Xiaoning Qian

Texas A&M University, College Station, Texas, United States

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Publications (63)119 Total impact

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    ABSTRACT: The intra-operative three-dimensional (3D) structure of tissue organs and laparoscope motion are the basis for many tasks in computer-assisted surgery (CAS), such as safe surgical navigation and registration of pre-operative and intra-operative data for soft tissues. This article provides a literature review on laparoscopic video-based intra-operative techniques of 3D surface reconstruction, laparoscope localization and tissue deformation recovery for abdominal minimally invasive surgery (MIS). This article introduces a classification scheme based on the motions of a laparoscope and the motions of tissues. In each category, comprehensive discussion is provided on the evolution of both classic and state-of-the-art methods. Video-based approaches have many advantages, such as providing intra-operative information without introducing extra hardware to the current surgical platform. However, an extensive discussion on this important topic is still lacking. This survey paper is therefore beneficial for researchers in this field. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
    No preview · Article · Apr 2015 · International Journal of Medical Robotics and Computer Assisted Surgery
  • Bingxiong Lin · Yu Sun · Jaime E Sanchez · Xiaoning Qian
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    ABSTRACT: Distinctive feature detection is an essential task in computer-assisted minimally invasive surgery (MIS). For special conditions in an MIS imaging environment, such as specular reflections and texture homogeneous areas, the feature points extracted by general feature point detectors are less distinctive and repeatable in MIS images. We observe that abundant blood vessels are available on tissue surfaces and can be extracted as a new set of image features. In this paper, two types of blood vessel features are proposed for endoscopic images: branching points and branching segments. Two novel methods, ridgenessbased circle test (RBCT) and ridgeness-based branching segment detection (RBSD) are presented to extract branching points and branching segments respectively. Extensive in vivo experiments were conducted to evaluate the performance of the proposed methods and compare them with the state-of-the-art methods. The numerical results verify that, in MIS images, the blood vessel features can produce a large number of points. More importantly, those points are more robust and repeatable than the other types of feature points. In addition, due to the difference in feature types, vessel features can be combined with other general features, which makes them new tools for MIS image analysis. These proposed methods are efficient and the code and datasets are made available to the public.
    No preview · Article · Nov 2014 · IEEE transactions on bio-medical engineering
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    ABSTRACT: Objective To identify the risk-predictive baseline profile patterns of demographic, genetic, immunologic, and metabolic markers and synthesize these patterns for risk prediction. Research Design and Methods RuleFit is used to identify the risk-predictive baseline profile patterns of demographic, immunologic, and metabolic markers, using 356 subjects who were randomized into the control arm of the prospective Diabetes Prevention Trial-Type 1 (DPT-1) study. A novel latent trait model is developed to synthesize these baseline profile patterns for disease risk prediction. The primary outcome was Type 1 Diabetes (T1D) onset. Results We identified ten baseline profile patterns that were significantly predictive to the disease onset. Using these ten baseline profile patterns, a risk prediction model was built based on the latent trait model, which produced superior prediction performance over existing risk score models for T1D. Conclusion Our results demonstrated that the underlying disease progression process of T1D can be detected through some risk-predictive patterns of demographic, immunologic, and metabolic markers. A synthesis of these patterns provided accurate prediction of disease onset, leading to more cost-effective design of prevention trials of T1D in the future.
    Full-text · Article · Jun 2014 · PLoS ONE
  • Shaogang Ren · Xiaoning Qian
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    ABSTRACT: This paper presents a new mathematical formulation and the corresponding algorithms for structured sparse principal component analysis (PCA). We introduce a new concept of support matrices with structured prior based on Markov Random Field (MRF). Both the support matrices and principal components are regularized by the L1 norm to be integrated in a coupled objective function to recover the structured sparsity from the given data. Block coordinate descent and subgradient-based optimization methods are utilized to search for proper local minima for the formulated non-convex optimization problem. We implement the proposed methods to jointly analyze micro-RNA (miRNA) and gene interaction data to identify miRNA-gene co-regulatory modules (co-modules). Our preliminary experiments demonstrate that our structured sparse PCA has the potential to identify meaningful co-regulatory modules with enriched cellular functionalities.
