Avni, Guy, et al. “Infinite-Duration All-Pay Bidding Games.”10.1137/1.9781611976465.38__Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms, edited by Dániel Marx, Society for Industrial and Applied Mathematics, 2021, pp. 617–36, doi:10.1137/1.9781611976465.38.

Tomásek, Petr, et al. “Solving Partially Observable Stochastic Shortest-Path Games.”10.24963/ijcai.2021/575__30th International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–89, doi:10.24963/ijcai.2021/575.

Fedorov, Alexander, et al. “A Scalable Concurrent Algorithm for Dynamic Connectivity.”10.1145/3409964.3461810__Proceedings of the 33rd ACM Symposium on Parallelism in Algorithms and Architectures, Association for Computing Machinery, 2021, pp. 208–20, doi:10.1145/3409964.3461810.

Lombardi, Fabrizio, et al. Quantifying the Coexistence of Neuronal Oscillations and Avalanches. arXiv, doi:10.48550/ARXIV.2108.06686.

Shadel, Gerald S., et al. “The San Diego Nathan Shock Center: Tackling the Heterogeneity of Aging.”10.1007/s11357-021-00426-x__GeroScience, vol. 43, no. 5, Springer Nature, 2021, pp. 2139–48, doi:10.1007/s11357-021-00426-x.

Krishna, Shefali, et al. “Identification of Long-Lived Proteins in the Mitochondria Reveals Increased Stability of the Electron Transport Chain.”10.1016/j.devcel.2021.10.008__Developmental Cell, vol. 56, no. 21, Elsevier, 2021, p. 52–2965.e9, doi:10.1016/j.devcel.2021.10.008.

Kungurtsev, Vyacheslav, et al. “Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees.” 35th AAAI Conference on Artificial Intelligence, AAAI 2021, vol. 35, no. 9B, AAAI Press, 2021, pp. 8209–16.

Braun, Lukas, and Tim P. Vogels. “Online Learning of Neural Computations from Sparse Temporal Feedback.” Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, vol. 20, Neural Information Processing Systems Foundation, 2021, pp. 16437–50.

Alimisis, Foivos, et al. “Distributed Principal Component Analysis with Limited Communication.” Advances in Neural Information Processing Systems - 35th Conference on Neural Information Processing Systems, vol. 4, Neural Information Processing Systems Foundation, 2021, pp. 2823–34.

Frantar, Elias, et al. “M-FAC: Efficient Matrix-Free Approximations of Second-Order Information.” 35th Conference on Neural Information Processing Systems, vol. 34, Curran Associates, 2021, pp. 14873–86.

Alistarh, Dan-Adrian, and Janne Korhonen. “Towards Tight Communication Lower Bounds for Distributed Optimisation.” 35th Conference on Neural Information Processing Systems, vol. 34, Curran Associates, 2021, pp. 7254–66.

Schmidt, K. B., et al. “Recovery and Analysis of Rest-Frame UV Emission Lines in 2052 Galaxies Observed with MUSE at 1.5 &; z &; 6.4.”10.1051/0004-6361/202140876__Astronomy &; Astrophysics, vol. 654, , EDP Sciences, 2021, doi:10.1051/0004-6361/202140876.

Bacon, R., et al. “The MUSE Extremely Deep Field: The Cosmic Web in Emission at High Redshift.”10.1051/0004-6361/202039887__Astronomy &; Astrophysics, vol. 647, 7, EDP Sciences, 2021, doi:10.1051/0004-6361/202039887.

Boogaard, Leindert A., et al. “Measuring the Average Molecular Gas Content of Star-Forming Galaxies at z = 3–4.”10.3847/1538-4357/ac01d7__The Astrophysical Journal, vol. 916, no. 1, 12, IOP Publishing, 2021, doi:10.3847/1538-4357/ac01d7.

Matthee, Jorryt J., et al. “The X-SHOOTER Lyman ;1 Survey at z = 2 (XLS-Z2) I: What Makes a Galaxy a Lyman ;1 Emitter?”10.1093/mnras/stab1304__Monthly Notices of the Royal Astronomical Society, vol. 505, no. 1, Oxford University Press, 2021, pp. 1382–412, doi:10.1093/mnras/stab1304.

Gallego, Sofia G., et al. “Constraining the Cosmic UV Background at z &; 3 with MUSE Lyman-;1 Emission Observations.”10.1093/mnras/stab796__Monthly Notices of the Royal Astronomical Society, vol. 504, no. 1, Oxford University Press, 2021, pp. 16–32, doi:10.1093/mnras/stab796.

Gronke, Max, et al. “Lyman-;1 Transmission Properties of the Intergalactic Medium in the CoDaII Simulation.”10.1093/mnras/stab2762__Monthly Notices of the Royal Astronomical Society, vol. 508, no. 3, Oxford University Press, 2021, pp. 3697–709, doi:10.1093/mnras/stab2762.

Mackenzie, Ruari, et al. “Revealing the Impact of Quasar Luminosity on Giant Ly;1 Nebulae.”10.1093/mnras/staa3277__Monthly Notices of the Royal Astronomical Society, vol. 502, no. 1, Oxford University Press, 2021, pp. 494–509, doi:10.1093/mnras/staa3277.

Santos, S., et al. “The Evolution of the UV Luminosity and Stellar Mass Functions of Lyman-;1 Emitters from z c 2 to z c 6.”10.1093/mnras/stab1218__Monthly Notices of the Royal Astronomical Society, vol. 505, no. 1, Oxford University Press, 2021, pp. 1117–34, doi:10.1093/mnras/stab1218.

Matthee, Jorryt J. “Differences in Galaxy Colours Are Not Just about the Mass.”10.1038/s41550-021-01415-y__Nature Astronomy, vol. 5, Springer Nature, 2021, pp. 984–85, doi:10.1038/s41550-021-01415-y.