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US HEAT-ML breakthrough accelerates fusion plasma heat protection

A public-private team of fusion pioneers – Commonwealth Fusion Systems (CFS), the U.S. Department of Energy’s Princeton Plasma Physics Laboratory (PPPL), and Oak Ridge National Laboratory – has unveiled an AI breakthrough that could reshape the future of fusion plasma research.

The new system, called HEAT-ML, can identify safe zones inside a reactor in milliseconds, replacing a process that once took more than 30 minutes.

By protecting sensitive components from the blistering heat of superheated plasma, this advance could accelerate the design and operation of next-generation fusion power plants.

An AI tool, developed by CFS, PPPL, and Oak Ridge, maps fusion plasma heat in milliseconds, protecting reactors and advancing clean energy.

Beyond words: Study maps the cognitive force of metaphor

Metaphors are a fundamental aspect of human language and cognition, allowing us to understand complex concepts and relationships by mapping them onto more familiar and concrete domains. However, the nature of metaphors and how they work is still not well understood.

In a new paper published in PLOS Complex Systems, Max-Planck-Institute for Mathematics in the Sciences researchers Marie Teich and Wilmer Leal together with director Jürgen Jost have developed a formal framework and large-scale empirical methodology to analyze metaphors and their role in conceptual theory.

The study confirms the fundamental assumption in conceptual metaphor theory that metaphors are enduring linguistic and cognitive structures, not merely rhetorical figures. Using complex systems tools, the researchers identified a metaphor network with distinctions between abstract and concrete categories, and two significant metaphorical processes: mappings from concrete to abstract topics and the emergence of new mappings between concrete domains.

Sweeping survey maps hundreds of satellite systems orbiting dwarf galaxies

We usually think of satellites as small objects orbiting planets or stars. But in the broader universe, galaxies themselves can have satellites—smaller galaxies bound by gravity that orbit a larger host, carrying with them stars, gas, dust, and dark matter.

Most of what we know about satellite galaxies comes from studying the Milky Way and other similarly large galaxies. But a new study led by Dartmouth astronomers broadens that understanding by exploring the satellites of dwarf galaxies—systems less than a tenth the size of the Milky Way.

The multi-institutional survey triples the number of dwarf galaxies surveyed for satellites, the researchers report in The Astrophysical Journal. The study identifies 355 candidate satellite galaxies, including 264 that were previously undocumented. The researchers suggest that 134 of these candidates are highly likely to be satellite galaxies.

Google DeepMind says its new AI can map the entire planet with unprecedented accuracy

Google DeepMind unveils AlphaEarth Foundations, an AI system that processes satellite data 16x more efficiently to create detailed Earth maps for tracking deforestation, climate change, and environmental shifts.

Nanometer-Resolved Images from Superconducting Technology

An imaging method provides unprecedented resolution for studies of quantum materials by relying on superconductors’ extreme sensitivity to light.

The energy deposited in a superconductor by a single photon can register a detectable signal, which is why superconductors are employed in some extremely sensitive detectors. Now researchers have shown how to use this sensitivity to create maps of the superconducting properties of a material with nanometer resolution [1]. The technique can also detect polaritons—hybrid light–matter excitations that may be useful in quantum technologies—with higher resolution than earlier methods. The researchers expect the new technique to be useful in fields as diverse as quantum information and nanophotonics.

When a superconductor held just below its critical temperature absorbs a single photon, the superconductivity can be destroyed in a small region of the material, triggering a small electrical signal. Recent advances have expanded the operating temperatures of such detectors and improved their sensitivities to photons over a wide range of frequencies, enabling many new applications. Mengkun Liu of Stony Brook University in New York and colleagues wondered if the same sensitivity might be employed to build high-resolution spatial maps of the properties of superconducting samples. “Spatial variations often influence superconducting strength and coherence, so an ability to image these properties locally would bring valuable insight,” says Stony Brook team member Ran Jing.

Scientists create a “time crystal” using giant atoms, a concept long thought to be impossible

Recent studies have already used Rydberg vapors to detect radio‑frequency fields with extreme sensitivity.

Persistent, phase‑locked oscillations promise low‑phase‑noise signals useful for clock recovery, precision spectroscopy, and perhaps gravitational‑wave detection, where any self‑referencing oscillator could serve as a phase tag.

On the theory side, researchers now have a platform for mapping phase diagrams that include stationary, bistable, and time‑crystalline regimes.

Tesla FSD Competitors Admit DEFEAT: “Elon Was Right”

Questions to inspire discussion.

Safety and Performance.

🛡️ Q: How does Tesla’s full self-driving system compare to human driving in terms of safety? A: According to Elon Musk, Tesla’s end-to-end neural networks trained on massive video datasets have been proven to be dramatically safer than average human driving.

⚡ Q: What recent hardware upgrade has improved Tesla’s full self-driving capabilities? A: Tesla’s AI4 hardware has been upgraded to 150–200 watts, enabling more complex neural networks and faster decision-making, achieving 36 frames per second processing.

Scalability and Efficiency.

📈 Q: Why is Tesla’s vision-only approach considered more scalable than competitors’ methods? A: Tesla’s vision-only approach is more scalable than competitors’ use of multiple sensors, sensor fusion, and high-definition maps, as stated by BU’s Robin Lee.

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