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Guoqiang Li

Publications and source records attributed to Guoqiang Li.

At least 55 records · Page 3Linked to original sources
arXiv2020

A New Action Recognition Framework for Video Highlights Summarization in Sporting Events

To date, machine learning for human action recognition in video has been widely implemented in sports activities. Although some studies have been successful in the past, precision is still the most significant concern. In this study, we present a high-accuracy framework to automatically clip the sports video stream by using a three-level prediction algorithm based on two classical open-source structures, i.e., YOLO-v3 and OpenPose. It is found that by using a modest amount of sports video training data, our methodology can perform sports activity highlights clipping accurately. Comparing with the previous systems, our methodology shows some advantages in accuracy. This study may serve as a new clipping system to extend the potential applications of the video summarization in sports field, as well as facilitates the development of match analysis system.

cs.CV↗
arXiv2020

Shannon Entropy for Time-Varying Persistence of Cell Migration

Cell migration, which can be significantly affected by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays a crucial role in many physiological and pathological processes. The efficiency of cell migration, which is typically modeled as a persistent random walk (PRW), depends on two critical motility parameters, i.e., migration speed and persistence. It is generally very challenging to efficiently and accurately extract these key dynamics parameters from noisy experimental data. Here, we employ the normalized Shannon entropy to quantify the deviation of cell migration dynamics from that of diffusive/ballistic motion as well as to derive the persistence of cell migration based on the Fourier power spectrum of migration velocities. Moreover, we introduce the time-varying Shannon entropy based on the wavelet power spectrum of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which is typically resulted from complex and time-varying intra or extra-cellular mechanisms. We employ our approach to analyze trajectory data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the combination of Shannon entropy and wavelet transform offers a simple and efficient tool to estimate the persistence of cell migration, which may also reflect the real-time effects of ICSP-ECM to some extent.

physics.bio-ph↗
arXiv2020

Statistical Detection of Collective Data Fraud

Statistical divergence is widely applied in multimedia processing, basically due to regularity and interpretable features displayed in data. However, in a broader range of data realm, these advantages may no longer be feasible, and therefore a more general approach is required. In data detection, statistical divergence can be used as a similarity measurement based on collective features. In this paper, we present a collective detection technique based on statistical divergence. The technique extracts distribution similarities among data collections, and then uses the statistical divergence to detect collective anomalies. Evaluation shows that it is applicable in the real world.

cs.DB↗
arXiv2020

Deriving Time-varying Cellular Motility Parameters via Wavelet Analysis

Cell migration is an indispensable physiological and pathological process for normal tissue development and cancer metastasis, which is greatly regulated by intracellular signal pathways and extracellular microenvironment (ECM). However, there is a lack of adequate tools to analyze the time-varying cell migration characteristics because of the effects of some factors, i.e., the ECM including the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop an approach to derive the time-dependent motility parameters from cellular trajectories, based on the time-varying persistent random walk model. In particular, we employ the wavelet denoising and wavelet transform to investigate cell migration velocities and obtain the wavelet power spectrum. The time-dependent motility parameters are subsequently derived via Lorentzian power spectrum. Our analysis shows that the combination of wavelet denoising, wavelet transform and Lorentzian power spectrum provides a powerful tool to derive accurately the time-dependent motility parameters, which reflects the time-varying microenvironment characteristics to some extent.

physics.bio-ph↗
arXiv2020

Morphing Attack Detection -- Database, Evaluation Platform and Benchmarking

Morphing attacks have posed a severe threat to Face Recognition System (FRS). Despite the number of advancements reported in recent works, we note serious open issues such as independent benchmarking, generalizability challenges and considerations to age, gender, ethnicity that are inadequately addressed. Morphing Attack Detection (MAD) algorithms often are prone to generalization challenges as they are database dependent. The existing databases, mostly of semi-public nature, lack in diversity in terms of ethnicity, various morphing process and post-processing pipelines. Further, they do not reflect a realistic operational scenario for Automated Border Control (ABC) and do not provide a basis to test MAD on unseen data, in order to benchmark the robustness of algorithms. In this work, we present a new sequestered dataset for facilitating the advancements of MAD where the algorithms can be tested on unseen data in an effort to better generalize. The newly constructed dataset consists of facial images from 150 subjects from various ethnicities, age-groups and both genders. In order to challenge the existing MAD algorithms, the morphed images are with careful subject pre-selection created from the contributing images, and further post-processed to remove morphing artifacts. The images are also printed and scanned to remove all digital cues and to simulate a realistic challenge for MAD algorithms. Further, we present a new online evaluation platform to test algorithms on sequestered data. With the platform we can benchmark the morph detection performance and study the generalization ability. This work also presents a detailed analysis on various subsets of sequestered data and outlines open challenges for future directions in MAD research.

cs.CV↗
arXiv2020

Object Detection for Graphical User Interface: Old Fashioned or Deep Learning or a Combination?

