4,803 research outputs found
The Entropy Principle and the Influence of Sociological Pressures on SETI
We begin with the premise that the law of entropy could prove to be
fundamental for the evolution of intelligent life and the advent of
technological civilization. Building on recent theoretical results, we combine
a modern approach to evolutionary theory with Monte Carlo Realization
Techniques. A numerical test for a proposed significance of the law of entropy
within the evolution of intelligent species is performed and results are
compared with a neutral test hypothesis. Some clarifying aspects on the
emergence of intelligent species arise and are discussed in the framework of
contemporary astrobiology.Comment: 11 pages, 5 figures, accepted for publication in the International
Journal of Astrobiolog
A Survey on Soft Subspace Clustering
Subspace clustering (SC) is a promising clustering technology to identify
clusters based on their associations with subspaces in high dimensional spaces.
SC can be classified into hard subspace clustering (HSC) and soft subspace
clustering (SSC). While HSC algorithms have been extensively studied and well
accepted by the scientific community, SSC algorithms are relatively new but
gaining more attention in recent years due to better adaptability. In the
paper, a comprehensive survey on existing SSC algorithms and the recent
development are presented. The SSC algorithms are classified systematically
into three main categories, namely, conventional SSC (CSSC), independent SSC
(ISSC) and extended SSC (XSSC). The characteristics of these algorithms are
highlighted and the potential future development of SSC is also discussed.Comment: This paper has been published in Information Sciences Journal in 201
Fusion based Image Enhancement Approach for Brain Tumor Detection
Magnetic Resonance Imaging (MRI), is a crucial technology used in the processing of medical images that provides insights into the anatomy of soft organs in the human body and helps in detecting brain tumors and spinal tumors. Despite advances in technology, most images have intrinsic drawbacks such as reduced contrast and brightness, and noise. Several contrast enhancement techniques are used such as, HE, BBHE, DSIHE, CLAHE, RMSHE, and their fusion, have been deployed on different MRI images to handle these problems. Metrics such as, entropy, PIQE and BRISQUE are used in the assessment of the results. Through the different fusion combinations, most prominent results are obtained from CLAHE-RMSHE fusion with an entropy value of 6.2516 and BRISQUE value of 40.14
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
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