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Institution

Tata Institute of Fundamental Research

EducationMumbai, Maharashtra, India
About: Tata Institute of Fundamental Research is a education organization based out in Mumbai, Maharashtra, India. It is known for research contribution in the topics: Magnetization & Large Hadron Collider. The organization has 7786 authors who have published 21742 publications receiving 622368 citations. The organization is also known as: TIFR.


Papers
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Journal ArticleDOI
TL;DR: In vivo and in vitro results support a role for thyroid hormone in the regulation of adult hippocampal neurogenesis and raise the possibility that altered Neurogenesis may contribute to the cognitive and behavioral deficits associated with adult-onset hypothyroidism.

208 citations

Journal ArticleDOI
Vardan Khachatryan1, Albert M. Sirunyan1, Armen Tumasyan1, Wolfgang Adam  +2325 moreInstitutions (191)
TL;DR: In this paper, an upper bound on the branching fraction of the Higgs boson decay to invisible particles, as a function of the assumed production cross-sections, was established, and the results were also interpreted in the context of Higgs-portal dark matter models.
Abstract: Searches for invisible decays of the Higgs boson are presented. The data collected with the CMS detector at the LHC correspond to integrated luminosities of 5.1, 19.7, and 2.3 fb−1 at centre-of-mass energies of 7, 8, and 13 TeV, respectively. The search channels target Higgs boson production via gluon fusion, vector boson fusion, and in association with a vector boson. Upper limits are placed on the branching fraction of the Higgs boson decay to invisible particles, as a function of the assumed production cross sections. The combination of all channels, assuming standard model production, yields an observed (expected) upper limit on the invisible branching fraction of 0.24 (0.23) at the 95% confidence level. The results are also interpreted in the context of Higgs-portal dark matter models.

208 citations

Journal ArticleDOI
TL;DR: Many of the results of this paper are new in the finite case (i.e., G is a finite-dimensional semisimple group over C) as well.
Abstract: Let G be a Kac-Moody group with Borel subgroup B and compact maximal torus T. Analogous to Kostant and Kumar [Kostant, B. & Kumar, S. (1986) Proc. Nati. Acad. Sci. USA 83, 1543-1545], we define a certain ring Y, purely in terms of the Weyl group W (associated to G) and its action on T. By dualizing Y we get another ring 1, which, we prove, is "canonically" isomorphic with the T-equivariant Ktheory KT(G/B) of GIB. Now KT(G/B), apart from being an algebra over KT(pt.) A(T), also has a Weyl group action and, moreover, KT(G/B) admits certain operators {DW},,w similar to the Demazure operators defined on A(T). We prove that these structures on KT(G/B) come naturally from the ring Y. By "evaluating" the A(T)-module W at 1, we recover K(G/B) together with the above-mentioned structures. We believe that many of the results of this paper are new in the finite case (i.e., G is a finite-dimensional semisimple group over C)

207 citations

Proceedings ArticleDOI
05 Dec 2004
TL;DR: The large deviations theory is used to develop a mathematically rigorous framework for determining the optimal allocation of computing resources even when the underlying variables have general, nonGaussian distributions.
Abstract: We consider the problem of optimal allocation of computing budget to maximize the probability of correct selection in the ordinal optimization setting. This problem has been studied in the literature in an approximate mathematical framework under the assumption that the underlying random variables have a Gaussian distribution. We use the large deviations theory to develop a mathematically rigorous framework for determining the optimal allocation of computing resources even when the underlying variables have general, non-Gaussian distributions. Further, in a simple setting we show that when there exists an indifference zone, quick stopping rules may be developed that exploit the exponential decay rates of the probability of false selection. In practice, the distributions of the underlying variables are estimated from generated samples leading to performance degradation due to estimation errors. On a positive note, we show that the corresponding estimates of optimal allocations converge to their true values as the number of samples used for estimation increases to infinity.

207 citations


Authors

Showing all 7857 results

NameH-indexPapersCitations
Pulickel M. Ajayan1761223136241
Suvadeep Bose154960129071
Subir Sarkar1491542144614
Sw. Banerjee1461906124364
Dipanwita Dutta1431651103866
Ajit Kumar Mohanty141112493062
Tariq Aziz138164696586
Andrew Mehta1371444101810
Suchandra Dutta134126587709
Kajari Mazumdar134129594253
Bobby Samir Acharya1331121100545
Gobinda Majumder133152387732
Eric Conte132120684593
Prashant Shukla131134185287
Alessandro Montanari131138793071
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
2022128
2021939
20201,085
20191,100
20181,040