为什么国家卫生研究院(NIH)应该用抽签替换同行评审(Why the National Institutes of Health Should Replace Peer Review With a Lottery)

PSMAGApril 11, 3:39 PM

BY Michael White

(Translated by – 李昉)

edited by 傅渥成

用抽签决定基金的主要好处是减少对同行评审排名的依赖,国家卫生研究院(NIH)将有更多的空间来解决这个紧急问题。适当设计的抽签系统可以帮助消除微小但持续的性别差距,以及获取基金方面巨大的种族差异,并给予 NIH 工作人员在选择具有代表性的研究计划时更多的回旋余地。重要的是,对于抽签,大多数科学家已经坦然承认,尽管根据价值作出决策取得很多成绩,但同行评审是一个比我们理解的更具随机性的过程。


A major advantage of using a funding lottery would be that, by reducing its reliance on peer-review rankings, the NIH would have more room to address the urgent problem of bias. A suitably designed lottery system could help eliminate the small but persistent gender gap, and the larger racial gap in the funding it awards, by giving NIH officers more leeway to include a representative set of proposals. And importantly, a lottery would be an honest acknowledgement of what most scientists already sense: that, despite its reputation for basing decisions on merit, peer review is a much more random process than we would like to admit.


混合型社会:设计自组织系统集群行为的挑战及前景 (Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems)

Robotics and AI, April 11, 5:44 PM

BY Heiko Hamann, Yara Khaluf, Jean Botev, Mohammad Divband Soorati, Eliseo Ferrante, Oliver Kosak, Jean-Marc Montanier, Sanaz Mostaghim, Richard Redpath, Jonathan Timmis, Frank Veenstra, Mostafa Wahby, Aleš Zamuda

(Translated by -xiaomuqiu)

edited by 傅渥成



Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies.We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge.There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area.



通过稀疏识别从数据中发掘非线性动力系统的控制方程(Discovering governing equations from data by sparse identification of nonlinear dynamical systems)

PNASApril 13, 3:45 PM

BY Steven L. Brunton, Joshua L. Proctor, and J. Nathan Kutz

(Translated by -Darthy)

(Edited by 唐璐)



Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive, as in climate science, neuroscience, ecology, finance, and epidemiology, to name only a few examples. In this work, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including linear and nonlinear oscillators and the chaotic Lorenz system, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.














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