121,623 research outputs found
Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models
Identifying the presence of a gravitational wave transient buried in
non-stationary, non-Gaussian noise which can often contain spurious noise
transients (glitches) is a very challenging task. For a given data set,
transient gravitational wave searches produce a corresponding list of triggers
that indicate the possible presence of a gravitational wave signal. These
triggers are often the result of glitches mimicking gravitational wave signal
characteristics. To distinguish glitches from genuine gravitational wave
signals, search algorithms estimate a range of trigger attributes, with
thresholds applied to these trigger properties to separate signal from noise.
Here, we present the use of Gaussian mixture models, a supervised machine
learning approach, as a means of modelling the multi-dimensional trigger
attribute space. We demonstrate this approach by applying it to triggers from
the coherent Waveburst search for generic bursts in LIGO O1 data. By building
Gaussian mixture models for the signal and background noise attribute spaces,
we show that we can significantly improve the sensitivity of the coherent
Waveburst search and strongly suppress the impact of glitches and background
noise, without the use of multiple search bins as employed by the original O1
search. We show that the detection probability is enhanced by a factor of 10,
leading enhanced statistical significance for gravitational wave signals such
as GW150914.Comment: 9 pages, 4 figure
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation
Many security and privacy problems can be modeled as a graph classification
problem, where nodes in the graph are classified by collective classification
simultaneously. State-of-the-art collective classification methods for such
graph-based security and privacy analytics follow the following paradigm:
assign weights to edges of the graph, iteratively propagate reputation scores
of nodes among the weighted graph, and use the final reputation scores to
classify nodes in the graph. The key challenge is to assign edge weights such
that an edge has a large weight if the two corresponding nodes have the same
label, and a small weight otherwise. Although collective classification has
been studied and applied for security and privacy problems for more than a
decade, how to address this challenge is still an open question. In this work,
we propose a novel collective classification framework to address this
long-standing challenge. We first formulate learning edge weights as an
optimization problem, which quantifies the goals about the final reputation
scores that we aim to achieve. However, it is computationally hard to solve the
optimization problem because the final reputation scores depend on the edge
weights in a very complex way. To address the computational challenge, we
propose to jointly learn the edge weights and propagate the reputation scores,
which is essentially an approximate solution to the optimization problem. We
compare our framework with state-of-the-art methods for graph-based security
and privacy analytics using four large-scale real-world datasets from various
application scenarios such as Sybil detection in social networks, fake review
detection in Yelp, and attribute inference attacks. Our results demonstrate
that our framework achieves higher accuracies than state-of-the-art methods
with an acceptable computational overhead.Comment: Network and Distributed System Security Symposium (NDSS), 2019.
Dataset link: http://gonglab.pratt.duke.edu/code-dat
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