Tootfinder

Summary:
A colossal ancient impact may have reshaped the Moon far more deeply than scientists once realized.
By analyzing rare lunar rocks brought back by China’s Chang’e-6 mission from the Moon’s largest crater,
researchers found unusual chemical fingerprints pointing to extreme heat and material loss caused by a giant impact.
The collision likely stripped away volatile elements,
reshaped volcanic activity,
and left a lasting chemical signature deep belo…

Chang’e-6 lunar samples reveal a giant impact reshaped the Moon’s interior
A colossal ancient impact may have reshaped the Moon far more deeply than scientists once realized. By analyzing rare lunar rocks brought back by China’s Chang’e-6 mission from the Moon’s largest crater, researchers found unusual chemical fingerprints pointing to extreme heat and material loss caused by a giant impact. The collision likely stripped away volatile elements, reshaped volcanic activity, and left a lasting chemical signature deep below the surface.

Isotopic evidence for volatile loss driven by South Pole-Aitken basin–forming impact: #Moon Is Asymmetrical: https://eos.org/articles/primordial-impact-may-explain-why-the-moon-is-asymmetrical - analysis of surface samples from the Chang’e-6 mission suggests that an asteroid may have vaporized parts of the lunar mantle, suppressing volcanic activity on the farside of the Moon.

Copy-Trasform-Paste: Zero-Shot Object-Object Alignment Guided by Vision-Language and Geometric Constraints
Rotem Gatenyo, Ohad Fried
https://arxiv.org/abs/2601.14207 https://arxiv.org/pdf/2601.14207 https://arxiv.org/html/2601.14207
arXiv:2601.14207v1 Announce Type: new
Abstract: We study zero-shot 3D alignment of two given meshes, using a text prompt describing their spatial relation -- an essential capability for content creation and scene assembly. Earlier approaches primarily rely on geometric alignment procedures, while recent work leverages pretrained 2D diffusion models to model language-conditioned object-object spatial relationships. In contrast, we directly optimize the relative pose at test time, updating translation, rotation, and isotropic scale with CLIP-driven gradients via a differentiable renderer, without training a new model. Our framework augments language supervision with geometry-aware objectives: a variant of soft-Iterative Closest Point (ICP) term to encourage surface attachment and a penetration loss to discourage interpenetration. A phased schedule strengthens contact constraints over time, and camera control concentrates the optimization on the interaction region. To enable evaluation, we curate a benchmark containing diverse categories and relations, and compare against baselines. Our method outperforms all alternatives, yielding semantically faithful and physically plausible alignments.
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