Tootfinder

»Parlament fordert Pilotprojekt für E-Collecting:
Der Nationalrat hat gleich zwei politische Vorstösse angenommen, in denen es um die Einführung von E-Collecting geht. Einer wurde zuvor allerdings noch abgeändert.«
Mal sehen wie sicher und Privatsphäre einhaltend diesmal E-Collection in der Schweiz sich ergibt/präsentiert. Simpel ist es nicht und die Privatsphäre hat Vorrang hoffentlich.
🗳️

Parlament fordert Pilotprojekt für E-Collecting
Der Nationalrat hat gleich zwei politische Vorstösse angenommen, in denen es um die Einführung von E-Collecting geht. Einer wurde zuvor allerdings noch abgeändert.

sp_colocation: Social co-locations (2018)
Network of colocations between peoople, based on the information on which RFID readers received information from the RFID tags. Namely, we define two individuals to be in co-presence if the same exact set of readers have received signals from both individuals during a 20s time window.
This network has 81 nodes and 150126 edges.
Tags: Social, Offline, Unweighted, Weighted, Temporal, Metadata

Interpretable Text-Guided Image Clustering via Iterative Search
Bingchen Zhao, Oisin Mac Aodha
https://arxiv.org/abs/2506.12514 https://

Interpretable Text-Guided Image Clustering via Iterative Search
Traditional clustering methods aim to group unlabeled data points based on their similarity to each other. However, clustering, in the absence of additional information, is an ill-posed problem as there may be many different, yet equally valid, ways to partition a dataset. Distinct users may want to use different criteria to form clusters in the same data, e.g. shape v.s. color. Recently introduced text-guided image clustering methods aim to address this ambiguity by allowing users to specify t…

Fair Bayesian Model-Based Clustering
Jihu Lee, Kunwoong Kim, Yongdai Kim
https://arxiv.org/abs/2506.12839 https://arxiv.org/pdf/2506.…

Fair Bayesian Model-Based Clustering
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based cl…

A kinematic and structural study of young open clusters in the Milky Way Galaxy using Gaia DR3 catalogue
Harsha K. H., Annapurni Subramaniam, S. R. Dhanush, Hariharan D. S
https://arxiv.org/abs/2507.13069

A kinematic and structural study of young open clusters in the Milky Way Galaxy using Gaia DR3 catalogue
We aim to identify the cluster members, estimate cluster properties, study the dynamical state of the clusters as a function of mass, trace the existence of dynamical effects in massive stars, and check for spatial patterns of members in young clusters. We studied 14 young open clusters located within 1 kpc using the data from Gaia DR3 with the membership estimated using the GMM method. The cluster parameters such as age, distance, metallicity, and extinction were estimated by fitting PARSEC is…

AI bots that scrape the internet for training data are hammering the servers of libraries, archives, museums, and galleries,
and are in some cases knocking their collections offline,
according to a new survey published today.
While the impact of AI bots on open collections has been reported anecdotally,
this survey is the first attempt at measuring the problem,
which in the worst cases can make valuable, public resources unavailable to humans
because the…

AI Scraping Bots Are Breaking Open Libraries, Archives, and Museums
"This is a moment where that community feels collectively under threat and isn't sure what the process is for solving the problem.”

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…

now that we have all collectively seen and acknowledged palantir fuel the efforts of the fascist US regime,
can we also collectively get rid of people who think even criticizing palantir is worthy of exclusion?
(yes, i'm looking at you, guy who was proud to shake dtolnay's hand and told us all about it. you're a part of the problem! you know who you are)

Explainable Evidential Clustering
Victor F. Lopes de Souza, Karima Bakhti, Sofiane Ramdani, Denis Mottet, Abdelhak Imoussaten
https://arxiv.org/abs/2507.12192

Explainable Evidential Clustering
Unsupervised classification is a fundamental machine learning problem. Real-world data often contain imperfections, characterized by uncertainty and imprecision, which are not well handled by traditional methods. Evidential clustering, based on Dempster-Shafer theory, addresses these challenges. This paper explores the underexplored problem of explaining evidential clustering results, which is crucial for high-stakes domains such as healthcare. Our analysis shows that, in the general case, repr…

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Zhiqiang Li, Haiyong Bao, Menghong Guan, Hao Pan, Cheng Huang, Hong-Ning Dai
https://arxiv.org/abs/2506.13612

EBS-CFL: Efficient and Byzantine-robust Secure Clustered Federated Learning
Despite federated learning (FL)'s potential in collaborative learning, its performance has deteriorated due to the data heterogeneity of distributed users. Recently, clustered federated learning (CFL) has emerged to address this challenge by partitioning users into clusters according to their similarity. However, CFL faces difficulties in training when users are unwilling to share their cluster identities due to privacy concerns. To address these issues, we present an innovative Efficient and R…