9810371
The edge-face choosability of plane graphs
A plane graph G is said to be k-edge-face choosable if, for every list L of colors satisfying |L(x)|=k for every edge and face x, there exists a coloring which assigns to each edge and each face a color from its list so that any adjacent or incident elements receive different colors. We prove that every plane graph G with maximum degree Δ(G) is (Δ(G)+3)-edge-face choosable.
6853966
Total chromatic number of planar graphs with maximum degree ten
41218825
Relaxed game chromatic number of graphs
This paper discusses a coloring game on graphs. Let k, d be non-negative integers and C a set of k colors. Two persons, Alice and Bob, alternately color the vertices of G with colors from C, with Alice having the first move. A color i is legal for an uncolored vertex x if by coloring x with color i, the subgraph of G induced by those vertices of color i has maximum degree at most d. Each move of Alice or Bob colors an uncolored vertex with a legal color. The game is over if either all vertices are colored, or no more vertices can be colored with a legal color. Alice's goal is to produce a legal coloring which colors all the vertices of G, and Bob's goal is to prevent this from happening. We shall prove that if G is a forest, then for k = 3, d ≥ 1, Alice has a winning strategy. If G is an outerplanar graph, then for k = 6 and d ≥ 1, Alice has a winning strategy.
1003852
Upper Bounds of Entire Chromatic Number of Plane Graphs
The entire chromatic number χvef (G) of a plane graph G is the least number of colors assigned to the vertices, edges and faces so that every two adjacent or incident pair of them receive different colors. Kronk and Mitchem (1973) conjectured that χvef (G) ≤ 1 + 4 for every plane graph G. In this paper we prove the conjecture for a plane graph G having χ ′(G) = 1 and give a upper bound χvef (G) ≤ 1+ 5 for all plane graphs, where χ ′(G) and 1 are the chromatic index and the maximum degree of G, respectively.
11186519
Edge-partitions of graphs of nonnegative characteristic and their game coloring numbers
Let G be a graph of nonnegative characteristic and let g(G) and @D(G) be its girth and maximum degree, respectively. We show that G has an edge-partition into a forest and a subgraph H so that (1) @D(H)= =11; (2) @D(H)= =7; (3) @D(H)= =5 or G does not contain 4-cycles and 5-cycles; (4) @D(H)= =11; (2) col"g(G)= =7; (3) col"g(G)= =5 or G contains no 4-cycles and 5-cycles; (4) col"g(G)=<10 if G does not contain 4-cycles.
26709203
The Linear 2-Arboricity of Outerplanar Graphs
1857380
Mining RNA Tertiary Motifs with Structure Graphs
We present a novel application of graph database mining to identify tertiary motifs in RNA structures. In our method, we abstract an RNA molecule as a labeled graph and use a frequent subgraph mining technique to derive tertiary motifs. By applying our technique to ribosome RNA and transfer RNA, we have identified known RNA tertiary motifs such as the ribose zipper and U-turn, plus candidates for novel tertiary motifs. Finally, we suggest an iterative multiple structure alignment algorithm to classify tertiary motifs and generate consensus motifs.
17218997
Profiling program behavior for anomaly intrusion detection based on the transition and frequency property of computer audit data
Intrusion detection is an important technique in the defense-in-depth network security framework. In recent years, it has been a widely studied topic in computer network security. In this paper, we present two methods, namely, the Hidden Markov Models (HMM) method and the Self Organizing Maps (SOM) method, to profile normal program behavior for anomaly intrusion detection based on computer audit data. The HMM method utilizes the transition property of events while SOM method relies on the frequency property of events. Two data sets, CERT synthetic Sendmail system call data collected in the University of New Mexico (UNM) and Live FTP system call data collected in the CNSIS lab of Xi'an Jiaotong University, were used to assess the two methods. Testing results show that the HMM method using the transition property of events produces good detection performance while high computational expense is required both for training and detection. The HMM method is better than other two methods reported previously in terms of detection accuracy for the same data set. The SOM method considering the frequency property of events, on the other hand, is suitable for real-time intrusion detection because of its capability of processing a large amount of data with low computational overhead.
38145479
Pre-scheduling on the domain of integers
A preschedule is a static schedule without assuming constant and completely predictable rate of resource supply. A generalized prescheduling framework and a sound, complete, and PTIME preschedule generator was proposed in Wang et al. (2004) based on linear programming (LP). Since infinitely small time slices are not implementable for resources with context switch overhead, it is desirable to define and solve the prescheduling problem on the domain of integers so that context switching can occur only at boundaries of time quantums. However, integral LP (ILP) is NP-hard in the strong sense in general, so the ILP approach is not applicable and better techniques are needed. This paper answers this challenge by giving a sound, complete and PTIME rational-to-integral preschedule transformer based on a technique which we call "round-and-compensate".
