Indian Institute of Technology Bombay

About: Indian Institute of Technology Bombay is a education organization based out in Mumbai, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 16756 authors who have published 33588 publications receiving 570559 citations.

Reptile: representative tiling for short read error correction

Abstract: MOTIVATION Error correction is critical to the success of next-generation sequencing applications, such as resequencing and de novo genome sequencing. It is especially important for high-throughput short-read sequencing, where reads are much shorter and more abundant, and errors more frequent than in traditional Sanger sequencing. Processing massive numbers of short reads with existing error correction methods is both compute and memory intensive, yet the results are far from satisfactory when applied to real datasets. RESULTS We present a novel approach, termed Reptile, for error correction in short-read data from next-generation sequencing. Reptile works with the spectrum of k-mers from the input reads, and corrects errors by simultaneously examining: (i) Hamming distance-based correction possibilities for potentially erroneous k-mers; and (ii) neighboring k-mers from the same read for correct contextual information. By not needing to store input data, Reptile has the favorable property that it can handle data that does not fit in main memory. In addition to sequence data, Reptile can make use of available quality score information. Our experiments show that Reptile outperforms previous methods in the percentage of errors removed from the data and the accuracy in true base assignment. In addition, a significant reduction in run time and memory usage have been achieved compared with previous methods, making it more practical for short-read error correction when sampling larger genomes. AVAILABILITY Reptile is implemented in C++ and is available through the link: http://aluru-sun.ece.iastate.edu/doku.php?id=software CONTACT aluru@iastate.edu.

Jitter compensation for real-time control systems

Abstract: In this paper, we first identify the potential violations of control assumptions inherent in standard real-time scheduling approaches (because of the presence of jitters) that causes, degradation in control performance and may even lead to instability. We then develop practical approaches founded on control theory to deal with these violations. Our approach is based on the notion of compensations wherein controller parameters are adjusted at runtime for the presence of jitters. Through time and memory overhead analysis, and by elaborating on the implementation details, we characterize when offline and on-line compensations are feasible. Our experimental results confirm that our approach does compensate for the degraded control performance when EDF and FPS algorithms are used for scheduling the control tasks. Our compensation approach provides us another advantage that leads to better schedulability of control tasks. This derives from the potential to derive more flexible timing constraints, beyond periods and deadlines necessary to apply EDF and FPS. Overall, our approach provides guarantees offline that the control system will be stable at runtime-if temporal requirements are met at runtime-even when actual execution patterns are not known beforehand. With our approach, we can address the problems due to (a) sampling jitters, (b) varying delays between sampling and actuation, or (c) both-not addressable using traditional EDF and FPS based scheduling, or by previous real-time and control integration approaches.

Learning 3D Human Pose from Structure and Motion

Abstract: 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data. We propose two anatomically inspired loss functions and use them with a weakly-supervised learning framework to jointly learn from large-scale in-the-wild 2D and indoor/synthetic 3D data. We also present a simple temporal network that exploits temporal and structural cues present in predicted pose sequences to temporally harmonize the pose estimations. We carefully analyze the proposed contributions through loss surface visualizations and sensitivity analysis to facilitate deeper understanding of their working mechanism. Jointly, the two networks capture the anatomical constraints in static and kinetic states of the human body. Our complete pipeline improves the state-of-the-art by 11.8% and 12% on Human3.6M and MPI-INF-3DHP, respectively, and runs at 30 FPS on a commodity graphics card.