The spectrum efficiency is a greatly important issue when we establish connection requests in elastic optical data center networks (EODCNs). In this article, we address the spectrum efficiency problems of the shared-path protection with spectrum-sharing-maximized approaches for the optical network survivability. Two integer linear program (ILP) models, i.e., flow-based and path-based ILP models, named FB-ILP and PB-ILP, are developed to minimize the frequency slots (FSs) occupied with the spectrum-sharing-maximized protection, and two heuristic approaches with the spectrum-sharing-maximized protection (HA-SSMP) and with the general spectrum-sharing protection (HA-GSSP) are also proposed to improve spectrum efficiency in EODCNs. For comparison, we introduce a heuristic approach with the existing shared-path protection (HA-ESPP) in EODCNs. On the one hand, simulation results show that FB-ILP can minimize the number of FSs occupied, but leads to the higher average number of hops and longer running time compared to PB-ILP, HA-SSMP, HA-GSSP, and HA-ESPP in static traffic scenario in EODCNs. On the other hand, the simulation results of our proposed HA-SSMP are very close to the solutions of FB-ILP. In dynamic traffic scenario, simulation results show that HA-SSMP significantly improves the spectrum efficiency and effectively suppresses the blocking probability, but leads to the higher average number of hops compared to HA-GSSP and HA-ESPP in EODCNs.

Many temporal problems arising in automated planning and scheduling can be expressed as disjunctive temporal problems (DTPs). Most of DTP solvers in the literature treat DTPs as constraint satisfaction problems (CSPs) or satisfiability problems (SATs), and solve them using standard CSP (SAT) techniques. Basically DTPs are represented through logically related topological relations between temporal variables, however, unfortunately little work has been done on exploiting the topological information to direct the search for DTP resolving. According to the "fail-first "(FF) principle for dynamic variable ordering (DVO) heuristics in CSP literature, this paper proposes a DVO which is based on the topological structure of DTP (which is defined to be Disjunctive Temporal Network). Experimental results reveal that the proposed DVO outperforms Minimal Remaining Values heuristics-a DVO that is widely used in existing DTP solvers, especially for the hard and large-scale problems. And, a CSP based procedure with the best of the heuristics wins TSAT++ on most of the test problems.

In face recognition, the dimensionality of raw data is very high, dimension reduction (Feature Extraction) should be applied before classification. There exist several feature extraction methods, commonly used are Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) techniques. In this paper, we present a comparative study of some feature extraction methods for face recognition in the same conditions. The methods evaluated here include Eigenfaces, Kernel Principal Component Analysis (KPCA), Fisherfaces, Direct Linear Discriminant Analysis (D-LDA), Regularized Linear Discriminant Analysis (R-LDA), and Kernel Direct Discriminant Analysis (KDDA). For the purpose of comparison on feature extraction methods, we adopt Nearest Neighbor (NN) algorithm from existed classifiers of face recognition, since this classifier is common and simpleness. Empirical studies are conducted to evaluate these feature extraction methods with images from ORL Face Database, and it is found that in most cases LDA-based methods are efficient than PCA-based ones.

We study an expressive quantitative temporal model: disjunctive temporal problem (DTP), which was first proposed only in 1998 (Stergiou and Koubarakis). As extension of temporal constraint satisfaction problem (TCSP) (Dechter et al. 1991), DTP differs from TCSP in that two disjuncts in a same disjunctive constraint do not necessarily refer to same temporal variables. Traditionally, most of the DTP algorithms in the literature solve DTPs by treating them as constraint satisfaction problems (CSPs), and searching for solutions using standard CSP techniques, e.g. backtracking, back-jumping, forward checking, semantic branching, removal of subsumed variables, nogood recording, etc. Those CSP techniques are powerful in solving DTPs. However, an evident drawback of viewing DTPs as general CSPs is that much semantic information encoded in DTPs is neglected. In fact we can mine rich semantic information that can be exploited to reduce search space for DTPs (more than semantic branching). Through some topological analysis on the graphical representation of the problems, some techniques are developed to help to search solutions for other temporal models (e.g. TCSP), or to identify "crucial subproblems" for CSP (Epstein and Wallace, 2006). However, little effort has been made to exploit the inherent topological information in solving DTPs. Our idea runs on a graphical representation of DTPs - disjunctive temporal network (DTN). We define DTN as an edge-labeled weighted digraph, on which some relevant concepts are identified. Then, we define the concept of equivalency between DTNs with respect to their consistency. For a given DTN, deciding its consistency is ascribed to check the consistency of a DTN which is equivalent to it and has less constraints (edges). We iteratively reduce a DTN to a simpler but equivalent one according to a set of designed reduction rules (which can be performed within polynomial time). It is hoped that when the DTN reaches a fixed point under such reduction operation, the resulted DTN has minimal edges (which is like backdoor in SAT, or "near clique"). At last the resulted DTN (DTP) is transferred to CSP search phase, where we derive a special variable ordering strategy again through the DTN structure. We shall describe the generation of DTN structure, the DTN reduction rules, the implementation of the complete graph-DTP algorithm, and some first results of this approach.