    No preview · Conference Paper · May 2014
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    ABSTRACT: OBJECTIVE To determine basal and stimulated C-peptide percentiles in North American children and adolescents at risk for type 1 diabetes (T1D) and to examine factors associated with this distribution in the Diabetes Prevention Trial of Type 1 (DPT-1).RESEARCH DESIGN AND METHODS We included 582 subjects aged 4-18 years at randomization in the DPT-1 trials. A 2-h oral glucose tolerance test (OGTT) was performed at baseline and every 6 months during the 5-year follow-up period. The percentile values of C-peptide after baseline OGTT were estimated according to age, BMI Z score (BMIZ), and/or sex categories. Conditional quantile regression was used to examine the relationship between C-peptide percentiles and various independent variables.RESULTSThe basal and stimulated C-peptide levels increased significantly as age and BMIZ increased (P < 0.05). Both age and BMIZ had a stronger impact on the upper quartile of C-peptide distributions than the lower quartile. Sex was only significantly associated with stimulated C-peptide. Higher stimulated C-peptide levels were generally observed in girls compared with boys at the same age and BMIZ (P < 0.05). HLA type and number of positive antibodies and antibody titers (islet cell antibody [ICA], insulin autoantibody, GAD65A, and ICA512A) were not significantly associated with C-peptide distribution after adjustment for age, BMIZ, and sex.CONCLUSIONS Age-, sex-, and BMIZ-specific C-peptide percentiles can be estimated for North American children and adolescents at risk for T1D. They can be used as an assessment tool that could impact the recommendations in T1D prevention trials.
    Full-text · Article · Apr 2014 · Diabetes care
  • Bingxiong Lin · Yu Sun · Jaime Sanchez · Xiaoning Qian
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    ABSTRACT: Distinctive features are crucial to many tasks in computer assisted minimally invasive surgeries (MIS). Most existing methods are difficult to extract distinctive features in MIS images. For better analysis of MIS images, we resort to blood vessels that are abundant and distinctive on the tissue surfaces. Based on vascular branching points, we propose a new type of vascular feature, branching segment. Two novel methods, Vesselness Based Circle Test (VBCT) and Vesselness based Branching Segment Detection (VBSD) are proposed to detect branching points and branching segments respectively. In the experiments, the performance of VBCT and VBSD is evaluated with in vivo images and VBCT is compared with other state-of-the-art feature point detectors. The numerical results verify that branching points and branching segments are highly repeatable under different viewpoints. Moreover, the computational complexity of VBCT and VBSD is linear to the number of pixels. As supplements to other types of feature point detectors, VBCT and VBSD provide researchers new tools for endoscopic image analysis.
    No preview · Conference Paper · Apr 2014
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    ABSTRACT: In order to have a better understanding of unexplained heritability for complex diseases in conventional Genome-Wide Association Studies (GWAS), aggregated association analyses based on predefined functional regions, such as genes and pathways, become popular recently as they enable evaluating joint effect of multiple Single-Nucleotide Polymorphisms (SNPs), which helps increase the detection power, especially when investigating genetic variants with weak individual effects. In this paper, we focus on aggregated analysis methods based on the idea of Principal Component Analysis (PCA). The past approaches using PCA mostly make some inherent genotype data and/or risk effect model assumptions, which may hinder the accurate detection of potential disease SNPs that influence disease phenotypes. In this paper, we derive a general Supervised Categorical Principal Component Analysis (SCPCA), which explicitly models categorical SNP data without imposing any risk effect model assumption. We have evaluated the efficacy of SCPCA with the comparison to a traditional Supervised PCA (SPCA) and a previously developed Supervised Logistic Principal Component Analysis (SLPCA) based on both the simulated genotype data by HAPGEN2 and the genotype data of Crohn's Disease (CD) from Wellcome Trust Case Control Consortium (WTCCC). Our preliminary results have demonstrated the superiority of SCPCA over both SPCA and SLPCA due to its modeling explicitly designed for categorical SNP data as well as its flexibility on the risk effect model assumption. Electronic supplementary material The online version of this article (doi:10.1186/1471-2164-15-S1-S10) contains supplementary material, which is available to authorized users.
