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

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

INFORMATION NEEDS AND SEEKING BEHAVIOR OF DISTANCE AND REGULAR UNDERGRADUATE UNIVERSITY STUDENTS OF KHYBER PAKHTUNKHWA-PAKISTAN

The main theme of this study is to investigate the information needs and seeking behavior of distance and regular Library and Information undergraduate students studying at different public sector universities of Khyber Pakhtunkhwa-Pakistan. Quantitative research method was used to carry out this research study. Adopted questionnaire was used as data collection tool. A total of 299 questionnaires were distributed among the respondents and 213 questionnaires were received. It was depicted that lectures and course books were the prime needed information of both groups for their academic qualifications. The prime needed information of both groups was found as “Information on course of study”, followed by Scholarships for onward education, job opportunities, postgraduate education and career development. It is recommended that with provision of course related information; some efforts should be made for provision of extra services like career development, job facilitation, scholarships searching and information for further education

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

Synthesis of 3-aryl-3,4-Dihydroisocoumarins and sterically encumbered Biaryls by [3+3] Cyclocondensation Reactions and synthesis of Functionalized Fluoranthenes, trifluoromethyl-Substituted Di- and Terphenyls and tetraaryl-p-Benzoquinones by Pd(0)-Catalysed Cross-Coupling Reactions

Salicylates, 3-aryl-3,4-dihydroisocoumarins and sterically encumbered and functionalized biaryls were synthesized using 1,3-bis(silyl enol ethers) by TiCl4-mediated regioselective [3+3] cyclocondensation strategy. While functionalized fluoranthenes, 4-aryl-1-bromo-2-trifluoromethylbenzenes, 2,5-diaryl-1-trifluoromethylbenzene and terphenyls and tetraaryl-p-benzoquinones were synthesized by palladium(0)-catalyzed Heck and Suzuki cross coupling reactions

State-owned Enterprises not Burden: A case study of Pakistan International Airline

Despite covering a large share of the Pakistan aviation industry, Pakistan International Airline Corporation (PIAC) has been facing financial crises and losses for the past few years. The government subsidizes it every year for about Rs 456 billion. This study examined the operating revenue and operating expenses of the Pakistan International Airline Corporation (PIAC). The study finds that the performance of Pakistan International Airlines is affected by high employee burden, lack of management, political interference, poor services, corruption and human resource policies etc. The study suggests reforms in PIA, Public Private Partnership and Privatization of the PIA

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