10,873 research outputs found
Information Surfaces in Systems Biology and Applications to Engineering Sustainable Agriculture
Systems biology of plants offers myriad opportunities and many challenges in
modeling. A number of technical challenges stem from paucity of computational
methods for discovery of the most fundamental properties of complex dynamical
systems. In systems engineering, eigen-mode analysis have proved to be a
powerful approach. Following this philosophy, we introduce a new theory that
has the benefits of eigen-mode analysis, while it allows investigation of
complex dynamics prior to estimation of optimal scales and resolutions.
Information Surfaces organizes the many intricate relationships among
"eigen-modes" of gene networks at multiple scales and via an adaptable
multi-resolution analytic approach that permits discovery of the appropriate
scale and resolution for discovery of functions of genes in the model plant
Arabidopsis. Applications are many, and some pertain developments of crops that
sustainable agriculture requires.Comment: 24 Pages, DoCEIS 1
Deep Feature-based Face Detection on Mobile Devices
We propose a deep feature-based face detector for mobile devices to detect
user's face acquired by the front facing camera. The proposed method is able to
detect faces in images containing extreme pose and illumination variations as
well as partial faces. The main challenge in developing deep feature-based
algorithms for mobile devices is the constrained nature of the mobile platform
and the non-availability of CUDA enabled GPUs on such devices. Our
implementation takes into account the special nature of the images captured by
the front-facing camera of mobile devices and exploits the GPUs present in
mobile devices without CUDA-based frameorks, to meet these challenges.Comment: ISBA 201
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Super-Efficient Cross-Correlation (SEC-C): A Fast Matched Filtering Code Suitable for Desktop Computers
A Comparison of Big Data Frameworks on a Layered Dataflow Model
In the world of Big Data analytics, there is a series of tools aiming at
simplifying programming applications to be executed on clusters. Although each
tool claims to provide better programming, data and execution models, for which
only informal (and often confusing) semantics is generally provided, all share
a common underlying model, namely, the Dataflow model. The Dataflow model we
propose shows how various tools share the same expressiveness at different
levels of abstraction. The contribution of this work is twofold: first, we show
that the proposed model is (at least) as general as existing batch and
streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to
understand high-level data-processing applications written in such frameworks.
Second, we provide a layered model that can represent tools and applications
following the Dataflow paradigm and we show how the analyzed tools fit in each
level.Comment: 19 pages, 6 figures, 2 tables, In Proc. of the 9th Intl Symposium on
High-Level Parallel Programming and Applications (HLPP), July 4-5 2016,
Muenster, German
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