Learning Deep and Compact Models for Gesture Recognition

We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression. The final model is less than $1~MB$ in size, which is less than one hundredth of our initial model, with a drop of $7\%$ in accuracy, and is suitable for real-time gesture recognition on mobile devices.Comment: Accepted at 2017 IEEE International Conference on Image Processing (ICIP 2017

Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

Semi-supervised and Active-learning Scenarios: Efficient Acoustic Model Refinement for a Low Resource Indian Language

We address the problem of efficient acoustic-model refinement (continuous retraining) using semi-supervised and active learning for a low resource Indian language, wherein the low resource constraints are having i) a small labeled corpus from which to train a baseline `seed' acoustic model and ii) a large training corpus without orthographic labeling or from which to perform a data selection for manual labeling at low costs. The proposed semi-supervised learning decodes the unlabeled large training corpus using the seed model and through various protocols, selects the decoded utterances with high reliability using confidence levels (that correlate to the WER of the decoded utterances) and iterative bootstrapping. The proposed active learning protocol uses confidence level based metric to select the decoded utterances from the large unlabeled corpus for further labeling. The semi-supervised learning protocols can offer a WER reduction, from a poorly trained seed model, by as much as 50% of the best WER-reduction realizable from the seed model's WER, if the large corpus were labeled and used for acoustic-model training. The active learning protocols allow that only 60% of the entire training corpus be manually labeled, to reach the same performance as the entire data

PCR-based sex determination of cetaceans and dugong from the Indian seas

A sex-determination technique based on PCR amplifi- cation of genomic DNA extracted from the skin tissue has been standardized in cetaceans and dugong sam-pled from the Indian seas. A Y-chromosome-specific region (SRY or Sex-determining Y-chromosome gene) of 210–224 bp size in the genome has been amplified (only in males) using specific PCR primers. A fragment of the ZFX/ZFY (zinc finger protein genes located both on the X and Y chromosomes respectively) re-gion in the size range 442–445 bp is also amplified (in both sexes) using another pair of primers simultaneously as positive controls for confirmation of sex. Molecular sexing was standardized in spinner dolphin (Stenella longirostris), bridled dolphin (Stenella attenuata), bottlenose dolphin (Tursiops aduncus), Indo-Pacific humpbacked dolphin (Sousa chinensis), Risso’s dolphin (Grampus griseus), finless porpoise (Neopho-caena phocaenoides), sperm whale (Physeter macro-cephalus), blue whale (Balaenoptera musculus), Bryde’s whale (Balaenoptera edeni) and dugong (Dugong du-gon), which are all vulnerable/endangered species pro- tected under the Indian Wildlife Act

A note on cetacean distribution in the Indian EEZ and contiguous seas during 2003-07

Relatively little is known about the distribution of cetaceans in Indian seas due to lack of systematic surveys. For collecting data on species distribution, 35 opportunistic surveys were conducted onboard FORV Sagar Sampada between October 2003 and February 2007 in the Indian EEZ and contiguous seas. In 5,254 hours of sighting effort, a total of 473 cetacean records were made with 5,865 individuals. The occurrence of 10 species from three cetacean families was confirmed. The Indo-Pacific bottlenose dolphin was the most frequently sighted species, whereas the spinner dolphin was dominant in terms of abundance. Long-beaked common dolphins, Indo-Pacific hump-backed dolphin and sperm whales were also recorded at frequent intervals. Cetaceans were found to have a wide geographical distribution in the Indian EEZ and contiguous seas. High abundance and species richness were recorded in the Southeastern Arabian Sea and southern Sri Lankan waters. From the information collected during the present study, the platform of opportunity has proved to be a useful means for cetacean surve

Indian Efforts on the Inventorization of Marine Mammal Species for their Conservation and Management

The present study is the first attempt to use molecular tools for identification of marine mammals in India. The objective was to develop a database of genetic sequences for future marine mammal research in addition to confirming the species identity of cetaceans and dugongs using a molecular approach. Partial sequencing of mitochondrial DNA loci was carried out in accidentally caught/stranded specimens of Spinner dolphin (Stenella longirostris), Pantropical spotted dolphin/bridled dolphin (Stenella attenuata), Bottlenose dolphin (Tursiops aduncus), Long-beaked common dolphin (Delphinus capensis), Indopacific humpbacked dolphin (Sousa chinensis), Risso’s dolphin (Grampus griseus), Finless porpoise (Neophocaena phocaenoides), Sperm whale (Physeter macrocephalus), Blue whale (Balaenoptera musculus), Bryde’s whale (Balaenoptera edeni) and Dugong (Dugong dugon). Molecular identification of species was done by phylogenetic reconstruction of the sequences using portals GenBank and DNA Surveillance. Apart from ratifying their morphological identification, the analysis was able to distinguish specimens that otherwise, could not have been identified using conventional approaches. Phylogenetic analysis of the Sousa-Stenella-Tursiops-Delphinus group indicated more or less robust monophyly for all species in this complex, except Delphinus capensis. A sister-group relationship for Sperm whales and Baleen whales was evident, that would place the former closer to the latter than to any other group of toothed whales