International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

DeepFly: towards complete autonomous navigation of MAVs with monocular camera

Abstract: Recently, the interest in Micro Aerial Vehicles (MAVs) and their autonomous flights has increased tremendously and significant advances have been made. The monocular camera has turned out to be most popular sensing modality for MAVs as it is light-weight, does not consume more power, and encodes rich information about the environment around. In this paper, we present DeepFly, our framework for autonomous navigation of a quadcopter equipped with monocular camera. The navigable space detection and waypoint selection are fundamental components of autonomous navigation system. They have broader meaning than just detecting and avoiding immediate obstacles. Finding the navigable space emphasizes equally on avoiding obstacles and detecting ideal regions to move next to. The ideal region can be defined by two properties: 1) All the points in the region have approximately same high depth value and 2) The area covered by the points of the region in the disparity map is considerably large. The waypoints selected from these navigable spaces assure collision-free path which is safer than path obtained from other waypoint selection methods which do not consider neighboring information.In our approach, we obtain a dense disparity map by performing a translation maneuver. This disparity map is input to a deep neural network which predicts bounding boxes for multiple navigable regions. Our deep convolutional neural network with shortcut connections regresses variable number of outputs without any complex architectural add on. Our autonomous navigation approach has been successfully tested in both indoors and outdoors environment and in range of lighting conditions.

Isothermal titration calorimetric and computational studies on the binding of chitooligosaccharides to pumpkin (Cucurbita maxima) phloem exudate lectin.

Abstract: The interaction of chitooligosaccharides [(GlcNAc) 2−6 ] with pumpkin phloem exudate lectin (PPL) was investigated by isothermal titration calorimetry and computational methods. The dimeric PPL binds to (GlcNAc) 3−5 with binding constants of 1.26−1.53 × 10 5 M −1 at 25 °C, whereas chitobiose exhibits approximately 66-fold lower affinity. Interestingly, chitohexaose shows nearly 40-fold higher affinity than chitopentaose with a binding constant of 6.16 × 10 6 M −1 . The binding stoichiometry decreases with an increase in the oligosaccharide size from 2.26 for chitobiose to 1.08 for chitohexaose. The binding reaction was essentially enthalpy driven with negative entropic contribution, suggesting that hydrogen bonds and van der Waals’ interactions are the main factors that stabilize PPL−saccharide association. The three-dimensional structure of PPL was predicted by homology modeling, and binding of chitooligosaccharides was investigated by molecular docking and molecular dynamics simulations, which showed that the protein binding pocket can accommodate up to three GlcNAc residues, whereas additional residues in chitotetraose and chitopentaose did not exhibit any interactions with the binding pocket. Docking studies with chitohexaose indicated that the two triose units of the molecule could interact with different protein binding sites, suggesting formation of higher order complexes by the higher oligomers of GlcNAc by their simultaneous interaction with two protein molecules.

A conceptual framework to analyse the land-use/land-cover changes and its impact on phytodiversity: a case study of North Andaman Islands, India

Abstract: Phytodiversity is affected both by natural and anthropogenic factors and in Island ecosystems these impacts can devastate or reduce diversity, if the native vegetation is lost. In addition to rich species richness and diversity, Island systems are the sites of high endemism and any threat to these ecosystems will consequently lead to loss and extinction of species. To understand the dynamics including feedbacks of these changes in phytodiversity of North Andaman Islands, a conceptual framework is proposed which focuses on understanding the land-use and land-cover changes and its impact with phytodiversity. In considering land-use and land-cover changes this work highlights the direct and indirect drivers of changes—socio-economic, biophysical and climatic factors. Migration of population, their socio economic needs and government policies were identified as major driving forces threatening the phytodiversity of these Islands. Apart from human beings, natural disasters like tsunami and introduced herbivorous animals like elephants also contributed to forest destruction in these Islands. The integrated analysis based on such framework will provide insights for holistic resource management including ecological conservation.

A Deep Learning Framework for Segmentation of Retinal Layers from OCT Images

Abstract: Segmentation of retinal layers from Optical Coherence Tomography (OCT) volumes is a fundamental problem for any computer aided diagnostic algorithm development. This requires preprocessing steps such as denoising, region of interest extraction, flattening and edge detection all of which involve separate parameter tuning. In this paper, we explore deep learning techniques to automate all these steps and handle the presence/absence of pathologies. A model is proposed consisting of a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The CNN is used to extract layers of interest image and extract the edges, while the LSTM is used to trace the layer boundary. This model is trained on a mixture of normal and AMD cases using minimal data. Validation results on three public datasets show that the pixel-wise mean absolute error obtained with our system is 1.30±0.48 which is lower than the inter-marker error of 1.79±0.76. Our model's performance is also on par with the existing methods.

Precise limits on the charge- 2 / 3 U 1 vector leptoquark

Abstract: The ${U}_{1}$ leptoquark is known to be a suitable candidate for explaining the semileptonic $B$-decay anomalies. We derive precise limits on its parameter space relevant for the anomalies from the current LHC high-${p}_{\mathrm{T}}$ dilepton data. We consider an exhaustive list of possible $B$-anomalies-motivated simple scenarios with one or two new couplings that can also be used as templates for obtaining bounds on more complicated scenarios. To obtain precise limits, we systematically consider all possible ${U}_{1}$ production processes that can contribute to the dilepton searches, including the resonant pair and single productions, nonresonant $t$-channel ${U}_{1}$ exchange, as well as its large interference with the Standard Model background. We demonstrate how the inclusion of resonant production contributions in the dilepton signal can lead to appreciably improved exclusion limits. We point out new search channels of ${U}_{1}$ that can act as unique tests of the flavor-motivated models. The template scenarios can also be used for future ${U}_{1}$ searches at the LHC. We compare the LHC limits with other relevant flavor bounds and find that a TeV-scale ${U}_{1}$ can accommodate both ${R}_{{D}^{(*)}}$ and ${R}_{{K}^{(*)}}$ anomalies while satisfying all the bounds.