Tootfinder

Wildbergers Wünsche: BMDS legt Eckpunkte für schnelleren Netzausbau vor
Wie soll es weitergehen mit dem Glasfaser-Ausbau? Der neue Digitalminister hat ein Sieben-Punkte-Papier vorgelegt, das für einige Diskussionen sorgen dürfte.

Learning from Uncertain Similarity and Unlabeled Data
Meng Wei, Zhongnian Li, Peng Ying, Xinzheng Xu
https://arxiv.org/abs/2509.11984 https://arxiv.org/pdf…

Learning from Uncertain Similarity and Unlabeled Data
Existing similarity-based weakly supervised learning approaches often rely on precise similarity annotations between data pairs, which may inadvertently expose sensitive label information and raise privacy risks. To mitigate this issue, we propose Uncertain Similarity and Unlabeled Learning (USimUL), a novel framework where each similarity pair is embedded with an uncertainty component to reduce label leakage. In this paper, we propose an unbiased risk estimator that learns from uncertain simil…

"Similarities between recharging and refuelling make the switch to electric cars an easier choice"
#EV #ElectricVehicles #Cars

Similarities between recharging and refuelling make the switch to electric cars an easier choice
It’s not only the appearance of petrol stations and fuel pumps that has been imitated. The basic principles of charging and refuelling are very similar.

heise | 20 Tipps: So schaffen Sie wieder Platz auf Ihrem Mac
Apple knausert in der Basisausstattung seiner Macs seit jeher mit der SSD. Diese füllt sich oft schneller, als einem lieb ist. Das bremst auch das System aus.

Similarity-Guided Diffusion for Contrastive Sequential Recommendation
Jinkyeong Choi, Yejin Noh, Donghyeon Park
https://arxiv.org/abs/2507.11866 https://…

Similarity-Guided Diffusion for Contrastive Sequential Recommendation
In sequential recommendation systems, data augmentation and contrastive learning techniques have recently been introduced using diffusion models to achieve robust representation learning. However, most of the existing approaches use random augmentation, which risk damaging the contextual information of the original sequence. Accordingly, we propose a Similarity-Guided Diffusion for Contrastive Sequential Recommendation. Our method leverages the similarity between item embedding vectors to gener…

SIEVE: Effective Filtered Vector Search with Collection of Indexes
Zhaoheng Li, Silu Huang, Wei Ding, Yongjoo Park, Jianjun Chen
https://arxiv.org/abs/2507.11907

SIEVE: Effective Filtered Vector Search with Collection of Indexes
Many real-world tasks such as recommending videos with the kids tag can be reduced to finding most similar vectors associated with hard predicates. This task, filtered vector search, is challenging as prior state-of-the-art graph-based (unfiltered) similarity search techniques quickly degenerate when hard constraints are considered. That is, effective graph-based filtered similarity search relies on sufficient connectivity for reaching the most similar items within just a few hops. To consider …

KI statt Models: Otto spart bei der Produktfotografie
Mit einem eigenen KI-Tool will Otto neue Kollektionen schneller und günstiger in den Verkauf bringen. Otto folgt einem allgemeinen Trend in der Branche.
https://www…

Mode stability for self-similar blowup of slightly supercritical NLS: II. high-energy spectrum
Zexing Li
https://arxiv.org/abs/2507.11259 https://

Mode stability for self-similar blowup of slightly supercritical NLS: II. high-energy spectrum
In continuation of the study of the companion work, we prove the high-energy mode stability for linearized operator around self-similar profiles in [Bahri-Martel-Raphaël, 2021] for slightly mass-supercritical NLS in $1 \le d \le 10$. This concludes the asymptotic stability of such self-similar blowup, and answers the question from [Bahri-Martel-Raphaël, 2021] and [Merle-Raphaël-Szeftel, 2010]. As a byproduct, we characterize the spectrum for linearized operator around mass-critical ground st…

Neuer Job: Humanoider Roboter-Marathon-Gewinner testet Laufschuhe
Der humanoide Tien-Kung-Roboter testet Laufschuhe und ermittelt dabei Leistungsdaten. Die sind besser und schneller verfügbar, als die von menschlichen Testern.

Als erster US-Bundesstaat will Kalifornien mit KI gegen Stromausfälle kämpfen
Mehr erneuerbare Energien sorgen für instabilere Netze. In Kalifornien soll jetzt KI helfen, sie zu managen. Netzbetreiber sollen so schneller reagieren können.