[{"external_id":{"arxiv":["2205.10009"]},"citation":{"ieee":"J. Barbier, T. Hou, M. Mondelli, and M. Saenz, “The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?,” <i>arXiv</i>. .","ama":"Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2205.10009\">10.48550/arXiv.2205.10009</a>","short":"J. Barbier, T. Hou, M. Mondelli, M. Saenz, ArXiv (n.d.).","ista":"Barbier J, Hou T, Mondelli M, Saenz M. The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv, 2205.10009.","apa":"Barbier, J., Hou, T., Mondelli, M., &#38; Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2205.10009\">https://doi.org/10.48550/arXiv.2205.10009</a>","mla":"Barbier, Jean, et al. “The Price of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix Estimation?” <i>ArXiv</i>, 2205.10009, doi:<a href=\"https://doi.org/10.48550/arXiv.2205.10009\">10.48550/arXiv.2205.10009</a>.","chicago":"Barbier, Jean, TianQi Hou, Marco Mondelli, and Manuel Saenz. “The Price of Ignorance: How Much Does It Cost to Forget Noise Structure in Low-Rank Matrix Estimation?” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2205.10009\">https://doi.org/10.48550/arXiv.2205.10009</a>."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2205.10009"}],"article_processing_charge":"No","type":"preprint","oa_version":"Preprint","language":[{"iso":"eng"}],"date_published":"2022-05-20T00:00:00Z","department":[{"_id":"MaMo"}],"publication_status":"accepted","doi":"10.48550/arXiv.2205.10009","date_created":"2023-02-10T13:45:41Z","publication":"arXiv","month":"05","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation?","oa":1,"article_number":"2205.10009","author":[{"last_name":"Barbier","first_name":"Jean","full_name":"Barbier, Jean"},{"last_name":"Hou","first_name":"TianQi","full_name":"Hou, TianQi"},{"first_name":"Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","full_name":"Mondelli, Marco"},{"full_name":"Saenz, Manuel","last_name":"Saenz","first_name":"Manuel"}],"_id":"12536","date_updated":"2023-02-16T09:41:25Z","abstract":[{"text":"We consider the problem of estimating a rank-1 signal corrupted by structured rotationally invariant noise, and address the following question: how well do inference algorithms perform when the noise statistics is unknown and hence Gaussian noise is assumed? While the matched Bayes-optimal setting with unstructured noise is well understood, the analysis of this mismatched problem is only at its premises. In this paper, we make a step towards understanding the effect of the strong source of mismatch which is the noise statistics. Our main technical contribution is the rigorous analysis of a Bayes estimator and of an approximate message passing (AMP) algorithm, both of which incorrectly assume a Gaussian setup. The first result exploits the theory of spherical integrals and of low-rank matrix perturbations; the idea behind the second one is to design and analyze an artificial AMP which, by taking advantage of the flexibility in the denoisers, is able to \"correct\" the mismatch. Armed with these sharp asymptotic characterizations, we unveil a rich and often unexpected phenomenology. For example, despite AMP is in principle designed to efficiently compute the Bayes estimator, the former is outperformed by the latter in terms of mean-square error. We show that this performance gap is due to an incorrect estimation of the signal norm. In fact, when the SNR is large enough, the overlaps of the AMP and the Bayes estimator coincide, and they even match those of optimal estimators taking into account the structure of the noise.","lang":"eng"}],"arxiv":1,"day":"20","year":"2022"},{"volume":35,"quality_controlled":"1","external_id":{"arxiv":["2205.10217"]},"article_processing_charge":"No","department":[{"_id":"MaMo"}],"language":[{"iso":"eng"}],"type":"conference","publication":"36th Conference on Neural Information Processing Systems","page":"7628-7640","date_created":"2023-02-10T13:46:37Z","title":"Memorization and optimization in deep neural networks