Institution
Santa Fe Institute
Nonprofit•Santa Fe, New Mexico, United States•
About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.
Papers published on a yearly basis
Papers
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TL;DR: Two recent workshops that were convened to describe the systems needed to synthesize simple life forms--called artificial cells or protocells--both in the laboratory and as simulations are discussed.
Abstract: Researchers interested in the origins of life on Earth have long pondered what constitutes the transition from nonliving to living matter. In this meeting report,
Rasmussen
and colleagues discuss two recent workshops that were convened to describe the systems needed to synthesize simple life forms--called artificial cells or protocells--both in the laboratory and as simulations.
306 citations
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TL;DR: In this article, the authors study the stochastic folding kinetics of RNA sequences into secondary structures with a new algorithm based on the formation, dissociation, and the shifting of individual base pairs.
Abstract: We study the stochastic folding kinetics of RNA sequences into secondary structures with a new algorithm based on the formation, dissociation, and the shifting of individual base pairs. We discuss folding mechanisms and the correlation between the barrier structure of the conformational landscape and the folding kinetics for a number of examples based on artificial and natural sequences, including the influence of base modification in tRNAs.
306 citations
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TL;DR: Methods from percolation theory are used to develop a mathematical framework for predicting disease transmission through semi-directed contact networks in which some contacts are undirected-the probability of transmission is symmetric between individuals-and others are directed-transmission is possible only in one direction.
305 citations
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TL;DR: In financial markets, an excess of buying tends to drive prices up, and a excess of selling tend to drive them down as mentioned in this paper, and this is called market impact, which is defined as the tendency for self-fulfilling prophesies.
Abstract: In financial markets an excess of buying tends to drive prices up, and an excess of selling tends to drive them down. This is called market impact. Based on a simplified model for market making, it is possible to derive a unique functional form for market impact. This can be used to formulate a nonequilibrium theory for price formation. Commonly used trading strategies such as value investing and trend following induce characteristic dynamics in the price. Although there is a tendency for self-fulfilling prophesies, this is not always the case; in particular, many value investing strategies fail to make prices reflect values. When there is a diversity of preceived values, nonlinear strategies give rise to excess volatility. Many market phenomena such as trends and temporal correlations in volume and volatility have simple explanations. The theory is both simple and experimentally testable. Under this theory there is an emphasis on the interrelationships of strategies that makes it natural to regard a market as a financial ecology. A variety of examples show how diversity emerges autmatically as new stategies exploit the inefficiencies of old strategies. This results in capital reallocations that evolve on longer timescales, and cause apparent nonstationarities on shorter timescales. The drive toward market efficiency can be studied in the dynamical context of pattern evolution. The evolution of the capital of a strategy is analogous to the evolution of the population of a biolgoical species. Several different arguments suggest that the timescale for market efficiency is years to decades.
303 citations
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TL;DR: Re-examination of single channel EEG data obtained from normal human subjects suggests that the previous indication of low-dimensional structure was an artifact of autocorrelation in the oversampled signal, and discriminatory analysis indicates that the correlation dimension is a poor discriminator for distinguishing between EEGs recorded at rest and during periods of cognitive activity.
302 citations
Authors
Showing all 606 results
Name | H-index | Papers | Citations |
---|---|---|---|
James Hone | 127 | 637 | 108193 |
James H. Brown | 125 | 423 | 72040 |
Alan S. Perelson | 118 | 632 | 66767 |
Mark Newman | 117 | 348 | 168598 |
Bette T. Korber | 117 | 392 | 49526 |
Marten Scheffer | 111 | 350 | 73789 |
Peter F. Stadler | 103 | 901 | 56813 |
Sanjay Jain | 103 | 881 | 46880 |
Henrik Jeldtoft Jensen | 102 | 1286 | 48138 |
Dirk Helbing | 101 | 642 | 56810 |
Oliver G. Pybus | 100 | 447 | 45313 |
Andrew P. Dobson | 98 | 322 | 44211 |
Carel P. van Schaik | 94 | 329 | 26908 |
Seth Lloyd | 92 | 490 | 50159 |
Andrew W. Lo | 85 | 378 | 51440 |