Jährlicher Zubau und Rückbau von #Balkonsolarleistung in Deutschland mit Stand vom 10.03.2026.
Unplausible Inbetriebnahmedaten von 1900 bis 2017 wurden durch das Registrierungsdatum der #Steckersolaranlage ersetzt.
👉 Zusatzlesestoff:
Microsoft stock plunged 23% in Q1, a steeper drop than any of its tech peers or the Nasdaq, and its steepest quarterly drop since the 2008 financial crisis (Jordan Novet/CNBC)
https://www.cnbc.com/2026/03/31/microsofts-stock-closes-worst…
It's probably a lost cause and also too short notice, but I'm still thinking about #AlgoApril, which I proposed back in 2022, but it's an incomplete list of prompts and sadly never took off...
https://github.com/alg…
I have recommended this album on here more than once. It's a marvellous reproduction of 70s aesthetics and the sonic texture of telephone hold music of yore.
The Beat Index, "I Don't Wanna Get Over You (Hotline Mix)" (2022)
https://thebeatindex.bandcamp.com/trac…
Sequoia says Doug Leone is returning in a newly created role of chairman, after he announced his retirement in 2022 from his role as "senior steward" (Iain Martin/Forbes)
http://www.forbes.com/sites/iainmartin/2026/03…
TIEG-Youpu Solution for NeurIPS 2022 WikiKG90Mv2-LSC
Feng Nie, Zhixiu Ye, Sifa Xie, Shuang Wu, Xin Yuan, Liang Yao, Jiazhen Peng, Xu Cheng
https://arxiv.org/abs/2603.28512 https://arxiv.org/pdf/2603.28512 https://arxiv.org/html/2603.28512
arXiv:2603.28512v1 Announce Type: new
Abstract: WikiKG90Mv2 in NeurIPS 2022 is a large encyclopedic knowledge graph. Embedding knowledge graphs into continuous vector spaces is important for many practical applications, such as knowledge acquisition, question answering, and recommendation systems. Compared to existing knowledge graphs, WikiKG90Mv2 is a large scale knowledge graph, which is composed of more than 90 millions of entities. Both efficiency and accuracy should be considered when building graph embedding models for knowledge graph at scale. To this end, we follow the retrieve then re-rank pipeline, and make novel modifications in both retrieval and re-ranking stage. Specifically, we propose a priority infilling retrieval model to obtain candidates that are structurally and semantically similar. Then we propose an ensemble based re-ranking model with neighbor enhanced representations to produce final link prediction results among retrieved candidates. Experimental results show that our proposed method outperforms existing baseline methods and improves MRR of validation set from 0.2342 to 0.2839.
toXiv_bot_toot
De uitslag in mijn gemeente. Wel een ruk naar rechts maar GL-PvdA houdt stand.