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The initial step in the search for extraterrestrial life involves identifying the presence of liquid water. The moons of Saturn and Jupiter like Enceladus, Ganymede, Europa, and Callisto are suspected of holding oceans of liquid water beneath icy crusts. Similarly, some exoplanets beyond our solar system likely host liquid water, crucial for habitability. But detecting water, when we can’t physically access these celestial bodies, poses challenges. Ice-penetrating radar, a geophysical tool, has proven capable of detecting liquid water on Earth and beneath Mars ’ South polar cap.

Now, this instrument is aboard the JUICE spacecraft and it is on its way to Jupiter’s icy moon Ganymede and will also be aboard the Europa Clipper spacecraft, which will be launched to Europa later this year. What can we expect to learn from these missions and how can we use ice-penetrating radar for future planetary exploration? Dr Elena Pettinelli of Roma Tre University, with extensive experience in planetary exploration using ice-penetrating radar, delved into the utility of this technology in her presentation recently presented at the European Geosciences Union General Assembly EGU24.

A new semipermeable membrane doubles the osmotic energy output in estuaries, showing potential for sustainable power generation.

Estuaries — where freshwater rivers meet the salty sea — are great locations for birdwatching and kayaking. In these areas, waters containing different salt concentrations mix and may be sources of sustainable, “blue” osmotic energy. In the journal ACS Energy Letters, researchers report creating a semipermeable membrane that harvests osmotic energy from salt gradients and converts it to electricity.

The new design had an output power density more than two times higher than commercial membranes in lab demonstrations.

A new project unites world-leading experts in quantum computing and genomics to develop new methods and algorithms to process biological data.

Researchers aim to harness quantum computing to speed up genomics, enhancing our understanding of DNA and driving advancements in personalized medicine

A new collaboration has formed, uniting a world-leading interdisciplinary team with skills across quantum computing, genomics, and advanced algorithms. They aim to tackle one of the most challenging computational problems in genomic science: building, augmenting, and analyzing pangenomic datasets for large population samples. Their project sits at the frontiers of research in both biomedical science and quantum computing.

A collaborative study by the University of Oxford and MIT has uncovered a 3.7-billion-year-old magnetic field record from Greenland, demonstrating that Earth’s ancient magnetic field was as strong as it is today, crucial for protecting life by shielding against cosmic and solar radiation.

A new study has recovered a 3.7-billion-year-old record of Earth’s magnetic field, and found that it appears remarkably similar to the field surrounding Earth today. The findings have been published today (April 24) in the Journal of Geophysical Research.

Without its magnetic field, life on Earth would not be possible since this shields us from harmful cosmic radiation and charged particles emitted by the Sun (the ‘solar wind’). But up to now, there has been no reliable date for when the modern magnetic field was first established.

NASA’s Mars Curiosity rover has made consistent and puzzling findings while roaming the barren surface of the planet’s Gale Crater: mysterious puffs of methane gas that only appear at night and vanish during the day.

Over the years, the rover’s Sample Analysis at Mars (SAM) instrument has repeatedly detected significant concentrations of the gas, sometimes spiking to 40 times the usual levels — and scientists are still trying to figure out the source, as NASA details in a new blog post.

It’s an especially intriguing finding, given that living creatures produce methane here on Earth, giving the findings special significance as NASA scans the Red Planet for signs of subterranean life.

PyTorch 2.3 is here 😎🔥

Details:


By Team PyTorch.

We are excited to announce the release of PyTorch® 2.3 (release note)! PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance regressions or graph breaks. Tensor Parallelism improves the experience for training Large Language Models using native PyTorch functions, which has been validated on training runs for 100B parameter models. As well, semi-structured sparsity implements semi-structured sparsity as a Tensor subclass, with observed speedups of up to 1.6 over dense matrix multiplication.

This release is composed of 3,393 commits and 426 contributors since PyTorch 2.2. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.3. More information about how to get started with the PyTorch 2-series can be found at our Getting Started page.