Detecting Graphical User Interface (GUI) elements in GUI images is a domain-specific object detection task. It supports many software engineering tasks, such as GUI animation and testing, GUI search and code generation. Existing studies for GUI element detection directly borrow the mature methods from computer vision (CV) domain, including old fashioned ones that rely on traditional image processing features (e.g., canny edge, contours), and deep learning models that learn to detect from large-scale GUI data. Unfortunately, these CV methods are not originally designed with the awareness of the unique characteristics of GUIs and GUI elements and the high localization accuracy of the GUI element detection task. We conduct the first large-scale empirical study of seven representative GUI element detection methods on over 50k GUI images to understand the capabilities, limitations and effective designs of these methods. This study not only sheds the light on the technical challenges to be addressed but also informs the design of new GUI element detection methods. We accordingly design a new GUI-specific old-fashioned method for non-text GUI element detection which adopts a novel top-down coarse-to-fine strategy, and incorporate it with the mature deep learning model for GUI text detection.Our evaluation on 25,000 GUI images shows that our method significantly advances the start-of-the-art performance in GUI element detection.

cs.CV↗
arXiv2020

Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning

According to the World Health Organization(WHO), it is estimated that approximately 1.3 billion people live with some forms of vision impairment globally, of whom 36 million are blind. Due to their disability, engaging these minority into the society is a challenging problem. The recent rise of smart mobile phones provides a new solution by enabling blind users' convenient access to the information and service for understanding the world. Users with vision impairment can adopt the screen reader embedded in the mobile operating systems to read the content of each screen within the app, and use gestures to interact with the phone. However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app. Unfortunately, more than 77% apps have issues of missing labels, according to our analysis of 10,408 Android apps. Most of these issues are caused by developers' lack of awareness and knowledge in considering the minority. And even if developers want to add the labels to UI components, they may not come up with concise and clear description as most of them are of no visual issues. To overcome these challenges, we develop a deep-learning based model, called LabelDroid, to automatically predict the labels of image-based buttons by learning from large-scale commercial apps in Google Play. The experimental results show that our model can make accurate predictions and the generated labels are of higher quality than that from real Android developers.

cs.HC↗
arXiv2020

A Survey on Unknown Presentation Attack Detection for Fingerprint

Fingerprint recognition systems are widely deployed in various real-life applications as they have achieved high accuracy. The widely used applications include border control, automated teller machine (ATM), and attendance monitoring systems. However, these critical systems are prone to spoofing attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed by presenting gummy fingers made from different materials such as silicone, gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex. Biometrics Researchers have developed Presentation Attack Detection (PAD) methods as a countermeasure to PA. PAD is usually done by training a machine learning classifier for known attacks for a given dataset, and they achieve high accuracy in this task. However, generalizing to unknown attacks is an essential problem from applicability to real-world systems, mainly because attacks cannot be exhaustively listed in advance. In this survey paper, we present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD. We categorize PAD algorithms, point out their advantages/disadvantages, and future directions for this area.

cs.CV↗
arXiv2019

Time-resolved Diffusing Wave Spectroscopy to Enable Ultrafast Electro-optic Relaxation Dynamic in Liquid Crystals

Enabling control over a spontaneous dynamic process is a very complicated task in practice because of its statistical nature, and electro-optic (E-O) relaxation dynamic in a nematic liquid crystal (NLC) system is not an exception. Controlling E-O relaxation time requires an appropriate microscopic visualization. For that, a time-resolved diffusing wave spectroscopy (TR-DWS) is developed first time, and further demonstrated with that the linear E-O relaxation time of a NLC can be improved typically, from 25msec to 350μsec at room temperature, by controlling the orientational ordering of NLC. Apart from improving the linear E-O dynamic, it is expected that the application range of the proposed TR-DWS is likely to be extended to applications such as optical imaging in turbid media, coherent controlling and optical manipulation in complex quantum systems including miniature lasers, amorphous photonic crystals, and more.

physics.optics↗
arXiv2018

Multi-View Factorization Machines

For a learning task, data can usually be collected from different sources or be represented from multiple views. For example, laboratory results from different medical examinations are available for disease diagnosis, and each of them can only reflect the health state of a person from a particular aspect/view. Therefore, different views provide complementary information for learning tasks. An effective integration of the multi-view information is expected to facilitate the learning performance. In this paper, we propose a general predictor, named multi-view machines (MVMs), that can effectively include all the possible interactions between features from multiple views. A joint factorization is embedded for the full-order interaction parameters which allows parameter estimation under sparsity. Moreover, MVMs can work in conjunction with different loss functions for a variety of machine learning tasks. A stochastic gradient descent method is presented to learn the MVM model. We further illustrate the advantages of MVMs through comparison with other methods for multi-view classification, including support vector machines (SVMs), support tensor machines (STMs) and factorization machines (FMs).

cs.LG↗
arXiv2017

Remarks on strong embeddability for discrete metric spaces and groups

In this paper, we show that the strong embeddability has fibering permanence property and is preserved under the direct limit for the metric space. Moreover, we show the following result: let $G$ is a finitely generated group with a coarse quasi-action on a metric space $X$. If $X$ has finite asymptotic dimension and the quasi-stabilizers are strongly embeddable, then $G$ is also strongly embeddable.

