A Bottom Up Procedure for Text Line Segmentation of Latin Script

In this paper we present a bottom up procedure for segmentation of text lines written or printed in the Latin script. The proposed method uses a combination of image morphology, feature extraction and Gaussian mixture model to perform this task. The experimental results show the validity of the procedure.Comment: Accepted and presented at the IEEE conference "International Conference on Advances in Computing, Communications and Informatics (ICACCI) 2017

The interplay between viral-derived miRNAs and host immunity during infection

MicroRNAs are short non-coding RNAs that play a crucial role in the regulation of gene expression during cellular processes. The host-encoded miRNAs are known to modulate the antiviral defense during viral infection. In the last decade, multiple DNA and RNA viruses have been shown to produce miRNAs known as viral miRNAs (v-miRNAs) so as to evade the host immune response. In this review, we highlight the origin and biogenesis of viral miRNAs during the viral lifecycle. We also explore the role of viral miRNAs in immune evasion and hence in maintaining chronic infection and disease. Finally, we offer insights into the underexplored role of viral miRNAs as potential targets for developing therapeutics for treating complex viral diseases

Gesture Recognition Using Hidden Markov Models Augmented with Active Difference Signatures

With the recent invention of depth sensors, human gesture recognition has gained significant interest in the fields of computer vision and human computer interaction. Robust gesture recognition is a difficult problem because of the spatiotemporal variations in gesture formation, subject size, subject location, image fidelity, and subject occlusion. Gesture boundary detection, or the automatic detection of the onset and offset of a gesture in a sequence of gestures, is critical toward achieving robust gesture recognition. Existing gesture recognition methods perform the task of gesture segmentation either using resting frames in a gesture sequence or by using additional information such as audio, depth images, or RGB images. This ancillary information introduces high latency in gesture segmentation and recognition, thus making it inappropriate for real time applications. This thesis proposes a novel method to recognize time-varying human gestures from continuous video streams. The proposed method passes skeleton joint information into a Hidden Markov Model augmented with active difference signatures to achieve state-of-the-art gesture segmentation and recognition. Active body parts are used to calculate the likelihood of previously unseen data to facilitate gesture segmentation. Active difference signatures are used to describe temporal motion as well as static differences from a canonical resting position. Geometric features, such as joint angles, and joint topological distances are used along with active difference signatures as salient feature descriptors. These feature descriptors serve as unique signatures which identify hidden states in a Hidden Markov Model. The Hidden Markov Model is able to identify gestures in a robust fashion which is tolerant to spatiotemporal and human-to-human variation in gesture articulation. The proposed method is evaluated on both isolated and continuous datasets. An accuracy of 80.7% is achieved on the isolated MSR3D dataset and a mean Jaccard index of 0.58 is achieved on the continuous ChaLearn dataset. Results improve upon existing gesture recognition methods, which achieve a Jaccard index of 0.43 on the ChaLearn dataset. Comprehensive experiments investigate the feature selection, parameter optimization, and algorithmic methods to help understand the contributions of the proposed method

Robust Loss Functions under Label Noise for Deep Neural Networks

In many applications of classifier learning, training data suffers from label noise. Deep networks are learned using huge training data where the problem of noisy labels is particularly relevant. The current techniques proposed for learning deep networks under label noise focus on modifying the network architecture and on algorithms for estimating true labels from noisy labels. An alternate approach would be to look for loss functions that are inherently noise-tolerant. For binary classification there exist theoretical results on loss functions that are robust to label noise. In this paper, we provide some sufficient conditions on a loss function so that risk minimization under that loss function would be inherently tolerant to label noise for multiclass classification problems. These results generalize the existing results on noise-tolerant loss functions for binary classification. We study some of the widely used loss functions in deep networks and show that the loss function based on mean absolute value of error is inherently robust to label noise. Thus standard back propagation is enough to learn the true classifier even under label noise. Through experiments, we illustrate the robustness of risk minimization with such loss functions for learning neural networks.Comment: Appeared in AAAI 201

Tunable Optoelectronic Properties of Triply-Bonded Carbon Molecules with Linear and Graphyne Substructures

In this paper we present a detailed computational study of the electronic structure and optical properties of triply-bonded hydrocarbons with linear, and graphyne substructures, with the aim of identifying their potential in opto-electronic device applications. For the purpose, we employed a correlated electron methodology based upon the Pariser-Parr-Pople model Hamiltonian, coupled with the configuration interaction (CI) approach, and studied structures containing up to 42 carbon atoms. Our calculations, based upon large-scale CI expansions, reveal that the linear structures have intense optical absorption at the HOMO-LUMO gap, while the graphyne ones have those at higher energies. Thus, the opto-electronic properties depend on the topology of the {graphyne substructures, suggesting that they can be tuned by means of structural modifications. Our results are in very good agreement with the available experimental data.Comment: main text 29 pages + 4 figures + 1 TOC graphic (included), supporting information 21 page