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Institution

Rensselaer Polytechnic Institute

EducationTroy, New York, United States
About: Rensselaer Polytechnic Institute is a education organization based out in Troy, New York, United States. It is known for research contribution in the topics: Terahertz radiation & Finite element method. The organization has 19024 authors who have published 39922 publications receiving 1414699 citations. The organization is also known as: RPI & Rensselaer Institute.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors describe an automated algorithm for selecting quasar candidates for optical spectroscopy in the Sloan Digital Sky Survey, which is sensitive to quasars at all redshifts lower than z ~ 5.8.
Abstract: We describe the algorithm for selecting quasar candidates for optical spectroscopy in the Sloan Digital Sky Survey. Quasar candidates are selected via their nonstellar colors in ugriz broadband photometry and by matching unresolved sources to the FIRST radio catalogs. The automated algorithm is sensitive to quasars at all redshifts lower than z ~ 5.8. Extended sources are also targeted as low-redshift quasar candidates in order to investigate the evolution of active galactic nuclei (AGNs) at the faint end of the luminosity function. Nearly 95% of previously known quasars are recovered (based on 1540 quasars in 446 deg2). The overall completeness, estimated from simulated quasars, is expected to be over 90%, whereas the overall efficiency (quasars/quasar candidates) is better than 65%. The selection algorithm targets ultraviolet excess quasars to i* = 19.1 and higher redshift (z 3) quasars to i* = 20.2, yielding approximately 18 candidates deg-2. In addition to selecting "normal" quasars, the design of the algorithm makes it sensitive to atypical AGNs such as broad absorption line quasars and heavily reddened quasars.

1,073 citations

Proceedings Article
01 Jan 2002
TL;DR: CHARM is an efficient algorithm for mining all frequent closed itemsets that enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels, and uses a technique called diffsets to reduce the memory footprint of intermediate computations.
Abstract: The set of frequent closed itemsets uniquely determines the exact frequency of all itemsets, yet it can be orders of magnitude smaller than the set of all frequent itemsets. In this paper we present CHARM, an efficient algorithm for mining all frequent closed itemsets. It enumerates closed sets using a dual itemset-tidset search tree, using an efficient hybrid search that skips many levels. It also uses a technique called diffsets to reduce the memory footprint of intermediate computations. Finally it uses a fast hash-based approach to remove any “non-closed” sets found during computation. An extensive experimental evaluation on a number of real and synthetic databases shows that CHARM significantly outperforms previous methods. It is also linearly scalable in the number of transactions.

1,068 citations

Journal ArticleDOI
TL;DR: These findings indicate that heat transport in a nanotube composite material will be limited by the exceptionally small interface thermal conductance and that the thermal conductivity of the composite will be much lower than the value estimated from the intrinsic thermal conductivities of the nanotubes and their volume fraction.
Abstract: The enormous amount of basic research into carbon nanotubes has sparked interest in the potential applications of these novel materials. One promising use of carbon nanotubes is as fillers in a composite material to improve mechanical behaviour, electrical transport and thermal transport. For composite materials with high thermal conductivity, the thermal conductance across the nanotube-matrix interface is of particular interest. Here we use picosecond transient absorption to measure the interface thermal conductance (G) of carbon nanotubes suspended in surfactant micelles in water. Classical molecular dynamics simulations of heat transfer from a carbon nanotube to a model hydrocarbon liquid are in agreement with experiment. Our findings indicate that heat transport in a nanotube composite material will be limited by the exceptionally small interface thermal conductance (G approximately 12 MW m(-2) K(-1)) and that the thermal conductivity of the composite will be much lower than the value estimated from the intrinsic thermal conductivity of the nanotubes and their volume fraction.