math.FA↗
arXiv2017

Warped cones and Haagerup property

In this paper we show that the fibred coarse embeddability of a warped cone implies the Haagerup property of the appropriate group. Moreover, Kazhdan's property (T) of the group implies geometric property (T) of the warped cone.

math.FA↗
arXiv2015

Space-variant polarized Airy beam

We experimentally generate an Airy beam with polarization structure while keeping its original amplitude and phase profile intact. This class of Airy beam preserves the acceleration properties. By monitoring their initial polarization structure we have provided insight concerning the self-healing mechanism of Airy beams. We investigate both theoretically and experimentally the self-healing polarization properties of the space-variant polarized Airy beams. Amplitude as well as the polarization structure tends to reform during propagation in spite of the severe truncation of the beam by finite apertures.

physics.optics↗
arXiv2015

Energy flow structuring in the focused field

We propose an iterative method of energy flow shaping in the focal region with the amplitude, phase and polarization modulation of incident light. By using an iterative optimization based on the diffraction calculation with help of the fast Fourier transform, we can tailor the polarization and phase structure in the focal plane. By appropriate design of the polarization and phase gradients, arbitrary energy flow including spin and orbital parts can be designed and tailored independently. The capability of energy flow structuring is demonstrated by the measurement of the Stokes parameters and self-interference pattern. This provides a novel method to control the vectorial feature of the focal volume.

physics.optics↗
arXiv2015

Iso-oriented monolayer α-MoO3(010) films epitaxially grown on SrTiO3(001)

The ability to synthesis well-ordered two-dimensional materials under ultra-high vacuum and directly characterize them by other techniques in-situ can greatly advance our current understanding on their physical and chemical properties. In this paper, we demonstrate that iso-oriented α-MoO3 films with as low as single monolayer thickness can be reproducibly grown on SrTiO3(001) (STO) substrates by molecular beam epitaxy ( (010)MoO3 || (001)STO, [100]MoO3 || [100]STO or [010]STO) through a self-limiting process. While one in-plane lattice parameter of the MoO3 is very close to that of the SrTiO3 (aMoO3 = 3.96 Å, aSTO = 3.905 Å), the lattice mismatch along other direction is large (~5%, cMoO3 = 3.70 Å), which leads to relaxation as clearly observed from the splitting of streaks in reflection high-energy electron diffraction (RHEED) patterns. A narrow range in the growth temperature is found to be optimal for the growth of monolayer α-MoO3 films. Increasing deposition time will not lead to further increase in thickness, which is explained by a balance between deposition and thermal desorption due to the weak van der Waals force between α-MoO3 layers. Lowering growth temperature after the initial iso-oriented α-MoO3 monolayer leads to thicker α-MoO3(010) films with excellent crystallinity.

cond-mat.mtrl-sci↗
arXiv2015

ZenLDA: An Efficient and Scalable Topic Model Training System on Distributed Data-Parallel Platform

This paper presents our recent efforts, zenLDA, an efficient and scalable Collapsed Gibbs Sampling system for Latent Dirichlet Allocation training, which is thought to be challenging that both data parallelism and model parallelism are required because of the Big sampling data with up to billions of documents and Big model size with up to trillions of parameters. zenLDA combines both algorithm level improvements and system level optimizations. It first presents a novel CGS algorithm that balances the time complexity, model accuracy and parallelization flexibility. The input corpus in zenLDA is represented as a directed graph and model parameters are annotated as the corresponding vertex attributes. The distributed training is parallelized by partitioning the graph that in each iteration it first applies CGS step for all partitions in parallel, followed by synchronizing the computed model each other. In this way, both data parallelism and model parallelism are achieved by converting them to graph parallelism. We revisited the tradeoff between system efficiency and model accuracy and presented approximations such as unsynchronized model, sparse model initialization and "converged" token exclusion. zenLDA is built on GraphX in Spark that provides distributed data abstraction (RDD) and expressive APIs to simplify the programming efforts and simultaneously hides the system complexities. This enables us to implement other CGS algorithm with a few lines of code change. To better fit in distributed data-parallel framework and achieve comparable performance with contemporary systems, we also presented several system level optimizations to push the performance limit. zenLDA was evaluated it against web-scale corpus, and the result indicates that zenLDA can achieve about much better performance than other CGS algorithm we implemented, and simultaneously achieve better model accuracy.

cs.DC↗
arXiv2015

Theoretical analysis of nanoparticle-induced homeotropic alignment in nematic liquid crystals

A theoretical analysis of homeotropic alignment induced by nanoparticles (NPs) in a nematic liquid crystal (NLC) sample cell is presented. It is found that such alignment on the surface of a NP causes a change in the orientation of the molecular director near the surface, which in turn induces variations in the elastic constants and free energy. The induced NLC properties allow coupling between nearby NPs, mediated by the NLC molecules. The rotation of the coupled NPs close to the substrate tends to induce a long-range orientation of the NLC molecular director, leading to modification in the alignment at the interface of NLC and substrate which induces the orientation from homogeneous (planar) to homeotropic (vertical) in the bulk material.

physics.optics↗