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Dwarka, India

Indraprastha Institute of Information Technology, Delhi is an autonomous university in Delhi, India. Wikipedia.


Sureka A.,Indraprastha Institute of Information Technology
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Bug reports in widely used defect tracking systems contains standard and mandatory fields like product name, component name, version number and operating system. Such fields provide important information required by developers during bug fixing. Previous research shows that bug reporters often assign incorrect values for such fields which cause problems and delays in bug fixing. We conduct an empirical study on the issue of incorrect component assignments or component reassignments in bug reports. We perform a case study on open-source Eclipse and Mozilla projects and report results on various aspects such as the percentage of reassignments, distribution across number of assignments until closure of a bug and time difference between creation and reassignment event. We perform a series of experiments using a machine learning framework for two prediction tasks: categorizing a given bug report into a pre-defined list of components and predicting whether a given bug report will be reassigned. Experimental results demonstrate correlation between terms present in bug reports (textual documents) and components which can be used as linguistic indicators for the task of component prediction. We study component reassignment graphs and reassignment probabilities and investigate their usefulness for the task of component reassignment prediction. © 2012 Springer-Verlag.


Majumdar A.,Indraprastha Institute of Information Technology
Magnetic Resonance Imaging | Year: 2015

In blind compressed sensing (BCS), both the sparsifying dictionary and the sparse coefficients are estimated simultaneously during signal recovery. A recent study adopted the BCS framework for recovering dynamic MRI sequences from under-sampled K-space measurements; the results were promising. Previous works in dynamic MRI reconstruction showed that, recovery accuracy can be improved by incorporating low-rank penalties into the standard compressed sensing (CS) optimization framework. Our work is motivated by these studies, and we improve upon the basic BCS framework by incorporating low-rank penalties into the optimization problem. The resulting optimization problem has not been solved before; hence we derive a Split Bregman type technique to solve the same. Experiments were carried out on real dynamic contrast enhanced MRI sequences. Results show that, with our proposed improvement, the reconstruction accuracy is better than BCS and other state-of-the-art dynamic MRI recovery algorithms. © 2014 Elsevier Inc.


Majumdar A.,Indraprastha Institute of Information Technology | Ward R.K.,University of British Columbia
Biomedical Signal Processing and Control | Year: 2014

This work addresses the problem of reconstructing EEG signals from lower dimensional projections. Unlike previous studies, we propose to reconstruct the EEG signal using an analysis prior formulation. Moreover we use the inter-channel correlation while reconstruction which leads to a row-sparse analysis prior multiple measurement vector (MMV) recovery problem. To improve the reconstruction, we formulate the recovery as a non-convex optimization problem. Such a non-convex row-sparse MMV recovery problem had not been encountered before; this work derives an efficient algorithm to solve it. The proposed reconstruction technique is compared with state-of-the-art methods and we find that our technique yields significant improvement over others. © 2014 Elsevier Ltd. All rights reserved.


Bera D.,Indraprastha Institute of Information Technology
Quantum Information Processing | Year: 2015

One of the early achievements of quantum computing was demonstrated by Deutsch and Jozsa (Proc R Soc Lond A Math Phys Sci 439(1907):553, 1992) regarding classification of a particular type of Boolean functions. Their solution demonstrated an exponential speedup compared to classical approaches to the same problem; however, their solution was the only known quantum algorithm for that specific problem so far. This paper demonstrates another quantum algorithm for the same problem, with the same exponential advantage compared to classical algorithms. The novelty of this algorithm is the use of quantum amplitude amplification, a technique that is the key component of another celebrated quantum algorithm developed by Grover (Proceedings of the twenty-eighth annual ACM symposium on theory of computing, ACM Press, New York, 1996). A lower bound for randomized (classical) algorithms is also presented which establishes a sound gap between the effectiveness of our quantum algorithm and that of any randomized algorithm with similar efficiency. © 2015, Springer Science+Business Media New York.


Majumdar A.,Indraprastha Institute of Information Technology
Magnetic Resonance Imaging | Year: 2013

In this paper we address the problem of dynamic MRI reconstruction from partially sampled K-space data. Our work is motivated by previous studies in this area that proposed exploiting the spatiotemporal correlation of the dynamic MRI sequence by posing the reconstruction problem as a least squares minimization regularized by sparsity and low-rank penalties. Ideally the sparsity and low-rank penalties should be represented by the l0-norm and the rank of a matrix; however both are NP hard penalties. The previous studies used the convex l1-norm as a surrogate for the l0-norm and the non-convex Schatten-q norm (0

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