8124049
Limit points for average genus. I. 3-Connected and 2-connected simplicial graphs
Abstract It is demonstrated that a give value of average genus is shared by at most finitely many 2-connected simplicial graphs and by at most finitely many 3-connected graphs. Moreover, the distribution of values of average genus is sparse, in the following sense: within any finite real interval, there are at most finitely many different numbers that are values of average genus for 2-connected simplicial graphs or for 3-connected graphs. Thus, there are no limit points for the values of average genus of graphs in these classes. The potential applicability of these results to graph isomorphism testing is considered.
5242879
Interactive rind modeling
In this paper, we describe a technique, with roots in topological graph theory, that we call rind modeling. It provides for the easy creation of surfaces resembling peeled and punctured rinds. We show how the method's two main steps of: 1) creation of a shell or crust like the rind of an orange, and 2) opening holes in the crust by punching or peeling can be encapsulated into a real time semi-automatic interactive algorithm. We include a number of worked examples, some by students in a first modeling course, that demonstrate the ease with which a large variety of intricate rind shapes can be created.
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.
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.
34159049
Approximation algorithms for scheduling real-time jobs with multiple feasible intervals
Time-critical jobs in many real-time applications have multiple feasible intervals. Such a job is constrained to execute from start to completion in one of its feasible intervals. A job fails if the job remains incomplete at the end of the last feasible interval. Earlier works developed an optimal off-line algorithm to schedule all the jobs in a given job set and on-line heuristics to schedule the jobs in a best effort manner. This paper is concerned with how to find a schedule in which the number of jobs completed in one of their feasible intervals is maximized. We show that the maximization problem is $${\cal N}{\cal P}$$-hard for both non-preemptible and preemptible jobs. This paper develops two approximation algorithms for non-preemptible and preemptible jobs. When jobs are non-preemptible, Algorithm Least Earliest Completion Time First (LECF) is shown to have a 2-approximation factor; when jobs are preemptible, Algorithm Least Execution Time First (LEF) is proved being a 3-approximation algorithm. We show that our analysis for the two algorithms are tight. We also evaluate our algorithms by extensive simulations. Simulation results show that Algorithms LECF and LEF not only guarantee the approximation factors but also outperform other multiple feasible interval scheduling algorithms in average.
36905463
Quasi-threshold Graphs
Abstract Quasi-threshold graphs are defined recursively by the following rules: 1. (1) K1 is a quasi-threshold graph, 2. (2) adding a new vertex adjacent to all vertices of a quasi-threshold graph results in a quasi-threshold graph, 3. (3) the disjoint union of two quasi-threshold graphs is a quasi-threshold graph. This paper gives some new equivalent definitions of a quasi-threshold graph. From them, linear time recognition algorithms follow. We also give linear time algorithms for the edge domination problem and the bandwidth problem in this class of graphs.
9663015
Customizing a geographical routing protocol for wireless sensor networks
Several problems are required to be fixed in order to apply geographical routing protocol greedy perimeter stateless routing (GPSR) in wireless sensor networks. First, GPSR is designed under the assumption of symmetric links (i.e., bidirectional reachability), which is not realistic for many practical sensor networks, since wireless links in sensor networks often are asymmetric. Second, in sensor networks, packet destinations are often marked with locations instead of identifiers like IP address. Therefore, packets are routed to the home node, which is the node geographically closest to the destined location. Under GPSR, when the target location is outside the exterior perimeter of the sensor network, each packet has to visit all nodes on the boundary of the sensor network topology before it identifies the home node, which results in energy inefficiency. Third, due to the dynamic nature of sensor networks, maintaining data consistency, that is, data retrieved from the home node for a location should be consistent with the data sent to the same location, becomes a challenge when home nodes change. We propose on-demand GPSR (OD-GPSR), a data driven geographical routing protocol customized for sensor networks with solutions to the above three problems. Simulation results show that OD-GPSR performs well in terms of energy efficiency and packet delivery rate at the cost of a little bit more packet delivery delay.
26267867
Optimal Bayesian sampling acceptance plan with random censoring
Abstract The sampling acceptance scheme with censoring is one of the important life inspection problems. In this article, a general model of sampling acceptance plan for the exponential distribution with exponentially distributed random censoring is presented and investigated using Bayesian decision theory. We consider a loss function which includes the sampling cost, time-consuming cost and decision loss to determine the optimal sampling acceptance plan. Under mild assumptions, the optimal Bayes rule can be proved to be of a monotonic form. Moreover, we obtain optimal Bayes rules and explicit expressions of the Bayes risk for two special decision loss functions. Finally, a numerical example is given to demonstrate the model.
628849
Learning a circuit by injecting values
We propose a new model for exact learning of acyclic circuits using experiments in which chosen values may be assigned to an arbitrary subset of wires internal to the circuit, but only the value of the circuit's single output wire may be observed. We give polynomial time algorithms to learn (1) arbitrary circuits with logarithmic depth and constant fan-in and (2) Boolean circuits of constant depth and unbounded fan-in over AND, OR, and NOT gates. Thus, both AC0 and NC1 circuits are learnable in polynomial time in this model. Negative results show that some restrictions on depth, fan-in and gate types are necessary: exponentially many experiments are required to learn AND/OR circuits of unbounded depth and fan-in; it is NP-hard to learn AND/OR circuits of unbounded depth and fan-in 2; and it is NP-hard to learn circuits of bounded depth and unbounded fan-in over AND, OR, and threshold gates, even when the target circuit is known to contain at most one threshold gate and that threshold gate has threshold 2. We also consider the effect of adding an oracle for behavioral equivalence. In this case there are polynomial-time algorithms to learn arbitrary circuits of constant fan-in and unbounded depth and to learn Boolean circuits with arbitrary fan-in and unbounded depth over AND, OR, and NOT gates. A corollary is that these two classes are PAC-learnable if experiments are available.