DNA Sequences Classification Based on Immune Evolution Network
Classification is a major task in the gene sequence analysis. Based on the general principle of artificial immune system, this paper first constructed a classifier which inducted antibody-antigen identification, immune colonel reproduction, hypermutation, affinity mature and the network suppression, by simulating how the antigens stimulate the immune network and how the immune network responds. Then, a "leave-one-out" method was adopted to test the classifier's performance, applying 1-20th DNA sequences of Art-model-data with class attribute. Its accuracy was up to 90%. At last, a well-pleasing result was got on the prediction of 21-40th DNA sequences of Art-model-data.

title: Application of Immune Classifier Based on Increment of Diversity in the Model Species Genomes Identification
abstract: The model species genomes project has played a very important role in the research of human genomes. By comparing and identifying the genomes' information in the different biological evolution stages with the model species genomes, it is favorable to understand deeply the genomes' structure and function of the advanced species, especially the human being, and to reveal the life's essential law. In the paper, the DNA sequences of three model species including C. elegans, S. cerevisiae and A.thaliana are divided into three types: intron, exon, intergenic DNA at first. The bases of each sequence are given number (1, 2, hellip, N) base by base from the beginning. According to three phases of the codons, we can extract three subsequences which numbers are respectively 3n+1, 3n+2 and 3n+3 (n=0, 1, 2, ...,N/3-1). Calculating respectively the probability of four bases in three subsequence we can attain 12 parameters which are regarded as the state parameters of the diversity sources. Each sequence of intron, exon and intergenic DNA is expressed by a 12-dimension eigenvector. All of these sequences' 12-dimension samples are divided into the training sample set and the testing sample set. Then, according to the immune network theory, a immune classifier based on the diversity increment is constructed, which considers the reciprocal of the diversity increment as affinity function and makes the immune network evolve in the direction of identifying the antigens by submitting unceasingly the antigens one by one, which is a record of the training sample set. Finally, the classifier has high performance and its prediction accuracy is up to 85% by test.

title: A Novel Approach Using PCA and SVM for Face Detection
abstract: Nowadays, face detection and recognition have gained importance in security and information access. In this paper, an efficient method of face detection based on principal components analysis (PCA) and support vector machine (SVM) is proposed. It firsly filter the face potential area using statistical feature which is generated by analyzing local histogram distribution. And then, SVM classifier is used to detect face feature in the test image, SVM has great performance in classification task. PCA is used to reduce dimension of sample data. After PCA transform, the feature vectors, which are used for training SVM classifier, are generated. Our tests in this paper are based on CMU face database. The experimental results demonstrate that the proposed method is encouraging with a successful detection rate.

title: EOS: expertise oriented search using social networks
abstract: In this paper, we present the design and implementation of our expertise oriented search system, EOS http://www.arnetminer.net. EOS is a researcher social network system. It has gathered information about a half-million computer science researchers from the Web and constructed a social network among the researchers through their co-authorship. In particular, the relationship in the social network information is used in both ranking experts for a given topic and searching for associations between researchers. Our experimental results demonstrate that the proposed methods for expert finding and association search in a social network are both more effective and efficient than the baseline methods.

title: Externality Identification and Quantification of Transmission Construction Projects
abstract: This paper provides the evaluation method and model of the externality of the transmission projects, and identifies both negative and positive externalities, as well as makes quantitative and qualitative analysis of externality. In order to quantify the impact of transmission construction projects on the power flow distribution, power supply and demand, as well as the power price of the neighboring power grid, this paper operates the optimal power flow calculation on the Matlab platform through using the IEEE 30-Node Prototype System, and figures out the construction project's influence on the economic benefits of different power market participants. Based on the existing environmental evaluation study, this paper analyzes how the transmission construction project affects the natural resource and the environment and the daily life of the inhabitants. Taking into account the externality and quantifying the externality when evaluating the investment project can help the government and the investors to make the investment decision which is beneficial to the whole society.

title: Explicit Routing for Traffic Engineering in Labeled Optical Burst-Switched WDM Networks
abstract: Optical burst switching (OBS) is a promising technique for supporting high-capacity bursty data traffic over optical wavelength-division-multiplexed (WDM) networks. A label-switched path can be established to forward a burst control packet (BCP) if each OBS node is augmented with an IP/MPLS controller. Such a network is called a labeled OBS (LOBS) network, and it can exploit the explicit routing and constraint-based routing properties supported in the MPLS framework to perform traffic and resource engineering. In this paper, we consider the traffic-engineering problem in a LOBS network, using explicit routing to balance network load and to reduce the burst-loss probability. We model the traffic-engineering problem in a LOBS network as an optimization problem and propose two new objective functions to minimize network congestion. Our illustrative numerical examples show that the burst-loss probability can be significantly reduced using optimization technique, when compared with shortest-path routing.

title: Design and analysis of bi-directional pumping silicon Raman laser
abstract: In this paper, a novel design of bi-directional pumping scheme is proposed and analyzed for the first time in an attempt to verify its validity as a mean for loss reduction and gain enhancement in Raman silicon laser. FCA loss is identified as the key loss contributor in silicon Raman process which is highly dependent on peak input pump power level, from the simulation results to account for various loss mechanisms in on-off gain simulation. To solve the problem of high threshold and low gain obtained in Raman silicon laser, bi-directional pumping scheme is proposed to have the effects of loss reduction by peak power limitation and Raman gain enhancement through average power increment. This is also substantiated by simulation of average and instantaneous pump power level along the silicon waveguide length. With bi-directional pumping, Raman silicon laser could improve in its threshold power and gain/slope efficiency to enable it better application credibility.