Tootfinder

Measuring the environmental impact of delivering AI at Google Scale
Cooper Elsworth, Keguo Huang, David Patterson, Ian Schneider, Robert Sedivy, Savannah Goodman, Ben Townsend, Parthasarathy Ranganathan, Jeff Dean, Amin Vahdat, Ben Gomes, James Manyika
https://arxiv.org/abs/2508.15734

Measuring the environmental impact of delivering AI at Google Scale
The transformative power of AI is undeniable - but as user adoption accelerates, so does the need to understand and mitigate the environmental impact of AI serving. However, no studies have measured AI serving environmental metrics in a production environment. This paper addresses this gap by proposing and executing a comprehensive methodology for measuring the energy usage, carbon emissions, and water consumption of AI inference workloads in a large-scale, AI production environment. Our approa…

Correcting systematic errors in the likelihood optimization of underdamped Langevin models of molecular dynamics trajectories
David Daniel Girardier, Hadrien Vroylandt, Sara Bonella, Fabio Pietrucci
https://arxiv.org/abs/2506.16272

Correcting systematic errors in the likelihood optimization of underdamped Langevin models of molecular dynamics trajectories
Since Kramers' pioneering work in 1940, significant efforts have been devoted to studying Langevin equations applied to physical and chemical reactions projected onto few collective variables, with particular focus on the inference of their parameters. While the inference for overdamped Langevin equations is well-established and widely applied, a notable gap remains in the literature for underdamped Langevin equations, which incorporate inertial effects and velocities. This gap arises from the …

Spectral gap with polynomial rate for Weil-Petersson random surfaces
Will Hide, Davide Macera, Joe Thomas
https://arxiv.org/abs/2508.14874 https://arxiv.or…

Spectral gap with polynomial rate for Weil-Petersson random surfaces
We show that there is a constant $c>0$ such that a genus $g$ closed hyperbolic surface, sampled at random from the moduli space $\mathcal{M}_{g}$ with respect to the Weil-Petersson probability measure, has Laplacian spectral gap at least $\frac{1}{4}-O\left(\frac{1}{g^{c}}\right)$ with probability tending to $1$ as $g\to\infty$. This extends and gives a new proof of a recent result of Anantharaman and Monk proved in the series of works [2,3,5,4,6]. Our approach adapts the polynomial method fo…

Putin is 'obstacle to peace,' retired US General Petraeus says: https://benborges.xyz/2025/08/25/putin-is-obstacle-to-peace.html

Quoting is coming to mastodon.social and mastodon.online next week. And then available later on for everyone else.
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Introducing quote posts
Over the years, we’ve learned just how essential quoting is to many of you. Here's a deep dive on how quote posts will work on Mastodon.

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
Tasnuva Chowdhury, Tadashi Maeno, Fatih Furkan Akman, Joseph Boudreau, Sankha Dutta, Shengyu Feng, Adolfy Hoisie, Kuan-Chieh Hsu, Raees Khan, Jaehyung Kim, Ozgur O. Kilic, Scott Klasky, Alexei Klimentov, Tatiana Korchuganova, Verena Ingrid Martinez Outschoorn, Paul Nilsson, David K. Park, Norbert Podhorszki, Yihui Ren, John Rembrandt Steele, Fr\'ed\'eric Suter, Sairam Sri Vatsavai, Torre Wenaus,…

Machine Learning-Driven Predictive Resource Management in Complex Science Workflows
The collaborative efforts of large communities in science experiments, often comprising thousands of global members, reflect a monumental commitment to exploration and discovery. Recently, advanced and complex data processing has gained increasing importance in science experiments. Data processing workflows typically consist of multiple intricate steps, and the precise specification of resource requirements is crucial for each step to allocate optimal resources for effective processing. Estimat…

Quantitative Stain Mapping in X-ray Virtual Histology
Dominik John, David M. Paganin, Marie-Christine Zdora, Lisa Marie Petzold, Patrick Ilg, Junan Chen, Sara Baggio, Johannes B. Thalhammer, Sami Wirtensohn, Julian Moosmann, J\"org U. Hammel, Felix Beckmann, Samantha J. Alloo, Jannis Ahlers, Madleen Busse, Julia Herzen, Kaye S. Morgan
https://

Quantitative Stain Mapping in X-ray Virtual Histology
Virtual histology is an emerging field in biomedicine that enables three-dimensional tissue visualization using X-ray micro-computed tomography. However, the method still lacks the specificity of conventional histology, in which parts of the tissue are selectively highlighted using targeted stains. Though some first X-ray stains have been developed to address this issue, their precise location and quantity inside the tissue volume remain largely unknown. In this work, we present a novel approac…

Real-Time RAG for the Identification of Supply Chain Vulnerabilities
Jesse Ponnock, Grace Kenneally, Michael Robert Briggs, Elinor Yeo, Tyrone Patterson III, Nicholas Kinberg, Matthew Kalinowski, David Hechtman
https://arxiv.org/abs/2509.10469

Real-Time RAG for the Identification of Supply Chain Vulnerabilities
New technologies in generative AI can enable deeper analysis into our nation's supply chains but truly informative insights require the continual updating and aggregation of massive data in a timely manner. Large Language Models (LLMs) offer unprecedented analytical opportunities however, their knowledge base is constrained to the models' last training date, rendering these capabilities unusable for organizations whose mission impacts rely on emerging and timely information. This research propo…

Space Mission Options for Reconnaissance and Mitigation of Asteroid 2024 YR4
Brent W. Barbee, Matthew A. Vavrina, Rylie Bull, Adrienne Rudolph, Davide Farnocchia, Russell TerBeek, Justin Atchison, Joshua Lyzhoft, Jessie Dotson, Patrick King, Paul W. Chodas, Dawn Graninger, Ronald G. Mink, Kathryn M. Kumamoto, Jason M. Pearl, Mary Burkey, Isaiah Santistevan, Catherine S. Plesko, Wendy K. Caldwell, Megan Harwell

Space Mission Options for Reconnaissance and Mitigation of Asteroid 2024 YR4
Near-Earth asteroid 2024 YR4 was discovered on 2024-12-27 and its probability of Earth impact in December 2032 peaked at about 3% on 2025-02-18. Additional observations ruled out Earth impact by 2025-02-23. However, the probability of lunar impact in December 2032 then rose, reaching about 4% by the end of the apparition in May 2025. James Webb Space Telescope (JWST) observations on 2025-03-26 estimated the asteroid's diameter at 60 +/- 7 m. Studies of 2024 YR4's potential lunar impact effects …