Optimizing Sensing: From Water to the Web

Where should we place sensors to quickly detect contamination in drinking water distribution networks? Which blogs should we read to learn about the biggest stories on the Web? Such problems are typically NP-hard in theory and extremely challenging in practice. The authors present algorithms that exploit submodularity to efficiently find provably near-optimal solutions to large, complex real-world sensing problems

Temperature Sensor Placement Including Routing Overhead and Sampling Inaccuracies

Dynamic thermal management techniques require a collection of on-chip thermal sensors that imply a significant area and power overhead. Finding the optimum number of temperature monitors and their location on the chip surface to optimize accuracy is an NP-hard problem. In this work we improve the modeling of the problem by including area, power and networking constraints along with the consideration of three inaccuracy terms: spatial errors, sampling rate errors and monitor-inherent errors. The problem is solved by the simulated annealing algorithm. We apply the algorithm to a test case employing three different types of monitors to highlight the importance of the different metrics. Finally we present a case study of the Alpha 21364 processor under two different constraint scenarios

Stochastic reconstruction of sandstones

A simulated annealing algorithm is employed to generate a stochastic model for a Berea and a Fontainebleau sandstone with prescribed two-point probability function, lineal path function, and ``pore size'' distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples.Comment: 12 pages, 5 figure

Parallel Quantum Rapidly-Exploring Random Trees

In this paper, we present the Parallel Quantum Rapidly-Exploring Random Tree (Pq-RRT) algorithm, a parallel version of the Quantum Rapidly-Exploring Random Trees (q-RRT) algorithm. Parallel Quantum RRT is a parallel quantum algorithm formulation of a sampling-based motion planner that uses Quantum Amplitude Amplification to search databases of reachable states for addition to a tree. In this work we investigate how parallel quantum devices can more efficiently search a database, as the quantum measurement process involves the collapse of the superposition to a base state, erasing probability information and therefore the ability to efficiently find multiple solutions. Pq-RRT uses a manager/parallel-quantum-workers formulation, inspired by traditional parallel motion planning, to perform simultaneous quantum searches of a feasible state database. We present results regarding likelihoods of multiple parallel units finding any and all solutions contained with a shared database, with and without reachability errors, allowing efficiency predictions to be made. We offer simulations in dense obstacle environments showing efficiency, density/heatmap, and speed comparisons for Pq-RRT against q-RRT, classical RRT, and classical parallel RRT. We then present Quantum Database Annealing, a database construction strategy for Pq-RRT and q-RRT that uses a temperature construct to define database creation over time for balancing exploration and exploitation.Comment: 14 pages, 15 figure

Elastic Chain in a Random Potential: Simulation of the Displacement Function <(u(x)−u(0))2><(u(x)-u(0))^2> and Relaxation

We simulate the low temperature behaviour of an elastic chain in a random potential where the displacements $u(x)$ are confined to the {\it longitudinal} direction ($u(x)$ parallel to $x$) as in a one dimensional charge density wave--type problem. We calculate the displacement correlation function $g(x)=< (u(x)-u(0))^2>$ and the size dependent average square displacement $W(L)=$. We find that $g(x)\sim x^{2\eta}$ with $\eta\simeq3/4$ at short distances and $\eta\simeq3/5$ at intermediate distances. We cannot resolve the asymptotic long distance dependence of $g$ upon $x$. For the system sizes considered we find $g(L/2)\propto W\sim L^{2\chi}$ with $\chi\simeq2/3$. The exponent $\eta\simeq3/5$ is in agreement with the Random Manifold exponent obtained from replica calculations and the exponent $\chi\simeq2/3$ is consistent with an exact solution for the chain with {\it transverse} displacements ($u(x)$ perpendicular to $x$).The distribution of nearest distances between pinning wells and chain-particles is found to develop forbidden regions.Comment: 19 pages of LaTex, 6 postscript figures available on request, submitted to Journal of Physics A, MAJOR CHANGE

Co-optimization: a generalization of coevolution

Many problems encountered in computer science are best stated in terms of interactions amongst individuals. For example, many problems are most naturally phrased in terms of finding a candidate solution which performs best against a set of test cases. In such situations, methods are needed to find candidate solutions which are expected to perform best over all test cases. Coevolution holds the promise of addressing such problems by employing principles from biological evolution, where populations of candidate solutions and test cases are evolved over time to produce higher quality solutions...This thesis presents a generalization of coevolution to co-optimization, where optimization techniques that do not rely on evolutionary principles may be used. Instead of introducing a new addition to coevolution in order to make it better suited for a particular class of problems, this thesis suggests removing the evolutionary model in favor of a technique better suited for that class of problems --Abstract, page iii

Drawing DNA Sequence Networks

We explore methods for drawing a graph of DNA sequences on a digital canvas such that the Euclidean distances between sequences on the canvas suggest the distances between the sequences as calculated from pairwise sequence alignment. We use data from three plant taxa, the genus Castilleja as well as the families Caryophyllaceae and Cactaceae, to test our methods. We discuss different possible measures of the cost of a drawing, and analyze heuristic approaches to the problem including random assignment, greedy assignment, the iterated hill-climber, and simulated annealing. We find that our hill-climbing method tends to return superior drawings. Our simulated annealing method also returns drawings with low costs, but in much less time than the hill-climbing method for large datasets