6,859 research outputs found
Insightful classification of crystal structures using deep learning
Computational methods that automatically extract knowledge from data are
critical for enabling data-driven materials science. A reliable identification
of lattice symmetry is a crucial first step for materials characterization and
analytics. Current methods require a user-specified threshold, and are unable
to detect average symmetries for defective structures. Here, we propose a
machine-learning-based approach to automatically classify structures by crystal
symmetry. First, we represent crystals by calculating a diffraction image, then
construct a deep-learning neural-network model for classification. Our approach
is able to correctly classify a dataset comprising more than 100 000 simulated
crystal structures, including heavily defective ones. The internal operations
of the neural network are unraveled through attentive response maps,
demonstrating that it uses the same landmarks a materials scientist would use,
although never explicitly instructed to do so. Our study paves the way for
crystal-structure recognition of - possibly noisy and incomplete -
three-dimensional structural data in big-data materials science.Comment: Nature Communications, in press (2018
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for
solving classification tasks on graphs. This survey gives a comprehensive
overview of techniques for kernel-based graph classification developed in the
past 15 years. We describe and categorize graph kernels based on properties
inherent to their design, such as the nature of their extracted graph features,
their method of computation and their applicability to problems in practice. In
an extensive experimental evaluation, we study the classification accuracy of a
large suite of graph kernels on established benchmarks as well as new datasets.
We compare the performance of popular kernels with several baseline methods and
study the effect of applying a Gaussian RBF kernel to the metric induced by a
graph kernel. In doing so, we find that simple baselines become competitive
after this transformation on some datasets. Moreover, we study the extent to
which existing graph kernels agree in their predictions (and prediction errors)
and obtain a data-driven categorization of kernels as result. Finally, based on
our experimental results, we derive a practitioner's guide to kernel-based
graph classification
A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs
Representing the reservoir as a network of discrete compartments with
neighbor and non-neighbor connections is a fast, yet accurate method for
analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale
compartments with distinct static and dynamic properties is an integral part of
such high-level reservoir analysis. In this work, we present a hybrid framework
specific to reservoir analysis for an automatic detection of clusters in space
using spatial and temporal field data, coupled with a physics-based multiscale
modeling approach. In this work a novel hybrid approach is presented in which
we couple a physics-based non-local modeling framework with data-driven
clustering techniques to provide a fast and accurate multiscale modeling of
compartmentalized reservoirs. This research also adds to the literature by
presenting a comprehensive work on spatio-temporal clustering for reservoir
studies applications that well considers the clustering complexities, the
intrinsic sparse and noisy nature of the data, and the interpretability of the
outcome.
Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal
Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin
Prototypes as Explanation for Time Series Anomaly Detection
Detecting abnormal patterns that deviate from a certain regular repeating
pattern in time series is essential in many big data applications. However, the
lack of labels, the dynamic nature of time series data, and unforeseeable
abnormal behaviors make the detection process challenging. Despite the success
of recent deep anomaly detection approaches, the mystical mechanisms in such
black-box models have become a new challenge in safety-critical applications.
The lack of model transparency and prediction reliability hinders further
breakthroughs in such domains. This paper proposes ProtoAD, using prototypes as
the example-based explanation for the state of regular patterns during anomaly
detection. Without significant impact on the detection performance, prototypes
shed light on the deep black-box models and provide intuitive understanding for
domain experts and stakeholders. We extend the widely used prototype learning
in classification problems into anomaly detection. By visualizing both the
latent space and input space prototypes, we intuitively demonstrate how regular
data are modeled and why specific patterns are considered abnormal
Optical Truss Interferometer for the LISA Telescope
The LISA telescopes must exhibit an optical path length stability of
in the mHz observation band to meet
mission requirements. The optical truss interferometer is a proposed method to
aid in the ground testing of the telescopes, as well as a risk-mitigation plan
for the flight units. This consists of three Fabry-Perot cavities mounted to
the telescope which are used to monitor structural displacements. We have
designed and developed a fiber-based cavity injection system that integrates
fiber components, mode-matching optics, and a cavity input mirror into a
compact input stage. The input stages, paired with return mirror stages, can be
mounted to the telescope to form the optical truss cavities. We performed a
thorough sensitivity analysis using various simulation methods to support the
fabrication and assembly of three first-generation prototype cavities, each of
which exhibited a satisfactory performance based on our models.Comment: 9 pages, 9 figures, 13 pdf figures attache
Human activity recognition based on evolving fuzzy systems
Environments equipped with intelligent sensors can be of much help if they can recognize the actions
or activities of their users. If this activity recognition is done automatically, it can be very useful for
different tasks such as future action prediction, remote health monitoring, or interventions. Although
there are several approaches for recognizing activities, most of them do not consider the changes in how
a human performs a specific activity. We present an automated approach to recognize daily activities
from the sensor readings of an intelligent home environment. However, as the way to perform an activity
is usually not fixed but it changes and evolves, we propose an activity recognition method based on
Evolving Fuzzy SystemsThis work has been partially supported by the
Spanish Government under project TRA2007-67374-C02-0
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