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ATEM is a novel framework for studying topic evolution in scientific archives. It is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles.

Example plain HTML site using GitLab Pages: https://pages.gitlab.io/plain-html

This is the source code of the 2021 replication for ReScience of the paper "Speedup Graph Processing by Graph Ordering" by Hao Wei, Jeffrey Xu Yu, Can Lu, and Xuemin Lin, published in Proceedings of SIGMOD 2016.

Example Hugo site using GitLab Pages: https://pages.gitlab.io/hugo

This is a tool intended to monitor the GPU usage on the various GPU-servers at the LIP6 Lab, Paris

This repository provides a template to generate webpages for the team of Matthieu Cord.