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Surprise Spiral Shape Revealed in The Darkness Surrounding The Solar System

The edge of the Solar System is a strange place, full of oddities we’ve only just begun to probe. But perhaps the oddest of all is the Oort Cloud, a vast field of icy debris extending out to 100,000 times the distance between Earth and the Sun.

We have a rough idea of the size and shape of this field, but the fine particulars elude our understanding. Now, a new computational study has revealed a surprising structure – a spiral generated by the tidal forces exerted by the Milky Way galaxy itself.

The finding, in press at The Astrophysical Journal, is currently available on preprint server arXiv.

Ancient Beaches Found on Mars Reveal The Red Planet Once Had Oceans

Mars – dusty, dry, and desert-clad – was once so rich in water it had not just lakes, but oceans, according to a new study.

Observations using ground-penetrating radar have revealed underground features consistent with beaches on the red planet, 4 billion years ago. It’s some of the best evidence to date that Mars was once so soggy as to host a northern sea.

The research team has named that sea Deuteronilus.

2025 Astronomical Wonders! February Sky Unveils New Technological Insights

February 2025 features Comet CK-25, observed with AI-driven telescopic networks for real-time imaging and analysis. A spectacular planetary alignment of Mercury, Venus, and Mars will be enhanced by augmented reality devices for interactive viewing. A partial lunar eclipse will occur on February 27th-28th, with an immersive experience via the Virtual Lunar Observation Platform (VLOP). Technological advancements highlight new methods of observing and interacting with space events, bridging Earth and the cosmos. February 2025 is set to mesmerize stargazers and tech enthusiasts alike, as the cosmos aligns with cutting-edge advancements in astronomical observation. This month isn’t just about celestial spectacles; it’s about witnessing how new technology is redefining our view of space from Earth.

Online test-time adaptation for better generalization of interatomic potentials to out-of-distribution data

Molecular Dynamics (MD) simulation serves as a crucial technique across various disciplines including biology, chemistry, and material science1,2,3,4. MD simulations are typically based on interatomic potential functions that characterize the potential energy surface of the system, with atomic forces derived as the negative gradients of the potential energies. Subsequently, Newton’s laws of motion are applied to simulate the dynamic trajectories of the atoms. In ab initio MD simulations5, the energies and forces are accurately determined by solving the equations in quantum mechanics. However, the computational demands of ab initio MD limit its practicality in many scenarios. By learning from ab initio calculations, machine learning interatomic potentials (MLIPs) have been developed to achieve much more efficient MD simulations with ab initio-level accuracy6,7,8.

Despite their successes, the crucial challenge of implementing MLIPs is the distribution shift between training and test data. When using MLIPs for MD simulations, the data for inference are atomic structures that are continuously generated during simulations based on the predicted forces, and the training set should encompass a wide range of atomic structures to guarantee the accuracy of predictions. However, in fields such as phaseion9,10, catalysis11,12, and crystal growth13,14, the configurational space that needs to be explored is highly complex. This complexity makes it challenging to sample sufficient data for training and easy to make a potential that is not smooth enough to extrapolate to every relevant point. Consequently, a distribution shift between training and test datasets often occurs, which causes the degradation of test performance and leads to the emergence of unrealistic atomic structures, and finally the MD simulations collapse15.

Are There Limits What Brains Can Learn?

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My name is Artem, I’m a graduate student at NYU Center for Neural Science and researcher at Flatiron Institute.

In this video video we are exploring a fascinating paper which revealed the role of biological constraints on what patterns of neural dynamics the brain and cannot learn.
Link to the paper: https://www.nature.com/articles/s4159… small correction: I didn’t mention in this in the video, but the dimensionality-reduction process for BCI was two-stage. First, the source 90D neural activity was non-linearly projected to 10 using Factor analysis, and only after that 2D projections of this 10D space were shown as cursor positions. It doesn’t change the interpretation of the result, just wanted to be more technically correct about the methods. Outline: 00:00 Introduction 01:01 Temporal sequences 02:10 The experimental challenge 4:42 Biofeedback and BCIs as a research tool 7:30 Sponsor: Squarespace 8:44 Experimental setup 11:36 Two 2D projections of neural activity 12:53 Switching BCI mapping reveals activity constraints 14:46 Conclusion Icons by Freepik and Biorender Music by Artlist.

A small correction: I didn’t mention in this in the video, but the dimensionality-reduction process for BCI was two-stage. First, the source 90D neural activity was non-linearly projected to 10 using Factor analysis, and only after that 2D projections of this 10D space were shown as cursor positions. It doesn’t change the interpretation of the result, just wanted to be more technically correct about the methods.

Outline: