Tootfinder

The crisis is coming: Trump's ultimatum hangs over Russia | News Pulse: https://benborges.xyz/2025/07/30/the-crisis-is-coming-trumps.html

Coinbase says its new "everything exchange" will include tokenized assets, stocks, derivatives, prediction markets, and more, for US users in the coming months (Tanaya Macheel/CNBC)
https://www.cnbc.com/2025/07/31/coinbase-t

Coinbase says it's launching tokenized stocks, predictions markets for U.S. users in coming months
The expansion puts Coinbase in even closer competition with Robinhood, Gemini and Kraken, all of whom have tokenized equity offerings for users outside the U.S.

A look at the potential future of personalized, AI-generated entertainment, and how it could both submerge human originality and enable new forms of expression (Joshua Rothman/New Yorker)
https://www.newyorker.com/magazine/2025/09/01/ai-is-coming-for-cu…

A.I. Is Coming for Culture
We’re used to algorithms guiding our choices. But when machines can effortlessly generate the content we consume, Joshua Rothman asks, what’s left for the human imagination?

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
Grace Nansamba, Evelyn Namugwanya, David Boehme, Dewi Yokelson, Riley Shipley, Derek Schafer, Michael McKinsey, Olga Pearce, Anthony Skjellum
https://arxiv.org/abs/2507.22372

Leveraging Caliper and Benchpark to Analyze MPI Communication Patterns: Insights from AMG2023, Kripke, and Laghos
We introduce ``communication regions'' into the widely used Caliper HPC profiling tool. A communication region is an annotation enabling capture of metrics about the data being communicated (including statistics of these metrics), and metrics about the MPI processes involved in the communications, something not previously possible in Caliper. We explore the utility of communication regions with three representative modeling and simulation applications, AMG2023, Kripke, and Laghos, all part of t…

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Ya-Wei Eileen Lin, Ron Levie
https://arxiv.org/abs/2509.24886 https://

Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks
Canonicalization is a widely used strategy in equivariant machine learning, enforcing symmetry in neural networks by mapping each input to a standard form. Yet, it often introduces discontinuities that can affect stability during training, limit generalization, and complicate universal approximation theorems. In this paper, we address this by introducing \emph{adaptive canonicalization}, a general framework in which the canonicalization depends both on the input and the network. Specifically, w…

Resigned health official: 'I only see harm coming' (David Cohen/Politico)
https://www.politico.com/news/2025/08/31/cdc-resignation-daskalakis-harm-coming-00538153
http://www.memeorandum.com/250831/p70#a250831p70

DeMarvion Overshown hopes to be second coming of a Cowboys legend https://www.si.com/nfl/cowboys/demarvion-overshown-hopes-to-be-second-coming-dallas-cowboys-legend

Predicting when each winless NFL team will get its first win: Saints' breakthrough coming soon?

https://www.cbssports.com/nfl/news/predict

A look at the potential future of personalized, AI-generated entertainment, and how it could both submerge human originality and enable new forms of expression (Joshua Rothman/New Yorker)
https://www.newyorker.com/magazine/2025/09/01/ai-is-coming-for-cu…

A.I. Is Coming for Culture
We’re used to algorithms guiding our choices. But when machines can effortlessly generate the content we consume, Joshua Rothman asks, what’s left for the human imagination?

Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
Asadullah Tariq, Tariq Qayyum, Mohamed Adel Serhani, Farag Sallabi, Ikbal Taleb, Ezedin S. Barka
https://arxiv.org/abs/2509.23419

Enhancing Communication Efficiency in FL with Adaptive Gradient Quantization and Communication Frequency Optimization
Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices, effectively addressing data privacy and computational concerns. However, FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server, limiting deployment in resource-constrained wireless networks. In this paper, we propose a three-fold strategy. Firstly, an Adaptive Feature-Eli…