Institution
Massachusetts Institute of Technology
Education•Cambridge, Massachusetts, United States•
About: Massachusetts Institute of Technology is a(n) education organization based out in Cambridge, Massachusetts, United States. It is known for research contribution in the topic(s): Population & Laser. The organization has 116795 authors who have published 268000 publication(s) receiving 18272025 citation(s). The organization is also known as: MIT & M.I.T..
Topics: Population, Laser, Galaxy, Gene, Scattering
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University of Genoa1, University of Manchester2, KEK3, CERN4, Imperial College London5, Stanford University6, Tata Institute of Fundamental Research7, Istituto Nazionale di Fisica Nucleare8, University of Pittsburgh9, Lyon College10, TRIUMF11, Northeastern University12, Thomas Jefferson National Accelerator Facility13, University of Córdoba (Spain)14, Goethe University Frankfurt15, University of Southampton16, University of Udine17, University of Alberta18, Tokyo Metropolitan University19, Helsinki Institute of Physics20, National Research Nuclear University MEPhI21, University of Bath22, Niigata University23, Naruto University of Education24, Kobe University25, University of Calabria26, University of Trieste27, European Space Agency28, University of Birmingham29, Ritsumeikan University30, Qinetiq31, École Polytechnique Fédérale de Lausanne32, Massachusetts Institute of Technology33, Brookhaven National Laboratory34
Abstract: G eant 4 is a toolkit for simulating the passage of particles through matter. It includes a complete range of functionality including tracking, geometry, physics models and hits. The physics processes offered cover a comprehensive range, including electromagnetic, hadronic and optical processes, a large set of long-lived particles, materials and elements, over a wide energy range starting, in some cases, from 250 eV and extending in others to the TeV energy range. It has been designed and constructed to expose the physics models utilised, to handle complex geometries, and to enable its easy adaptation for optimal use in different sets of applications. The toolkit is the result of a worldwide collaboration of physicists and software engineers. It has been created exploiting software engineering and object-oriented technology and implemented in the C++ programming language. It has been used in applications in particle physics, nuclear physics, accelerator design, space engineering and medical physics.
16,046 citations
14
University of Udine1, National Research Council2, International School for Advanced Studies3, Massachusetts Institute of Technology4, University of Paris5, Princeton University6, University of Minnesota7, ParisTech8, University of Milan9, International Centre for Theoretical Physics10, University of Paderborn11, ETH Zurich12, École Polytechnique Fédérale de Lausanne13
Abstract: QUANTUM ESPRESSO is an integrated suite of computer codes for electronic-structure calculations and materials modeling, based on density-functional theory, plane waves, and pseudopotentials (norm-conserving, ultrasoft, and projector-augmented wave). The acronym ESPRESSO stands for opEn Source Package for Research in Electronic Structure, Simulation, and Optimization. It is freely available to researchers around the world under the terms of the GNU General Public License. QUANTUM ESPRESSO builds upon newly-restructured electronic-structure codes that have been developed and tested by some of the original authors of novel electronic-structure algorithms and applied in the last twenty years by some of the leading materials modeling groups worldwide. Innovation and efficiency are still its main focus, with special attention paid to massively parallel architectures, and a great effort being devoted to user friendliness. QUANTUM ESPRESSO is evolving towards a distribution of independent and interoperable codes in the spirit of an open-source project, where researchers active in the field of electronic-structure calculations are encouraged to participate in the project by contributing their own codes or by implementing their own ideas into existing codes.
15,767 citations
Abstract: Finite systems of deterministic ordinary nonlinear differential equations may be designed to represent forced dissipative hydrodynamic flow. Solutions of these equations can be identified with trajectories in phase space For those systems with bounded solutions, it is found that nonperiodic solutions are ordinarily unstable with respect to small modifications, so that slightly differing initial states can evolve into considerably different states. Systems with bounded solutions are shown to possess bounded numerical solutions.
15,109 citations
TL;DR: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key.
Abstract: An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: (1) Couriers or other secure means are not needed to transmit keys, since a message can be enciphered using an encryption key publicly revealed by the intented recipient. Only he can decipher the message, since only he knows the corresponding decryption key. (2) A message can be “signed” using a privately held decryption key. Anyone can verify this signature using the corresponding publicly revealed encryption key. Signatures cannot be forged, and a signer cannot later deny the validity of his signature. This has obvious applications in “electronic mail” and “electronic funds transfer” systems. A message is encrypted by representing it as a number M, raising M to a publicly specified power e, and then taking the remainder when the result is divided by the publicly specified product, n, of two large secret primer numbers p and q. Decryption is similar; only a different, secret, power d is used, where e * d ≡ 1(mod (p - 1) * (q - 1)). The security of the system rests in part on the difficulty of factoring the published divisor, n.
14,611 citations
TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.
14,128 citations
Authors
Showing all 116795 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
Robert Langer | 281 | 2324 | 326306 |
George M. Whitesides | 240 | 1739 | 269833 |
Trevor W. Robbins | 231 | 1137 | 164437 |
George Davey Smith | 224 | 2540 | 248373 |
Yi Cui | 220 | 1015 | 199725 |
Robert J. Lefkowitz | 214 | 860 | 147995 |
David J. Hunter | 213 | 1836 | 207050 |
Daniel Levy | 212 | 933 | 194778 |
Rudolf Jaenisch | 206 | 606 | 178436 |
Mark J. Daly | 204 | 763 | 304452 |
David Miller | 203 | 2573 | 204840 |
David Baltimore | 203 | 876 | 162955 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Ronald M. Evans | 199 | 708 | 166722 |