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1. Likelihood of back-to-back tropical cyclone hazards is increasing

   https://doi.org/10.1038/s41558-023-01596-6  

   Lin, N.; Xi, D (March 2023, Nature Climate Change)

   An integrated Earth system analysis is applied to project the probability of sequential hazards from tropical cyclones along the US East and Gulf coasts. Even a moderate-emissions scenario increases the chances of back-to-back tropical cyclone hazards and, possibly, two extreme tropical cyclone events impacting the United States within a short period of time. 

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2. Acute effects of back-to-back hurricanes on the underwater light regime of a coral reef

   https://doi.org/10.1007/s00227-018-3459-z  

   Edmunds, Peter J.; Tsounis, Georgios; Boulon, Ralf; Bramanti, Lorenzo (February 2019, Marine Biology)
3. Sherali--Adams strikes back

   O'Donnell, Ryan; Schramm, Tselil (January 2019, Computational Complexity Conference)
4. Observation of Correlations between Spin and Transverse Momenta in Back-to-Back Dihadron Production at CLAS12

   https://doi.org/10.1103/PhysRevLett.130.022501  

   Avakian, H.; Hayward, T. B.; Kotzinian, A.; Armstrong, W. R.; Atac, H.; Ayerbe Gayoso, C.; Baashen, L.; Baltzell, N. A.; Barion, L.; Bashkanov, M.; et al (January 2023, Physical Review Letters)
5. Pull-back Geometry of Persistent Homology Encodings

   Liang, Shuang; Turkes, Renata; Li, Jiayi; Otter, Nina; Montufar, Guido (February 2024, Transactions on Machine Learning Research)

   Persistent homology (PH) is a method for generating topology-inspired representations of data. Empirical studies that investigate the properties of PH, such as its sensitivity to perturbations or ability to detect a feature of interest, commonly rely on training and testing an additional model on the basis of the PH representation. To gain more intrinsic insights about PH, independently of the choice of such a model, we propose a novel methodology based on the pull-back geometry that a PH encoding induces on the data manifold. The spectrum and eigenvectors of the induced metric help to identify the most and least significant information captured by PH. Furthermore, the pull-back norm of tangent vectors provides insights about the sensitivity of PH to a given perturbation, or its potential to detect a given feature of interest, and in turn its ability to solve a given classification or regression problem. Experimentally, the insights gained through our methodology align well with the existing knowledge about PH. Moreover, we show that the pull-back norm correlates with the performance on downstream tasks, and can therefore guide the choice of a suitable PH encoding. 

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