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Institution

Indian Institute of Technology Bombay

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


Papers
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Journal ArticleDOI
TL;DR: The effectiveness of low cost agro-based materials namely, Tamarindus indica seed (TS), crushed coconut shell (CS), almond shell (AS), ground nut shell (GS) and walnut shell (WS) were evaluated for Cr(VI) removal and batch test indicated that hexavalent chromium sorption capacity (q(e)) followed the sequence.

276 citations

Journal ArticleDOI
Betty Abelev1, Jaroslav Adam2, Dagmar Adamová3, Madan M. Aggarwal4  +989 moreInstitutions (101)
TL;DR: In this paper, the authors measured the transverse momentum spectra of pi(+/-), K-+/- and p((p) over bar) up to p(T) = 20 GeV/c at mid-rapidity in pp, peripheral (60-80%) and central (0-5%) Pb-Pb collisions.

276 citations

Proceedings Article
15 Feb 2018
TL;DR: Empirical evaluation on three different applications establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbations methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.
Abstract: We present CROSSGRAD, a method to use multi-domain training data to learn a classifier that generalizes to new domains. CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain. Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training. In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains. We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances. CROSSGRAD parallelly trains a label and a domain classifier on examples perturbed by loss gradients of each other's objectives. This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions. Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and that (2) data augmentation is a more stable and accurate method than domain adversarial training.

276 citations

Journal ArticleDOI
TL;DR: In this article, a clear correlation between defect-related emissions and the magnetization of ZnO nanorods synthesized by a one-step aqueous chemical method is demonstrated.
Abstract: A clear correlation between defect-related emissions and the magnetization of ZnO nanorods synthesized by a one-step aqueous chemical method is demonstrated. The relative contribution of the emission bands arising from various types of defects is determined and found to be linked with the size of the nanorods and annealing conditions. When the size of the nanorods and the annealing temperature are increased, the magnetization of pure ZnO nanorods decreases with the reduction of a defect-rotated band originating from singly charged oxygen vacancies (V + o ). With a sufficient increase of annealing temperature (at 900 °C), the nanorods show diamagnetic behavior. Combining with the electron paramagnetic resonance results, a direct link between the magnetization and the relative occupancy of the singly charged oxygen vacancies present on the surface of ZnO nanorods is established.

276 citations

Journal ArticleDOI
TL;DR: Different applications of fiber reinforced polymer composites (FRPCs) for external strengthening in civil construction are reviewed in this paper, where experimental as well as analytical and numerical research contributions have been focussed in the review.

275 citations


Authors

Showing all 17055 results

NameH-indexPapersCitations
Jovan Milosevic1521433106802
C. N. R. Rao133164686718
Robert R. Edelman11960549475
Claude Andre Pruneau11461045500
Sanjeev Kumar113132554386
Basanta Kumar Nandi11257243331
Shaji Kumar111126553237
Josep M. Guerrero110119760890
R. Varma10949741970
Vijay P. Singh106169955831
Vinayak P. Dravid10381743612
Swagata Mukherjee101104846234
Anil Kumar99212464825
Dhiman Chakraborty9652944459
Michael D. Ward9582336892
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023175
2022433
20213,013
20203,093
20192,760
20182,549