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ABSTRACT AND APPLIED ANALYSIS 2014年第SI64期2014卷 1-8页

作者： Han, Xu Wu, Jiasong Wang, Lu Chen, Yang Senhadji, Lotfi Shu, Huazhong Southeast Univ Lab Image Sci & Technol Nanjing 210096 Jiangsu Peoples R China Ctr Rech Informat Med Sino Francais CRIBs Rennes France INSERM U1099 F-35000 Rennes France Univ Rennes 1 LTSI F-35042 Rennes France

Matrix completion that estimates missing values in visual data is an important topic in computer vision. Most of the recent studies focused on the low rank matrix approximation via the nuclear norm. However, the visual data, such as images, is rich in texture which may not be well approximated by low rank constraint. In this paper, we propose a novel matrix completion method, which combines the nuclear norm with the local geometric regularizer to solve the problem of matrix completion for redundant texture images. And in this paper we mainly consider one of the most commonly graph regularized parameters: the total variation norm which is a widely used measure for enforcing intensity continuity and recovering a piecewise smooth image. The experimental results show that the encouraging results can be obtained by the proposed method on real texture images compared to the stateof-the-art methods.

关键词： MATHEMATICAL optimization computer vision TEXTURE analysis (Image processing) computer algorithms MATHEMATICAL models NUMERICAL analysis

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Dosage Individualization of Warfarin Using Artificial Neural Networks

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MOLECULAR DIAGNOSIS & THERAPY 2014年第3期18卷 371-379页

作者： Saleh, Mohammad I. Alzubiedi, Sameh Univ Jordan Fac Pharm Amman 11942 Jordan

Our objective was to explore artificial neural networks (ANNs) as a possible tool for dosage individualization of warfarin. Demographic, clinical, and genetic data were gathered from a previously collected cohort of patients with a stable warfarin dosage who were able to achieve an observed international normalized ratio of 2-3. Data from a cohort of 3,415 patients were used to develop an ANN dosing algorithm. Data from another cohort of 856 were used to validate the algorithm. The clinical significance of the ANN dosing algorithm was evaluated by calculating the percentage of patients whose predicted dosage of warfarin was within 20 % of the actual stable therapeutic dose. The clinical significance was also compared with a previously published dosing algorithm. A feed-forward neural network with three layers was able to successfully predict the ideal warfarin dosage in 48 % of the patients. The neural network model explained 48 % and 43 % of the dosage variability observed among patients in the derivation and validation cohorts, respectively. ANN analysis identified several predictors of warfarin dosage including race;age;height;weight;cytochrome P450 (CYP)2C9 genotype;VKORC1 genotype;sulfonamide, azole antifungals, or macrolide administration;carbamazepine, phenytoin, or rifampicin administration;and amiodarone administration. An ANN was applied to develop a warfarin dosing algorithm. The proposed dosing algorithm has the potential to recommend warfarin dosages that are close to the appropriate dosages.

关键词： WARFARIN DOSAGE of drugs NEURAL networks (computer science) GENETIC databases computer algorithms ADMINISTRATION of drugs

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Virtual network embedding with discrete particle swarm optimisation

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ELECTRONICS LETTERS 2014年第4期50卷 285-U127页

作者： Wang, Li Qu, Hua Zhao, Jihong Guo, Ya Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China Xian Univ Posts & Telecommun Sch Commun & Informat Engn Xian 710061 Peoples R China

Network virtualisation is organised as a fundamental technology for eradicating the ossified architecture of the Internet. Virtual network embedding (VNE) instantiates virtual networks on a substrate network to carry ... 详细信息

关键词： computer algorithms INTERNET WIDE area networks (computer networks) PARTICLE swarm optimization GENETIC algorithms

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Controllability and observability analysis for vertex domination centrality in directed networks

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SCIENTIFIC REPORTS 2014年第1期4卷 1-10页

作者： Wang, Bingbo Gao, Lin Gao, Yong Deng, Yue Wang, Yu Xidian Univ Sch Comp Sci & Technol Xian Peoples R China Xian Univ Technol Sch Comp Sci & Technol Xian Peoples R China Univ British Columbia Okanagan Irving K Barber Sch Arts & Sci Dept Comp Sci Kelowna BC Canada

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

关键词： SUBSPACES (Mathematics) ARITHMETIC mean SCALE-free network (Statistical physics) ENZYMES GENE ontology computer algorithms

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A comparison of 12 algorithms for matching on the propensity score

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STATISTICS IN MEDICINE 2014年第6期33卷 1057-1069页