with minimum over-parameterization","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Curran Associates","author":[{"full_name":"Bombari, Simone","id":"ca726dda-de17-11ea-bc14-f9da834f63aa","last_name":"Bombari","first_name":"Simone"},{"full_name":"Amani, Mohammad Hossein","first_name":"Mohammad Hossein","last_name":"Amani"},{"full_name":"Mondelli, Marco","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425","first_name":"Marco","orcid":"0000-0002-3242-7020"}],"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"day":"24","acknowledgement":"The authors were partially supported by the 2019 Lopez-Loreta prize, and they would like to thank\r\nQuynh Nguyen, Mahdi Soltanolkotabi and Adel Javanmard for helpful discussions.\r\n","citation":{"short":"S. Bombari, M.H. Amani, M. Mondelli, in:, 36th Conference on Neural Information Processing Systems, Curran Associates, 2022, pp. 7628–7640.","ieee":"S. Bombari, M. H. Amani, and M. Mondelli, “Memorization and optimization in deep neural networks with minimum over-parameterization,” in <i>36th Conference on Neural Information Processing Systems</i>, 2022, vol. 35, pp. 7628–7640.","ama":"Bombari S, Amani MH, Mondelli M. Memorization and optimization in deep neural networks with minimum over-parameterization. In: <i>36th Conference on Neural Information Processing Systems</i>. Vol 35. Curran Associates; 2022:7628-7640.","chicago":"Bombari, Simone, Mohammad Hossein Amani, and Marco Mondelli. “Memorization and Optimization in Deep Neural Networks with Minimum Over-Parameterization.” In <i>36th Conference on Neural Information Processing Systems</i>, 35:7628–40. Curran Associates, 2022.","mla":"Bombari, Simone, et al. “Memorization and Optimization in Deep Neural Networks with Minimum Over-Parameterization.” <i>36th Conference on Neural Information Processing Systems</i>, vol. 35, Curran Associates, 2022, pp. 7628–40.","ista":"Bombari S, Amani MH, Mondelli M. 2022. Memorization and optimization in deep neural networks with minimum over-parameterization. 36th Conference on Neural Information Processing Systems. vol. 35, 7628–7640.","apa":"Bombari, S., Amani, M. H., &#38; Mondelli, M. (2022). Memorization and optimization in deep neural networks with minimum over-parameterization. In <i>36th Conference on Neural Information Processing Systems</i> (Vol. 35, pp. 7628–7640). Curran Associates."},"intvolume":"        35","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2205.10217"}],"date_published":"2022-07-24T00:00:00Z","oa_version":"Preprint","publication_status":"published","month":"07","publication_identifier":{"isbn":["9781713871088"]},"oa":1,"status":"public","_id":"12537","year":"2022","arxiv":1,"abstract":[{"text":"The Neural Tangent Kernel (NTK) has emerged as a powerful tool to provide memorization, optimization and generalization guarantees in deep neural networks. A line of work has studied the NTK spectrum for two-layer and deep networks with at least a layer with Ω(N) neurons, N being the number of training samples. Furthermore, there is increasing evidence suggesting that deep networks with sub-linear layer widths are powerful memorizers and optimizers, as long as the number of parameters exceeds the number of samples. Thus, a natural open question is whether the NTK is well conditioned in such a challenging sub-linear setup. In this paper, we answer this question in the affirmative. Our key technical contribution is a lower bound on the smallest NTK eigenvalue for deep networks with the minimum possible over-parameterization: the number of parameters is roughly Ω(N) and, hence, the number of neurons is as little as Ω(N−−√). To showcase the applicability of our NTK bounds, we provide two results concerning memorization capacity and optimization guarantees for gradient descent training.","lang":"eng"}],"date_updated":"2024-09-10T13:03:19Z"},{"scopus_import":"1","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2205.08199"}],"citation":{"short":"M.H. Amani, S. Bombari, M. Mondelli, R. Pukdee, S. Rini, IEEE Information Theory Workshop (2022) 588–593.","ama":"Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. Sharp asymptotics on the compression of two-layer neural networks. <i>IEEE Information Theory Workshop</i>. 