Issue #234

February 15, 2024

This week's book is "Computing the Climate" by Steve M. Easterbrook, a captivating journey into the synergy of climate science and computing, making it a must-read for anyone intrigued by the intersection of these fields. Easterbrook's engaging writing style effortlessly demystifies complex concepts, ensuring accessibility for readers with diverse backgrounds. The book's strength lies in its seamless blend of theoretical discussions with real-world examples, showcasing the instrumental role of computing in unraveling the intricacies of climate dynamics.

Easterbrook's balanced perspective sets this book apart, acknowledging the uncertainties in climate science while underscoring the transformative impact of technological advancements. By delving into interdisciplinary connections with policy, economics, and environmental science, Easterbrook provides a holistic understanding of the challenges associated with climate change. This comprehensive approach educates and empowers readers to recognize the pivotal role of computational progress in shaping our collective response to climate-related issues.

In essence, "Computing the Climate" stands as a persuasive testament to the indispensable role of computing in climate research. Easterbrook's skillful narrative not only informs but also inspires readers to grasp the significance of technological innovation in confronting the pressing challenges of our changing climate. This book is an essential addition to the literature, urging readers to actively engage in the ongoing dialogue surrounding the future of our planet.

Computing the Climate

1. SQL for the Weary [gvwilson.github.io]
2. A/B Testing [An interactive look at Thompson sampling]
3. A Camera-Wearing Baby Taught an AI to Learn Words [scientificamerican.com]
4. Introduction to Thompson Sampling: the Bernoulli bandit [gdmarmerola.github.io]
5. Researchers Approach New Speed Limit for Seminal Problem [quantamagazine.org]
6. The Math Behind the Adam Optimizer [towardsdatascience.com]
7. Hash collisions and exploitations [github.com/corkami]

• Sequential stacking link prediction algorithms for temporal networks (X. He, A. Ghasemian, E. Lee, A. Clauset, P. J. Mucha)
• The 15-minute city quantified using human mobility data (T. Abbiasov, C. Heine, S. Sabouri, A. Salazar-Miranda, P. Santi, E. Glaeser, C. Ratti)
• Identifying key players in dark web marketplaces through Bitcoin transaction networks (E. Fonseca dos Reis, A. Teytelboym, A. ElBahrawy, I. De Loizaga, A. Baronchelli)
• Identifying the systemic importance and systemic vulnerability of financial institutions based on portfolio similarity correlation network (M. Shao, H. Fan)
• Effectiveness of contact tracing on networks with cliques (A. K. Rizi, L. A. Keating, J. P. Gleeson, D. J. P. O'Sullivan, M. Kivelä)
• Hidden multiscale organization and robustness of real multiplex networks (G. Son, M. Ha, H. Jeong)
• Insights and caveats from mining local and global temporal motifs in cryptocurrency transaction networks (N. A. Arnold, P. Zhong, C. T. Ba, B. Steer, R. Mondragon, F. Cuadrado, R. Lambiotte, R. G. Clegg)
• Cooperation and the social brain hypothesis in primate social networks (N. G. MacLaren, L. Meng, M. Collier, N. Masuda)

Causal Factor Investing

February 15, 2024

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