Coloring the Faces of Convex Polyhedra so That Like Colors Are Far Apart
This paper proves the conjecture of Hornak and Jendrol' that the faces of a convex polyhedron with maximum vertex degree ? can be colored with 1+(?+7)(??1)d colors in such a way that each pair of faces that are distance at most d apart receives different colors.

title: On the Edge Reconstruction of Graphs Embedded in Surfaces, III,
abstract: In this paper, we prove that if a connected graphGembeddable in a surface?of characteristic?(?) satisfies (1) the minimum degree ofG?5, (2) |V(G)|??43?(?), thenGis edge reconstructible. A direct consequence of the above result is that a triangulationGof a surface of characteristic?(?) is edge reconstructible if |V(G)|??43?(?).

title: On the edge-reconstruction of graphs embedded in surfaces IV
abstract: In this paper, by applying the discharging method, we give simple proofs that (1) every n-vertex graph with minimum degree at least 3 and girth at least 5 that is embeddable in a surface Σ of Euler characteristic at least -n/8 is edge-reconstructible, and (2) every n-vertex graph with minimum degree at least 4 that is embeddable without faces of length at most 3 in a surface Σ of Euler characteristic at least -n/6 is edge-reconstructible.

title: On spanning trees and walks of low maximum degree

title: On Diagonally 10-Coloring Plane Triangulations
abstract: This article shows that the vertices of a plane triangulation may be colored with 10 colors such that every pair of vertices has different colors if they are either adjacent or diagonal, that is, that they are not adjacent but are adjacent to two faces which share an edge. This improves a result of Borodin, who showed that 11 colors were sufficient.

title: On cyclic colorings and their generalizations
abstract: A cyclic coloring is a vertex coloring such that vertices in a face receive different colors. Let Δ be the maximum face degree of a graph. This article shows that plane graphs have cyclic 9 5 Δ -colorings, improving results of Ore and Plummer, and of Borodin. The result is mainly a corollary of a best-possible upper bound on the minimum cyclic degree of a vertex of a plane graph in terms of its maximum face degree. The proof also yields results on the projective plane, as well as for d-diagonal colorings. Also, it is shown that plane graphs with Δ=5 have cyclic 8-colorings. This result and also the 9 5 Δ result are not necessarily best possible.

title: Recursive Algorithms for a Novel Identification Criterion
abstract: A new identification criterion is concerned for disturbed system with color noise in this paper. The cost function considers both the errors between the actual outputs and the estimation result and the difference of each estimation error. Under this performance function, a new recursive algorithm DSCN (Disturbed System with Color Noise) is proposed. It could reduce the influence of noise in system identification, further decrease the prediction error. Several simulation examples are given to illustrate the method's anti-disturbance performance.

title: Optimal Spectrum Management in Multiuser Interference Channels
abstract: In this paper, we study the problem of continuous frequency optimal spectrum management in multiuser frequency selective interference channels. We assume that interference is treated as noise by the decoders, and separate encoding is applied. First, a simple pair-wise channel condition for frequency division multiple access schemes to achieve all Pareto optimal points of the rate region is derived. It enables fully distributed global optimal decision making on whether any two users should use orthogonal channels. Next, we present an analytical solution to finding the maximum sum-rate in two-user symmetric frequency flat channels. Generalizing this solution to frequency selective channels, a convex optimization is established that yields the global optimum. Finally, we show that our method generalizes to K-user (K ≥ 2) weighted sum-rate maximization in asymmetric frequency selective channels, and we transform this classic nonconvex optimization to an equivalent convex optimization in the primal domain.

title: Tibetan Language Continuous Speech Recognition Based on Dynamic Bayesian Network
abstract: Dynamic Bayesian Networks (DBN) area subset of the probabilistic graphical models (PGM) which include hidden Markov model (HMM) as a special case. One of the principle weaknesses of HMMs is the independence assumptions on the observed and hidden processes of speech. This paper proposed to use the DBN for Tibetan language continuous speech recognition.The proposed approach is based on structure learning paradigm in DBN framework. This approach has the advantage to guaranty that the resulting model represents speech with higher fidelity than HMM. The results of recognition experiments show that the proposed algorithm has better performance of recognition rate and noise suppression compared with HMM.

title: Evolving Model for Small-World Network Based on Benefit Choice
abstract: Based on reviewing the research progress of evolving model for small-world network, the defects of present studies were pointed out. Taking a square grid as the basis, an evolving model for small-world network based on benefit choice was built. This model is different from other papers in which limitation of vector' degree was considered. Then, the author proposed a simulation scheme and compared the network generated by simulation with a same scale random network. The results show that this network has small-world characteristic. Finally, several directions were given.

title: MBBNTree Classifier Algorithm Based on Active Learning from Unlabeled Samples
abstract: MBBNTree algorithm, which integrates the advantage of Markov blanket Bayesian networks (MBBN) and decision tree, would behave better performance than other Bayesian networks for classification. But the available training samples with actual classes are not enough for building MBBNTree classifier in practice. Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, then selecting the most informative ones with respect to a given cost function for a human to label. In this paper, the MBBNTree classifier algorithm based on active learning would be presented to solve the problem of learning MBBNTree classifier from unlabeled samples. Experimental results show that the proposed algorithm can reach the same accuracy as passive learning with few labeled training examples.