22,909 research outputs found
Robust Execution of Contact-Rich Motion Plans by Hybrid Force-Velocity Control
In hybrid force-velocity control, the robot can use velocity control in some
directions to follow a trajectory, while performing force control in other
directions to maintain contacts with the environment regardless of positional
errors. We call this way of executing a trajectory hybrid servoing. We propose
an algorithm to compute hybrid force-velocity control actions for hybrid
servoing. We quantify the robustness of a control action and make trade-offs
between different requirements by formulating the control synthesis as
optimization problems. Our method can efficiently compute the dimensions,
directions and magnitudes of force and velocity controls. We demonstrated by
experiments the effectiveness of our method in several contact-rich
manipulation tasks. Link to the video: https://youtu.be/KtSNmvwOenM.Comment: Proceedings of IEEE International Conference on Robotics and
Automation (ICRA2019
Exploratory Mediation Analysis with Many Potential Mediators
Social and behavioral scientists are increasingly employing technologies such
as fMRI, smartphones, and gene sequencing, which yield 'high-dimensional'
datasets with more columns than rows. There is increasing interest, but little
substantive theory, in the role the variables in these data play in known
processes. This necessitates exploratory mediation analysis, for which
structural equation modeling is the benchmark method. However, this method
cannot perform mediation analysis with more variables than observations. One
option is to run a series of univariate mediation models, which incorrectly
assumes independence of the mediators. Another option is regularization, but
the available implementations may lead to high false positive rates. In this
paper, we develop a hybrid approach which uses components of both filter and
regularization: the 'Coordinate-wise Mediation Filter'. It performs filtering
conditional on the other selected mediators. We show through simulation that it
improves performance over existing methods. Finally, we provide an empirical
example, showing how our method may be used for epigenetic research.Comment: R code and package are available online as supplementary material at
https://github.com/vankesteren/cmfilter and
https://github.com/vankesteren/ema_simulation
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
Learning continuous representations of nodes is attracting growing interest
in both academia and industry recently, due to their simplicity and
effectiveness in a variety of applications. Most of existing node embedding
algorithms and systems are capable of processing networks with hundreds of
thousands or a few millions of nodes. However, how to scale them to networks
that have tens of millions or even hundreds of millions of nodes remains a
challenging problem. In this paper, we propose GraphVite, a high-performance
CPU-GPU hybrid system for training node embeddings, by co-optimizing the
algorithm and the system. On the CPU end, augmented edge samples are parallelly
generated by random walks in an online fashion on the network, and serve as the
training data. On the GPU end, a novel parallel negative sampling is proposed
to leverage multiple GPUs to train node embeddings simultaneously, without much
data transfer and synchronization. Moreover, an efficient collaboration
strategy is proposed to further reduce the synchronization cost between CPUs
and GPUs. Experiments on multiple real-world networks show that GraphVite is
super efficient. It takes only about one minute for a network with 1 million
nodes and 5 million edges on a single machine with 4 GPUs, and takes around 20
hours for a network with 66 million nodes and 1.8 billion edges. Compared to
the current fastest system, GraphVite is about 50 times faster without any
sacrifice on performance.Comment: accepted at WWW 201
A cooperative conjugate gradient method for linear systems permitting multithread implementation of low complexity
This paper proposes a generalization of the conjugate gradient (CG) method
used to solve the equation for a symmetric positive definite matrix
of large size . The generalization consists of permitting the scalar control
parameters (= stepsizes in gradient and conjugate gradient directions) to be
replaced by matrices, so that multiple descent and conjugate directions are
updated simultaneously. Implementation involves the use of multiple agents or
threads and is referred to as cooperative CG (cCG), in which the cooperation
between agents resides in the fact that the calculation of each entry of the
control parameter matrix now involves information that comes from the other
agents. For a sufficiently large dimension , the use of an optimal number of
cores gives the result that the multithread implementation has worst case
complexity in exact arithmetic. Numerical experiments, that
illustrate the interest of theoretical results, are carried out on a multicore
computer.Comment: Expanded version of manuscript submitted to the IEEE-CDC 2012
(Conference on Decision and Control
Learning Large-Scale Bayesian Networks with the sparsebn Package
Learning graphical models from data is an important problem with wide
applications, ranging from genomics to the social sciences. Nowadays datasets
often have upwards of thousands---sometimes tens or hundreds of thousands---of
variables and far fewer samples. To meet this challenge, we have developed a
new R package called sparsebn for learning the structure of large, sparse
graphical models with a focus on Bayesian networks. While there are many
existing software packages for this task, this package focuses on the unique
setting of learning large networks from high-dimensional data, possibly with
interventions. As such, the methods provided place a premium on scalability and
consistency in a high-dimensional setting. Furthermore, in the presence of
interventions, the methods implemented here achieve the goal of learning a
causal network from data. Additionally, the sparsebn package is fully
compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure
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