Welcome to the Sept 4th edition of the Data Science Briefing!
We want to remind you that the very first edition of the LLMs for Data Science webinar will be held in just under two weeks on Sep 19. There's still a few spots left, so Register now so you don't miss out!
The most recent post on the Epidemiology series, Epidemiology 303: Metapopulation Models, explores how to connect multiple populations through a travel matrix. In the graph subsection, you'll find all you need to know about k-core Decomposition, while in the Visualization section, you can explore The Effects of Vaccination through a WSJ visualization.
In our regularly scheduled content, we learn What Is a Knowledge Graph, move Towards Geometric Deep Learning, explore The Simple Algorithm That Ants Use to Build Bridges and how to leverage AI for efficient incident response.
On the academic front, we Gauge Societal Impacts of Large Language Models, review the 2023 state of the climate report, and explore frustrated random walks: A fast method to compute node distances on hypergraphs.
This week's book recommendation is "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. You can find all the previous book recommendations on our website. In this week's video, we have a 1982 Lecture by Computing Pioneer Rear Admiral Grace Hopper.
Data shows that the best way for a newsletter to grow is by word of mouth, so if you think one of your friends or colleagues would enjoy this newsletter, go ahead and forward this email to them. This will help us spread the word!
Semper discentes,
The D4S Team
This week's book is "Working with Network Data" by J. Bagrow and Y.-Y. Ahn. Networks are the keystone concept necessary to understand a wealth of real-world complex systems whose behavior is characterized by interactions between individual components. In this book, J. Bagrow and Y.-Y. Ahn, two leading researchers in the field of Complex Networks, introduce readers to the fundamental concepts of Network Science and how to apply them to practical datasets. Their hands-on approach will get you up to speed quickly, allowing you to develop effective approaches to understanding your own network datasets.
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What Is a Knowledge Graph? [neo4j.com]
- Towards Geometric Deep Learning [thegradient.pub]
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Experiments Prepare to Test Whether Consciousness Arises from Quantum Weirdness [scientificamerican.com]
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Introduction to Objective Bayesian Hypothesis Testing [objectivebayesian.com]
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The Simple Algorithm That Ants Use to Build Bridges [quantamagazine.org]
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Leveraging AI for efficient incident response [engineering.fb.com]
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Foundation for Human Vision Models [github.com/facebookresearch]
- Gauging Societal Impacts of Large Language Models (E. Shein)
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Diffusion Models Are Real-Time Game Engines (D. Valevski, Y. Leviathan, M. Arar, S. Fruchter)
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The 2023 state of the climate report: Entering uncharted territory (W. J. Ripple, C. Wolf, J. W. Gregg, J. Rockström, T. M. Newsome, B. E. Law, L. Marques, T. M. Lenton, C. Xu, S. Huq, L. Simons, D. A. King)
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Experimental Design in Two-Sided Platforms: An Analysis of Bias (R. Johari, H. Li, I. Liskovich, G. Weintraub)
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Large Model Strategic Thinking, Small Model Efficiency: Transferring Theory of Mind in Large Language Models (N. Lore, A. S. Ilami, B. Heydari)
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Higher-Order Null Models as a Lens for Social Systems (G. Preti, A. Fazzone, G. Petri, G. D. F. Morales)
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Frustrated random walks: A fast method to compute node distances on hypergraphs (E. Li, S. Nickleach, B. Fadlallah)
1982 Lecture by Computing Pioneer Rear Admiral Grace Hopper
All the videos of the week are now available in our Youtube playlist.
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