    Full-text · Article · Jan 2014 · BMC Genomics
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    Yijie Wang · Xiaoning Qian
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    ABSTRACT: Biological networks obtained by high-throughput profiling or human curation are typically noisy. For functional module identification, single network clustering algorithms may not yield accurate and robust results. In order to borrow information across multiple sources to alleviate such problems due to data quality, we propose a new joint network clustering algorithm ASModel in this paper. We construct an integrated network to combine network topological information based on protein-protein interaction (PPI) datasets and homological information introduced by constituent similarity between proteins across networks. A novel random walk strategy on the integrated network is developed for joint network clustering and an optimization problem is formulated by searching for low conductance sets defined on the derived transition matrix of the random walk, which fuses both topology and homology information. The optimization problem of joint clustering is solved by a derived spectral clustering algorithm. Network clustering using several state-of-the-art algorithms has been implemented to both PPI networks within the same species (two yeast PPI networks and two human PPI networks) and those from different species (a yeast PPI network and a human PPI network). Experimental results demonstrate that ASModel outperforms the existing single network clustering algorithms as well as another recent joint clustering algorithm in terms of complex prediction and Gene Ontology (GO) enrichment analysis.
    Full-text · Article · Jan 2014 · BMC Systems Biology
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    Xiaoning Qian · Edward R Dougherty
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    ABSTRACT: There are two distinct issues regarding network validation: (1) Does an inferred network provide good predictions relative to experimental data? (2) Does a network inference algorithm applied within a certain network model framework yield networks that are accurate relative to some criterion of goodness? The first issue concerns scientific validation and the second concerns algorithm validation. In this paper we consider inferential validation relative to controllability; that is, if an inference procedure is applied to data generated from a gene regulatory network and an intervention procedure is designed on the inferred network, how well does it perform on the true network? The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network. We will consider the problem from the perspectives of stationary control, which involves designing a control policy to be applied over time based on the current state of the network, with the decision procedure itself being time independent. The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes), which is equivalent to optimally increasing the total steady-state mass of the desirable states. Based on this criterion we compare several proposed network inference procedures. We will see that inference procedure ψ may perform poorer than inference procedure ξ relative to inferring the full network structure but perform better than ξ relative to controllability. Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.
    Preview · Article · Dec 2013 · Frontiers in Genetics
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    ABSTRACT: We hypothesized that elucidating the interactome of epidermal growth factor receptor (EGFR) forms that are mutated in lung cancer, via global analysis of protein-protein interactions, phosphorylation, and systematically perturbing the ensuing network nodes, should offer a new, more systems-level perspective of the molecular etiology. Here, we describe an EGFR interactome of 263 proteins and offer a 14-protein core network critical to the viability of multiple EGFR-mutated lung cancer cells. Cells with acquired resistance to EGFR tyrosine kinase inhibitors (TKIs) had differential dependence of the core network proteins based on the underlying molecular mechanisms of resistance. Of the 14 proteins, 9 are shown to be specifically associated with survival of EGFR-mutated lung cancer cell lines. This included EGFR, GRB2, MK12, SHC1, ARAF, CD11B, ARHG5, GLU2B, and CD11A. With the use of a drug network associated with the core network proteins, we identified two compounds, midostaurin and lestaurtinib, that could overcome drug resistance through direct EGFR inhibition when combined with erlotinib. Our results, enabled by interactome mapping, suggest new targets and combination therapies that could circumvent EGFR TKI resistance.