2022:588-593. doi:<a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">10.1109/ITW54588.2022.9965870</a>","ieee":"M. H. Amani, S. Bombari, M. Mondelli, R. Pukdee, and S. Rini, “Sharp asymptotics on the compression of two-layer neural networks,” <i>IEEE Information Theory Workshop</i>. IEEE, pp. 588–593, 2022.","chicago":"Amani, Mohammad Hossein, Simone Bombari, Marco Mondelli, Rattana Pukdee, and Stefano Rini. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” <i>IEEE Information Theory Workshop</i>. IEEE, 2022. <a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">https://doi.org/10.1109/ITW54588.2022.9965870</a>.","apa":"Amani, M. H., Bombari, S., Mondelli, M., Pukdee, R., &#38; Rini, S. (2022). Sharp asymptotics on the compression of two-layer neural networks. <i>IEEE Information Theory Workshop</i>. Mumbai, India: IEEE. <a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">https://doi.org/10.1109/ITW54588.2022.9965870</a>","mla":"Amani, Mohammad Hossein, et al. “Sharp Asymptotics on the Compression of Two-Layer Neural Networks.” <i>IEEE Information Theory Workshop</i>, IEEE, 2022, pp. 588–93, doi:<a href=\"https://doi.org/10.1109/ITW54588.2022.9965870\">10.1109/ITW54588.2022.9965870</a>.","ista":"Amani MH, Bombari S, Mondelli M, Pukdee R, Rini S. 2022. Sharp asymptotics on the compression of two-layer neural networks. IEEE Information Theory Workshop., 588–593."},"doi":"10.1109/ITW54588.2022.9965870","conference":{"location":"Mumbai, India","end_date":"2022-11-09","name":"ITW: Information Theory Workshop","start_date":"2022-11-01"},"publication_status":"published","date_published":"2022-11-16T00:00:00Z","oa_version":"Preprint","oa":1,"publication_identifier":{"isbn":["9781665483414"]},"status":"public","month":"11","year":"2022","abstract":[{"text":"In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes. More precisely, we consider the setting in which the weights of the target network are i.i.d. sub-Gaussian, and we minimize the population L_2 loss between the outputs of the target and of the compressed network, under the assumption of Gaussian inputs. By using tools from high-dimensional probability, we show that this non-convex problem can be simplified when the target network is sufficiently over-parameterized, and provide the error rate of this approximation as a function of the input dimension and N. In this mean-field limit, the simplified objective, as well as the optimal weights of the compressed network, does not depend on the realization of the target network, but only on expected scaling factors. Furthermore, for networks with ReLU activation, we conjecture that the optimum of the simplified optimization problem is achieved by taking weights on the Equiangular Tight Frame (ETF), while the scaling of the weights and the orientation of the ETF depend on the parameters of the target network. Numerical evidence is provided to support this conjecture.","lang":"eng"}],"arxiv":1,"date_updated":"2023-12-18T11:31:47Z","_id":"12538","article_type":"original","article_processing_charge":"No","quality_controlled":"1","external_id":{"arxiv":["2205.08199"]},"publication":"IEEE Information Theory Workshop","date_created":"2023-02-10T13:47:56Z","page":"588-593","department":[{"_id":"MaMo"}],"language":[{"iso":"eng"}],"type":"journal_article","title":"Sharp asymptotics on the compression of two-layer neural networks","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"IEEE","day":"16","author":[{"full_name":"Amani, Mohammad Hossein","first_name":"Mohammad