Transcendence, Facticity, and Modes of Non-Being

A.I. Laboratory working papers are produced for internal circulation, and may contain information that is, for example, to preliminary, too detailed, or too silly for formal publication. This paper handsomely satisfies all three criteria. While it is destined to become a landmark in its genre, readers are cautioned against making reference to this paper in the literature, as the authors would like to rejoin society with a clean slate. This paper could not have been produced without the assistance of many brilliant but unstable individuals who could not be reached for comment, and whose names have been suppressed pending determination of competence.Research in artificial intelligence has yet to satisfactorily address the primordial fissure between human consciousness and the material order. How is this split reconciled in terms of human reality? By what duality is Bad Faith possible? We show that the answer is quite subtle, and of particular relevance to certain classical A.I. problems in introspection and intensional belief structure. A principled approach to bad faith and the consciousness of the other is suggested. We present ideas for an implementation in the domain of chemical engineering.MIT Artificial Intelligence Laborator

Indoor Calibration using Segment Chains

International audienceIn this paper, we present a new method for line segments matching for indoor reconstruction. Instead of matching individual seg- ments via a descriptor like most methods do, we match segment chains that have a distinctive topology using a dynamic programing formulation. Our method relies solely on the geometric layout of the segment chain and not on photometric or color profiles. Our tests showed that the presented method is robust and manages to produce calibration information even under a drastic change of viewpoint

On the segmentation and classification of hand radiographs

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components

Unsupervised Polygonal Reconstruction of Noisy Contours by a Discrete Irregular Approach

International audienceIn this paper, we present an original algorithm to build a polygonal reconstruction of noisy digital contours. For this purpose, we first improve an algorithm devoted to the vectorization of discrete irregular isothetic objects. Afterwards we propose to use it to define a reconstruction process of noisy digital contours. More precisely, we use a local noise detector, introduced by Kerautret and Lachaud in IWCIA 2009, that builds a multi-scale representation of the digital contour, which is composed of pixels of various size depending of the local amount of noise. Finally, we compare our approach with previous works, by con- sidering the Hausdorff distance and the error on tangent orientations of the computed line segments to the original perfect contour. Thanks to both synthetic and real noisy objects, we show that our approach has interesting performance, and could be applied in document analysis systems

Timed inhibition of CDC7 increases CRISPR-Cas9 mediated templated repair.

Repair of double strand DNA breaks (DSBs) can result in gene disruption or gene modification via homology directed repair (HDR) from donor DNA. Altering cellular responses to DSBs may rebalance editing outcomes towards HDR and away from other repair outcomes. Here, we utilize a pooled CRISPR screen to define host cell involvement in HDR between a Cas9 DSB and a plasmid double stranded donor DNA (dsDonor). We find that the Fanconi Anemia (FA) pathway is required for dsDonor HDR and that other genes act to repress HDR. Small molecule inhibition of one of these repressors, CDC7, by XL413 and other inhibitors increases the efficiency of HDR by up to 3.5 fold in many contexts, including primary T cells. XL413 stimulates HDR during a reversible slowing of S-phase that is unexplored for Cas9-induced HDR. We anticipate that XL413 and other such rationally developed inhibitors will be useful tools for gene modification

Sampling-based Algorithms for Optimal Motion Planning

During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.Comment: 76 pages, 26 figures, to appear in International Journal of Robotics Researc

Parental transfer of the antimicrobial protein LBP/BPI protects Biomphalaria glabrata eggs against oomycete infections

Copyright: © 2013 Baron et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was funded by ANR (ANR-07-BLAN-0214 and ANR-12-EMMA-00O7-01), CNRS and INRA. PvW was financially supported by the BBSRC. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Motion Planning via Manifold Samples

We present a general and modular algorithmic framework for path planning of robots. Our framework combines geometric methods for exact and complete analysis of low-dimensional configuration spaces, together with practical, considerably simpler sampling-based approaches that are appropriate for higher dimensions. In order to facilitate the transfer of advanced geometric algorithms into practical use, we suggest taking samples that are entire low-dimensional manifolds of the configuration space that capture the connectivity of the configuration space much better than isolated point samples. Geometric algorithms for analysis of low-dimensional manifolds then provide powerful primitive operations. The modular design of the framework enables independent optimization of each modular component. Indeed, we have developed, implemented and optimized a primitive operation for complete and exact combinatorial analysis of a certain set of manifolds, using arrangements of curves of rational functions and concepts of generic programming. This in turn enabled us to implement our framework for the concrete case of a polygonal robot translating and rotating amidst polygonal obstacles. We demonstrate that the integration of several carefully engineered components leads to significant speedup over the popular PRM sampling-based algorithm, which represents the more simplistic approach that is prevalent in practice. We foresee possible extensions of our framework to solving high-dimensional problems beyond motion planning.Comment: 18 page