    Full-text · Article · Nov 2013 · Molecular Systems Biology
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    Yijie Wang · Xiaoning Qian
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    ABSTRACT: Identifying functional modules in protein-protein interaction (PPI) networks may shed light on cellular functional organization and thereafter underlying cellular mechanisms. Many existing module identification algorithms aim to detect densely connected groups of proteins as potential modules. However, based on this simple topological criterion of "higher than expected connectivity", those algorithms may miss biologically meaningful modules of functional significance, in which proteins have similar interaction patterns to other proteins in networks but may not be densely connected to each other. A few blockmodel module identification algorithms have been proposed to address the problem but the lack of global optimum guarantee and the prohibitive computational complexity have been the bottleneck of their applications in real-world large-scale PPI networks. In this paper, we propose a novel optimization formulation LCP(2) using the concept of Markov random walk on graphs, which enables simultaneous identification of both dense and sparse modules based on protein interaction patterns in given networks through searching for low two-hop conductance sets by random walk. A spectral approximate algorithm SLCP(2) is derived to identify non-overlapping functional modules. Based on a bottom-up greedy strategy, we further extend LCP(2) to a new algorithm GLCP(2) to identify overlapping functional modules. We compare SLCP(2) and GLCP(2) with a range of state-of-the-art algorithms on synthetic networks and real-world PPI networks. The performance evaluation based on several criteria with respect to protein complex prediction, high level GO (Gene Ontology) term prediction, and especially, sparse module detection, has demonstrated that our algorithms based on searching for low two-hop conductance sets outperform all other compared algorithms. All data and code are available at http://www.cse.usf.edu/xqian/fmi/slcp2hop/. xqian@cse.usf.edu, yijie@mail.usf.edu.
    Full-text · Article · Oct 2013 · Bioinformatics
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    ABSTRACT: Interaction among different risk factors plays an important role in the development and progress of complex disease, such as diabetes. However, traditional epidemiological methods often focus on analyzing individual or a few 'essential' risk factors, hopefully to obtain some insights into the etiology of complex disease. In this paper, we propose a systematic framework for risk factor analysis based on a synergy network, which enables better identification of potential risk factors that may serve as prognostic markers for complex disease. A spectral approximate algorithm is derived to solve this network optimization problem, which leads to a new network-based feature ranking method that improves the traditional feature ranking by taking into account the pairwise synergistic interactions among risk factors in addition to their individual predictive power. We first evaluate the performance of our method based on simulated datasets, and then, we use our method to study immunologic and metabolic indices based on the Diabetes Prevention Trial-Type 1 (DPT-1) study that may provide prognostic and diagnostic information regarding the development of type 1 diabetes. The performance comparison based on both simulated and DPT-1 datasets demonstrates that our network-based ranking method provides prognostic markers with higher predictive power than traditional analysis based on individual factors.
    Full-text · Article · Sep 2013 · EURASIP Journal on Bioinformatics and Systems Biology
  • Byung-Jun Yoon · Xiaoning Qian · Edward R. Dougherty
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    ABSTRACT: Real-world problems often involve complex systems that cannot be perfectly modeled or identified, and many engineering applications aim to design operators that can perform reliably in the presence of such uncertainty. In this paper, we propose a novel Bayesian framework for objective-based uncertainty quantification (UQ), which quantifies the uncertainty in a given system based on the expected increase of the operational cost that it induces. This measure of uncertainty, called MOCU (mean objective cost of uncertainty), provides a practical way of quantifying the effect of various types of system uncertainties on the operation of interest. Furthermore, the proposed UQ framework provides a general mathematical basis for designing robust operators, and it can be applied to diverse applications, including robust filtering, classification, and control. We demonstrate the utility and effectiveness of the proposed framework by applying it to the problem of robust structural intervention of gene regulatory networks, an important application in translational genomics.
    No preview · Article · May 2013 · IEEE Transactions on Signal Processing
  • Bingxiong Lin · Yu Sun · Xiaoning Qian
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    ABSTRACT: 3D reconstruction of internal organ surfaces provides useful information for better control and guidance of the operations of surgical tools for minimally invasive surgery (MIS). The current reconstruction techniques using stereo cameras are still challenging due to the difculties in correspondence matching in MIS, since there is very limited texture but signicant specular reection on organ surfaces. This paper proposes a new approach to overcome the problem by introducing weakly structured light actively casting surgical tool shadows on organ surfaces. The contribution of this paper is two-fold: rst, we propose a robust approach to extract shadow edges from a sequence of shadowed images; second, we develop a novel eld surface interpolation (FSI) approach to obtain an accurate and dense disparity map. Our approach does not rely on texture information and is able to reconstruct accurate 3D information by exploiting shadows from surgical tools. One advantage is that the point correspondences are directly calculated and no explicit stereo matching is required, which ensures the efciency of the method. Another advantage is the minimum hardware requirement because only stereo cameras and a separated single-point light source are required. We evaluated the proposed approach using both phantom models and ex vivo images. Based on the experimental results, we achieved the precision of the recovered 3D surfaces within 0:7mm for phantom models and 1:2mm for ex vivo images. The comparison of disparity maps indicates that with the addition of shadows, the proposed method signicantly outperforms the state-of-theart stereo algorithms for MIS.