Hossein","last_name":"Amani"},{"full_name":"Bombari, Simone","first_name":"Simone","last_name":"Bombari","id":"ca726dda-de17-11ea-bc14-f9da834f63aa"},{"full_name":"Mondelli, Marco","orcid":"0000-0002-3242-7020","first_name":"Marco","last_name":"Mondelli","id":"27EB676C-8706-11E9-9510-7717E6697425"},{"full_name":"Pukdee, Rattana","last_name":"Pukdee","first_name":"Rattana"},{"first_name":"Stefano","last_name":"Rini","full_name":"Rini, Stefano"}]},{"citation":{"ista":"Venkataramanan R, Kögler K, Mondelli M. 2022. Estimation in rotationally invariant generalized linear models via approximate message passing. Proceedings of the 39th International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 162, 22.","apa":"Venkataramanan, R., Kögler, K., &#38; Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162). Baltimore, MD, United States: ML Research Press.","mla":"Venkataramanan, Ramji, et al. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, 22, ML Research Press, 2022.","chicago":"Venkataramanan, Ramji, Kevin Kögler, and Marco Mondelli. “Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, Vol. 162. ML Research Press, 2022.","short":"R. Venkataramanan, K. Kögler, M. Mondelli, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022.","ieee":"R. Venkataramanan, K. Kögler, and M. Mondelli, “Estimation in rotationally invariant generalized linear models via approximate message passing,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162.","ama":"Venkataramanan R, Kögler K, Mondelli M. Estimation in rotationally invariant generalized linear models via approximate message passing. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022."},"intvolume":"       162","file_date_updated":"2023-02-13T10:53:11Z","ddc":["000"],"oa_version":"Published Version","date_published":"2022-01-01T00:00:00Z","conference":{"start_date":"2022-07-17","name":"ICML: International Conference on Machine Learning","end_date":"2022-07-23","location":"Baltimore, MD, United States"},"publication_status":"published","status":"public","oa":1,"article_number":"22","_id":"12540","abstract":[{"text":"We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral distribution, this model is well suited for capturing complex correlation structures which often arise in applications. We propose a novel family of approximate message passing (AMP) algorithms for signal estimation, and rigorously characterize their performance in the high-dimensional limit via a state evolution recursion. Our rotationally invariant AMP has complexity of the same order as the existing AMP derived under the restrictive assumption of a Gaussian design; our algorithm also recovers this existing AMP as a special case. Numerical results showcase a performance close to Vector AMP (which is conjectured to be Bayes-optimal in some settings), but obtained with a much lower complexity, as the proposed algorithm does not require a computationally expensive singular value decomposition.","lang":"eng"}],"date_updated":"2024-09-10T13:03:17Z","year":"2022","volume":162,"quality_controlled":"1","article_processing_charge":"No","type":"conference","department":[{"_id":"MaMo"}],"language":[{"iso":"eng"}],"has_accepted_license":"1","publication":"Proceedings of the 39th International Conference on Machine Learning","date_created":"2023-02-10T13:49:04Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ML Research Press","title":"Estimation in rotationally invariant generalized linear models via approximate message passing","author":[{"last_name":"Venkataramanan","first_name":"Ramji","full_name":"Venkataramanan, Ramji"},{"full_name":"Kögler, Kevin","first_name":"Kevin","id":"94ec913c-dc85-11ea-9058-e5051ab2428b","last_name":"Kögler"},{"id":"27EB676C-8706-11E9-9510-7717E6697425","last_name":"Mondelli","orcid":"0000-0002-3242-7020","first_name":"Marco","full_name":"Mondelli, Marco"}],"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"file":[{"date_updated":"2023-02-13T10:53:11Z","file_name":"2022_PMLR_Venkataramanan.pdf","date_created":"2023-02-13T10:53:11Z","file_id":"12547","creator":"dernst","checksum":"67436eb0a660789514cdf9db79e84683","access_level":"open_access","relation":"main_file","content_type":"application/pdf","success":1,"file_size":2341343}],"acknowledgement":"The authors would like to thank the anonymous reviewers for their helpful comments. KK and MM were partially supported by the 2019 Lopez-Loreta Prize."