Institution
Indian Institute of Technology Madras
Facility•Chennai, Tamil Nadu, India•
About: Indian Institute of Technology Madras is a facility organization based out in Chennai, Tamil Nadu, India. It is known for research contribution in the topics: Heat transfer & Finite element method. The organization has 20118 authors who have published 36499 publications receiving 590447 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, a GW signal from the merger of two stellar-mass black holes was observed by the two Advanced Laser Interferometer Gravitational-Wave Observatory detectors with a network signal-to-noise ratio of 13.5%.
Abstract: On 2017 June 8 at 02:01:16.49 UTC, a gravitational-wave (GW) signal from the merger of two stellar-mass black holes was observed by the two Advanced Laser Interferometer Gravitational-Wave Observatory detectors with a network signal-to-noise ratio of 13. This system is the lightest black hole binary so far observed, with component masses of ${12}_{-2}^{+7}\,{M}_{\odot }$ and ${7}_{-2}^{+2}\,{M}_{\odot }$ (90% credible intervals). These lie in the range of measured black hole masses in low-mass X-ray binaries, thus allowing us to compare black holes detected through GWs with electromagnetic observations. The source's luminosity distance is ${340}_{-140}^{+140}\,\mathrm{Mpc}$, corresponding to redshift ${0.07}_{-0.03}^{+0.03}$. We verify that the signal waveform is consistent with the predictions of general relativity.
1,268 citations
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TL;DR: In 2019, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9 and the Virgo detector was also taking data that did not contribute to detection due to a low SINR but were used for subsequent parameter estimation as discussed by the authors.
Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.
1,189 citations
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TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.
Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.
1,165 citations
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TL;DR: In this paper, the authors present an exhaustive review of the literature in this area and suggest a direction for future developments, including heat transfer, material science, physics, chemical engineering and synthetic chemistry.
Abstract: Suspended nanoparticles in conventional fluids, called nanofluids, have been the subject of intensive study worldwide since pioneering researchers recently discovered the anomalous thermal behavior of these fluids. The enhanced thermal conductivity of these fluids with small-particle concentration was surprising and could not be explained by existing theories. Micrometer-sized particle-fluid suspensions exhibit no such dramatic enhancement. This difference has led to studies of other modes of heat transfer and efforts to develop a comprehensive theory. This article presents an exhaustive review of these studies and suggests a direction for future developments. The review and suggestions could be useful because the literature in this area is spread over a wide range of disciplines, including heat transfer, material science, physics, chemical engineering and synthetic chemistry.
1,069 citations
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University of Lyon1, University of Burgundy2, Université de Sherbrooke3, The Chinese University of Hong Kong4, Pompeu Fabra University5, Stanford University6, Queen Mary University of London7, University of Crete8, Indian Institute of Technology Madras9, French Institute for Research in Computer Science and Automation10, German Cancer Research Center11, Mannheim University of Applied Sciences12, ETH Zurich13, Utrecht University14, Yonsei University15, University of Nice Sophia Antipolis16
TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.
Abstract: Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.
1,056 citations
Authors
Showing all 20385 results
Name | H-index | Papers | Citations |
---|---|---|---|
Pulickel M. Ajayan | 176 | 1223 | 136241 |
Xiaodong Wang | 135 | 1573 | 117552 |
C. N. R. Rao | 133 | 1646 | 86718 |
Archana Sharma | 126 | 1162 | 75902 |
Rama Chellappa | 120 | 1031 | 62865 |
R. Graham Cooks | 110 | 736 | 47662 |
Angel Rubio | 110 | 930 | 52731 |
Prafulla Kumar Behera | 109 | 1204 | 65248 |
J. Andrew McCammon | 106 | 669 | 55698 |
M. Santosh | 103 | 1344 | 49846 |
Sandeep Kumar | 94 | 1563 | 38652 |
Tom L. Blundell | 86 | 687 | 56613 |
R. Srikant | 84 | 432 | 26439 |
Zdenek P. Bazant | 82 | 301 | 20908 |
Raghavan Srinivasan | 80 | 959 | 37821 |