    No preview · Article · Apr 2013 · IEEE transactions on bio-medical engineering
  • Bingxiong Lin · Yu Sun · Xiaoning Qian
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    ABSTRACT: Robust feature point matching for images with large view angle changes in Minimally Invasive Surgery (MIS) is a challenging task due to low texture and specular reflections in these images. This paper presents a new approach that can improve feature matching performance by exploiting the inherent geometric property of the organ surfaces. Recently, intensity based template image tracking using a Thin Plate Spline (TPS) model has been extended for 3D surface tracking with stereo cameras. The intensity based tracking is also used here for 3D reconstruction of internal organ surfaces. To overcome the small displacement requirement of intensity based tracking, feature point correspondences are used for proper initialization of the nonlinear optimization in the intensity based method. Second, we generate simulated images from the reconstructed 3D surfaces under all potential view positions and orientations, and then extract feature points from these simulated images. The obtained feature points are then filtered and re-projected to the common reference image. The descriptors of the feature points under different view angles are stored to ensure that the proposed method can tolerate a large range of view angles. We evaluate the proposed method with silicon phantoms and in vivo images. The experimental results show that our method is much more robust with respect to the view angle changes than other state-of-the-art methods.
    No preview · Article · Mar 2013 · Proceedings of SPIE - The International Society for Optical Engineering
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    Yijie Wang · Xiaoning Qian
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    ABSTRACT: Functional module identification in biological networks may provide new insights into the complex interactions among biomolecules for a better understanding of cellular functional organization. Most of existing functional module identification methods are based on the optimization of network modularity and cluster networks into groups of nodes within which there are a higher-than-expectation number of edges. However, module identification simply based on this topological criterion may not discover certain kinds of biologically meaningful modules within which nodes are sparsely connected but have similar interaction patterns with the rest of the network. In order to unearth more biologically meaningful functional modules, we propose a novel efficient convex programming algorithm based on the subgradient method with heuristic path generation to solve the problem in a recently proposed framework of blockmodel module identification. We have implemented our algorithm for large-scale protein-protein interaction (PPI) networks, including Saccharomyces cerevisia and Homo sapien PPI networks collected from the Database of Interaction Proteins (DIP) and Human Protein Reference Database (HPRD). Our experimental results have shown that our algorithm achieves comparable network clustering performance in comparison to the more time-consuming simulated annealing (SA) optimization. Furthermore, preliminary results for identifying fine-grained functional modules in both biological networks and the comparison with the commonly adopted Markov Clustering (MCL) algorithm have demonstrated the potential of our algorithm to discover new types of modules, within which proteins are sparsely connected but with significantly enriched biological functionalities.
    Full-text · Article · Jan 2013 · BMC Bioinformatics
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    Shaogang Ren · Bo Zeng · Xiaoning Qian
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    ABSTRACT: Background Optimization procedures to identify gene knockouts for targeted biochemical overproduction have been widely in use in modern metabolic engineering. Flux balance analysis (FBA) framework has provided conceptual simplifications for genome-scale dynamic analysis at steady states. Based on FBA, many current optimization methods for targeted bio-productions have been developed under the maximum cell growth assumption. The optimization problem to derive gene knockout strategies recently has been formulated as a bi-level programming problem in OptKnock for maximum targeted bio-productions with maximum growth rates. However, it has been shown that knockout mutants in fact reach the steady states with the minimization of metabolic adjustment (MOMA) from the corresponding wild-type strains instead of having maximal growth rates after genetic or metabolic intervention. In this work, we propose a new bi-level computational framework--MOMAKnock--which can derive robust knockout strategies under the MOMA flux distribution approximation. Methods In this new bi-level optimization framework, we aim to maximize the production of targeted chemicals by identifying candidate knockout genes or reactions under phenotypic constraints approximated by the MOMA assumption. Hence, the targeted chemical production is the primary objective of MOMAKnock while the MOMA assumption is formulated as the inner problem of constraining the knockout metabolic flux to be as close as possible to the steady-state phenotypes of wide-type strains. As this new inner problem becomes a quadratic programming problem, a novel adaptive piecewise linearization algorithm is developed in this paper to obtain the exact optimal solution to this new bi-level integer quadratic programming problem for MOMAKnock. Results Our new MOMAKnock model and the adaptive piecewise linearization solution algorithm are tested with a small E. coli core metabolic network and a large-scale iAF1260 E. coli metabolic network. The derived knockout strategies are compared with those from OptKnock. Our preliminary experimental results show that MOMAKnock can provide improved targeted productions with more robust knockout strategies.
