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   https://doi.org/10.1016/j.scitotenv.2021.146922  

   Yuan, Zijie ; Wu, Duo ; Niu, Lili ; Ma, Xuyi ; Li, Youmo ; Hillman, Aubrey L. ; Abbott, Mark B. ; Zhou, Aifeng ( August 2021 , Science of The Total Environment)
2. Invertibility aware Integration of Static and Time-series data: An application to Lake Temperature Modeling

   https://doi.org/10.1137/1.9781611977172.79  

   Tayal, K. ; Jia X. ; Ghosh R. ; Willard J. ; Read J. ; Kumar V. ( April 2022 , 2022 SIAM International Conference on Data Mining (SDM))

   Accurate predictions of water temperature are the foundation for many decisions and regulations, with direct impacts on water quality, fishery yields, and power production. Building accurate broad-scale models for lake temperature prediction remains challenging in practice due to the variability in the data distribution across different lake systems monitored by static and time-series data. In this paper, to tackle the above challenges, we propose a novel machine learning based approach for integrating static and time-series data in deep recurrent models, which we call Invertibility-Aware-Long Short-Term Memory(IA-LSTM), and demonstrate its effectiveness in predicting lake temperature. Our proposed method integrates components of the Invertible Network and LSTM to better predict temperature profiles (forward modeling) and infer the static features (i.e., inverse modeling) that can eventually enhance the prediction when static variables are missing. We evaluate our method on predicting the temperature profile of 450 lakes in the Midwestern U.S. and report a relative improvement of 4\% to capture data heterogeneity and simultaneously outperform baseline predictions by 12\% when static features are unavailable.
3. On studying extreme values and systematic risks with nonlinear time series models and tail dependence measures

   https://doi.org/10.1080/24754269.2020.1856590  

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4. Using extremal events to characterize noisy time series

   https://doi.org/10.1007/s00285-020-01471-4  

   Berry, Eric ; Cummins, Bree ; Nerem, Robert R. ; Smith, Lauren M. ; Haase, Steven B. ; Gedeon, Tomas ( April 2020 , Journal of Mathematical Biology)
5. Mid-infrared time-domain study of recent dust production events in the extreme debris disc of TYC 4209-1322-1

   https://doi.org/10.1093/mnras/stac2595  

   Moór, Attila ; Ábrahám, Péter ; Kóspál, Ágnes ; Su, Kate Y. L. ; Rieke, George H. ; Vida, Krisztián ; Cataldi, Gianni ; Bódi, Attila ; Bognár, Zsófia ; Cseh, Borbála ; et al ( September 2022 , Monthly Notices of the Royal Astronomical Society)

   Extreme debris discs are characterized by unusually strong mid-infrared excess emission, which often proves to be variable. The warm dust in these discs is of transient nature and is likely related to a recent giant collision occurring close to the star in the terrestrial region. Here we present the results of a 877 d long, gap-free photometric monitoring performed by the Spitzer Space Telescope of the recently discovered extreme debris disc around TYC 4209-1322-1. By combining these observations with other time-domain optical and mid-infrared data, we explore the disc variability of the last four decades with particular emphasis on the last 12 yr. During the latter interval the disc showed substantial changes, the most significant was the brightening and subsequent fading between 2014 and 2018 as outlined in WISE data. The Spitzer light curves outline the fading phase and a subsequent new brightening of the disc after 2018, revealing an additional flux modulation with a period of ∼39 d on top of the long-term trend. We found that all these variations can be interpreted as the outcome of a giant collision that happened at an orbital radius of ∼0.3 au sometime in 2014. Our analysis implies that a collision on a similar scale couldmore »have taken place around 2010, too. The fact that the disc was already peculiarly dust rich 40 yr ago, as implied by IRAS data, suggests that these dust production events belong to a chain of large impacts triggered by an earlier even more catastrophic collision.

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