Sampling Biases in IP Topology Measurements

Considerable attention has been focused on the properties of graphs derived from Internet measurements. Router-level topologies collected via traceroute studies have led some authors to conclude that the router graph of the Internet is a scale-free graph, or more generally a power-law random graph. In such a graph, the degree distribution of nodes follows a distribution with a power-law tail. In this paper we argue that the evidence to date for this conclusion is at best insufficient. We show that graphs appearing to have power-law degree distributions can arise surprisingly easily, when sampling graphs whose true degree distribution is not at all like a power-law. For example, given a classical Erdös-Rényi sparse, random graph, the subgraph formed by a collection of shortest paths from a small set of random sources to a larger set of random destinations can easily appear to show a degree distribution remarkably like a power-law. We explore the reasons for how this effect arises, and show that in such a setting, edges are sampled in a highly biased manner. This insight allows us to distinguish measurements taken from the Erdös-Rényi graphs from those taken from power-law random graphs. When we apply this distinction to a number of well-known datasets, we find that the evidence for sampling bias in these datasets is strong.

Influence of network topology and data collection on network inference.

We recently developed an approach for testing the accuracy of network inference algorithms by applying them to biologically realistic simulations with known network topology. Here, we seek to determine the degree to which the network topology and data sampling regime influence the ability of our Bayesian network inference algorithm, NETWORKINFERENCE, to recover gene regulatory networks. NETWORKINFERENCE performed well at recovering feedback loops and multiple targets of a regulator with small amounts of data, but required more data to recover multiple regulators of a gene. When collecting the same number of data samples at different intervals from the system, the best recovery was produced by sampling intervals long enough such that sampling covered propagation of regulation through the network but not so long such that intervals missed internal dynamics. These results further elucidate the possibilities and limitations of network inference based on biological data.

Feature-oriented grid topology design for aerospace configurations

The last few years have seen a substantial increase in the geometric complexity for 3D flow simulation. In this paper we describe the challenges in generating computation grids for 3D aerospace configuations and demonstrate the progress made to eventually achieve a push button technology for CAD to visualized flow. Special emphasis is given to the interfacing from the grid generator to the flow solver by semi-automatic generation of boundary conditions during the grid generation process. In this regard, once a grid has been generated, push button technology of most commercial flow solvers has been achieved. This will be demonstrated by the ad hoc simulation for the Hopper configuration.

6-T SRAM cell design with nanoscale double-gate SOI MOSFETs: impact of source/drain engineering and circuit topology

The impact of source/drain engineering on the performance of a six-transistor (6-T) static random access memory (SRAM) cell, based on 22 nm double-gate (DG) SOI MOSFETs, has been analyzed using mixed-mode simulation, for three different circuit topologies for low voltage operation. The trade-offs associated with the various conflicting requirements relating to read/write/standby operations have been evaluated comprehensively in terms of eight performance metrics, namely retention noise margin, static noise margin, static voltage/current noise margin, write-ability current, write trip voltage/current and leakage current. Optimal design parameters with gate-underlap architecture have been identified to enhance the overall SRAM performance, and the influence of parasitic source/drain resistance and supply voltage scaling has been investigated. A gate-underlap device designed with a spacer-to-straggle (s/sigma) ratio in the range 2-3 yields improved SRAM performance metrics, regardless of circuit topology. An optimal two word-line double-gate SOI 6-T SRAM cell design exhibits a high SNM similar to 162 mV, I-wr similar to 35 mu A and low I-leak similar to 70 pA at V-DD = 0.6 V, while maintaining SNM similar to 30% V-DD over the supply voltage (V-DD) range of 0.4-0.9 V.

Effect of Selfish Node Behavior on Efficient Topology Design

The problem of topology control is to assign per-node transmission power such that the resulting topology is energy efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a noncooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady-state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed, and the network performance is comparable to that obtained from centralized algorithms.

Interaction strength, food web topology and the relative importance of species in food webs

P>1. We established complex marine communities, consisting of over 100 species, in large subtidal experimental mesocosms. We measured the strength of direct interactions and the net strength of direct and indirect interactions between the species in those communities, using a combination of theoretical and empirical approaches.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes.A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.