    Preview · Article · Jan 2013 · BMC Bioinformatics
  • Bingxiong Lin · Adrian Johnson · Xiaoning Qian · Jaime Sanchez · Yu Sun
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    ABSTRACT: Tissue deformation is one of the major difficulties in the registration of pre-operative and intra-operative data. Vision based techniques have shown the potential to simultaneously track the endoscope and recover a sparse 3D structure of the tissue. However, most of such methods either assume a static environment or require the tissue organ to have a periodic motion such as respiration. To deal with the general tissue deformation, a new framework is proposed in this paper with the ability of simultaneous stereoscope tracking, 3D reconstruction and deforming point detection in the Minimally Invasive Surgery (MIS) environment. First, we adopt a Parallel Tracking and Mapping (PTAM) framework and extend it for the use of stereoscope in MIS. Second, this newly extended framework enables the detection of deforming points without restricted periodic motion model assumptions. Our proposed method has been evaluated on a phantom model, and in vivo experiments demonstrate its capability for accurate tracking in nearly real time speed as well as 3D reconstruction with hundreds of 3D points. Those experiments have shown that our method is robust towards tissue deformation and hence have promising potential for information integration by registration with pre-operative data.
    No preview · Chapter · Jan 2013
  • Wei Dai · Yu Sun · Xiaoning Qian
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    ABSTRACT: This paper presents a novel grasping motion analysis technique based on functional principal component analysis (fPCA). The functional analysis of grasping motion provides an effective representation of grasping motion and emphasizes motion dynamic features that are omitted by classic PCA-based approaches. The proposed approach represents, processes, and compares grasping motion trajectories in a low-dimensional space. An experiment was conducted to record grasping motion trajectories of 15 different grasp types in Cutkosky grasp taxonomy. We implemented our method for the analysis of collected grasping motion in the PCA+fPCA space, which generated a new data-driven taxonomy of the grasp types, and naturally clustered grasping motion into 5 consistent groups across 5 different subjects. The robustness of the grouping was evaluated and confirmed using a tenfold cross validation approach.
    No preview · Conference Paper · Jan 2013
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    ABSTRACT: Blogging is a popular activity with high impact on marketing, shaping public opinions, and informing the world about major events from a grassroots point of view. Influential bloggers are recognized by businesses as significant forces for product promotion or demotion, and by oppressive political regimes as serious threats to their power. This paper studies the problem of identifying influential bloggers in a blogging community, BlogCatalog, by using network centrality metrics. Our analysis shows that bloggers are connected in a core-periphery network structure, with the highly influential bloggers well connected with each others forming the core, and the non-influential bloggers at the periphery. The six node centrality metrics we analyzed are highly correlated, showing that an aggregate centrality score as a measure of influence will be stable to variations in centrality metrics.
    No preview · Conference Paper · Dec 2012

Publication Stats

437 Citations
119.00 Total Impact Points

Institutions

  • 2008-2015
    • Texas A&M University
      • Department of Electrical and Computer Engineering
      College Station, Texas, United States
  • 2009-2014
    • University of South Florida
      • Department of Computer Science & Engineering
      Tampa, Florida, United States
  • 2002-2010
    • Yale University
      • • Department of Electrical Engineering
      • • Department of Diagnostic Radiology and Pediatric Diagnostic Radiology
      New Haven, CT, United States