Tootfinder

»Datenleck bei Dating-App "Tea" – Frauen fürchten um ihre Sicherheit:
Die App "Tea" sollte im Dating-Bereich eigentlich als Sicherheitstool für Frauen dienen. Jetzt entwickelt sich die Plattform nach einem Verlust umfangreicher Daten aber zum Albtraum für zahlreiche Nutzerinnen.«
Mehr muss mensch wohl zu deren lehren Versprechen nicht sagen: "Gut gemeint ist nicht gut gemacht"
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Datenleck bei Dating-App "Tea": Frauen fürchten um ihre Sicherheit
Die App "Tea" sollte im Dating-Bereich eigentlich als Sicherheits-Tool für Frauen dienen. Jetzt entwickelt sich die Plattform nach einem Verlust umfangreicher Daten aber zum Alptraum für zahlreiche Nutzerinnen.

Esels Alptraum - Abrissbirne der Volksmusik*
#AntiCapitalist #Yodelling
“Love Yodel
Hate Fascism!”
https://youtu.be/sDHQaWQIF1g

#NDR Fernsehen Niedersachsen live
https://www.ardmediathek.de/tv-programm/68ae45e39c8b015142123981 (2017, )
Nu te zien (niet in ardmediathek.de)
22:00 – 23:30 Uhr

I don’t GM nearly as often as I used to. However, today was the first #DCCRPG game I’ve run, and surprisingly it wasn’t a disaster.
I may have to do this again soon.
#ttrpg

You know that it's a rough day at #SCOTUS when it hands down a ruling from which *eight* justices dissent (albeit in different parts).
https://bsky.app/profile/stevevladeck.bsky.soc…

Adaptive Model Ensemble for Continual Learning
Yuchuan Mao, Zhi Gao, Xiaomeng Fan, Yuwei Wu, Yunde Jia, Chenchen Jing
https://arxiv.org/abs/2509.19819 https://

Adaptive Model Ensemble for Continual Learning
Model ensemble is an effective strategy in continual learning, which alleviates catastrophic forgetting by interpolating model parameters, achieving knowledge fusion learned from different tasks. However, existing model ensemble methods usually encounter the knowledge conflict issue at task and layer levels, causing compromised learning performance in both old and new tasks. To solve this issue, we propose meta-weight-ensembler that adaptively fuses knowledge of different tasks for continual le…

World4RL: Diffusion World Models for Policy Refinement with Reinforcement Learning for Robotic Manipulation
Zhennan Jiang, Kai Liu, Yuxin Qin, Shuai Tian, Yupeng Zheng, Mingcai Zhou, Chao Yu, Haoran Li, Dongbin Zhao
https://arxiv.org/abs/2509.19080

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization
Connor Scully-Allison, Kevin Menear, Kristin Potter, Andrew McNutt, Katherine E. Isaacs, Dmitry Duplyakin
https://arxiv.org/abs/2507.17898

Same Data, Different Audiences: Using Personas to Scope a Supercomputing Job Queue Visualization
Domain-specific visualizations sometimes focus on narrow, albeit important, tasks for one group of users. This focus limits the utility of a visualization to other groups working with the same data. While tasks elicited from other groups can present a design pitfall if not disambiguated, they also present a design opportunity -- development of visualizations that support multiple groups. This development choice presents a trade off of broadening the scope but limiting support for the more narro…

Neue Karten zeigen, wo #Aufforstung das #Klima wirklich kühlt und wo nicht.
Denn #Bäume speichern zwar CO₂, können aber durch veränderte

Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering
Jiangxue Yu, Hui Wang, San Jiang, Xing Zhang, Dejin Zhang, Qingquan Li
https://arxiv.org/abs/2509.19898 …

Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering
The integration of aerial and ground images has been a promising solution in 3D modeling of complex scenes, which is seriously restricted by finding reliable correspondences. The primary contribution of this study is a feature matching algorithm for aerial and ground images, whose core idea is to generate intermediate views to alleviate perspective distortions caused by the extensive viewpoint changes. First, by using aerial images only, sparse models are reconstructed through an incremental Sf…