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Val Kilmer Resurrected by AI: âAs Deep as the Graveâ Trailer Brings Late Actor Back to the Big Screen (EXCLUSIVE)
The filmmakers behind âAs Deep as the Graveâ have debuted the trailer for the upcoming historical drama, giving viewers a first look at the AI technology that was used to create Val Kilmerâs performance.
Kilmer, who died in 2025 after battling throat cancer, was cast as Father Fintan, a Catholic priest and Native American spiritualist, but was too sick to shoot his role. With the cooperation of Kilmerâs estate and his daughter Mercedes, the âAs Deep as the Graveâ team used generative AI to include the actor in the finished film.
DESI Completes Planned 3D Map of the Universe and Continues Exploring
DESI has mapped more than 47 million galaxies and quasars, creating the largest high-resolution 3D map of our Universe to date. Because of the instrumentâs excellent performance and hints that dark energy might evolve, DESI will continue observations into 2028 and further expand the map. DESI was constructed with funding from the U.S. Department of Energy Office of Science and is mounted on the U.S. National Science Foundation Nicholas U. Mayall 4-meter telescope.
Last night, the 5,000 fiber-optic eyes of the Dark Energy Spectroscopic Instrument (DESI) swiveled onto a patch of sky near the Little Dipper. Roughly every 20 minutes, they locked onto distant pinpricks of light, gathering photons that had traveled toward Earth for billions of years. When the Sun rose, DESI collaborators marked the completion of a major milestone: successfully surveying all of the area in DESIâs planned map of the Universe.
The five-year survey, finished ahead of schedule and with vastly more data than expected, has produced the largest high-resolution 3D map of the Universe ever made. Researchers use that map to explore dark energy, the fundamental ingredient that makes up about 70% of our Universe and is driving its accelerating expansion.
Superconductor Theory Under Cold-Atom Scrutiny
Snapshot measurements of cold-atom gases reveal hidden spin correlations that could force an update of some superconductivity theories.
Our understanding of nature is inherently bound to the experimental tools we build to observe the world. Superconductivity, for example, has been traditionally studied using current and voltage meters under a variety of temperatures and other environmental conditions. From these observations, theorists have developed modelsânotably the Bardeen-Cooper-Schrieffer (BCS) theory, which assumes that the zero-resistance flow in a superconductor arises from electrons forming so-called Cooper pairs. This theory has been successful in explaining a large class of superconductors, but Tarik Yefsah from the Ecole Normale SupĂ©rieure in Paris and colleagues have now observed behavior that contradicts BCS predictions [1]. Using a recently developed technique called atom-resolved continuum quantum gas microscopy, the researchers directly observed spatial correlations in cold atoms that mimic superconducting electrons.
Machine learning accelerates analysis of fusion materials
Tungstenâs superior performance in extreme environments makes it a leading candidate for plasma-facing components (PFCs) in fusion reactors, but the ultra-high heat can damage its microscopic structure and lead to component failure. Scanning electron microscopy (SEM) can capture and quantify these microstructure changes, but assembling a sufficiently large dataset of SEM imagery is expensive and logistically challenging.
To augment this dataset, researchers at Oak Ridge National Laboratory trained a generative machine learning model using 3,200 SEM images of tungsten samples exposed to fusion-relevant conditions. The model can generate novel SEM images with realistic microstructures and surface features, such as cracks and pores, without replicating the original images.
âThis work is not about making pretty pictures, itâs about capturing the statistics of real damage on these materials,â said ORNLâs Rinkle Juneja, the projectâs principal investigator. âWe train our generative workflow to learn tungstenâs microstructure signatures, like crack patterns, so it can generate new, statistically consistent microstructures, laying the groundwork for robust, data-driven assessment of PFC fusion materials.â
Gravity follows Newton and Einsteinâs rules, even at cosmic scales
Gravity, as most people understand it, is the familiar force that pulls a falling apple toward Earth. But for astronomers and theoretical physicists, it is also a vexing invisible architect that guides the shape and evolution of the largest cosmic structures across the universe.
For decades, puzzling observations of unusually fast-moving galaxies have forced cosmologists like the University of Pennsylvaniaâs Patricio A. Gallardo to revisit the fundamentals of physics, exploring, for example, whether the laws of gravity as described by Isaac Newton and Albert Einstein truly apply everywhere.
âAstrophysics has been plagued by a massive discrepancy in the cosmic ledger,â says Gallardo. âWhen we look at how stars orbit within galaxies or how galaxies move within galaxy clusters, some appear to be traveling way too fast for the amount of visible matter they contain.â
Quantum-inspired algorithm solves 268 million-site quasicrystal simulation in a heartbeat
Quantum technologies like quantum computers are built from quantum materials. These types of materials exhibit quantum properties when exposed to the right conditions. Curiously, engineers can also trigger quantum behavior by manipulating a materialâs structure; for example, by stacking layers of graphene on top of each other and twisting them to create a moirĂ© pattern, which suddenly turns them into a superconductor.
The layers can be arranged in increasingly complex ways all the way to quasicrystals and super-moirĂ© materials. The fundamental problem is that scientists must first calculate the properties of potential new materials to predict if they could be useful. Quasicrystals, for example, are so complex they can require processing more than a quadrillion numbersâfar beyond the capacity of the worldâs most powerful supercomputers.
Now researchers at Aalto Universityâs Department of Applied Physics have shown how a quantum-inspired algorithm makes solving these colossal, non-periodic quantum materials possible in a heartbeat. The research is published in the journal Physical Review Letters as an Editorâs suggestion.
âInterstellar glaciersâ: NASAâs SPHEREx maps vast galactic ice regions
NASAâs SPHEREx (Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer) mission has mapped interstellar ice at an unprecedented scale. Covering regions in our Milky Way galaxy more than 600 light-years across, the ice was found inside giant molecular cloudsâvast regions of gas and dust where dense clumps of matter collapse under gravity, giving birth to stars. A study describing these findings was published Wednesday in The Astrophysical Journal.
One of SPHERExâs main goals is to map the chemical signatures of various types of interstellar ice. This ice includes molecules like water, carbon dioxide, and carbon monoxide, which are vital to the chemistry that allows life to develop. Researchers believe these ice reservoirs, attached to the surfaces of tiny dust grains, are where most of the universeâs water is formed and stored. The water in Earthâs oceans âand the ices in comets and on other planets and moons in our galaxyâoriginates from these regions.
âThese vast frozen complexes are like âinterstellar glaciersâ that could deliver a massive water supply to new solar systems that will be born in the region,â said study co-author Phil Korngut, the instrument scientist for SPHEREx at Caltech in Pasadena, California. âItâs a profound idea that we are looking at a map of material that could rain on nascent planets and potentially support future life.â