},{"article_processing_charge":"No","volume":36,"quality_controlled":"1","external_id":{"arxiv":["2203.01640"]},"publication":"Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022","date_created":"2023-02-19T23:00:56Z","page":"9858-9867","type":"conference","department":[{"_id":"KrCh"}],"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Association for the Advancement of Artificial Intelligence","title":"Risk-aware stochastic shortest path","day":"28","author":[{"full_name":"Meggendorfer, Tobias","first_name":"Tobias","orcid":"0000-0002-1712-2165","last_name":"Meggendorfer","id":"b21b0c15-30a2-11eb-80dc-f13ca25802e1"}],"scopus_import":"1","main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2203.01640","open_access":"1"}],"citation":{"chicago":"Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, 36:9858–67. Association for the Advancement of Artificial Intelligence, 2022. <a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">https://doi.org/10.1609/aaai.v36i9.21222</a>.","mla":"Meggendorfer, Tobias. “Risk-Aware Stochastic Shortest Path.” <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, vol. 36, no. 9, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–67, doi:<a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">10.1609/aaai.v36i9.21222</a>.","apa":"Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i> (Vol. 36, pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence. <a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">https://doi.org/10.1609/aaai.v36i9.21222</a>","ista":"Meggendorfer T. 2022. Risk-aware stochastic shortest path. Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022. Conference on Artificial Intelligence vol. 36, 9858–9867.","short":"T. Meggendorfer, in:, Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022, Association for the Advancement of Artificial Intelligence, 2022, pp. 9858–9867.","ama":"Meggendorfer T. Risk-aware stochastic shortest path. In: <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>. Vol 36. Association for the Advancement of Artificial Intelligence; 2022:9858-9867. doi:<a href=\"https://doi.org/10.1609/aaai.v36i9.21222\">10.1609/aaai.v36i9.21222</a>","ieee":"T. Meggendorfer, “Risk-aware stochastic shortest path,” in <i>Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022</i>, Virtual, 2022, vol. 36, no. 9, pp. 9858–9867."},"intvolume":"        36","doi":"10.1609/aaai.v36i9.21222","conference":{"name":"Conference on Artificial Intelligence","start_date":"2022-02-22","location":"Virtual","end_date":"2022-03-01"},"publication_status":"published","oa_version":"Preprint","date_published":"2022-06-28T00:00:00Z","status":"public","publication_identifier":{"isbn":["1577358767"],"eissn":["2374-3468"]},"oa":1,"month":"06","arxiv":1,"abstract":[{"text":"We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.","lang":"eng"}],"issue":"9","date_updated":"2023-02-20T07:19:12Z","year":"2022","_id":"12568"},{"scopus_import":"1","main_file_link":[{"url":"https://doi.org/10.1038/s43247-022-00588-2","open_access":"1"}],"citation":{"ista":"McCarthy M, Miles E, Kneib M, Buri P, Fugger S, Pellicciotti F. 2022. Supraglacial debris thickness and supply rate in High-Mountain Asia. Communications Earth &#38; Environment. 