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Towards sustainability: Does energy efficiency reduce unemployment in African societies? 科技报告
来源:Environment for Development Initiative. 出版年: 2022
作者:  Mawunyo Agradi;  Philip K. Adom;  Andrea Vezzulli
收藏  |  浏览/下载:9/0  |  提交时间:2022/06/24
A multivariate approach to generate synthetic short-to-medium range hydro-meteorological forecasts across locations, variables, and lead times 期刊论文
Water Resources Research, 2021
作者:  Zachary P. Brodeur;  Scott Steinschneider
收藏  |  浏览/下载:6/0  |  提交时间:2021/06/07
Long-Term Aging of Asphalt Mixtures for Performance Testing and Prediction: Phase III Results 科技报告
来源:National Academies Press. 出版年: 2021
作者:  National Academies of Sciences, Engineering, and Medicine
收藏  |  浏览/下载:1/0  |  提交时间:2021/04/20
Impacts of soil water stress on the acclimated stomatal limitation of photosynthesis: Insights from stable carbon isotope data 期刊论文
Global Change Biology, 2020
作者:  Alié;  nor Lavergne;  David Sandoval;  Vincent J. Hare;  Heather Graven;  Iain Colin Prentice
收藏  |  浏览/下载:7/0  |  提交时间:2020/10/26
Can we predict solar flares? 期刊论文
Science, 2020
作者:  Astrid M. Veronig
收藏  |  浏览/下载:0/0  |  提交时间:2020/08/09
Coupled Time‐lapse Full Waveform Inversion for Subsurface Flow Problems using Intrusive Automatic Differentiation 期刊论文
Water Resources Research, 2020
作者:  Dongzhuo Li;  Kailai Xu;  Jerry M. Harris;  Eric Darve
收藏  |  浏览/下载:4/0  |  提交时间:2020/07/09
Simplified Green-Ampt Model, Imbibition-Based Estimates of Permeability, and Implications for Leak-off in Hydraulic Fracturing 期刊论文
WATER RESOURCES RESEARCH, 2020, 56 (4)
作者:  Tokunaga, Tetsu K.
收藏  |  浏览/下载:6/0  |  提交时间:2020/07/02
permeability  imbibition  capillary pressure  sorptivity  hydraulic fracturing  
A distributional code for value in dopamine-based reinforcement learning 期刊论文
NATURE, 2020, 577 (7792) : 671-+
作者:  House, Robert A.;  Maitra, Urmimala;  Perez-Osorio, Miguel A.;  Lozano, Juan G.;  Jin, Liyu;  Somerville, James W.;  Duda, Laurent C.;  Nag, Abhishek;  Walters, Andrew;  Zhou, Ke-Jin;  Roberts, Matthew R.;  Bruce, Peter G.
收藏  |  浏览/下载:61/0  |  提交时间:2020/07/03

Since its introduction, the reward prediction error theory of dopamine has explained a wealth of empirical phenomena, providing a unifying framework for understanding the representation of reward and value in the brain(1-3). According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. Here we propose an account of dopamine-based reinforcement learning inspired by recent artificial intelligence research on distributional reinforcement learning(4-6). We hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea implies a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.


Analyses of single-cell recordings from mouse ventral tegmental area are consistent with a model of reinforcement learning in which the brain represents possible future rewards not as a single mean of stochastic outcomes, as in the canonical model, but instead as a probability distribution.


  
A simple dynamic model explains the diversity of island birds worldwide 期刊论文
NATURE, 2020
作者:  Li, Junxue;  Wilson, C. Blake;  Cheng, Ran;  Lohmann, Mark;  Kavand, Marzieh;  Yuan, Wei;  Aldosary, Mohammed;  Agladze, Nikolay;  Wei, Peng;  Sherwin, Mark S.;  Shi, Jing
收藏  |  浏览/下载:12/0  |  提交时间:2020/07/03

Colonization, speciation and extinction are dynamic processes that influence global patterns of species richness(1-6). Island biogeography theory predicts that the contribution of these processes to the accumulation of species diversity depends on the area and isolation of the island(7,8). Notably, there has been no robust global test of this prediction for islands where speciation cannot be ignored(9), because neither the appropriate data nor the analytical tools have been available. Here we address both deficiencies to reveal, for island birds, the empirical shape of the general relationships that determine how colonization, extinction and speciation rates co-vary with the area and isolation of islands. We compiled a global molecular phylogenetic dataset of birds on islands, based on the terrestrial avifaunas of 41 oceanic archipelagos worldwide (including 596 avian taxa), and applied a new analysis method to estimate the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation). Our model predicts-with high explanatory power-several global relationships. We found a decline in colonization with isolation, a decline in extinction with area and an increase in speciation with area and isolation. Combining the theoretical foundations of island biogeography(7,8) with the temporal information contained in molecular phylogenies(10) proves a powerful approach to reveal the fundamental relationships that govern variation in biodiversity across the planet.


Using a global molecular phylogenetic dataset of birds on islands, the sensitivity of island-specific rates of colonization, speciation and extinction to island features (area and isolation) is estimated.


  
A Rainfall-Runoff Model With LSTM-Based Sequence-to-Sequence Learning 期刊论文
WATER RESOURCES RESEARCH, 2020, 56 (1)
作者:  Xiang, Zhongrun;  Yan, Jun;  Demir, Ibrahim
收藏  |  浏览/下载:7/0  |  提交时间:2020/07/02