About Pyramid Structure in Convolutional Neural Networks

Deep convolutional neural networks (CNN) brought revolution without any doubt to various challenging tasks, mainly in computer vision. However, their model designing still requires attention to reduce number of learnable parameters, with no meaningful reduction in performance. In this paper we investigate to what extend CNN may take advantage of pyramid structure typical of biological neurons. A generalized statement over convolutional layers from input till fully connected layer is introduced that helps further in understanding and designing a successful deep network. It reduces ambiguity, number of parameters, and their size on disk without degrading overall accuracy. Performance are shown on state-of-the-art models for MNIST, Cifar-10, Cifar-100, and ImageNet-12 datasets. Despite more than 80% reduction in parameters for Caffe_LENET, challenging results are obtained. Further, despite 10-20% reduction in training data along with 10-40% reduction in parameters for AlexNet model and its variations, competitive results are achieved when compared to similar well-engineered deeper architectures.Comment: Published in 2016 International Joint Conference on Neural Networks (IJCNN

Machine Learning and Location Verification in Vehicular Networks

Location information will play a very important role in emerging wireless networks such as Intelligent Transportation Systems, 5G, and the Internet of Things. However, wrong location information can result in poor network outcomes. It is therefore critical to verify all location information before further utilization in any network operation. In recent years, a number of information-theoretic Location Verification Systems (LVSs) have been formulated in attempts to optimally verify the location information supplied by network users. Such LVSs, however, are somewhat limited since they rely on knowledge of a number of channel parameters for their operation. To overcome such limitations, in this work we introduce a Machine Learning based LVS (ML-LVS). This new form of LVS can adapt itself to changing environments without knowing the channel parameters. Here, for the first time, we use real-world data to show how our ML-LVS can outperform information-theoretic LVSs. We demonstrate this improved performance within the context of vehicular networks using Received Signal Strength (RSS) measurements at multiple verifying base stations. We also demonstrate the validity of the ML-LVS even in scenarios where a sophisticated adversary optimizes her attack location.Comment: 5 pages, 3 figure

An Automated System for Epilepsy Detection using EEG Brain Signals based on Deep Learning Approach

Epilepsy is a neurological disorder and for its detection, encephalography (EEG) is a commonly used clinical approach. Manual inspection of EEG brain signals is a time-consuming and laborious process, which puts heavy burden on neurologists and affects their performance. Several automatic techniques have been proposed using traditional approaches to assist neurologists in detecting binary epilepsy scenarios e.g. seizure vs. non-seizure or normal vs. ictal. These methods do not perform well when classifying ternary case e.g. ictal vs. normal vs. inter-ictal; the maximum accuracy for this case by the state-of-the-art-methods is 97+-1%. To overcome this problem, we propose a system based on deep learning, which is an ensemble of pyramidal one-dimensional convolutional neural network (P-1D-CNN) models. In a CNN model, the bottleneck is the large number of learnable parameters. P-1D-CNN works on the concept of refinement approach and it results in 60% fewer parameters compared to traditional CNN models. Further to overcome the limitations of small amount of data, we proposed augmentation schemes for learning P-1D-CNN model. In almost all the cases concerning epilepsy detection, the proposed system gives an accuracy of 99.1+-0.9% on the University of Bonn dataset.Comment: 18 page

The interrelationship between phagocytosis, autophagy and formation of neutrophil extracellular traps following infection of human neutrophils by Streptococcus pneumoniae

Neutrophils play an important role in the innate immune response to infection with Streptococcus pneumoniae, the pneumococcus. Pneumococci are phagocytosed by neutrophils and undergo killing after ingestion. Other cellular processes may also be induced, including autophagy and the formation of neutrophil extracellular traps (NETs), which may play a role in bacterial eradication. We set out to determine how these different processes interacted following pneumococcal infection of neutrophils, and the role of the major pneumococcal toxin pneumolysin in these various pathways. We found that pneumococci induced autophagy in neutrophils in a type III phosphatidylinositol-3 kinase dependent fashion that also required the autophagy gene Atg5. Pneumolysin did not affect this process. Phagocytosis was inhibited by pneumolysin but enhanced by autophagy, while killing was accelerated by pneumolysin but inhibited by autophagy. Pneumococci induced extensive NET formation in neutrophils that was not influenced by pneumolysin but was critically dependent on autophagy. While pneumolysin did not affect NET formation, it had a potent inhibitory effect on bacterial trapping within NETs. These findings show a complex interaction between phagocytosis, killing, autophagy and NET formation in neutrophils following pneumococcal infection that contribute to host defence against this pathogen

Potential ecological effects of Piriformospora indica, a possible biocontrol agent, in UK agricultural systems

Piriformospora indica (Sebacinaceae), a root endophytic fungus, was originally isolated from an arid sub-tropical soil. P. indica forms mutualistic symbioses with a broad range of host plants, increases biomass production, resistance and tolerance to fungal pathogens and abiotic stresses. These characteristics make it a very attractive component of more sustainable agriculture. So, it is desirable to understand its wider ecosystem effects. We determined how long P. indica could survive in the soil and how it interacts with other soil microorganisms and some important arable weeds. Survival of P. indica in the soil, under winter and summer conditions in the UK was tested by isolating DNA and RNA of P. indica from pots of soil which had been left open to winter-summer weather conditions without host plants, followed by PCR and reverse transcription-PCR (RT-PCR) with P. indica-specific primers. P. indica effects on other soil and root microorganisms were tested by PCR-denaturing gradient gel electrophoresis analysis of DNA extracted from soil and roots from pots in which P. indica-infected wheat had been grown. The effect of P. indica on growth of black-grass (Alopecuris myosuroides), wild-oat (Avena fatua) and cleavers (Galium aparine) was tested alone and in competition with wheat. In soil P. indica-mRNA and DNA could still be detected after eight months, but not after 15 months. Soils from P. indica-inoculated pots had distinct fungal and bacterial species communities which were more diverse than non-inoculated controls. P. indica infected A. myosuroides and A. fatua but was not detected in G. aparine. The average above-ground competitiveness of the weeds with wheat was decreased. If applied to field crops in the UK, P. indica would be persistent for up to 15 months and likely to alter competitive relations within vegetation. Increased soil microbial diversity during the first eight weeks after inoculation, although usually desirable, could alter soil composition or functioning

Identification, Screening, and Molecular Characterization of Bacterial Microbiota in the guts of Epinephelus sp.

Bacterial microbiota is predominantly present in all living organisms. Most of the bacteria present in the gut of the fish are contaminating the food chain. In the present study, we aimed to isolate and characterize the bacteria in the gut of Epinephelus sp. in the red sea of Jeddah, Kingdom of Saudi Arabia. Bacteria were isolated from the guts of 10 fish samples and were grown on Luria Bertani (LB) and nutrient agar media. Total thirteen bacterial colonies were screen out by morphological identification i.e., color, shape, structure, etc. which were further reduced to 7 colonies e.g., IF001, IF002, F003, IF004, F005, IF006, and IF007. The bacterial isolates were also identified through molecular identification using 16S-rDNA sequencing. The genomic DNA was isolated and was sequenced using the Sanger® sequencing method. BLAST alignment results that IF001 and IF002 were members Bacillus sp. IF003 was a strain of photobacterium damselae, IF004 and IF006 were strains of Rothia endophytica, IF005 was a strain of Acinetobacter bouvetiiand IF007 was belonged to Shewanella oneidensis. The molecular identification confirmed the identification of bacterial isolates in the Epinephelus sp. obtained from the red sea