110,386 research outputs found
Learning a Bias Correction for Lidar-only Motion Estimation
This paper presents a novel technique to correct for bias in a classical
estimator using a learning approach. We apply a learned bias correction to a
lidar-only motion estimation pipeline. Our technique trains a Gaussian process
(GP) regression model using data with ground truth. The inputs to the model are
high-level features derived from the geometry of the point-clouds, and the
outputs are the predicted biases between poses computed by the estimator and
the ground truth. The predicted biases are applied as a correction to the poses
computed by the estimator.
Our technique is evaluated on over 50km of lidar data, which includes the
KITTI odometry benchmark and lidar datasets collected around the University of
Toronto campus. After applying the learned bias correction, we obtained
significant improvements to lidar odometry in all datasets tested. We achieved
around 10% reduction in errors on all datasets from an already accurate lidar
odometry algorithm, at the expense of only less than 1% increase in
computational cost at run-time.Comment: 15th Conference on Computer and Robot Vision (CRV 2018
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Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.
As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing
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Using formal methods to support testing
Formal methods and testing are two important approaches that assist in the development of high quality software. While traditionally these approaches have been seen as rivals, in recent
years a new consensus has developed in which they are seen as complementary. This article reviews the state of the art regarding ways in which the presence of a formal specification can be used to assist testing
A Survey on Compiler Autotuning using Machine Learning
Since the mid-1990s, researchers have been trying to use machine-learning
based approaches to solve a number of different compiler optimization problems.
These techniques primarily enhance the quality of the obtained results and,
more importantly, make it feasible to tackle two main compiler optimization
problems: optimization selection (choosing which optimizations to apply) and
phase-ordering (choosing the order of applying optimizations). The compiler
optimization space continues to grow due to the advancement of applications,
increasing number of compiler optimizations, and new target architectures.
Generic optimization passes in compilers cannot fully leverage newly introduced
optimizations and, therefore, cannot keep up with the pace of increasing
options. This survey summarizes and classifies the recent advances in using
machine learning for the compiler optimization field, particularly on the two
major problems of (1) selecting the best optimizations and (2) the
phase-ordering of optimizations. The survey highlights the approaches taken so
far, the obtained results, the fine-grain classification among different
approaches and finally, the influential papers of the field.Comment: version 5.0 (updated on September 2018)- Preprint Version For our
Accepted Journal @ ACM CSUR 2018 (42 pages) - This survey will be updated
quarterly here (Send me your new published papers to be added in the
subsequent version) History: Received November 2016; Revised August 2017;
Revised February 2018; Accepted March 2018
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