Charges against an American influencer and teen pilot who has been stranded on a remote island in the Antarctic since June have been dropped. Ethan Guo, 19, is alleged to have illegally landed his plane in Chilean territory after embarking on a solo trip to all seven continents to raise money for cancer research, according to local authorities. They accused him of providing false flight plan information to officials who detained him and opened an investigation. A judge has ordered him to leave the area, pay a $30,000 (£22,332) donation to a children's cancer foundation and is banned from re-entering Chilean territory for three years. Mr Guo made headlines last year when he began an attempt to become the youngest person to fly solo to all seven continents and collect donations for research into childhood cancer.

Protein design with desirable properties has been a significant challenge for many decades. Generative artificial intelligence is a promising approach and has achieved great success in various protein generation tasks. Notably, diffusion models stand out for their robust mathematical foundations and impressive generative capabilities, offering unique advantages in certain applications such as protein design. In this review, we first give the definition and characteristics of diffusion models and then focus on two strategies: Denoising Diffusion Probabilistic Models and Score-based Generative Models, where DDPM is the discrete form of SGM. Furthermore, we discuss their applications in protein design, peptide generation, drug discovery, and protein-ligand interaction. Finally, we outline the future perspectives of diffusion models to advance autonomous protein design and engineering. The E(3) group consists of all rotations, reflections, and translations in three-dimensions. The equivariance on the E(3) group can keep the physical stability of the frame of each amino acid as much as possible, and we reflect on how to keep the diffusion model E(3) equivariant for protein generation.

The expedition to find the wreck of Sir Ernest Shackleton's Endurance is set to sail next month, it was announced today on the centenary of the polar explorer's death. Endurance was one of two ships used by the Imperial Trans-Antarctic expedition of 1914–1917, which hoped to make the first land crossing of the Antarctic. Carrying an expedition crew of 28 men, the 144-foot-long Endurance was a three-masted schooner barque sturdily built for operations in polar waters. Aiming to land at Vahsel Bay, the vessel became stuck in pack ice on the Weddell Sea on January 18, 1915 -- where she and her crew would remain for many months. In late October, however, a drop in temperature from 42 F to -14 F saw the ice pack begin to steadily crush the Endurance, which finally sank on November 21, 1915.

Researchers on the OcéanIA project are developing new artificial intelligence and mathematical modelling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling climate change. You may have seen our recent interview with the director of the project, and of Inria Chile, Nayat Sánchez-Pi. She explained the challenges of research in the field, what they are working on as part of the project, and the role that AI methods play. A key part of the project is data, and much of this is being collected by the Tara Microbiome-CEODOS expedition. The objective of this expedition is to study the marine microorganisms which play a fundamental role in ocean ecosystems.

Nayat Sánchez-Pi is the Director of the Inria Chile Research Center. We asked her about her research and about the OcéanIA project which she leads. The aim of the OcéanIA project is to develop new artificial intelligence and mathematical modeling tools to contribute to the understanding of the oceans and their role in regulating and sustaining the biosphere, and tackling the climate change. I have been working in the area of artificial intelligence and machine learning for more than 15 years now. During this time I have always had an interest in finding ways of taking the state-of-the-art of my area of research and applying it to have a direct impact on the real world.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After a general introduction, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.