Tootfinder

Please repost: #job in #climatescience
I am looking to fill positions in my group at Universitaet Hamburg and @…
👉Postdoc examining the response of the ocean carbon st…

Ausschreibung

En Abitibi-Temiscamingue, la culture c'est ferme ta gueule.
https://www.lapresse.ca/actualites/2024-04-02/festival-musical-alienfest-en-abitibi-temiscamingue/un-g…

Festival musical Alienfest en Abitibi-Témiscamingue | Un groupe déprogrammé pour ses critiques de la Fonderie Horne
Les critiques acerbes de la Fonderie Horne du groupe de métal hardcore engagé Guhn Twei lui ont coûté sa participation au festival de musique Alienfest de La Sarre, en Abitibi-Témiscamingue, une situation qui en dit long sur « la culture du silence qui règne » dans la région, déplore le chanteur du groupe.

Survey on AI in Cataloging Open for Contributions - open until June 5
Survey: https://www.rdatoolkit.org/news/rsc/AIsurvey_2024_May
Background:

Physics-Informed Graph Neural Networks for Water Distribution Systems
Inaam Ashraf, Janine Strotherm, Luca Hermes, Barbara Hammer
https://arxiv.org/abs/2403.18570

Physics-Informed Graph Neural Networks for Water Distribution Systems
Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model…

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
https://arxiv.org/abs/2403.17416

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…

Higher-dimensional grid-imprimitive block-transitive designs
Seyed Hassan Alavi, Carmen Amarra, Ashraf Daneshkhah, Alice Devillers, Cheryl E. Praeger
https://arxiv.org/abs/2404.11241

Higher-dimensional grid-imprimitive block-transitive designs
It was shown in 1989 by Delandtsheer and Doyen that, for a $2$-design with $v$ points and block size $k$, a block-transitive group of automorphisms can be point-imprimitive (that is, leave invariant a nontrivial partition of the point set) only if $v$ is small enough relative to $k$. Recently, exploiting a construction of block-transitive point-imprimitive $2$-designs given by Cameron and the last author, four of the authors studied $2$-designs admitting a block-transitive group that preserves …

This https://arxiv.org/abs/2403.17416 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal of providing personalized recommendations. While existing research predominantly ascribes this…