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Institution

IBM

CompanyArmonk, New York, United States
About: IBM is a company organization based out in Armonk, New York, United States. It is known for research contribution in the topics: Layer (electronics) & Cache. The organization has 134567 authors who have published 253905 publications receiving 7458795 citations. The organization is also known as: International Business Machines Corporation & Big Blue.


Papers
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Journal ArticleDOI
TL;DR: In this article, the microscopic mechanisms responsible for both the formation and coupling of magnetic moments in Heusler alloys were identified, and it was shown that the $X$ atoms (e.g., Cu, Pd) serve primarily to determine the lattice constant, while the $Y$ atoms mediate the interaction between the $\mathrm{Mn}d$ states.
Abstract: The microscopic mechanisms responsible for both the formation and coupling of magnetic moments in Heusler alloys (${X}_{2}\mathrm{Mn}Y$) are identified. We find that the $X$ atoms (e.g., Cu, Pd) serve primarily to determine the lattice constant, while the $Y$ atoms (e.g., Al, In, Sb) mediate the interaction between the $\mathrm{Mn}d$ states. There is no significant direct interaction between the Mn atoms, but the occupied $d$ states of Mn are delocalized by their strong interaction with the $X$-atom $d$ states. The localized character of the magnetization results from the exclusion of minority-spin (defined locally) electrons from the $\mathrm{Mn}3d$ shell. The coupling between the localized magnetic Mn moments can be described with the Heisenberg Hamiltonian and the sign of the exchange constants results from a competition between the intra-atomic magnetic energy and interatomic $Y$-atom mediated covalent interactions between the the $\mathrm{Mn}d$ states. These effects compete because the covalent mechanism is possible only for antiferromagnetic alignments, but necessarily reduces the magnitude of the local moments. The sensitive dependence of magnetic order on the occupation of the mediating $p\ensuremath{-}d$ hybrid states accounts well for experiments by Webster in which this occupation is varied by alloying. Our analysis is based on self-consistent, spin-polarized energy-band calculations for ${\mathrm{Co}}_{2}$MnAl, ${\mathrm{Co}}_{2}$MnSn, ${\mathrm{Ni}}_{2}$MnSn, ${\mathrm{Cu}}_{2}$MnAl, ${\mathrm{Cu}}_{2}$MnSn, ${\mathrm{Pd}}_{2}$MnIn, ${\mathrm{Pd}}_{2}$MnSn, and ${\mathrm{Pd}}_{2}$MnSb, for both ferromagnetic and antiferromagnetic spin alignments.

753 citations

Book ChapterDOI
01 Jan 2002
TL;DR: It is argued that future database systems must include responsibility for the privacy of data that they manage as a founding tenet, because of the explosive progress in networking, storage, and processor technologies.
Abstract: Publisher Summary The Hippocratic Oath has guided the conduct of physicians for centuries. Inspired by its tenet of preserving privacy, it has been argued that future database systems must include responsibility for the privacy of data that they manage as a founding tenet. The explosive progress in networking, storage, and processor technologies is resulting in an unprecedented amount of digitization of information. It is estimated that the amount of information in the world is doubling every 20 months, and the size and number of databases are increasing even faster. In concert with this dramatic and escalating increase in digital data, concerns about the privacy of personal information have emerged globally. Privacy issues have been further exacerbated, now that the Internet makes it easy for new data to be automatically collected and added to databases. Privacy is the fight of individuals to determine for themselves when, how, and to what extent information about them is communicated to others. Privacy concerns are being fueled by an ever-increasing list of privacy violations, ranging from privacy accidents to illegal actions. Lax security for sensitive data is of equal concern.

753 citations

Journal ArticleDOI
Gerald Tesauro1
TL;DR: TD-GAMMON is a neural network that trains itself to be an evaluation function for the game of backgammon by playing against itself and learning from the outcome.
Abstract: Ever since the days of Shannon's proposal for a chess-playing algorithm [12] and Samuel's checkers-learning program [10] the domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a variety of concepts and approaches in artificial intelligence and machine learning Such board games offer the challenge of tremendous complexity and sophistication required to play at expert level At the same time, the problem inputs and performance measures are clear-cut and well defined, and the game environment is readily automated in that it is easy to simulate the board, the rules of legal play, and the rules regarding when the game is over and determining the outcome

751 citations

Journal ArticleDOI
A.F. Diaz1, J.A. Logan1

751 citations

Journal ArticleDOI
TL;DR: In this article, the ring-opening polymerization of cyclic monomers is used as a representative polymerization process to illustrate some of the features of organic catalysts and initiators and compare them to metal-based approaches.
Abstract: Organocatalysis offers a number of opportunities in polymer synthesis and was among the earliest methods of catalyzing the synthesis of polyesters. In the following Perspective we attempt to highlight the opportunities and challenges in the use of organic molecules as catalysts or initiators for polymerization reactions. The ring-opening polymerization of cyclic monomers is used as a representative polymerization process to illustrate some of the features of organic catalysts and initiators and to compare them to metal-based approaches. The convergence of convenience, functional group tolerance, fast rates, and selectivities will continue to drive innovations in polymerization catalysis, and it is our perspective that organocatalysis will continue to play an important role in these developments.

749 citations


Authors

Showing all 134658 results

NameH-indexPapersCitations
Zhong Lin Wang2452529259003
Anil K. Jain1831016192151
Hyun-Chul Kim1764076183227
Rodney S. Ruoff164666194902
Tobin J. Marks1591621111604
Jean M. J. Fréchet15472690295
Albert-László Barabási152438200119
György Buzsáki15044696433
Stanislas Dehaene14945686539
Philip S. Yu1481914107374
James M. Tour14385991364
Thomas P. Russell141101280055
Naomi J. Halas14043582040
Steven G. Louie13777788794
Daphne Koller13536771073
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202330
2022137
20213,163
20206,336
20196,427
20186,278