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Physics#Supergravity, Domain Walls, Holography🔬 ResearchAnalyzed: Jan 3, 2026 17:09

Non-SUSY Domain Walls in ISO(7) Gauged Supergravity

Published:Dec 31, 2025 08:04
1 min read
ArXiv

Analysis

This paper explores non-supersymmetric domain walls in 4D maximal ISO(7) gauged supergravity, a theory derived from massive IIA supergravity. The authors use fake supergravity and the Hamilton-Jacobi formalism to find novel domain walls interpolating between different AdS vacua. The work is relevant for understanding holographic RG flows and calculating quantities like free energy and anomalous dimensions.
Reference

“The paper finds novel non-supersymmetric domain walls interpolating between different pairs of AdS extrema.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 08:23

Smoothed Quantile Estimation: A Unified Framework Interpolating to the Mean

Published:Dec 22, 2025 09:19
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, likely presents a novel statistical method for quantile estimation. The title suggests a focus on smoothing techniques and a connection to the mean, potentially offering improvements over existing methods. Further analysis would require reading the paper to understand the specific approach, its advantages, and its potential applications.

Key Takeaways

    Reference

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    PermalinkArXiv
    Research#llm📝 BlogAnalyzed: Jan 3, 2026 07:15

    Interpolation, Extrapolation and Linearisation (Prof. Yann LeCun, Dr. Randall Balestriero)

    Published:Jan 4, 2022 12:59
    1 min read
    ML Street Talk Pod

    Analysis

    This article discusses the concepts of interpolation, extrapolation, and linearization in the context of neural networks, particularly focusing on the perspective of Yann LeCun and his research. It highlights the argument that in high-dimensional spaces, neural networks primarily perform extrapolation rather than interpolation. The article references a paper by LeCun and others on this topic and suggests that this viewpoint has significantly impacted the understanding of neural network behavior. The structure of the podcast episode is also outlined, indicating the different segments dedicated to these concepts.
    Reference

    “Yann LeCun thinks that it's specious to say neural network models are interpolating because in high dimensions, everything is extrapolation.”

    PermalinkML Street Talk Pod