A component-oriented approach to simultaneous localization and mapping

The simultaneous localization and mapping (SLAM) problem is central to many mobile robots. The construction of a map of the local environment and the localization of the robot in that map must be accomplished in an incremental manner, even in the presence of signi cant error and uncertainty in sensor data. Further, the cyclic data dependency presents a challenge that does not lend itself to robust solutions. Solutions to the SLAM problem have been a major success in robotics research in the past 20 years. Existing solutions fully embrace the self-referential nature of the problem. There is a certain art to designing and tuning systems that achieve stability and conver- gence properties. Most robotics platforms implement a customized version of SLAM with modi cations to improve performance, given certain assumptions and a priori knowledge of the environment. Borrowing techniques and tools from the parallel composition research community, we aimed to design a robust, extensible, and e cient framework for SLAM solutions using a component- based architecture. This begins with a domain analysis, characterizing the breadth of existing solutions and factoring the logical function of various components in a modular way. We then de ne the interfaces for these components, provide implementations, and connect them in a data ow or dependency graph. The nal implementation presented supports, in theory, particle lter localization and can bene t from automated parallelization. However, it has not yet run successfully at the time of writing. The reasons for this include missing key features and non-working implementations of purported features in the tools used, PCOM2 and CODE. These limitations are described in detail and motivated, and should serve as justi cation for future work in parallel component composition research. Unfortunately, these are the only presentable results of the research done here at this time.Computer Science

Long Range Linkage Disequilibrium across the Human Genome

Long-range linkage disequilibria (LRLD) between sites that are widely separated on chromosomes may suggest that population admixture, epistatic selection, or other evolutionary forces are at work. We quantified patterns of LRLD on a chromosome-wide level in the YRI population of the HapMap dataset of single nucleotide polymorphisms (SNPs). We calculated the disequilibrium between all pairs of SNPs on each chromosome (a total of >2×1011 values) and evaluated significance of overall disequilibrium using randomization. The results show an excess of associations between pairs of distant sites (separated by >0.25 cM) on all of the 22 autosomes. We discuss possible explanations for this observation.Koch E, Ristroph M, Kirkpatrick M (2013) Long Range Linkage Disequilibrium across the Human Genome. PLoS ONE 8(12): e80754. doi:10.1371/journal.pone.0080754Integrative BiologyE-mail: [email protected]

Multiagent interactions in urban driving

In Fall 2007, the US Defense Advanced Research Projects Agency (DARPA) held the Urban Challenge, a street race between fully autonomous vehicles. Unlike previous challenges, the Urban Challenge vehicles had to follow the California laws for driving, including properly handling traffic. This article presents the modular algorithms developed largely by undergraduates at The University of Texas at Austin as part of the Austin Robot Technology team. We emphasize the aspects of the system that are relevant to multiagent interactions. Specifically, we discuss how our vehicle tracked and reacted to nearby traffic in order to allow our autonomous vehicle to safely follow and pass, merge into moving traffic, obey intersection precedence, and park.This research is supported in part by NSF CAREER award IIS-0237699 and ONR YIP award N00014-04-1-0545

Patches of LRLD on Chromosome 1.

<p><i>Left:</i> The circle plot represents patches by lines that connect the chromosome blocks involved. The most extreme value of <i>p_D</i> in each patch is represented by the color of the segment. The line segments on the outside of the circle show regions identified in Akey's <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0080754#pone.0080754-Akey1" target="_blank">[36]</a> catalogue of genomic targets of positive selection. <i>Right:</i> The triangle plot shows the patches as circles whose size is scaled to the value of the most extreme value of <i>p_D</i> in that patch.</p

Schematic of the structure of long range linkage disequilibria.

<p><i>Right:</i> Sites <i>A</i> and <i>B</i> are the target of selection or other force that generates disequilibria between them (red arrow). Site <i>A</i> is in disequilibrium with site <i>A′</i> nearby as the result of shared ancestry of that small segment of chromosome; likewise <i>B</i> and <i>B</i>′ also show short range disequilibrium (blue arrows). <i>Right:</i> A triangle plot of LD. Disequilibria between nearby sites appear as the band along the diagonal. The dash line encloses a “patch” consisting of pairs of widely-separated SNPs that are in LD.</p

The number of patches that appear in the Akey's [36] catalogue of putative targets of selection.

<p><i>p</i> values give the significance for individual chromosomes tested individually. Values significant at the 0.05 level are in bold; none is significant after a Bonferroni correction for tests of multiple chromosomes. Akey 1 gives the number of patches in which one of the two participating sites is in the Akey catalogue; Akey 2 is the number in which both sites do. The </p

Patches of LRLD on Chromosome 12 to 22.

<p>Patches of LRLD on Chromosome 12 to 22.</p

Scatter plot of the maximum value of −ln(p_D) in every patch across all chromosomes vs the distance r between the patches.

<p>Recombination is expected to make this correlation negative.</p

Patches of LRLD on Chromosome 12.

<p>Patches of LRLD on Chromosome 12.</p

Sved's [17] measure for the correlation of heterozygosity between blocks of 50 SNPs as a function of the recombination rate between the blocks.

<p>The squares show the genome-wide average for the Yoruba population (calculated by Sved). The circles pertain to the 10 cM region of Chromosome 8 that has the highest density of LRLD patches identified by our method. The difference is highly significant (<i>p</i><10⁻³).</p