2261180
Graph Ear Decompositions and Graph Embeddings
Ear decomposition of a graph has been extensively studied in relation to graph connectivity. In this paper, a connection of ear decomposition to graph embeddings is exhibited. It is shown that constructing a maximum-paired ear decomposition of a graph and constructing a maximum-genus embedding of the graph are polynomial-time equivalent. Applications of this connection are discussed.
16171801
Regular Mesh Construction Algorithms using Regular Handles
This paper presents our recent theoretical results on high genus modeling. We introduce a new concept called regular handles. Using regular handles it is possible to increase genus without increasing the number of vertices. Using regular handles a wide variety of mesh structures can be constructed. One of the usages of regular handles is to construct families of regular meshes, which is useful to create a wide variety of high genus mesh structures
3146463
On Strong Menger-Connectivity of Star Graphs
Motivated by parallel routing in networks with faults, we study the following graph theoretical problem. Let G be a d-regular graph. We say that G is strongly Menger-connected if for any copy Gf of G with at most d - 2 nodes removed, every pair of nodes u and v in Gf are connected by min{deg f(u), deg f(v)} node-disjoint paths in Gf, where deg f(u) and deg f(v) are the degrees of the nodes u and v in Gf, respectively. We show that the star graphs, which are a recently proposed attractive alternative to the widely used hypercubes as network models, are strongly Menger-connected. An algorithm of optimal running time is developed that constructs the maximum number of node-disjoint paths of nearly optimal length in star graphs with faults.
31382426
Strong Fault-Tolerance: Parallel Routing in Networks with Faults
A d-regular network G is strongly fault-tolerant if for any copy Gf of G with at most d - 2 faulty nodes, every pair of nonfaulty nodes u and v in Gf admits min{df(u), df (v)} node-disjoint paths in Gf from u to v, where df(u) and df(v) are the degrees of the nodes u and v in Gf. We show that popular network structures, such as hypercube and star networks, are strongly fault-tolerant. We develop efficient algorithms that construct the maximum number of node-disjoint paths of optimal or nearly optimal length in these networks with faulty nodes. Our algorithms are optimal in terms of their running time.
1021255
A minimal and complete set of operators for the development of robust manifold mesh modelers
In this paper, we identify a minimal and complete set of fundamental operators, which is necessary and sufficient for performing all homeomorphic and topological operations on 2- manifold mesh structures. Efficient algorithms are developed for the implementation of these operators. We also developed a set of powerful, user-friendly, and effective operators at the level of user-interface. Using these operators, we have developed a prototype system for robust, interactive, and user-friendly modeling of orientable 2-manifold meshes. Users of our system can perform a large set of homeomorphic and topological changes with these user-interface level operators. Our system is topologically robust in the sense that users will never create invalid 2-manifold mesh structure with these operators. In our system, the homeomorphic and topological surgery operations can be applied alternatively on 2-manifold meshes. With our system, users can blend surfaces, construct rinds, and open holes on these rind shapes. With our system, the shapes that look like solid, non-manifold, or 2-manifold with boundary can be manipulated. The system also provides automatic texture mapping during topology changes.
16331940
Optimal Parallel Routing in Star Networks
Star networks have recently been proposed as attractive alternatives to the popular hypercube for interconnecting processors on a parallel computer. In this paper, we present an efficient algorithm that constructs an optimal parallel routing in star networks. Our result improves previous results for the problem.
204766
Scalability, fidelity, and containment in the potemkin virtual honeyfarm
The rapid evolution of large-scale worms, viruses and bot-nets have made Internet malware a pressing concern. Such infections are at the root of modern scourges including DDoS extortion, on-line identity theft, SPAM, phishing, and piracy. However, the most widely used tools for gathering intelligence on new malware -- network honeypots -- have forced investigators to choose between monitoring activity at a large scale or capturing behavior with high fidelity. In this paper, we describe an approach to minimize this tension and improve honeypot scalability by up to six orders of magnitude while still closely emulating the execution behavior of individual Internet hosts. We have built a prototype honeyfarm system, called Potemkin, that exploits virtual machines, aggressive memory sharing, and late binding of resources to achieve this goal. While still an immature implementation, Potemkin has emulated over 64,000 Internet honeypots in live test runs, using only a handful of physical servers.
62212743
Resampling for face detection by self-adaptive genetic algorithm
Over the past ten years, face detection has been thoroughly studied in computer vision research for its interesting applications. However, all of the state-of-the-art statistical methods suffer from the data collection for training a classifier. This paper presents a self-adaptive genetic algorithm (GA)-based method to swell face database through re-sampling from the existing faces. The basic idea is that a face is composed of a limited components set, and the GA can simulate the procedure of heredity. This simulation can also cover the variations of faces in different lighting conditions, poses, accessories, and quality conditions. To verify the generalization capability of the proposed method, we also use the expanded database to train an Adaboost-based face detector and test it on the MIT+CMU frontal face test set. The experimental results show that the data collection can be efficiently speeded up by the proposed methods.
2647804
Extended DBP for (m, k)-Firm Based QoS
In this paper, an extended DBP (E_DBP) scheme is studied for (m,k)-firm constraint. The basic idea of the proposed algorithm takes into account the distance to exit a failure state, which is a symmetrical notion of distance to fall into a failure state in DBP. Quality of Service (QoS) in terms of dynamic failure and delay is evaluated. Simulation results reveal the effectiveness of E_DBP to provide better QoS.
6798576
Locational pricing and scheduling for an integrated energy-reserve market
It is well known that given a network that can become constrained on voltage or real power flows, reserves must also be spatially located in order to handle all credible contingencies. However, to date, there is no credible science-based method for assigning and pricing reserves in this way. Presented in this paper is a new scheduling algorithm incorporating constraints imposed by grid security considerations, which include one base case (intact system) and a list of possible contingencies (line-out, unit-lost, and load-growth) of the system. By following a cost-minimizing co-optimization procedure, both power and reserve are allocated spatially for the combined energy and reserve markets. With the Lagrange multipliers (dual variables) obtained, the scheduling algorithm also reveals the locational shadow prices for the reserve and energy requirements. Unlike other pricing and scheduling methods in use, which are usually ad-hoc and are based on engineering judgment and experience, this proposed formulation is likely to perform better in restructured markets when market power is a potential problem. An illustrative example of a modified IEEE 30-bus system is used to introduce concepts and present results.
33412733
Learning a Hidden Graph Using O(log n) Queries Per Edge
We consider the problem of learning a general graph using edge-detecting queries. In this model, the learner may query whether a set of vertices induces an edge of the hidden graph. This model has been studied for particular classes of graphs by Kucherov and Grebinski [1] and Alon et al.[2], motivated by problems arising in genome sequencing. We give an adaptive deterministic algorithm that learns a general graph with n vertices and m edges using O(m log n) queries, which is tight up to a constant factor for classes of non-dense graphs. Allowing randomness, we give a 5-round Las Vegas algorithm using \(O(m {\rm log}n+\sqrt{m}{\rm log}^{2}n)\) queries in expectation. We give a lower bound of Ω((2m/r) r/2) for learning the class of non-uniform hypergraphs of dimension r with m edges. For the class of r-uniform hypergraphs with bounded degree d, where d≤ n 1/( r− − 1)/(2r 1 + 2/( r− − 1)), we give a non-adaptive Monte Carlo algorithm using O(dnlog n) queries, which succeeds with probability at least 1–n − − c, where c is any constant.