5599627
Choosability and edge choosability of planar graphs without five cycles
Abstract It is proved that a planar graph G without five cycles is three degenerate, hence, four choosable, and it is also edge-( Δ ( G ) + 1)-choosable.
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
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
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.
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.
1177051
Visualization assisted detection of sybil attacks in wireless networks
In wireless networks,the authenticity and uniqueness of node identities are essential to the fundamental operations such as routing, resource allocation, and intrusion detection. In this paper, we investigate Sybil attack, an attack in which a malicious node illegitimately acquires multiple identities and performs as these nodes simultaneously. We propose an effective approach to monitoring and detecting such attacks by integrating network security and visualization methods. The security component explores the time-varying network topology and its statistical and geometry information to detect the existence of Sybil attacks. The visualization component incorporates the detection results and provides an effective mechanism to illustrate abnormal topology patterns and locate fake identities. These two components are integrated into a practical system that takes advantage of both interactive visualization and intelligent security methods. Experimental studies are conducted to investigate the impacts of the network parameters such as node connectivity on the detection capability of the proposed mechanism.
15982467
On intrinsic representations of 3D polygons for shape blending
Abstract An intrinsic representation of a geometric object comprises a set of parameters that are invariant under Euclidean transformations and determine the shape and size of the object uniquely. Unlike the case of a two-dimensional polygon which has essentially only one intrinsic representation, a three-dimensional (3D) polygon allows several variations. We study three different intrinsic representations of a 3D polygon, focusing on properties regarding their applications in shape blending. We show that only one of these representations, which is a natural extension to the intrinsic functions of a 3D differentiable curve, is suitable for shape blending, while the others lead to discontinuous shape transformation. Detailed discussions about the evaluation criteria, numerical sensitivity, and an inherent difficulty in shape blending of 3D polygons are also presented.
68223
Off-line Chinese signature verification based on support vector machines
This paper proposes a novel off-line Chinese signature verification method based on support vector machines. The method uses both static features and dynamic features. The static features include moment features and 16-direction distribution (an improvement on 4-direction distribution). The dynamic features include gray distribution and stroke width distribution. At last, support vector machine is used to classify the signatures. The main steps of constructing a signature verification system are discussed and experiments on real data sets show that the average error rate can reach 5%, which is obviously satisfactory. w that the average error rate can reach 5%, which is obviously satisfactory.
206580995
Discovering compact and highly discriminative features or combinations of drug activities using support vector machines
Nowadays, high throughput experimental techniques make it feasible to examine and collect massive data at the molecular level. These data, typically mapped to a very high dimensional feature space, carry rich information about functionalities of certain chemical or biological entities and can be used to infer valuable knowledge for the purposes of classification and prediction. Typically, a small number of features or feature combinations may play determinant roles in functional discrimination. The identification of such features or feature combinations is of great importance. In this paper, we study the problem of discovering compact and highly discriminative features or feature combinations from a rich feature collection. We employ the support vector machine as the classification means and aim at finding compact feature combinations. Comparing to previous methods on feature selection, which identify features solely based on their individual roles in the classification, our method is able to identify minimal feature combinations that ultimately have determinant roles in a systematic fashion. Experimental study on drug activity data shows that our method can discover descriptors that are not necessarily significant individually but are most significant collectively.