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www.nature.com | ||
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judithcurry.com
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| | | | | by John Ridgway Any politician faced with the challenge of protecting the public from a natural threat, such as a pandemic or climate change, will be keen to stress how much they are 'following the science' - by which they ... Continue reading | |
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progearthplanetsci.springeropen.com
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| | | | | Proposed in 1954, Alisov's climate classification (CC) focuses on climatic changes observed in January-July in large-scale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east-west climatic differences. This study renews Alisov's CC for the first time in almost 70years and employs data-driven machine learning to establish a standard for causal CC based on air masses. | |
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educationaltechnologyjournal.springeropen.com
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| | | | | Universities are facing growing internal and external pressures to generate income, educate a widening continuum of learners, and make effective use of digital technologies. One response has been growth of online education, catalysed by Massive Open Online Courses, availability of digital devices and technologies, and notions of borderless global education. In growing online education, learning and teaching provision has become increasingly disaggregated, and universities are partnering with a range of private companies to reach new learners, and commercialise educational provision. In this paper, we explore the competing drivers which impact decision making within English universities and their strategies to grow online education provision, through intervie... | |
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ssc.io
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| | | Domain generalization aims to design models that can effectively generalize to unseen target domains by learning from observed source domains. Domain generalization poses a significant challenge for time series data, due to varying data distributions and temporal dependencies. Existing approaches to domain generalization are not designed for time series data, which often results in suboptimal or unstable performance when confronted with diverse temporal patterns and complex data characteristics. We propose a novel approach to tackle the problem of domain generalization in time series forecasting. We focus on a scenario where time series domains share certain common attributes and exhibit no abrupt distribution shifts. Our method revolves around the incorpora... | ||
