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Plate tectonics is not something most people would associate with Mars. In fact, the planet’s dead core is one of the primary reasons for its famous lack of a magnetic field. And since active planetary cores are one of the primary driving factors of plate tectonics, it seems obvious why that general conception holds.

However, Mars has some features that we think of as corresponding with plate tectonics—volcanoes. A new paper from researchers at the University of Hong Kong (HKU) looks at how different types of plate tectonics could have formed different types of volcanoes on the surface of Mars.

Typically, when you think of volcanoes on Mars, you think of massive shield volcanoes like Olympus Mons, similar to those seen in some locations on Earth, such as Hawai’i. These form when repeated eruptions deposit layers of lava for millions of years. Those eruptions aren’t impacted by how any underlying plates move underneath them. But they do create a different underlying landscape than elsewhere on the planet.

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When it was first discovered in 2004, Apophis was identified as one of the most dangerous asteroids in that there was a risk that it could impact Earth. But that impact assessment changed over the years after astronomers tracked Apophis, also known as asteroid 99,942, and its orbit became better determined, and it became clear that it was on course to miss our planet.

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In a study published in The Astrophysical Journal, researchers from the Yunnan Observatories of the Chinese Academy of Sciences depicted a complete physical image of the anomalous heating in the upper atmosphere of the sun (the solar corona and the solar chromosphere).

The enigma of the corona’s anomalous heating stands as one of the eight challenges in modern astronomy. Similarly, the anomalous heating of the chromosphere continues to baffle solar physicists.

Observations gleaned from large telescopes and satellites have revealed potential magnetic activities that could be the cause of this heating. Theoretical research has proposed various heating modes, yet none have been definitively proven to be the cause. As it stands, our understanding of how the sun’s upper atmosphere is heated remains incomplete.

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New research has identified iron deficiencies in the blood as a major culprit in long COVID cases.

A new report from the University of Cambridge was able to connect that low iron levels contributed to inflammation and anemia and halted healthy red blood cell production in patients just two weeks after being diagnosed with COVID-19.

Many of those individuals reported having long COVID — which has recently been associated with a frightening IQ loss from brain fog — within months, according to the study, published in Nature Immunology.

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Artificial Intelligence (AI) and Deep Learning, with a focus on Natural Language Processing (NLP), have seen substantial changes in the last few years. The area has advanced quickly in both theoretical development and practical applications, from the early days of Recurrent Neural Networks (RNNs) to the current dominance of Transformer models.

Models that are capable of processing and producing natural language with efficiency have advanced significantly as a result of research and development in the field of neural networks, particularly with regard to managing sequences. RNN’s innate ability to process sequential data makes them well-suited for tasks involving sequences, such as time-series data, text, and speech. Though RNNs are ideally suited for these kinds of jobs, there are still problems with scalability and training complexity, particularly with lengthy sequences.

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One of the cornerstone challenges in machine learning, time series forecasting has made groundbreaking contributions to several domains. However, forecasting models can’t generalize the distribution shift that changes with time because time series data is inherently non-stationary. Based on the assumptions about the inter-instance and intra-instance temporal distribution shifts, two main types of techniques have been suggested to address this issue. Both stationary and nonstationary dependencies can be separated using these techniques. Existing approaches help reduce the impact of the shift in the temporal distribution. Still, they are overly prescriptive because, without known environmental labels, every sequence instance or segment might not be stable.

Before learning about the changes in the stationary and nonstationary states throughout time, there is a need to identify when the shift in the temporal distribution takes place. By assuming nonstationarity in observations, it is possible to theoretically identify the latent environments and stationary/nonstationary variables according to this understanding.

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