Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cascaded Region-based Densely Connected Network for Event Detection: A
  Seismic Application   Automatic event detection from time series signals has wide applications,
such as abnormal event detection in video surveillance and event detection in
geophysical data. Traditional detection methods detect events primarily by the
use of similarity and correlation in data. Those methods can be inefficient and
yield low accuracy. In recent years, because of the significantly increased
computational power, machine learning techniques have revolutionized many
science and engineering domains. In this study, we apply a deep-learning-based
method to the detection of events from time series seismic signals. However, a
direct adaptation of the similar ideas from 2D object detection to our problem
faces two challenges. The first challenge is that the duration of earthquake
event varies significantly; The other is that the proposals generated are
temporally correlated. To address these challenges, we propose a novel cascaded
region-based convolutional neural network to capture earthquake events in
different sizes, while incorporating contextual information to enrich features
for each individual proposal. To achieve a better generalization performance,
we use densely connected blocks as the backbone of our network. Because of the
fact that some positive events are not correctly annotated, we further
formulate the detection problem as a learning-from-noise problem. To verify the
performance of our detection methods, we employ our methods to seismic data
generated from a bi-axial "earthquake machine" located at Rock Mechanics
Laboratory, and we acquire labels with the help of experts. Through our
numerical tests, we show that our novel detection techniques yield high
accuracy. Therefore, our novel deep-learning-based detection methods can
potentially be powerful tools for locating events from time series data in
various applications.

22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org