Institution
California Institute of Technology
Education•Pasadena, California, United States•
About: California Institute of Technology is a education organization based out in Pasadena, California, United States. It is known for research contribution in the topics: Galaxy & Population. The organization has 57649 authors who have published 146691 publications receiving 8620287 citations. The organization is also known as: Caltech & Cal Tech.
Topics: Galaxy, Population, Star formation, Redshift, Mars Exploration Program
Papers published on a yearly basis
Papers
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TL;DR: With energy swiftly rising to the top of the world's agenda, Harry B. Gray at the California Institute of Technology looks at how chemistry can help to harness the power of the Sun to meet the world't energy needs.
Abstract: With energy swiftly rising to the top of the world's agenda, Harry B. Gray at the California Institute of Technology looks at how chemistry can help to harness the power of the Sun to meet the world's energy needs.
1,464 citations
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TL;DR: Since the late 1940s, the field of electron transfer processes has grown enormously, both in chemistry and biology as discussed by the authors, and the development of the field, experimentally and theoretically, as well as its relation to the study of other kinds of chemical reactions, presents an intriguing history, one in which many threads have been brought together.
Abstract: Since the late 1940s, the field of electron transfer processes has grown enormously, both in chemistry and biology. The development of the field, experimentally and theoretically, as well as its relation to the study of other kinds of chemical reactions, presents to us an intriguing history, one in which many threads have been brought together. In this lecture, some history, recent trends, and my own involvement in this research are described.
1,459 citations
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TL;DR: In this paper, the authors use brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity, and game theory to understand human decision making.
Abstract: Neuroeconomics uses knowledge about brain mechanisms to inform economic analysis, and roots economics in biology. It opens up the "black box" of the brain, much as organizational economics adds detail to the theory of the firm. Neuroscientists use many tools— including brain imaging, behavior of patients with localized brain lesions, animal behavior, and recording single neuron activity. The key insight for economics is that the brain is composed of multiple systems which interact. Controlled systems ("executive function") interrupt automatic ones. Emotions and cognition both guide decisions. Just as prices and allocations emerge from the interaction of two processes—supply and demand— individual decisions can be modeled as the result of two (or more) processes interacting. Indeed, "dual-process" models of this sort are better rooted in neuroscientific fact, and more empirically accurate, than single-process models (such as utility-maximization). We discuss how brain evidence complicates standard assumptions about basic preference, to include homeostasis and other kinds of state-dependence. We also discuss applications to intertemporal choice, risk and decision making, and game theory. Intertemporal choice appears to be domain-specific and heavily influenced by emotion. The simplified s-d of quasi-hyperbolic discounting is supported by activation in distinct regions of limbic and cortical systems. In risky decision, imaging data tentatively support the idea that gains and losses are coded separately, and that ambiguity is distinct from risk, because it activates fear and discomfort regions. (Ironically, lesion patients who do not receive fear signals in prefrontal cortex are "rationally" neutral toward ambiguity.) Game theory studies show the effect of brain regions implicated in "theory of mind", correlates of strategic skill, and effects of hormones and other biological variables. Finally, economics can contribute to neuroscience because simple rational-choice models are useful for understanding highly-evolved behavior like motor actions that earn rewards, and Bayesian integration of sensorimotor information.
1,454 citations
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TL;DR: It is reported that the NSAIDs ibuprofen, indomethacin and sulindac sulphide preferentially decrease the highly amyloidogenic Aβ42 peptide (the 42-residue isoform of the amyloids-β peptide) produced from a variety of cultured cells by as much as 80%.
Abstract: Epidemiological studies have documented a reduced prevalence of Alzheimer's disease among users of nonsteroidal anti-inflammatory drugs (NSAIDs). It has been proposed that NSAIDs exert their beneficial effects in part by reducing neurotoxic inflammatory responses in the brain, although this mechanism has not been proved. Here we report that the NSAIDs ibuprofen, indomethacin and sulindac sulphide preferentially decrease the highly amyloidogenic Aβ42 peptide (the 42-residue isoform of the amyloid-β peptide) produced from a variety of cultured cells by as much as 80%. This effect was not seen in all NSAIDs and seems not to be mediated by inhibition of cyclooxygenase (COX) activity, the principal pharmacological target of NSAIDs. Furthermore, short-term administration of ibuprofen to mice that produce mutant β-amyloid precursor protein (APP) lowered their brain levels of Aβ42. In cultured cells, the decrease in Aβ42 secretion was accompanied by an increase in the Aβ(1–38) isoform, indicating that NSAIDs subtly alter γ-secretase activity without significantly perturbing other APP processing pathways or Notch cleavage. Our findings suggest that NSAIDs directly affect amyloid pathology in the brain by reducing Aβ42 peptide levels independently of COX activity and that this Aβ42-lowering activity could be optimized to selectively target the pathogenic Aβ42 species.
1,452 citations
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TL;DR: Experience Weighted Attraction (EWA) as mentioned in this paper is a special case of reinforcement learning that combines reinforcement learning and belief learning, and hybridizes their key elements, allowing attractions to begin and grow flexibly as choice reinforcement does but reinforcing unchosen strategies substantially as belief-based models implicitly do.
Abstract: In ‘experience-weighted attraction’ (EWA) learning, strategies have attractions that reflect initial predispositions, are updated based on payoff experience, and determine choice probabilities according to some rule (e.g., logit). A key feature is a parameter δ that weights the strength of hypothetical reinforcement of strategies that were not chosen according to the payoff they would have yielded, relative to reinforcement of chosen strategies according to received payoffs. The other key features are two discount rates, φ and ρ, which separately discount previous attractions, and an experience weight. EWA includes reinforcement learning and weighted fictitious play (belief learning) as special cases, and hybridizes their key elements. When δ= 0 and ρ= 0, cumulative choice reinforcement results. When δ= 1 and ρ=φ, levels of reinforcement of strategies are exactly the same as expected payoffs given weighted fictitious play beliefs. Using three sets of experimental data, parameter estimates of the model were calibrated on part of the data and used to predict a holdout sample. Estimates of δ are generally around .50, φ around .8 − 1, and ρ varies from 0 to φ. Reinforcement and belief-learning special cases are generally rejected in favor of EWA, though belief models do better in some constant-sum games. EWA is able to combine the best features of previous approaches, allowing attractions to begin and grow flexibly as choice reinforcement does, but reinforcing unchosen strategies substantially as belief-based models implicitly do.
1,450 citations
Authors
Showing all 58155 results
Name | H-index | Papers | Citations |
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Eric S. Lander | 301 | 826 | 525976 |
Donald P. Schneider | 242 | 1622 | 263641 |
George M. Whitesides | 240 | 1739 | 269833 |
Yi Chen | 217 | 4342 | 293080 |
David Baltimore | 203 | 876 | 162955 |
Edward Witten | 202 | 602 | 204199 |
George Efstathiou | 187 | 637 | 156228 |
Michael A. Strauss | 185 | 1688 | 208506 |
Jing Wang | 184 | 4046 | 202769 |
Ruedi Aebersold | 182 | 879 | 141881 |
Douglas Scott | 178 | 1111 | 185229 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
Phillip A. Sharp | 172 | 614 | 117126 |
Timothy M. Heckman | 170 | 754 | 141237 |
Zhenan Bao | 169 | 865 | 106571 |