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Billionaire Drahi Strikes €20 Billion Deal to Sell France’s SFR
A SFR mobile phone and telecom store, operated by Altice France SA, in Paris, France, on Wednesday, Oct. 15, 2025. A consortium of French telecom carriers — Bouygues Telecom, Iliad SA and Orange SA — made a €17 billion offer to buy their rival SFR from billionaire Patrick Drahi’s Altice France.
Drahi Reaches €20 Billion Deal to Sell French Phone Carrier SFR
A SFR mobile phone and telecom store, operated by Altice France SA, in Paris, France, on Wednesday, Oct. 15, 2025. A consortium of French telecom carriers — Bouygues Telecom, Iliad SA and Orange SA — made a €17 billion offer to buy their rival SFR from billionaire Patrick Drahi’s Altice France.
Bouygues Telecom consortium agrees to buy Patrick Drahi’s SFR for €20.35bn
Bid from group including Orange and Free-Iliad faces showdown with antitrust regulators in Paris and Brussels
JWST finds a stellar bar in the early universe that breaks all rules
May 31, 2026 report JWST finds a stellar bar in the early universe that breaks all rules Shreejaya Karantha Author Sadie Harley Scientific Editor Robert Egan Associate Editor Astronomers using the James Webb Space Telescope (JWST) have discovered a stellar bar in GN20, a massive galaxy seen just 1.5 billion years after the Big Bang. The new paper was submitted to the preprint server arXiv on May 14. Cosmic funnels Stellar bars are elongated arrangements of stars that cut across the center of...
Beyond Instance-Level Alignment and Uniformity: Semantic Factor Learning for Collaborative Filtering
Announce Type: new Abstract: Collaborative filtering (CF) is widely used in recommender systems (RecSys) due to its simplicity and efficiency. However, existing CF methods follow an instance-level learning paradigm. During the instance learning stage, a large number of uninteracted user-item instances, of which items are potential interested by the user, are incorrectly treated as true negative samples resulting in a severe limitation to the generalization and scalability of models.
ResCLIP: Residual Attention for Training-free Dense Vision-language Inference
Announce Type: replace Abstract: While vision-language models like CLIP have shown remarkable success in open-vocabulary tasks, their application is currently confined to image-level tasks, and they still struggle with dense predictions. Recent works often attribute such deficiency in dense predictions to the self-attention layers in the final block, and have achieved commendable results by modifying the original query-key attention to self-correlation attention, (e.g., query-query and...