11,716 research outputs found
Cosmic Swarms: A search for Supermassive Black Holes in the LISA data stream with a Hybrid Evolutionary Algorithm
We describe a hybrid evolutionary algorithm that can simultaneously search
for multiple supermassive black hole binary (SMBHB) inspirals in LISA data. The
algorithm mixes evolutionary computation, Metropolis-Hastings methods and
Nested Sampling. The inspiral of SMBHBs presents an interesting problem for
gravitational wave data analysis since, due to the LISA response function, the
sources have a bi-modal sky solution. We show here that it is possible not only
to detect multiple SMBHBs in the data stream, but also to investigate
simultaneously all the various modes of the global solution. In all cases, the
algorithm returns parameter determinations within (as estimated from
the Fisher Matrix) of the true answer, for both the actual and antipodal sky
solutions.Comment: submitted to Classical & Quantum Gravity. 19 pages, 4 figure
Recent Advances in Graph Partitioning
We survey recent trends in practical algorithms for balanced graph
partitioning together with applications and future research directions
ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space
Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The difficulties are often due to the essential role of spatial
and topological structures in the determination of protein functions in living
cells. In this paper, we propose ProtNN, a novel approach for protein function
prediction. Given an unannotated protein structure and a set of annotated
proteins, ProtNN finds the nearest neighbor annotated structures based on
protein-graph pairwise similarities. Given a query protein, ProtNN finds the
nearest neighbor reference proteins based on a graph representation model and a
pairwise similarity between vector embedding of both query and reference
protein-graphs in structural and topological spaces. ProtNN assigns to the
query protein the function with the highest number of votes across the set of k
nearest neighbor reference proteins, where k is a user-defined parameter.
Experimental evaluation demonstrates that ProtNN is able to accurately classify
several datasets in an extremely fast runtime compared to state-of-the-art
approaches. We further show that ProtNN is able to scale up to a whole PDB
dataset in a single-process mode with no parallelization, with a gain of
thousands order of magnitude of runtime compared to state-of-the-art
approaches
Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament
In this paper, we want to study the informative value of negative links in
signed complex networks. For this purpose, we extract and analyze a collection
of signed networks representing voting sessions of the European Parliament
(EP). We first process some data collected by the VoteWatch Europe Website for
the whole 7 th term (2009-2014), by considering voting similarities between
Members of the EP to define weighted signed links. We then apply a selection of
community detection algorithms, designed to process only positive links, to
these data. We also apply Parallel Iterative Local Search (Parallel ILS), an
algorithm recently proposed to identify balanced partitions in signed networks.
Our results show that, contrary to the conclusions of a previous study focusing
on other data, the partitions detected by ignoring or considering the negative
links are indeed remarkably different for these networks. The relevance of
negative links for graph partitioning therefore is an open question which
should be further explored.Comment: in 2nd European Network Intelligence Conference (ENIC), Sep 2015,
Karlskrona, Swede
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