3, 269.","mla":"McCarthy, Michael, et al. “Supraglacial Debris Thickness and Supply Rate in High-Mountain Asia.” <i>Communications Earth &#38; Environment</i>, vol. 3, 269, Springer Nature, 2022, doi:<a href=\"https://doi.org/10.1038/s43247-022-00588-2\">10.1038/s43247-022-00588-2</a>.","apa":"McCarthy, M., Miles, E., Kneib, M., Buri, P., Fugger, S., &#38; Pellicciotti, F. (2022). Supraglacial debris thickness and supply rate in High-Mountain Asia. <i>Communications Earth &#38; Environment</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s43247-022-00588-2\">https://doi.org/10.1038/s43247-022-00588-2</a>","chicago":"McCarthy, Michael, Evan Miles, Marin Kneib, Pascal Buri, Stefan Fugger, and Francesca Pellicciotti. “Supraglacial Debris Thickness and Supply Rate in High-Mountain Asia.” <i>Communications Earth &#38; Environment</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/s43247-022-00588-2\">https://doi.org/10.1038/s43247-022-00588-2</a>.","ieee":"M. McCarthy, E. Miles, M. Kneib, P. Buri, S. Fugger, and F. Pellicciotti, “Supraglacial debris thickness and supply rate in High-Mountain Asia,” <i>Communications Earth &#38; Environment</i>, vol. 3. Springer Nature, 2022.","ama":"McCarthy M, Miles E, Kneib M, Buri P, Fugger S, Pellicciotti F. Supraglacial debris thickness and supply rate in High-Mountain Asia. <i>Communications Earth &#38; Environment</i>. 2022;3. doi:<a href=\"https://doi.org/10.1038/s43247-022-00588-2\">10.1038/s43247-022-00588-2</a>","short":"M. McCarthy, E. Miles, M. Kneib, P. Buri, S. Fugger, F. Pellicciotti, Communications Earth &#38; Environment 3 (2022)."},"intvolume":"         3","doi":"10.1038/s43247-022-00588-2","publication_status":"published","date_published":"2022-11-05T00:00:00Z","oa_version":"Published Version","publication_identifier":{"issn":["2662-4435"]},"oa":1,"status":"public","month":"11","year":"2022","abstract":[{"text":"Supraglacial debris strongly modulates glacier melt rates and can be decisive for ice dynamics and mountain hydrology. It is ubiquitous in High-Mountain Asia, yet because its thickness and supply rate from local topography are poorly known, our ability to forecast regional glacier change and streamflow is limited. Here we combined remote sensing and numerical modelling to resolve supraglacial debris thickness by altitude for 4689 glaciers in High-Mountain Asia, and debris-supply rate to 4141 of those glaciers. Our results reveal extensively thin supraglacial debris and high spatial variability in both debris thickness and supply rate. Debris-supply rate increases with the temperature and slope of debris-supply slopes regionally, and debris thickness increases as ice flow decreases locally. Our centennial-scale estimates of debris-supply rate are typically an order of magnitude or more lower than millennial-scale estimates of headwall-erosion rate from Beryllium-10 cosmogenic nuclides, potentially reflecting episodic debris supply to the region’s glaciers.","lang":"eng"}],"date_updated":"2023-02-28T14:02:22Z","_id":"12573","article_number":"269","article_type":"original","article_processing_charge":"No","keyword":["General Earth and Planetary Sciences","General Environmental Science"],"quality_controlled":"1","volume":3,"publication":"Communications Earth & Environment","date_created":"2023-02-20T08:09:27Z","language":[{"iso":"eng"}],"type":"journal_article","title":"Supraglacial debris thickness and supply rate in High-Mountain Asia","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Springer Nature","extern":"1","day":"05","author":[{"first_name":"Michael","last_name":"McCarthy","full_name":"McCarthy, Michael"},{"full_name":"Miles, Evan","first_name":"Evan","last_name":"Miles"},{"full_name":"Kneib, Marin","first_name":"Marin","last_name":"Kneib"},{"first_name":"Pascal","last_name":"Buri","full_name":"Buri, Pascal"},{"first_name":"Stefan","last_name":"Fugger","full_name":"Fugger, Stefan"},{"last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","first_name":"Francesca","full_name":"Pellicciotti, Francesca"}]},{"quality_controlled":"1","keyword":["Earth-Surface Processes","Water Science and Technology"],"volume":16,"article_type":"original","article_processing_charge":"No","language":[{"iso":"eng"}],"type":"journal_article","publication":"The Cryosphere","date_created":"2023-02-20T08:09:42Z","page":"4701-4725","title":"Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Copernicus Publications","author":[{"full_name":"Kneib, Marin","first_name":"Marin","last_name":"Kneib"},{"full_name":"Miles, Evan S.","first_name":"Evan S.","last_name":"Miles"},{"full_name":"Buri, Pascal","last_name":"Buri","first_name":"Pascal"},{"first_name":"Stefan","last_name":"Fugger","full_name":"Fugger, Stefan"},{"first_name":"Michael","last_name":"McCarthy","full_name":"McCarthy, Michael"},{"last_name":"Shaw","first_name":"Thomas E.","full_name":"Shaw, Thomas E."