Aiming at DNA encoding sequences design, a micro-genetic algorithm (MGA) is proposed by introducing a sharing function based on similarity and H-measure of DNA sequences. In the algorithm, six design criteria are adop...
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ISBN:
(纸本)9781467398732
Aiming at DNA encoding sequences design, a micro-genetic algorithm (MGA) is proposed by introducing a sharing function based on similarity and H-measure of DNA sequences. In the algorithm, six design criteria are adopted and four genetic operators are applied. Compared with the previous results, the algorithm can get better DNA sequences and improve the computational efficiency.
Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- m...
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Single particle analysis, which can be regarded as an average of signals from thousands or even millions of particle projections, is an efficient method to study the three-dimensional structures of biological macro- molecules. An intrinsic assumption in single particle analysis is that all the analyzed particles must have identical composition and conformation. Thus specimen heterogeneity in either composition or conformation has raised great challenges for high-resolution analysis. For particles with multiple conformations, inaccurate align- ments and orientation parameters will yield an averaged map with diminished resolution and smeared density. Besides extensive classification approaches, here based on the assumption that the macromolecular complex is made up of multiple rigid modules whose relative orien- tations and positions are in slight fluctuation around equilibriums, we propose a new method called as local optimization refinement to address this conformational heterogeneity for an improved resolution. The key idea is to optimize the orientation and shift parameters of each rigid module and then reconstruct their three-dimen- sional structures individually. Using simulated data of 80S/70S ribosomes with relative fluctuations between the large (60S/50S) and the small (40S/30S) subunits, we tested this algorithm and found that the resolutions of both subunits are significantly improved. Our method provides a proof-of-principle solution for high-resolutionsingle particle analysis of macromolecular complexes with dynamic conformations.
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will...
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ISBN:
(纸本)9781509008070
Along with the development of social network, more and more people know the world by reading news. The problem about what kind of emotion is inspired when people read news is very worthy of discussion. This paper will mix Deep Belief Networks (DBN) model and Support Vector Machine (SVM) to a hybrid neural network model by using the Contrast Divergence (CD) algorithm to estimate the weights when training a generating model, ensure that each layer of the Restricted Boltzmann Machine (RBM) mapping the features of the inputs to the best. At the same time, we cascade the last layer of DBN and a SVM classifier to adjust judging performance. And a set of tags will be attached to the top (Associative Memory), through a process of parameter tuning, learn the identifying weights to obtain a network for the task of text classification. The experimental results show that the hybrid neural network model works better than the traditional text categorization method based on simple characteristics (such as CHI), and it is more suitable for extracting text semantic characteristics.
By using facial motion data from passive optical motion capture system, this paper proposed an animation reconstruction approach, which is aimed to solve the common problem: model discontinuity and reconstructing a re...
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The protein structure folding is one of the most challenging problems in the field of bioinformatics. The main problem of protein structure prediction in the 3D toy model is to find the lowest energy conformation. Alt...
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The protein structure folding is one of the most challenging problems in the field of bioinformatics. The main problem of protein structure prediction in the 3D toy model is to find the lowest energy conformation. Although many heuristic algorithms have been proposed to solve the protein structure prediction (PSP) problem, the existing algorithms are far from perfect since PSP is an NP-problem. In this paper, we proposed an artificial bee colony (ABC) algorithm based on the toy model to solve PSP problem. In order to improve the global convergence ability and convergence speed of the ABC algorithm, we adopt a new search strategy by combining the global solution into the search equation. Experimental results illustrate that the suggested algorithm can get the lowest energy when the algorithm is applied to the Fibonacci sequences and to four real protein sequences which come from the Protein Data Bank (PDB). Compared with the results obtained by PSO, LPSO, PSO-TS, PGATS, our algorithm is more efficient.
In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the ...
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In this article, we investigate the use of joint a-entropy for 3D ear matching by incorporating the local shape feature of 3D ears into the joint a-entropy. First, we extract a sut^cient number of key points from the 3D ear point cloud, and fit the neighborhood of each key point to a single-value quadric surface on product parameter regions. Second, we define the local shape feature vector of each key point as the sampling depth set on the parametric node of the quadric surface. Third, for every pair of gallery ear and probe ear, we construct the minimum spanning tree (MST) on their matched key points. Finally, we minimize the total edge weight of MST to estimate its joint a-entropy the smaller the entropy is, the more similar the ear pair is. We present several examples to demonstrate the advantages of our algorithm, including low time complexity, high recognition rate, and high robustness. To the best of our knowledge, it is the first time that, in computer graphics, the classical information theory of joint a-entropy is used to deal with 3D ear shape recognition.
This study investigates the emergency decision-making problem in a multi-agent system. Departments are modeled as agents to perform coordinated planning to obtain a global action plan with a short execution time const...
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We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propos...
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We investigate quantum state tomography(QST) for pure states and quantum process tomography(QPT) for unitary channels via adaptive measurements. For a quantum system with a d-dimensional Hilbert space, we first propose an adaptive protocol where only 2d. 1 measurement outcomes are used to accomplish the QST for all pure states. This idea is then extended to study QPT for unitary channels, where an adaptive unitary process tomography(AUPT) protocol of d2+d.1measurement outcomes is constructed for any unitary channel. We experimentally implement the AUPT protocol in a 2-qubit nuclear magnetic resonance system. We examine the performance of the AUPT protocol when applied to Hadamard gate, T gate(/8 phase gate), and controlled-NOT gate,respectively, as these gates form the universal gate set for quantum information processing purpose. As a comparison, standard QPT is also implemented for each gate. Our experimental results show that the AUPT protocol that reconstructing unitary channels via adaptive measurements significantly reduce the number of experiments required by standard QPT without considerable loss of fidelity.
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