700 research outputs found
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
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
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
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
Seeing Tree Structure from Vibration
Humans recognize object structure from both their appearance and motion;
often, motion helps to resolve ambiguities in object structure that arise when
we observe object appearance only. There are particular scenarios, however,
where neither appearance nor spatial-temporal motion signals are informative:
occluding twigs may look connected and have almost identical movements, though
they belong to different, possibly disconnected branches. We propose to tackle
this problem through spectrum analysis of motion signals, because vibrations of
disconnected branches, though visually similar, often have distinctive natural
frequencies. We propose a novel formulation of tree structure based on a
physics-based link model, and validate its effectiveness by theoretical
analysis, numerical simulation, and empirical experiments. With this
formulation, we use nonparametric Bayesian inference to reconstruct tree
structure from both spectral vibration signals and appearance cues. Our model
performs well in recognizing hierarchical tree structure from real-world videos
of trees and vessels.Comment: ECCV 2018. The first two authors contributed equally to this work.
Project page: http://tree.csail.mit.edu
Canny Algorithm, Cosmic Strings and the Cosmic Microwave Background
We describe a new code to search for signatures of cosmic strings in cosmic
microwave anisotropy maps. The code implements the Canny Algorithm, an edge
detection algorithm designed to search for the lines of large gradients in
maps. Such a gradient signature which is coherent in position space is produced
by cosmic strings via the Kaiser-Stebbins effect. We test the power of our new
code to set limits on the tension of the cosmic strings by analyzing simulated
data with and without cosmic strings. We compare maps with a pure Gaussian
scale-invariant power spectrum with maps which have a contribution of a
distribution of cosmic strings obeying a scaling solution. The maps have
angular scale and angular resolution comparable to what current and future
ground-based small-scale cosmic microwave anisotropy experiments will achieve.
We present tests of the codes, indicate the limits on the string tension which
could be set with the current code, and describe various ways to refine the
analysis. Our results indicate that when applied to the data of ongoing cosmic
microwave experiments such as the South Pole Telescope project, the sensitivity
of our method to the presence of cosmic strings will be more than an order of
magnitude better than the limits from existing analyses.Comment: 19 pp, 14 figures; v4. minor corrections, as appears in journa
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
Recommended from our members
Automated Segmentation of HeLa Nuclear Envelope from Electron Microscopy Images
This paper describes an image-processing pipeline for the automatic segmentation of the nuclear envelope of HeLcells observed through Electron Microscopy. The pipeline was applied to a 3D stack of 300 images. The intermediate results of neighbouring slices are further combined to improve the final results. Comparison with a handsegmented ground truth reported Jaccard similarity values between 94-98% on the central slices with a decrease towards the edges of the cell where the structure was considerably more complex. The processing is unsupervised and each 2D slice is processed in about 5-10 seconds running on a MacBook Pro. No systematic attempt to make the code faster was made. These encouraging results could be further used to provide data for more complex segmentation techniques like Deep Learning, which require a considerable amount of data to train architectures like Convolutional Neural Networks. The code is freely available from https://github.com/reyesaldasoro/HeLa-Cell-Segmentatio
- âŠ