A new concurrency control protocol for parallel Btree structures, MARK-OPT, is proposed. MARK-OPT marks the lowest structure-modification-operation (SMO) occurrence point during optimistic latch coupling operations, t...
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In this paper, we propose a nonstop system upgrade method without significant performance degradation for data management software. To reduce disk accesses and network traffic, we construct logical nodes inside a phys...
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Recovery of 3D body pose is a fundamental problem for human motion analysis in many applications. This paper introduces a new silhouette-based framework for inferring human pose from monocular uncalibrated camera and ...
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Recovery of 3D body pose is a fundamental problem for human motion analysis in many applications. This paper introduces a new silhouette-based framework for inferring human pose from monocular uncalibrated camera and pays more attention on how to retrieving 3D body pose by matching 2D silhouette based on shape context. Finally we retrieve a group of 2D silhouettes from one human motion which has two different viewpoints. By many experiments, the rate of 2D silhouette matching we got is been proved more accurate than that generated by using other methods we have known in this field. To accelerate the matching process of calculating shape context, we use PCA (principal components analysis) to reduce the computation of complexity. In the paper we use trampoline and box sport as examples of complex human motion, to demonstrate the effectiveness of our approach and compare the results with those obtained with Hu moments methods
This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. ...
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This paper proposes a supervised version of a learning algorithm for a constructive neuro-immune network. The proposed methodology is developed by taking ideas from the immune system and learning vector quantization. The resulting classification algorithm is characterized by high-performance, similar to the ones produced by alternative methods in the literature, and parsimonious solutions, with a much smaller set of prototypes per class when compared with the other approaches. The number of prototypes is automatically defined by the convergence criterion. The algorithm requires a single user-defined parameter for training, associated with the convergence criterion, and the computational cost is sufficiently reduced to support applications involving large data sets.
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from support vector machines (SVM) that, for classification problems, ...
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ISBN:
(纸本)0769525210
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from support vector machines (SVM) that, for classification problems, examples lying close to class boundary usually have more influence and thus are more informative than those far from the boundary. We utilize a nonlinear technique - reduced set (RS) method and a new image distance metric to generate new examples, and then add them to the original collected database to enhance it. Extensive experiments show that the proposed approach has an encouraging performance
Aiming at the two main shortcomings in Homology Modeling, we have designed and established a domain clustering database. Searching the database is a fundamental work for it. However, current alignment algorithms are m...
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Aiming at the two main shortcomings in Homology Modeling, we have designed and established a domain clustering database. Searching the database is a fundamental work for it. However, current alignment algorithms are mainly based on the sequences, ignoring the structure conservation in domain. This paper proposed a profile-based alignment which considers the structure information into the profile, based on the character of our domain database. We designed an experiment within the database. The results show that both the quality and sensitivity of our scheme are better than pure Smith-Waterman and sequence-based profile algorithms. We strongly believe that this work can help to improve the protein structure prediction
In this paper, we introduce a novel discriminative feature which is efficient for pose estimation. The multi-view face representation is based on local Gabor binary patterns (LGBP) and encodes the local facial charact...
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ISBN:
(纸本)0769525210
In this paper, we introduce a novel discriminative feature which is efficient for pose estimation. The multi-view face representation is based on local Gabor binary patterns (LGBP) and encodes the local facial characteristics in to a compact feature histogram. In LGBP, Gabor filters can extract the feature of the orientation of head and local binary pattern (LBP) can extract the features official local orientation. To keep the spatial information of the multi-view face images, LGBP is operated on many sub-regions of the images. The combination of them can represent well and truly the multi-view face images. Considering the derived feature space, a radial basis function (RBF) kernel SVM classifier is trained to estimate pose. Extensive experiments demonstrate that the facial representation can be effective for pose estimation
Recently, large-scale protein-protein interactions were recovered using the similar two-hybrid system for the model systems. This information allows us to investigate the protein interaction network from a systematic ...
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Recently, large-scale protein-protein interactions were recovered using the similar two-hybrid system for the model systems. This information allows us to investigate the protein interaction network from a systematic point of view. However, experimentally determined interactions are susceptible to errors. A previous assessment estimated that only ~10% of the interactions can be supported by more than one independent experiment, and about half of the interactions may be false positives. These false positives might unnecessarily link unrelated proteins, resulting in huge apparent interaction clusters, which complicate elucidation for the biological importance of these interactions. Address this problem, we present an approach to integrate, assess and characterize all available protein-protein interactions in model organisms yeast and fly. We first integrate all available protein-protein interaction databases of yeast and fly, and merge all the datasets. We then use machine learning techniques to score the reliability for each interaction, and to rigorously validate the scoring scheme of yeast protein-protein interactions from different aspects. Our results show that this scoring scheme provides a good basis for selecting reliable protein-protein interaction dataset
This paper describes the development of a Multi-Electrode Array(MEA)with Guided Network for Cell-to-Cell Communication Transduction using a standard integrated circuit(IC) fabrication *** conventional electronic syste...
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ISBN:
(纸本)1424401607
This paper describes the development of a Multi-Electrode Array(MEA)with Guided Network for Cell-to-Cell Communication Transduction using a standard integrated circuit(IC) fabrication *** conventional electronic system,bio-system requires a special handling environment that is uncommon in conventional IC *** work presented here demonstrated the interaction between electrically active cells such as neuron and cardiac cells and an IC based *** a carefully designed array,and ionic action of a cell, extracellular signals can be captured and imaged.
The CICQ switch fabric is an ideal solution to multi-terabit switch implementation owing to its nice distributed scheduling property. Round-robin algorithms have been extensively studied because of their simplicity fo...
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The CICQ switch fabric is an ideal solution to multi-terabit switch implementation owing to its nice distributed scheduling property. Round-robin algorithms have been extensively studied because of their simplicity for hardware implementation. It is known that round-robin algorithms provide high throughput under uniform traffic;however, the performance is degraded under nonuniform traffic. In this paper, the reason for the performance degradation of the existing round-robin algorithms is pointed out and then a class of dual round-robin algorithms is proposed. For the proposed algorithms, each input arbiter is associated with dual round-robin pointers named the primary pointer and the secondary pointer respectively. The input queue corresponding to the primary pointer has the highest priority being scheduled, and the decision for updating the primary pointer can be dynamically made relying on the input queue status. When the input queue corresponding to the primary pointer is blocked, other input queues can be uniformly scheduled according to the secondary pointer position. Simulations show that the dual round-robin algorithms can significantly improve the performance of the CICQ switch under nonuniform traffic.
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