Modeling and analysis of a deep learning pipeline for cloud based video analytics.

Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time to perform fine tuning of hyper-parameters for object classification. We propose a cloud based video analytics system built upon an optimally tuned deep learning model to classify objects from video streams. The tuning of the hyper-parameters including learning rate, momentum, activation function and optimization algorithm is optimized through a mathematical model for efficient analysis of video streams. The system is capable of enhancing its own training data by performing transformations including rotation, flip and skew on the input dataset making it more robust and self-adaptive. The use of in-memory distributed training mechanism rapidly incorporates large number of distinguishing features from the training dataset - enabling the system to perform object classification with least human assistance and external support. The validation of the system is performed by means of an object classification case-study using a dataset of 100GB in size comprising of 88,432 video frames on an 8 node cloud. The extensive experimentation reveals an accuracy and precision of 0.97 and 0.96 respectively after a training of 6.8 hours. The system is scalable, robust to classification errors and can be customized for any real-life situation.N/

Biological effects of naturally occurring and man-made fibres: in vitro cytotoxicity and mutagenesis in mammalian cells

Cytotoxicity and mutagenicity of tremolite, erionite and the man-made ceramic (RCF-1) fibre were studied using the human– hamster hybrid A L cells. Results from these fibres were compared with those of UICC Rhodesian chrysotile fibres. The A L cell mutation assay, based on the S1 gene marker located on human chromosome 11, the only human chromosome contained in the hybrid cell, has been shown to be more sensitive than conventional assays in detecting deletion mutations. Tremolite, erionite and RCF-1 fibres were significantly less cytotoxic to A L cells than chrysotile. Mutagenesis studies at the HPRT locus revealed no significant mutant yield with any of these fibres. In contrast, both erionite and tremolite induced dose-dependent S1− mutations in fibre-exposed cells, with the former inducing a significantly higher mutant yield than the latter fibre type. On the other hand, RCF-1 fibres were largely non-mutagenic. At equitoxic doses (cell survival at ∼ 0.7), erionite was found to be the most potent mutagen among the three fibres tested and at a level comparable to that of chrysotile fibres. These results indicate that RCF-1 fibres are non-genotoxic under the conditions used in the studies and suggest that the high mesothelioma incidence previously observed in hamster may either be a result of selective sensitivity of hamster pleura to fibre-induced chronic irritation or as a result of prolonged fibre treatment. Furthermore, the relatively high mutagenic potential for erionite is consistent with its documented carcinogenicity. © 1999 Cancer Research Campaig

Peer-to-peer live video streaming with rateless codes for massively multiplayer online games

International audienc

Quality of spirometry and related diagnosis in primary care with a focus on clinical use

Contains fulltext : 220998.pdf (publisher's version ) (Open Access

Regulation of early signaling and gene expression in the α-particle and bystander response of IMR-90 human fibroblasts

<p>Abstract</p> <p>Background</p> <p>The existence of a radiation bystander effect, in which non-irradiated cells respond to signals from irradiated cells, is well established. To understand early signaling and gene regulation in bystander cells, we used a bio-informatics approach, measuring global gene expression at 30 minutes and signaling pathways between 30 minutes and 4 hours after exposure to α-particles in IMR-90 fibroblasts.</p> <p>Methods</p> <p>We used whole human genome microarrays and real time quantitative PCR to measure and validate gene expression. Microarray analysis was done using BRB-Array Tools; pathway and ontology analyses were done using Ingenuity Pathway Analysis and PANTHER, respectively. We studied signaling in irradiated and bystander cells using immunoblotting and semi-quantitative image analysis.</p> <p>Results</p> <p>Gene ontology suggested signal transduction and transcriptional regulation responding 30 minutes after treatment affected cell structure, motility and adhesion, and interleukin synthesis. We measured time-dependent expression of genes controlled by the NF-κB pathway; matrix metalloproteinases 1 and 3; <it/>chemokine ligands 2, 3 and 5 and <it/>interleukins 1β, 6 and 33. There was an increased response of this set of genes 30 minutes after treatment and another wave of induction at 4 hours. We investigated AKT-GSK3β signaling and found both AKT and GSK3β are hyper-phosphorylated 30 minutes after irradiation and this effect is maintained through 4 hours. In bystander cells, a similar response was seen with a delay of 30 minutes. We proposed a network model where the observed decrease in phosphorylation of β-catenin protein after GSK3β dependent inactivation can trigger target gene expression at later times after radiation exposure</p> <p>Conclusions</p> <p>These results are the first to show that the radiation induced bystander signal induces a widespread gene expression response at 30 minutes after treatment and these changes are accompanied by modification of signaling proteins in the PI3K-AKT-GSK3β pathway.</p