Institution
Princeton University
Education•Princeton, New Jersey, United States•
About: Princeton University is a education organization based out in Princeton, New Jersey, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 60542 authors who have published 146772 publications receiving 9158888 citations. The organization is also known as: College of New Jersey & Princeton.
Topics: Population, Galaxy, Redshift, Laser, Quasar
Papers published on a yearly basis
Papers
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20 Jun 2009TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Abstract: The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500-1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.
49,639 citations
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01 Jan 1979TL;DR: The relationship between Stimulation and Stimulus Information for visual perception is discussed in detail in this article, where the authors also present experimental evidence for direct perception of motion in the world and movement of the self.
Abstract: Contents: Preface. Introduction. Part I: The Environment To Be Perceived.The Animal And The Environment. Medium, Substances, Surfaces. The Meaningful Environment. Part II: The Information For Visual Perception.The Relationship Between Stimulation And Stimulus Information. The Ambient Optic Array. Events And The Information For Perceiving Events. The Optical Information For Self-Perception. The Theory Of Affordances. Part III: Visual Perception.Experimental Evidence For Direct Perception: Persisting Layout. Experiments On The Perception Of Motion In The World And Movement Of The Self. The Discovery Of The Occluding Edge And Its Implications For Perception. Looking With The Head And Eyes. Locomotion And Manipulation. The Theory Of Information Pickup And Its Consequences. Part IV: Depiction.Pictures And Visual Awareness. Motion Pictures And Visual Awareness. Conclusion. Appendixes: The Principal Terms Used in Ecological Optics. The Concept of Invariants in Ecological Optics.
21,493 citations
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TL;DR: The revised RECIST includes a new imaging appendix with updated recommendations on the optimal anatomical assessment of lesions, and a section on detection of new lesions, including the interpretation of FDG-PET scan assessment is included.
20,760 citations
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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
TL;DR: QUANTUM ESPRESSO as discussed by the authors 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).
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.
19,985 citations
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TL;DR: In this paper, the theoretical foundation for topological insulators and superconductors is reviewed and recent experiments are described in which the signatures of topologically insulators have been observed.
Abstract: Topological insulators are electronic materials that have a bulk band gap like an ordinary insulator but have protected conducting states on their edge or surface. These states are possible due to the combination of spin-orbit interactions and time-reversal symmetry. The two-dimensional (2D) topological insulator is a quantum spin Hall insulator, which is a close cousin of the integer quantum Hall state. A three-dimensional (3D) topological insulator supports novel spin-polarized 2D Dirac fermions on its surface. In this Colloquium the theoretical foundation for topological insulators and superconductors is reviewed and recent experiments are described in which the signatures of topological insulators have been observed. Transport experiments on $\mathrm{Hg}\mathrm{Te}∕\mathrm{Cd}\mathrm{Te}$ quantum wells are described that demonstrate the existence of the edge states predicted for the quantum spin Hall insulator. Experiments on ${\mathrm{Bi}}_{1\ensuremath{-}x}{\mathrm{Sb}}_{x}$, ${\mathrm{Bi}}_{2}{\mathrm{Se}}_{3}$, ${\mathrm{Bi}}_{2}{\mathrm{Te}}_{3}$, and ${\mathrm{Sb}}_{2}{\mathrm{Te}}_{3}$ are then discussed that establish these materials as 3D topological insulators and directly probe the topology of their surface states. Exotic states are described that can occur at the surface of a 3D topological insulator due to an induced energy gap. A magnetic gap leads to a novel quantum Hall state that gives rise to a topological magnetoelectric effect. A superconducting energy gap leads to a state that supports Majorana fermions and may provide a new venue for realizing proposals for topological quantum computation. Prospects for observing these exotic states are also discussed, as well as other potential device applications of topological insulators.
15,562 citations
Authors
Showing all 61034 results
Name | H-index | Papers | Citations |
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Donald P. Schneider | 242 | 1622 | 263641 |
Edward Witten | 202 | 602 | 204199 |
David J. Schlegel | 193 | 600 | 193972 |
Michael Rutter | 188 | 676 | 151592 |
Michael A. Strauss | 185 | 1688 | 208506 |
David H. Weinberg | 183 | 700 | 171424 |
Xiaohui Fan | 183 | 878 | 168522 |
Robert H. Lupton | 179 | 415 | 151608 |
Bruce M. Spiegelman | 179 | 434 | 158009 |
Daniel J. Eisenstein | 179 | 672 | 151720 |
David A. Weitz | 178 | 1038 | 114182 |
David R. Williams | 178 | 2034 | 138789 |
Michael I. Jordan | 176 | 1016 | 216204 |
Timothy M. Heckman | 170 | 754 | 141237 |
Zhenan Bao | 169 | 865 | 106571 |