},{"first_name":"Zhao","last_name":"Chuanxi","full_name":"Chuanxi, Zhao"},{"first_name":"Martin","last_name":"Truffer","full_name":"Truffer, Martin"},{"full_name":"Westoby, Matthew J.","last_name":"Westoby","first_name":"Matthew J."},{"full_name":"Yang, Wei","first_name":"Wei","last_name":"Yang"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"}],"extern":"1","day":"11","citation":{"ista":"Kneib M, Miles ES, Buri P, Fugger S, McCarthy M, Shaw TE, Chuanxi Z, Truffer M, Westoby MJ, Yang W, Pellicciotti F. 2022. Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry. The Cryosphere. 16(11), 4701–4725.","mla":"Kneib, Marin, et al. “Sub-Seasonal Variability of Supraglacial Ice Cliff Melt Rates and Associated Processes from Time-Lapse Photogrammetry.” <i>The Cryosphere</i>, vol. 16, no. 11, Copernicus Publications, 2022, pp. 4701–25, doi:<a href=\"https://doi.org/10.5194/tc-16-4701-2022\">10.5194/tc-16-4701-2022</a>.","apa":"Kneib, M., Miles, E. S., Buri, P., Fugger, S., McCarthy, M., Shaw, T. E., … Pellicciotti, F. (2022). Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry. <i>The Cryosphere</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/tc-16-4701-2022\">https://doi.org/10.5194/tc-16-4701-2022</a>","chicago":"Kneib, Marin, Evan S. Miles, Pascal Buri, Stefan Fugger, Michael McCarthy, Thomas E. Shaw, Zhao Chuanxi, et al. “Sub-Seasonal Variability of Supraglacial Ice Cliff Melt Rates and Associated Processes from Time-Lapse Photogrammetry.” <i>The Cryosphere</i>. Copernicus Publications, 2022. <a href=\"https://doi.org/10.5194/tc-16-4701-2022\">https://doi.org/10.5194/tc-16-4701-2022</a>.","ieee":"M. Kneib <i>et al.</i>, “Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry,” <i>The Cryosphere</i>, vol. 16, no. 11. Copernicus Publications, pp. 4701–4725, 2022.","ama":"Kneib M, Miles ES, Buri P, et al. Sub-seasonal variability of supraglacial ice cliff melt rates and associated processes from time-lapse photogrammetry. <i>The Cryosphere</i>. 2022;16(11):4701-4725. doi:<a href=\"https://doi.org/10.5194/tc-16-4701-2022\">10.5194/tc-16-4701-2022</a>","short":"M. Kneib, E.S. Miles, P. Buri, S. Fugger, M. McCarthy, T.E. Shaw, Z. Chuanxi, M. Truffer, M.J. Westoby, W. Yang, F. Pellicciotti, The Cryosphere 16 (2022) 4701–4725."},"intvolume":"        16","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5194/tc-16-4701-2022"}],"scopus_import":"1","date_published":"2022-11-11T00:00:00Z","oa_version":"Published Version","doi":"10.5194/tc-16-4701-2022","publication_status":"published","month":"11","oa":1,"publication_identifier":{"issn":["1994-0424"]},"status":"public","_id":"12574","year":"2022","abstract":[{"text":"Melt from supraglacial ice cliffs is an important contributor to the mass loss of debris-covered glaciers. However, ice cliff contribution is difficult to quantify as they are highly dynamic features, and the paucity of observations of melt rates and their variability leads to large modelling uncertainties. We quantify monsoon season melt and 3D evolution of four ice cliffs over two debris-covered glaciers in High Mountain Asia (Langtang Glacier, Nepal, and 24K Glacier, China) at very high resolution using terrestrial photogrammetry applied to imagery captured from time-lapse cameras installed on lateral moraines. We derive weekly flow-corrected digital elevation models (DEMs) of the glacier surface with a maximum vertical bias of ±0.2 m for Langtang Glacier and ±0.05 m for 24K Glacier and use change detection to determine distributed melt rates at the surfaces of the ice cliffs throughout the study period. We compare the measured melt patterns with those