1,066 citations

Book
01 Jan 1976

1,066 citations

Journal ArticleDOI
Elena Aprile1, Jelle Aalbers2, F. Agostini, M. Alfonsi3, F. D. Amaro4, M. Anthony1, F. Arneodo5, P. Barrow6, Laura Baudis6, Boris Bauermeister7, M. L. Benabderrahmane5, T. Berger8, P. A. Breur2, April S. Brown2, Ethan Brown8, S. Bruenner9, Giacomo Bruno, Ran Budnik10, L. Bütikofer11, J. Calvén7, João Cardoso4, M. Cervantes12, D. Cichon9, D. Coderre11, Auke-Pieter Colijn2, Jan Conrad7, Jean-Pierre Cussonneau13, M. P. Decowski2, P. de Perio1, P. Di Gangi14, A. Di Giovanni5, Sara Diglio13, G. Eurin9, J. Fei15, A. D. Ferella7, A. Fieguth16, W. Fulgione, A. Gallo Rosso, Michelle Galloway6, F. Gao1, M. Garbini14, Robert Gardner17, C. Geis3, Luke Goetzke1, L. Grandi17, Z. Greene1, C. Grignon3, C. Hasterok9, E. Hogenbirk2, J. Howlett1, R. Itay10, B. Kaminsky11, Shingo Kazama6, G. Kessler6, A. Kish6, H. Landsman10, R. F. Lang12, D. Lellouch10, L. Levinson10, Qing Lin1, Sebastian Lindemann9, Manfred Lindner9, F. Lombardi15, J. A. M. Lopes4, A. Manfredini10, I. Mariș5, T. Marrodán Undagoitia9, Julien Masbou13, F. V. Massoli14, D. Masson12, D. Mayani6, M. Messina1, K. Micheneau13, A. Molinario, K. Morâ7, M. Murra16, J. Naganoma18, Kaixuan Ni15, Uwe Oberlack3, P. Pakarha6, Bart Pelssers7, R. Persiani13, F. Piastra6, J. Pienaar12, V. Pizzella9, M.-C. Piro8, Guillaume Plante1, N. Priel10, L. Rauch9, S. Reichard6, C. Reuter12, B. Riedel17, A. Rizzo1, S. Rosendahl16, N. Rupp9, R. Saldanha17, J.M.F. dos Santos4, Gabriella Sartorelli14, M. Scheibelhut3, S. Schindler3, J. Schreiner9, Marc Schumann11, L. Scotto Lavina19, M. Selvi14, P. Shagin18, E. Shockley17, Manuel Gameiro da Silva4, H. Simgen9, M. V. Sivers11, A. Stein20, S. Thapa17, Dominique Thers13, A. Tiseni2, Gian Carlo Trinchero, C. Tunnell17, M. Vargas16, N. Upole17, Hui Wang20, Zirui Wang, Yuehuan Wei6, Ch. Weinheimer16, J. Wulf6, J. Ye15, Yanxi Zhang1, T. Zhu1 
TL;DR: The first dark matter search results from XENON1T, a ∼2000-kg-target-mass dual-phase (liquid-gas) xenon time projection chamber in operation at the Laboratori Nazionali del Gran Sasso in Italy, are reported and a profile likelihood analysis shows that the data are consistent with the background-only hypothesis.
Abstract: We report the first dark matter search results from XENON1T, a ∼2000-kg-target-mass dual-phase (liquid-gas) xenon time projection chamber in operation at the Laboratori Nazionali del Gran Sasso in Italy and the first ton-scale detector of this kind The blinded search used 342 live days of data acquired between November 2016 and January 2017 Inside the (1042±12)-kg fiducial mass and in the [5,40] keVnr energy range of interest for weakly interacting massive particle (WIMP) dark matter searches, the electronic recoil background was (193±025)×10-4 events/(kg×day×keVee), the lowest ever achieved in such a dark matter detector A profile likelihood analysis shows that the data are consistent with the background-only hypothesis We derive the most stringent exclusion limits on the spin-independent WIMP-nucleon interaction cross section for WIMP masses above 10 GeV/c2, with a minimum of 77×10-47 cm2 for 35-GeV/c2 WIMPs at 90% CL

1,061 citations


Authors

Showing all 19133 results

NameH-indexPapersCitations
Pulickel M. Ajayan1761223136241
Zhenan Bao169865106571
Murray F. Brennan16192597087
Ashok Kumar1515654164086
Joseph R. Ecker14838194860
Bruce E. Logan14059177351
Shih-Fu Chang13091772346
Michael G. Rossmann12159453409
Richard P. Van Duyne11640979671
Michael Lynch11242263461
Angel Rubio11093052731
Alan Campbell10968753463
Boris I. Yakobson10744345174
O. C. Zienkiewicz10745571204
John R. Reynolds10560750027
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202334
2022177
20211,118
20201,356
20191,328
20181,245