Climate change: Thousands of penguins die in Antarctic ice breakup | Solar Orbiter observes jets of material escaping the sun | CNN | Time-Series Forecasting: Deep Learning vs Statistics - Who Wins?

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Don't like ads? Go ad-free with TradeBriefs Premium Climate change: Thousands of penguins die in Antarctic ice breakup - BBC News    A catastrophic die-off of emperor penguin chicks has been observed in the Antarctic, with up to 10,000 young birds estimated to have been killed. The sea-ice underneath the chicks melted and broke apart before they could develop the waterproof feathers needed to swim in the ocean. The event, in late 2022, occurred in the west of the continent in an area fronting on to the Bellingshausen Sea. Continued here You Might Like � � � � � � � � � Solar Orbiter observes jets of material escaping the sun | CNN - CNN    The Solar Orbiter mission has discovered jets of material rapidly releasing from the sun's outer atmosphere. Astronomers believe these jets could be the source of the solar wind, a stream of charged particles that continuously flows from the sun across the solar system. The jets of charged particles, called plasma, last between 20 and 100 seconds each and move at about 223,694 miles per hour (360,000 kilometers per hour). Solar Orbiter, a joint mission between NASA and the European Space Agency, launched in 2020 to capture an unprecedented look at the sun by providing images of its north and south poles. Having a visual understanding of the sun's poles is important because it can provide more insight about the star's powerful magnetic field and how it affects Earth. Continued here Time-Series Forecasting: Deep Learning vs Statistics - Who Wins? - Medium    Time series, also sequential in nature, raise the question: what happens if we bring the full power of pretrained transformers to time-series forecasting? However, some papers, such as [2] and [3] have scrutinized Deep Learning models. These papers do not present the full picture. Even for NLP cases, some people attribute the breakthrough of GPT models to "more data and computing power" instead of "better ML research". This article aims to clear the confusion and provide an unbiased view, using reliable data and sources from both academia and industry. Specifically, we will cover: Continued here

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