作者： Austin, Peter C. Inst Clin Evaluat Sci Toronto ON M4N 3M5 Canada Univ Toronto Inst Hlth Policy Management & Evaluat Toronto ON Canada Sunnybrook Res Inst Schulich Heart Res Program Toronto ON Canada

Propensity-score matching is increasingly being used to reduce the confounding that can occur in observational studies examining the effects of treatments or interventions on outcomes. We used Monte Carlo simulations to examine the following algorithms for forming matched pairs of treated and untreated subjects: optimal matching, greedy nearest neighbor matching without replacement, and greedy nearest neighbor matching without replacement within specified caliper widths. For each of the latter two algorithms, we examined four different sub-algorithms defined by the order in which treated subjects were selected for matching to an untreated subject: lowest to highest propensity score, highest to lowest propensity score, best match first, and random order. We also examined matching with replacement. We found that (i) nearest neighbor matching induced the same balance in baseline covariates as did optimal matching;(ii) when at least some of the covariates were continuous, caliper matching tended to induce balance on baseline covariates that was at least as good as the other algorithms;(iii) caliper matching tended to result in estimates of treatment effect with less bias compared with optimal and nearest neighbor matching;(iv) optimal and nearest neighbor matching resulted in estimates of treatment effect with negligibly less variability than did caliper matching;(v) caliper matching had amongst the best performance when assessed using mean squared error;(vi) the order in which treated subjects were selected for matching had at most a modest effect on estimation;and (vii) matching with replacement did not have superior performance compared with caliper matching without replacement. (c) 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.

关键词： propensity score matching computer algorithms optimal matching Monte Carlo simulations propensity-score matching

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A robust two-stage face recognition system with localisation error compensation

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IET computer VISION 2014年第6期8卷 690-700页

作者： Su, Ching-Yao Yang, Jar-Ferr Natl Cheng Kung Univ Dept Elect Engn Inst Comp & Commun Engn Adv Optoelect Technol Ctr Tainan 70101 Taiwan

In practical systems, face recognition under unconstrained conditions is a very challenging task, where their input images are first pre-processed and initially aligned by a face detection algorithm. However, there are still some residual localisation errors after the initial alignment. If we do not take these errors into account, the recognition performance should be greatly degraded for most face recognition algorithms. Generally, when designing a practical face recognition system, we need to compromise the capability of residual error tolerance and the discriminating capability. Although it is feasible to apply an iterative alignment algorithm to fine-tune alignment, it will increase the computation load significantly. In this study, we propose an adaptive two-stage face recognition system consisting of two block-based recognition stages with a relatively larger cell size (i.e. the size of local regions) in the first stage to provide sufficient tolerance for geometric errors followed by a smaller one in the second stage to accurately evaluate a most probable candidate subset, which is adaptively determined according to the proposed confidence measure. In addition, an iterative gradient-based alignment algorithm is incorporated into the two-stage system to refine the alignment such that the recognition performance can be improved and the computation load can be saved simultaneously.

关键词： FACE perception computer algorithms FEASIBILITY studies ITERATIVE methods (Mathematics) COMPUTATIONAL complexity

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High Performance Motion Trajectory Tracking Control of Pneumatic Cylinders: A Comparison of Some Nonlinear Control algorithms

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ADVANCES IN MECHANICAL ENGINEERING 2014年第Pt.5期000卷

作者： Meng, Deyuan Li, Aimin Tao, Guoliang Li, Wei China Univ Min & Technol Sch Mechatron Engn Xuzhou 221116 Peoples R China Zhejiang Univ State Key Lab Fluid Power Transmiss & Control Hangzhou 310027 Zhejiang Peoples R China

The dynamics of pneumatic systems are highly nonlinear, and there normally exists a large extent of model uncertainties;the precision motion trajectory tracking control of pneumatic cylinders is still a challenge. In this paper, two typical nonlinear controllers-adaptive controller and deterministic robust controller-are constructed firstly. Considering that they have both benefits and limitations, an adaptive robust controller (ARC) is further proposed. The ARC is a combination of the first two controllers;it employs online recursive least squares estimation (RLSE) to reduce the extent of parametric uncertainties, and utilizes the robust control method to attenuate the effects of parameter estimation errors, unmodeled dynamics, and disturbances. In order to solve the conflicts between the robust control design and the parameter adaption law design, the projection mapping is used to condition the RLSE algorithm so that the parameter estimates are kept within a known bounded convex set. Theoretically, ARC possesses the advantages of the adaptive control and the deterministic robust control, and thus an even better tracking performance can be expected. Extensive comparative experimental results are presented to illustrate the achievable performance of the three proposed controllers and their performance robustness to the parameter variations and sudden disturbance.