45794206
Rational Quadratic Parameterizations of Quadrics
Every irreducible quadric in E3 has infinitely many different rational quadratic parameterizations. These parameterizations and the relationships between them are investigated. It is shown that every faithful rational quadratic parameterization of a quadric can be generated by a stereographic projection from a point on the quadric, called the center of projection (COP). Two such parameterizations for the same quadric are related by a rational linear reparameterization if they have the same COP; otherwise they are related by a rational quadratic reparameterization. We also consider unfaithful parameterizations for which, in general, a one-to-one correspondence between points on the surface and parameters in the plane does not exist. It is shown that all unfaithful rational quadratic parameterizations of a properly degenerate quadric can be characterized by a simple canonical form, and there exist no unfaithful rational quadratic parameterizations for a nondegenerate quadric. In addition, given a faithful rational quadratic parameterization of a quadric, a new technique is presented to compute its base points and inversion formula. These results are applied to solve the problems of parameterizing the intersection of two quadrics and reparameterizing a given quadric parameterization with respect to a different COP without implicitization.
36057715
Guest Editors' Introduction: Special Issue on Mining Biological Data
MINING biological data is an emerging area of intersection between data mining and bioinformatics. Bioinformaticians have been working on the research and development of computational methodologies and tools for expanding the use of biological, medical, behavioral, or health-related data. Data mining researchers have been making substantial contribution to the development of models and algorithms to meet challenges posed by the bioinformatics research. Some successful examples are frequent pattern discovery on biological molecules, text mining in biomedical literature, information integration, probabilistic modeling of genome sequences, etc. This special issue of the IEEE Transactions on Knowledge and Data Engineering features a collection of 11 papers, selected from 54 submissions, representing recent advances at the frontier of mining biological data. Mining frequent trees is very useful in bioinformatics applications. The first paper, “Efficiently Mining Frequent Trees in a Forest: Algorithms and Applications” by Mohammed J. Zaki, formulates the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. The author presents TreeMiner, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scope-list. TreeMiner has been proven to be superior to previous methods such as PatternMatcher and has shown promising results in analyzing RNA structure and phylogenetics data sets. In the second paper, “Frequent Substructure-Based Approaches for Classifying Chemical Compounds” by Mukund Deshpande, Michihiro Kuramochi, Nikil Wale, and George Karypis, the authors devise a substructurebased classification algorithm that decouples the substructure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topological and geometric substructures present in the data set. The advantage of this approach is that, during classification model construction, all relevant substructures are available and thus allow the classifier to intelligently select the most discriminating ones. This approach is employed to build models to correctly assign chemical compounds to various classes of interests, which have many applications in pharmaceutical research and are used extensively at various phases during the drug development process. Glycans, or carbohydrate sugar chains, are regarded as the third class of biological molecules, subsequent to DNA and proteins, and the recent advent of glycome informatics has generated an increasing number of glycan structures and annotation data. Glycans play important roles in the development and functioning of multicellular organisms and their structures can be represented by labeled ordered trees. The third paper, “A Probabilistic Model for Mining Labeled Ordered Trees: Capturing Patterns in Carbohydrate Sugar Chains” byNobuhisaUeda, Kiyoko F. Aoki-Kinoshita, Atsuko Yamaguchi, Tatsuya Akustu, and Hiroshi Mamitsuka, proposes a probabilistic model for mining labeled ordered trees and an EM algorithm for efficient learning. Proteins are the machinery of life. A number of techniques have been developed to classify proteins according to important features in their sequences, secondary structures, or three-dimensional structures. The fourth paper, “Finding Patterns on Protein Surfaces: Algorithms and Applications to Protein Classification” by Xiong Wang, introduces a novel approach to protein classification based on significant geometric patterns on the surface of a protein. The binding in protein-protein interactions exhibits a kind of biochemical stability in cells, which can be described by the mathematical notion of the fixed points. In the fifth paper, “Using Fixed Point Theorems to Model the Binding in Protein-Protein Interactions” by Jinyan Li and Haiquan Li, the authors define a point as a protein motif pair consisting of two traditional protein motifs. They propose a method to discover stable motif pairs of a given function from a large protein interaction sequence data set. With the rapid growth of articles on genomics research, it has become a challenge for biomedical researchers to access this ever-increasing quantity of information to understand the newest discovery of functions of proteins they are studying. To facilitate functional annotation of proteins by utilizing the huge amounts of biomedical literature and transforming the knowledge into easily accessible database formats, the text mining technique thus becomes essential. The sixth paper, “Literature Extraction of Protein Functions Using Sentence Pattern Mining” by Jung-Hsien Chiang and Hsu-Chun Yu, proposes the method of sentence pattern mining to extract protein functions from biomedical literature. Identifying concepts that have already been patented is essential for undertaking new biomedical research. Traditional keyword-based search on patent databases may not be sufficient enough to retrieve all the relevant information, especially for the biomedical domain. The seventh paper, “Information Retrieval and Knowledge Discovery Utilizing a BioMedical Patent Semantic Web” by Sougata Mukherjea, Bhuvan Bamba, and Pankaj Kankar, presents BioPatentMiner, a system that facilitates information retrieval and knowledge discovery from biomedical patents. BioPatentMiner first identifies biological terms IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 17, NO. 8, AUGUST 2005 1019