822 research outputs found
Electrostatic Field Classifier for Deficient Data
This paper investigates the suitability of recently developed models based on the physical
field phenomena for classification problems with incomplete datasets. An original approach
to exploiting incomplete training data with missing features and labels, involving extensive use
of electrostatic charge analogy, has been proposed. Classification of incomplete patterns has been
investigated using a local dimensionality reduction technique, which aims at exploiting all available
information rather than trying to estimate the missing values. The performance of all proposed
methods has been tested on a number of benchmark datasets for a wide range of missing data scenarios
and compared to the performance of some standard techniques. Several modifications of the
original electrostatic field classifier aiming at improving speed and robustness in higher dimensional
spaces are also discussed
Neuronal human BACE1 knock-in induces systemic diabetes in mice
Acknowledgements The authors thank S. Tammireddy (Diabetes and Cardiovascular Science, University of the Highlands and Islands, Inverness, UK) for technical support with the lipidomics component. Funding We would like to thank R. Simcox, Romex Oilfield Chemicals, for financial support for KP, and acknowledge additional contributions from the Scottish Alzheimer’s Research UK network for the lipidomics work. The College of Life Science and Medicine, University of Aberdeen, sponsored the imaging study. MD was funded by British Heart Foundation and Diabetes UK; NM was funded by a British Heart Foundation Intermediate Fellowship; KS was funded by a European Foundation for the Study of Diabetes/Lilly programme grant; and RD was funded by an Institute of Medical Sciences PhD studentship.Peer reviewedPublisher PDFPublisher PD
Coverage and Energy Analysis of Mobile Sensor Nodes in Obstructed Noisy Indoor Environment: A Voronoi Approach
The rapid deployment of wireless sensor network (WSN) poses the challenge of
finding optimal locations for the network nodes, especially so in (i) unknown
and (ii) obstacle-rich environments. This paper addresses this challenge with
BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi
algorithm. In line with the scenario challenges, BISON nodes are restricted to
(i) locally sensed as well as (ii) noisy information on the basis of which they
move, avoid obstacles and connect with neighboring nodes. Performance is
measured as (i) the percentage of area covered, (ii) the total distance
traveled by the nodes, (iii) the cumulative energy consumption and (iv) the
uniformity of nodes distribution. Obstacle constellations and noise levels are
studied systematically and a collision-free recovery strategy for failing nodes
is proposed. Results obtained from extensive simulations show the algorithm
outperforming previously reported approaches in both, convergence speed, as
well as deployment cost.Comment: 17 pages, 24 figures, 1 tabl
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Diabetic Retinopathy is the leading cause of blindness in the working-age
population of the world. The main aim of this paper is to improve the accuracy
of Diabetic Retinopathy detection by implementing a shadow removal and color
correction step as a preprocessing stage from eye fundus images. For this, we
rely on recent findings indicating that application of image dehazing on the
inverted intensity domain amounts to illumination compensation. Inspired by
this work, we propose a Shadow Removal Layer that allows us to learn the
pre-processing function for a particular task. We show that learning the
pre-processing function improves the performance of the network on the Diabetic
Retinopathy detection task.Comment: Accepted to International Conference on Image Analysis and
Recognition ICIAR 2019 Published at
https://doi.org/10.1007/978-3-030-27272-2_3
Spectral and polarization study of the double relics in Abell 3376 using the GMRT and the VLA
Double radio relics in galaxy clusters are rare phenomena that trace shocks
in the outskirts of merging galaxy clusters. We have carried out a spectral and
polarization study of the spectacular double relics in the galaxy cluster A3376
using the Giant Metrewave Radio Telescope at 150 and 325 MHz and the Very Large
Array at 1400 MHz. The polarization study at 1400 MHz reveals a high degree of
polarization (~30%) and aligned magnetic field vectors (not corrected for
Faraday rotation) in the eastern relic. A highly polarized (>60%) filamentary
radio source of size ~300 kpc near the eastern relic and north of the bent-jet
radio galaxy is detected for the first time. The western relic is less
polarized and does not show aligned magnetic field vectors. The distribution of
spectral indices between 325 and 1400 MHz over the radio relics show steepening
from the outer to the inner edges of the relics. The spectral indices of the
eastern and the western relics imply Mach numbers in the range 2.2 to 3.3.
Remarkable features such as the inward filament extending from the eastern
relic, the highly polarized filament, the complex polarization properties of
the western relic and the separation of the BCG from the ICM by a distance >900
kpc are noticed in the cluster. A comparison with simulated cluster mergers is
required to understand the complex properties of the double relics in the
context of the merger in A3376. An upper limit (log(P(1.4GHz) W/Hz < 23.0) on
the strength of a Mpc size radio halo in A3376 is estimated.Comment: 9 pages, 4 figures, accepted for publication in MNRA
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