Issue #155
May 16, 2022
- 1. Powerful ‘Machine Scientists’ Distill the Laws of Physics From Raw Data [quantamagazine.org]
- 2. Gnu Regression, Econometrics and Time-series Library [gretl.sourceforge.net]
- 3. Beyond Message Passing: a Physics-Inspired Paradigm for Graph Neural Networks [thegradient.pub]
- 4. We have early warnings for hurricanes. We need one for pandemics. [washingtonpost.com]
- 5. Climate simulations are mostly accurate, study finds [apnews.com]
- 6. Web Scraping with Python: Everything you need to know [scrapingbee.com]
- 7. Introduction to Diffusion Models for Machine Learning [assemblyai.com]
- 8. Time-series compression algorithms, explained [timescale.com]
- • Graph Neural Networks for Natural Language Processing: A Survey (L. Wu, Y. Chen, K. Shen, X. Guo, H. Gao, S. Li, J. Pei, B. Long)
- • A crash course in pandemic traffic (M. Buchanan)
- • Weak links in finance and supply chains are easily weaponized (H. Farrell, A. L. Newman)
- • Ancient Babylonian astronomers calculated Jupiter’s position from the area under a time-velocity graph (M. Ossendrijver)
- • On learning agent-based models from data (C. Monti, M. Pangallo, G. De F. Morales, F. Bonchi)
- • Using mobile money data and call detail records to explore the risks of urban migration in Tanzania (R. Lavelle-Hill, J. Harvey, G. Smith, A. Mazumder, M. Ellis, K. Mwantimwa, J. Goulding)
- • Geographical Theories of Migration (M. Price)
- • Ukraine Crisis: Monitoring population displacement through social media activity (D. R. Leasure, R. Kashyap, F. Rampazzo, B. Elbers, C. Dooley, I. Weber, M. Fatehkia, M. Bondarenko, M. D. Verhagen, A. Frey, J. Yan, E. Akimova, A. Sorichetta, A. J. Tatem, M. C. Mills)
- • Gromov Centrality: A Multi-Scale Measure of Network Centrality Using Triangle Inequality Excess (S. Babul, K. Devriendt, R. Lambiotte)
The arrow of time in causal networks
All our videos are also available in our YouTube playlist.
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