21,853 research outputs found
SensibleSleep: A Bayesian Model for Learning Sleep Patterns from Smartphone Events
We propose a Bayesian model for extracting sleep patterns from smartphone
events. Our method is able to identify individuals' daily sleep periods and
their evolution over time, and provides an estimation of the probability of
sleep and wake transitions. The model is fitted to more than 400 participants
from two different datasets, and we verify the results against ground truth
from dedicated armband sleep trackers. We show that the model is able to
produce reliable sleep estimates with an accuracy of 0.89, both at the
individual and at the collective level. Moreover the Bayesian model is able to
quantify uncertainty and encode prior knowledge about sleep patterns. Compared
with existing smartphone-based systems, our method requires only screen on/off
events, and is therefore much less intrusive in terms of privacy and more
battery-efficient
ProtoMD: A Prototyping Toolkit for Multiscale Molecular Dynamics
ProtoMD is a toolkit that facilitates the development of algorithms for
multiscale molecular dynamics (MD) simulations. It is designed for multiscale
methods which capture the dynamic transfer of information across multiple
spatial scales, such as the atomic to the mesoscopic scale, via coevolving
microscopic and coarse-grained (CG) variables. ProtoMD can be also be used to
calibrate parameters needed in traditional CG-MD methods. The toolkit
integrates `GROMACS wrapper' to initiate MD simulations, and `MDAnalysis' to
analyze and manipulate trajectory files. It facilitates experimentation with a
spectrum of coarse-grained variables, prototyping rare events (such as chemical
reactions), or simulating nanocharacterization experiments such as terahertz
spectroscopy, AFM, nanopore, and time-of-flight mass spectroscopy. ProtoMD is
written in python and is freely available under the GNU General Public License
from github.com/CTCNano/proto_md
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