Tootfinder

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Ahh... Dr is een fix voor de onofficiële DeepL app in Fdroid.
Uit de mail:
'F-Droid users, please wait a few days for the update to be distributed.'
📸
#Fdroid #DeepL

If you don’t support #AbolishICE, then you support ICE.
As in white supremacist murder.
New #NurembergTrials

On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning
Andreas Tritsarolis, Gil Sampaio, Nikos Pelekis, Yannis Theodoridis
https://arxiv.org/abs/2602.20782 https://arxiv.org/pdf/2602.20782 https://arxiv.org/html/2602.20782
arXiv:2602.20782v1 Announce Type: new
Abstract: The wide spread of new energy resources, smart devices, and demand side management strategies has motivated several analytics operations, from infrastructure load modeling to user behavior profiling. Energy Demand Forecasting (EDF) of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability, since it enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability. However, accurate EDF is a challenging problem due to external factors, such as the varying user routines, weather conditions, driving behaviors, unknown state of charge, etc. Furthermore, as concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented, resulting in distributed datasets scattered across different data silos and/or edge devices, calling for federated learning solutions. In this paper, we investigate different well-established time series forecasting methodologies to address the EDF problem, from statistical methods (the ARIMA family) to traditional machine learning models (such as XGBoost) and deep neural networks (GRU and LSTM). We provide an overview of these methods through a performance comparison over four real-world EVSE datasets, evaluated under both centralized and federated learning paradigms, focusing on the trade-offs between forecasting fidelity, privacy preservation, and energy overheads. Our experimental results demonstrate, on the one hand, the superiority of gradient boosted trees (XGBoost) over statistical and NN-based models in both prediction accuracy and energy efficiency and, on the other hand, an insight that Federated Learning-enabled models balance these factors, offering a promising direction for decentralized energy demand forecasting.
toXiv_bot_toot

Nice overview of Matrix in Europe, an open standard for decentralised communications.
https://element.io/blog/digital-sovereignty-is-built-on-an-open-standard-that-enables-federation/

Digital sovereignty is built on an open standard that enables federation
Across Europe, sovereign communications systems are already being deployed, and crucially, they don’t have to exist in isolation.

My own bike is almost exactly the one in the featured photo! And yes, it’s fantastic. https://tech.lgbt/@LilahTovMoon/116121175084366601

2026 NFL mock draft, Rounds 2 and 3: Garrett Nussmeier to Jets; Chiefs add big-play WR https://www.nytimes.com/athletic/7224130/2026/04/24/nfl-mock-draft-2026-rounds-2-3-quarterbacks/

To give myself a break tonight, I'm focused on the state of the Federation instead. #StarTrek

Die @… featuret heute einen schönen Blogbeitrag (aus dem Jahr 2017) über Fahrbibliotheken, vielleicht also für die #bibliothek|sbubble hier interessant. Es geht um Bücherbusse, Feldbüchereien ("Bildungskanonen" bzw. "Schützengräben-Büchereie…