Forget no regrets
نویسندگان
چکیده
منابع مشابه
Co‐benefits and ‘no regrets’ benefits of influenza pandemic planning
To the editor: The emergence of the influenza A (H1N1) pandemic strain in 2009 and its subsequent spread has stimulated national and international interest in pandemic influenza control. It is likely to also be stimulating further revisions of pandemic plans around the world, especially given the possibility of on-going waves. Such pandemic planning can involve sunk costs that might never be re...
متن کاملGetting started No-regrets strategies for reducing greenhouse gas emissions
An integrated approach for choosing among energy supplyand demand-side measures shows that, compared to business-as-usual demand patterns, global greenhouse-gas emissions can be reduced well below current levels with net economic benefits to society. Given these findings, a 'wait-and-see' stance towards new initiatives in energy and environmental policy is not economically justifiable. Achievin...
متن کاملChilling without regrets
I n 2014, Facebook and Apple announced that they would pay for female employees to have their oocytes frozen to allow them to delay having children and instead focus on their careers. Whatever motivated the companies to make their offers, the fact that they did so highlights a prevalent problem faced by many young women: Their most fertile years are also a crucial period for building a career, ...
متن کامل"Have no regrets:" Parents' experiences and developmental tasks in pregnancy with a lethal fetal diagnosis.
SIGNIFICANCE Lethal fetal diagnoses are made in 2% of all pregnancies. The pregnancy experience is certainly changed for the parents who choose to continue the pregnancy with a known fetal diagnosis but little is known about how the psychological and developmental processes are altered. METHODS This longitudinal phenomenological study of 16 mothers and 14 fathers/partners sought to learn the ...
متن کاملOnline PCA with Optimal Regrets
We carefully investigate the online version of PCA, where in each trial a learning algorithm plays a k-dimensional subspace, and suffers the compression loss on the next instance when projected into the chosen subspace. In this setting, we give regret bounds for two popular online algorithms, Gradient Descent (GD) and Matrix Exponentiated Gradient (MEG). We show that both algorithms are essenti...
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ژورنال
عنوان ژورنال: Nature Climate Change
سال: 2015
ISSN: 1758-678X,1758-6798
DOI: 10.1038/nclimate2675