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