (ii) predictions are obtained by an end-to-end framework, without explicitly selecting high-level representations. Finally we point out related datasets for experiments and future research directions.

cs.CL↗

arXiv2018

Student's t-Generative Adversarial Networks

Generative Adversarial Networks (GANs) have a great performance in image generation, but they need a large scale of data to train the entire framework, and often result in nonsensical results. We propose a new method referring to conditional GAN, which equipments the latent noise with mixture of Student's t-distribution with attention mechanism in addition to class information. Student's t-distribution has long tails that can provide more diversity to the latent noise. Meanwhile, the discriminator in our model implements two tasks simultaneously, judging whether the images come from the true data distribution, and identifying the class of each generated images. The parameters of the mixture model can be learned along with those of GANs. Moreover, we mathematically prove that any multivariate Student's t-distribution can be obtained by a linear transformation of a normal multivariate Student's t-distribution. Experiments comparing the proposed method with typical GAN, DeliGAN and DCGAN indicate that, our method has a great performance on generating diverse and legible objects with limited data.

cs.LG↗

arXiv2018

Structure Learning of Deep Networks via DNA Computing Algorithm

Convolutional Neural Network (CNN) has gained state-of-the-art results in many pattern recognition and computer vision tasks. However, most of the CNN structures are manually designed by experienced researchers. Therefore, auto- matically building high performance networks becomes an important problem. In this paper, we introduce the idea of using DNA computing algorithm to automatically learn high-performance architectures. In DNA computing algorithm, we use short DNA strands to represent layers and long DNA strands to represent overall networks. We found that most of the learned models perform similarly, and only those performing worse during the first runs of training will perform worse finally than others. The indicates that: 1) Using DNA computing algorithm to learn deep architectures is feasible; 2) Local minima should not be a problem of deep networks; 3) We can use early stop to kill the models with the bad performance just after several runs of training. In our experiments, an accuracy 99.73% was obtained on the MNIST data set and an accuracy 95.10% was obtained on the CIFAR-10 data set.

cs.NE↗

arXiv2018

Amending coherence-breaking channels via unitary operations

The coherence-breaking channels play a significant role in quantum information theory. We study the coherence-breaking channels and give a method to amend the coherence-breaking channels by applying unitary operations. For given incoherent channel $Φ$, we give necessary and sufficient conditions for the channel to be a coherence-breaking channel and amend it via unitary operations. For qubit incoherent channels $Φ$ that are not coherence-breaking ones, we consider the mapping $Φ\circΦ$ and present the conditions for coherence-breaking and channel amendment as well.

quant-ph↗

arXiv2018

GPU based Parallel Optimization for Real Time Panoramic Video Stitching

Panoramic video is a sort of video recorded at the same point of view to record the full scene. With the development of video surveillance and the requirement for 3D converged video surveillance in smart cities, CPU and GPU are required to possess strong processing abilities to make panoramic video. The traditional panoramic products depend on post processing, which results in high power consumption, low stability and unsatisfying performance in real time. In order to solve these problems,we propose a real-time panoramic video stitching framework.The framework we propose mainly consists of three algorithms, LORB image feature extraction algorithm, feature point matching algorithm based on LSH and GPU parallel video stitching algorithm based on CUDA.The experiment results show that the algorithm mentioned can improve the performance in the stages of feature extraction of images stitching and matching, the running speed of which is 11 times than that of the traditional ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based on analyzing the GPU resources occupancy rate of each resolution image stitching, we further propose a stream parallel strategy to maximize the utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU resources. The performance of the system accomplished in the paper is 29.2 times than that of the former embedded one, while the power dissipation is reduced to 10W.

cs.CV↗