关键词： design control Parameter estimation trajectory tracking parameter variation Control Methods computer algorithms nonlinear control adaptive control dynamics model uncertainty Pneumatic systems Pneumatic cylinders Controllers Robust control

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Autofocused 3D Classification of Cryoelectron Subtomograms

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STRUCTURE 2014年第10期22卷 1528-1537页

作者： Chen, Yuxiang Pfeffer, Stefan Jesus Fernandez, Jose Sorzano, Carlos Oscar S. Foerster, Friedrich Max Planck Inst Biochem Dept Mol Struct Biol D-82152 Martinsried Germany Tech Univ Munich D-85748 Garching Germany Natl Res Council CNB CSIC Natl Biotechnol Ctr Madrid 28049 Spain

Classification of subtomograms obtained by cryoelectron tomography (cryo-ET) is a powerful approach to study the conformational landscapes of macromolecular complexes in situ. Major challenges in subtomogram classification are the low signal-to-noise ratio (SNR) of cryo-tomograms, their incomplete angular sampling, the unknown number of classes and the typically unbalanced abundances of structurally distinct complexes. Here, we propose a clustering algorithm named AC3D that is based on a similarity measure, which automatically focuses on the areas of major structural discrepancy between respective subtomogram class averages. Furthermore, we incorporate a spherical-harmonics-based fast subtomogram alignment algorithm, which provides a significant speedup. Assessment of our approach on simulated data sets indicates substantially increased classification accuracy of the presented method compared to two state-of-the-art approaches. Application to experimental subtomograms depicting endoplasmic-reticulum-associated ribosomal particles shows that AC3D is well suited to deconvolute the compositional heterogeneity of macromolecular complexes in situ.

关键词： CRYOELECTRONICS SIGNAL-to-noise ratio computer algorithms DATA analysis SPHERICAL harmonics

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A New Adaptive Controller for Performance Improvement of Automotive Suspension Systems with MR Dampers

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SAE INTERNATIONAL JOURNAL OF PASSENGER CARS-MECHANICAL SYSTEMS 2014年第3期7卷 959-971页

作者： Khajepour, Amir Agrawal, Ankur Univ Waterloo Waterloo ON Canada

A control algorithm is developed for active/semi-active suspensions which can provide more comfort and better handling simultaneously. A weighting parameter is tuned online which is derived from two components - slow and fast adaptation to assign weights to comfort and handling. After establishing through simulations that the proposed adaptive control algorithm can demonstrate a performance better than some controllers in prior-art, it is implemented on an actual vehicle (Cadillac STS) which is equipped with MR dampers and several sensors. The vehicle is tested on smooth and rough roads and over speed bumps.

关键词： automotive engineering Suspension System adaptive controller DAMPER COMFORT computer algorithms rough road Speed Bumps Handling (Psychology) performance boost

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Massive fungal biodiversity data re-annotation with multi-level clustering

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SCIENTIFIC REPORTS 2014年第1期4卷 1-9页

作者： Duong Vu Szoke, Szaniszlo Wiwie, Christian Baumbach, Jan Cardinali, Gianluigi Rottger, Richard Robert, Vincent CBS KNAW Fungal Biodivers Ctr Bioinformat Grp Utrecht Netherlands Max Planck Inst Informat D-66123 Saarbrucken Germany Univ Southern Denmark Inst Math & Comp Sci Odense Denmark Univ Perugia I-06100 Perugia Italy

With the availability of newer and cheaper sequencing methods, genomic data are being generated at an increasingly fast pace. In spite of the high degree of complexity of currently available search routines, the massive number of sequences available virtually prohibits quick and correct identification of large groups of sequences sharing common traits. Hence, there is a need for clustering tools for automatic knowledge extraction enabling the curation of large-scale databases. Current sophisticated approaches on sequence clustering are based on pairwise similarity matrices. This is impractical for databases of hundreds of thousands of sequences as such a similarity matrix alone would exceed the available memory. In this paper, a new approach called MultiLevel Clustering (MLC) is proposed which avoids a majority of sequence comparisons, and therefore, significantly reduces the total runtime for clustering. An implementation of the algorithm allowed clustering of all 344,239 ITS (Internal Transcribed Spacer) fungal sequences from GenBank utilizing only a normal desktop computer within 22 CPU-hours whereas the greedy clustering method took up to 242 CPU-hours.

关键词： GENOMICS RESEARCH DATABASES DOCUMENT clustering computer algorithms FUNGAL remediation

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