9,800 research outputs found
DeepSoft: A vision for a deep model of software
Although software analytics has experienced rapid growth as a research area,
it has not yet reached its full potential for wide industrial adoption. Most of
the existing work in software analytics still relies heavily on costly manual
feature engineering processes, and they mainly address the traditional
classification problems, as opposed to predicting future events. We present a
vision for \emph{DeepSoft}, an \emph{end-to-end} generic framework for modeling
software and its development process to predict future risks and recommend
interventions. DeepSoft, partly inspired by human memory, is built upon the
powerful deep learning-based Long Short Term Memory architecture that is
capable of learning long-term temporal dependencies that occur in software
evolution. Such deep learned patterns of software can be used to address a
range of challenging problems such as code and task recommendation and
prediction. DeepSoft provides a new approach for research into modeling of
source code, risk prediction and mitigation, developer modeling, and
automatically generating code patches from bug reports.Comment: FSE 201
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations
Recent advances in scanning transmission electron and scanning probe
microscopies have opened exciting opportunities in probing the materials
structural parameters and various functional properties in real space with
angstrom-level precision. This progress has been accompanied by an exponential
increase in the size and quality of datasets produced by microscopic and
spectroscopic experimental techniques. These developments necessitate adequate
methods for extracting relevant physical and chemical information from the
large datasets, for which a priori information on the structures of various
atomic configurations and lattice defects is limited or absent. Here we
demonstrate an application of deep neural networks to extract information from
atomically resolved images including location of the atomic species and type of
defects. We develop a 'weakly-supervised' approach that uses information on the
coordinates of all atomic species in the image, extracted via a deep neural
network, to identify a rich variety of defects that are not part of an initial
training set. We further apply our approach to interpret complex atomic and
defect transformation, including switching between different coordination of
silicon dopants in graphene as a function of time, formation of peculiar
silicon dimer with mixed 3-fold and 4-fold coordination, and the motion of
molecular 'rotor'. This deep learning based approach resembles logic of a human
operator, but can be scaled leading to significant shift in the way of
extracting and analyzing information from raw experimental data
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