39858986
On the Connectivity of (4;g)-Cages
17919993
A Note on the Edge-Connectivity of Cages
A (k; g)-graph is a k-regular graph with girth g. A (k; g)-cage is a (k; g)-graph with the smallest possible number of vertices. In this paper we prove that (k; g)cages are k-edge-connected if k ≥ 3 and g is odd.
8410122
On connectivity of reconfigurable impact networks in ageless aerospace vehicles
The research results presented in this paper were obtained as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) project. We describe the underlying principles, methodology, and preliminary results of modelling and simulating a multi-cellular sensor and communication network in a dynamic decentralised setting, motivated by a self-monitoring, self-repairing AAV. Such networks are expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we address the problem of forming a reconfigurable network (a minimum spanning tree) connecting cells that detected non-critical impacts, in presence of connectivity disruptions caused by critical impacts. The presented algorithm is based on the ant colony metaphor and may be complemented by gradient-based techniques. In addition, we measure the robustness of impact networks and present quantitative metrics that clearly identify phase transitions in network connectivity, separating chaotic dynamics from ordered and robust patterns. © 2005 Elsevier B.V. All rights reserved.
14807870
Modeling and Predicting Face Recognition System Performance Based on Analysis of Similarity Scores
This paper presents methods of modeling and predicting face recognition (FR) system performance based on analysis of similarity scores. We define the performance of an FR system as its recognition accuracy, and consider the intrinsic and extrinsic factors affecting its performance. The intrinsic factors of an FR system include the gallery images, the FR algorithm, and the tuning parameters. The extrinsic factors include mainly query image conditions. For performance modeling, we propose the concept of "perfect recognition", based on which a performance metric is extracted from perfect recognition similarity scores (PRSS) to relate the performance of an FR system to its intrinsic factors. The PRSS performance metric allows tuning FR algorithm parameters offline for near optimal performance. In addition, the performance metric extracted from query images is used to adjust face alignment parameters online for improved performance. For online prediction of the performance of an FR system on query images, features are extracted from the actual recognition similarity scores and their corresponding PRSS. Using such features, we can predict online if an individual query image can be correctly matched by the FR system, based on which we can reduce the incorrect match rates. Experimental results demonstrate that the performance of an FR system can be significantly improved using the presented methods
13042163
Non-Axiomatic Reasoning System (Version 4.1)
NARS (Non-Axiomatic Reasoning System) is an intelligent reasoning system. It answers questions according to the knowledge originally provided by its user. What makes it different from conventional reasoning systems is its ability to learn from its experience and to work with insufficient knowledge and resources. The NARS 4.1 demo is a Java applet. It comes with help information and simple examples to show how the system does deduction, induction, abduction, analogy, belief revision, membership evaluation, relational inference, backward inference, new concept formation, and so on, in a unified manner. The demo also allows its user to create new examples to test the system, as well as to see the internal structure and process when the system is running. The on-line help document contains links to relevant publications. A previous version of the system, NARS 3.0, is described in detail in (Wang, 1995), which, and other related publications, are available at the author’s web page.
8406634
Cycles in 2-connected graphs
Let Gn be a class of graphs on n vertices. For an integer c, let ex(gn, c) be the smallest integer such that if G is a graph in Gn with more than ex(Gn, c) edges, then G contains a cycle of length more than c. A classical result of Erdos and Gallai is that if Gn is the class of all simple graphs on n vertices, then ex(Gn, c) = c/2 (n - 1). The result is best possible when n - 1 is divisible by c - 1, in view of the graph consisting of copies of Kc all having exactly one vertex in common. Woodall improved the result by giving best possible bounds for the remaining cases when n - 1 is not divisible by c - 1, and conjectured that if Gn is the class of all 2-connected simple graphs on n vertices, then ex(Gn, c) = max{f(n,2,c), f(n, ⌊c/2 ⌋,c)}, where f(n, t, c) = (c+1-t 2) + t(n - c - 1 + t), 2 ≤ t ≤ c/2, is the number of edges in the graph obtained from Kc+1-t by adding n-(c + 1 - t) isolated vertices each joined to the same t vertices of Kc+1-t. By using a result of Woodall together with an edge-switching technique, we confirm Woodall's conjecture in this paper.
15398175
Hardware-based Packet Classification Made Fast and Efficient
To achieve fast packet classification, a hardware-based scheme, cross-producting recurrence (CPR), based on a formerly proposed cross-producting scheme is proposed. This scheme simplifies the classification procedure and decrease the distinct combinations of fields by hierarchically decomposing the multi-dimensional space. In the new scheme, the multi-dimensional space is endowed with a hierarchical property which self-divides into several smaller subspaces, whereas the procedure of packet classification is translated into repeatedly searching for matching subspaces. The required storage of the proposed scheme is significantly reduced since the distinct fields of subspaces is controlled by a pre-defined configuration and can be much less than that of the filters. The experimental results demonstrate the effectiveness and scalability of the proposed scheme