Simon Kahan

On the Recognition of Printed Characters of Any Font and Size

We describe the current state of a system that recognizes printed text of various fonts and sizes for the Roman alphabet. The system combines several techniques in order to improve the overall recognition rate. Thinning and shape extraction are performed directly on a graph of the run-length encoding of a binary image. The resulting strokes and other shapes are mapped, using a shape-clustering approach, into binary features which are then fed into a statistical Bayesian classifier. Large-scale trials have shown better than 97 percent top choice correct performance on mixtures of six dissimilar fonts, and over 99 percent on most single fonts, over a range of point sizes. Certain remaining confusion classes are disambiguated through contour analysis, and characters suspected of being merged are broken and reclassified. Finally, layout and linguistic context are applied. The results are illustrated by sample pages.

Biocellion: accelerating computer simulation of multicellular biological system models

MOTIVATION Biological system behaviors are often the outcome of complex interactions among a large number of cells and their biotic and abiotic environment. Computational biologists attempt to understand, predict and manipulate biological system behavior through mathematical modeling and computer simulation. Discrete agent-based modeling (in combination with high-resolution grids to model the extracellular environment) is a popular approach for building biological system models. However, the computational complexity of this approach forces computational biologists to resort to coarser resolution approaches to simulate large biological systems. High-performance parallel computers have the potential to address the computing challenge, but writing efficient software for parallel computers is difficult and time-consuming. RESULTS We have developed Biocellion, a high-performance software framework, to solve this computing challenge using parallel computers. To support a wide range of multicellular biological system models, Biocellion asks users to provide their model specifics by filling the function body of pre-defined model routines. Using Biocellion, modelers without parallel computing expertise can efficiently exploit parallel computers with less effort than writing sequential programs from scratch. We simulate cell sorting, microbial patterning and a bacterial system in soil aggregate as case studies. AVAILABILITY AND IMPLEMENTATION Biocellion runs on x86 compatible systems with the 64 bit Linux operating system and is freely available for academic use. Visit http://biocellion.com for additional information.

Simulating Microbial Community Patterning Using Biocellion

Mathematical modeling and computer simulation are important tools for understanding complex interactions between cells and their biotic and abiotic environment: similarities and differences between modeled and observed behavior provide the basis for hypothesis formation. Momeni et al. (Elife 2:e00230, 2013) investigated pattern formation in communities of yeast strains engaging in different types of ecological interactions, comparing the predictions of mathematical modeling, and simulation to actual patterns observed in wet-lab experiments. However, simulations of millions of cells in a three-dimensional community are extremely time consuming. One simulation run in MATLAB may take a week or longer, inhibiting exploration of the vast space of parameter combinations and assumptions. Improving the speed, scale, and accuracy of such simulations facilitates hypothesis formation and expedites discovery. Biocellion is a high-performance software framework for accelerating discrete agent-based simulation of biological systems with millions to trillions of cells. Simulations of comparable scale and accuracy to those taking a week of computer time using MATLAB require just hours using Biocellion on a multicore workstation. Biocellion further accelerates large scale, high resolution simulations using cluster computers by partitioning the work to run on multiple compute nodes. Biocellion targets computational biologists who have mathematical modeling backgrounds and basic C++ programming skills. This chapter describes the necessary steps to adapt the original Momeni et al.s model to the Biocellion framework as a case study.

An O(n log n) algorithm for 1-D tile compaction

A simple O(n log n) algorithm is presented for performing one-dimensional tile compaction on a planar constraint network. The algorithm works only on planar constraints networks: those resulting from considering all components to be on single layer. The top and bottom zones in this example attain their minimum widths of 15 only by forcing the other zone to have width 25, while the best configuration has width 20 which is the minimum width of the middle zone. VLSI circuits often result in nonplanar constraint networks, even though interconnect parallel to the direction of compaction is not part of the constraint network. The algorithm could be used to obtain an initial guess at the width, possibly saving several iterations of the more expensive minimum-cost flow method. >

Single-layer cylindrical compaction

In VLSI compaction, an array composed of a single cell warrants special consideration. Standard methods of compaction [MS] result in either nonuniform cell layouts or unnecessarily large cell spacing. These inadequacies would be lessened were the array compacted instead by compacting a single instance of the cell against itself: in essence, the cell would be compacted on a torus. Equivalently, the problem becomes that of compacting a layout to fit into a minimal area shape 4-tiling the plane.Only the one-dimensional version of the problem has been addressed: that equivalent to compaction on a cylinder. Unfortunately, the efficient longest-path approach to one-dimensional compaction is not directly applicable since there is no origin to compact against. Eichenberger and Horowitz solve the problem in polynomial time by using a min-cost flow approach to assign positions to the nodes of a constraint network embedded on a cylinder [EH]. Mehlhorn and Rülling found an iterative approach running in timeO(n2 logn) when restricted to networks abstracted from layouts having any fixed number of layers [MR]. In this paper the longest-path approach is adapted to solve the same cylindrical compaction problem on planar networks—those abstracted from single-layer layouts—in justO(n logn) time.