PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be employed to learn new tasks. By performing iterative pruning and network re-training, we are able to sequentially "pack" multiple tasks into a single network while ensuring minimal drop in performance and minimal storage overhead. Unlike prior work that uses proxy losses to maintain accuracy on older tasks, we always optimize for the task at hand. We perform extensive experiments on a variety of network architectures and large-scale datasets, and observe much better robustness against catastrophic forgetting than prior work. In particular, we are able to add three fine-grained classification tasks to a single ImageNet-trained VGG-16 network and achieve accuracies close to those of separately trained networks for each task. Code available at https://github.com/arunmallya/packne

Does India Really Need a Stealth Fighter?

With the advent of unmanned combat aerial vehicles (UCAV) and given the nature of limited scope of a conventional military clash between India and its neighbors warranting the active use of the Air Force, the need of a manned stealth capable aerial platform holds limited utility at the moment. Moreover, the need to address the existing operational gulf and upgrading the current fleet, which remains adept at handling any contingency threatening the Indian airspace over the short-to-mid-term remains a more prudent choice

R tools for MicroRNA pathway analysis

In the early 2000s, microRNAs (miRNAs) were discovered as segments of a new class of highly conserved and small non-coding RNA molecules of 20-25 nucleotides that are transcribed from DNA.
They do not translation into proteins, rather they inhibit protein expression by binding to the 3’untranslated regions (3’ UTRs) of specific mRNA targets (that is/are complementary to them) and guiding their translational repression or complete degradation and gene silencing. With this, miRNAs provide a second level of regulation beyond primary gene expression. Integrative study of cellular pathways is pivotal to understanding the functions of individual genes and proteins in terms of systems and processes that contribute to normal physiology and to disease. "WikiPathways":http://wikipathways.org is an open, collaborative platform dedicated to the curation of biological pathways by and for the scientific community. The collection of pathways is publicly available to the researchers. The miRNA’s predicted by TargetScan in cardiomyocytes hypertrophy pathway has already been visualized on WikiPathways (WP1560). Since more studies investigate miRNAs using microarray technologies it would be desirable to be able to use information about miRNA’s in that analysis. One way to do that is to add the miRNA’s to all pathways. Therefore, we are integrating both validated and predicted miRNA information into biological pathways and making them available in WikiPathways. Initially, we focused on pathways related to the heart because miRNAs created a true revolution in the cardiovascular research field. The validated miRNAs have been downloaded from miRNA databases such as TarBase or miRTarbase. In order to link the validated miRNA targets to the genes in the pathways of our interest, we use "BridgeDb":http://www.bridgedb.org for identifier mapping. BridgeDb is a middleware between the relational databases, files and mapping services. BridgeDb is available in two forms. The first is a framework suitable for integration in Java applications. The other is based on Representational State Transfer (REST) webservices and is suitable for all other programming languages. The identifier mapping has been done in the R statistical environment as the connected Bioconductor repository has many pre-existing packages for microarray data analysis. For now we used the REST interface from R but we will also submit BridgeDb R package to Bioconductor.
Predicted miRNA targets by different prediction algorithms were verified by co evaluating miRNA and mRNA expression using microarray analysis. Quality control and normalization of the microarray datasets was done using the current functionality of the arrayanalysis.org web portal. Statistical analysis was done using Limma and the miRNAs were visualized in the pathways of interest using "PathVisio":http://www.pathvisio.org. Modules for statistical and pathway analysis have been developed which will be added to the "arrayanalysis.org":http://www.arrayanalysis.org portal. This also required connecting R to PathVisio, for which a new XMLRPC interface was developed. Through this PathVisio can be controlled by R scripts.
In conclusion, these R tools can help to integrate information about miRNAs with other knowledge about biological pathways and used for research purposes