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Wavelet-based response spectrum compatible synthesis of accelerograms-Eurocode application (EC8)
An integrated approach for addressing the problem of synthesizing artificial seismic accelerograms compatible with a given displacement design/target spectrum is presented in conjunction with aseismic design applications. Initially, a stochastic dynamics solution is used to obtain a family of simulated non-stationary earthquake records whose response spectrum is on the average in good agreement with the target spectrum. The degree of the agreement depends significantly on the adoption of an appropriate parametric evolutionary power spectral form, which is related to the target spectrum in an approximate manner. The performance of two commonly used spectral forms along with a newly proposed one is assessed with respect to the elastic displacement design spectrum defined by the European code regulations (EC8). Subsequently, the computational versatility of the family of harmonic wavelets is employed to modify iteratively the simulated records to satisfy the compatibility criteria for artificial accelerograms prescribed by EC8. In the process, baseline correction steps, ordinarily taken to ensure that the obtained accelerograms are characterized by physically meaningful velocity and displacement traces, are elucidated. Obviously, the presented approach can be used not only in the case of the EC8, for which extensive numerical results/examples are included, but also for any code provisions mandated by regulatory agencies. In any case, the presented numerical results can be quite useful in any aseismic design process dominated by the EC8 specifications
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
Fairness in overloaded parallel queues
Maximizing throughput for heterogeneous parallel server queues has received
quite a bit of attention from the research community and the stability region
for such systems is well understood. However, many real-world systems have
periods where they are temporarily overloaded. Under such scenarios, the
unstable queues often starve limited resources. This work examines what happens
during periods of temporary overload. Specifically, we look at how to fairly
distribute stress. We explore the dynamics of the queue workloads under the
MaxWeight scheduling policy during long periods of stress and discuss how to
tune this policy in order to achieve a target fairness ratio across these
workloads
Automated Detection of Solar Eruptions
Observation of the solar atmosphere reveals a wide range of motions, from
small scale jets and spicules to global-scale coronal mass ejections.
Identifying and characterizing these motions are essential to advancing our
understanding the drivers of space weather. Both automated and visual
identifications are currently used in identifying CMEs. To date, eruptions near
the solar surface (which may be precursors to CMEs) have been identified
primarily by visual inspection. Here we report on EruptionPatrol (EP): a
software module that is designed to automatically identify eruptions from data
collected by SDO/AIA. We describe the method underlying the module and compare
its results to previous identifications found in the Heliophysics Event
Knowledgebase. EP identifies eruptions events that are consistent with those
found by human annotations, but in a significantly more consistent and
quantitative manner. Eruptions are found to be distributed within 15Mm of the
solar surface. They possess peak speeds ranging from 4 to 100 km/sec and
display a power-law probability distribution over that range. These
characteristics are consistent with previous observations of prominences.Comment: 6 pages, 4 figures, 7th Solar Information Processing Workshop, to
appear in Space Weather and Space Climat
WxBS: Wide Baseline Stereo Generalizations
We have presented a new problem -- the wide multiple baseline stereo (WxBS)
-- which considers matching of images that simultaneously differ in more than
one image acquisition factor such as viewpoint, illumination, sensor type or
where object appearance changes significantly, e.g. over time. A new dataset
with the ground truth for evaluation of matching algorithms has been introduced
and will be made public.
We have extensively tested a large set of popular and recent detectors and
descriptors and show than the combination of RootSIFT and HalfRootSIFT as
descriptors with MSER and Hessian-Affine detectors works best for many
different nuisance factors. We show that simple adaptive thresholding improves
Hessian-Affine, DoG, MSER (and possibly other) detectors and allows to use them
on infrared and low contrast images.
A novel matching algorithm for addressing the WxBS problem has been
introduced. We have shown experimentally that the WxBS-M matcher dominantes the
state-of-the-art methods both on both the new and existing datasets.Comment: Descriptor and detector evaluation expande
Feature-based time-series analysis
This work presents an introduction to feature-based time-series analysis. The
time series as a data type is first described, along with an overview of the
interdisciplinary time-series analysis literature. I then summarize the range
of feature-based representations for time series that have been developed to
aid interpretable insights into time-series structure. Particular emphasis is
given to emerging research that facilitates wide comparison of feature-based
representations that allow us to understand the properties of a time-series
dataset that make it suited to a particular feature-based representation or
analysis algorithm. The future of time-series analysis is likely to embrace
approaches that exploit machine learning methods to partially automate human
learning to aid understanding of the complex dynamical patterns in the time
series we measure from the world.Comment: 28 pages, 9 figure
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