derived from a 3D energy balance model to derive the contribution of the main energy fluxes. We find that ice cliff melt varies considerably throughout the melt season, with maximum melt rates of 5 to 8 cm d−1, and their average melt rates are 11–14 (Langtang) and 4.5 (24K) times higher than the surrounding debris-covered ice. Our results highlight the influence of redistributed supraglacial debris on cliff melt. At both sites, ice cliff albedo is influenced by the presence of thin debris at the ice cliff surface, which is largely controlled on 24K Glacier by liquid precipitation events that wash away this debris. Slightly thicker or patchy debris reduces melt by 1–3 cm d−1 at all sites. Ultimately, our observations show a strong spatio-temporal variability in cliff area at each site, which is controlled by supraglacial streams and ponds and englacial cavities that promote debris slope destabilisation and the lateral expansion of the cliffs. These findings highlight the need to better represent processes of debris redistribution in ice cliff models, to in turn improve estimates of ice cliff contribution to glacier melt and the long-term geomorphological evolution of debris-covered glacier surfaces.","lang":"eng"}],"issue":"11","date_updated":"2023-02-28T13:59:22Z"},{"date_updated":"2023-02-28T13:55:32Z","abstract":[{"text":"The current Chilean megadrought has led to acute water shortages in central Chile since 2010. Glaciers have provided vital fresh water to the region's rivers, but the quantity, timing and sustainability of that provision remain unclear. Here we combine in-situ, remote sensing and climate reanalysis data to show that from 2010 to 2018 during the megadrought, unsustainable imbalance ablation of glaciers (ablation not balanced by new snowfall) strongly buffered the late-summer discharge of the Maipo River, a primary source of water to Santiago. If there had been no glaciers, water availability would have been reduced from December through May, with a 31 ± 19% decrease during March. Our results indicate that while the annual contributions of imbalance ablation to river discharge during the megadrought have been small compared to those from precipitation and sustainable balance ablation, they have nevertheless been a substantial input to a hydrological system that was already experiencing high water stress. The water-equivalent volume of imbalance ablation generated in the Maipo Basin between 2010 and 2018 was 740 × 106 m3 (19 ± 12 mm yr−1), approximately 3.4 times the capacity of the basin's El Yeso Reservoir. This is equivalent to 14% of Santiago's potable water use in that time, while total glacier ablation was equivalent to 59%. We show that glacier retreat will exacerbate river discharge deficits and further jeopardize water availability in central Chile if precipitation deficits endure, and conjecture that these effects will be amplified by climatic warming.","lang":"eng"}],"issue":"10","year":"2022","article_number":"e2022EF002852","_id":"12575","status":"public","oa":1,"publication_identifier":{"issn":["2328-4277"]},"month":"10","publication_status":"published","doi":"10.1029/2022ef002852","oa_version":"Published Version","date_published":"2022-10-01T00:00:00Z","scopus_import":"1","main_file_link":[{"url":"https://doi.org/10.1029/2022EF002852","open_access":"1"}],"intvolume":"        10","citation":{"mla":"McCarthy, Michael, et al. “Glacier Contributions to River Discharge during the Current Chilean Megadrought.” <i>Earth’s Future</i>, vol. 10, no. 10, e2022EF002852, American Geophysical Union, 2022, doi:<a href=\"https://doi.org/10.1029/2022ef002852\">10.1029/2022ef002852</a>.","apa":"McCarthy, M., Meier, F., Fatichi, S., Stocker, B. D., Shaw, T. E., Miles, E., … Pellicciotti, F. (2022). Glacier contributions to river discharge during the current Chilean megadrought. <i>Earth’s Future</i>. American Geophysical Union. <a href=\"https://doi.org/10.1029/2022ef002852\">https://doi.org/10.1029/2022ef002852</a>","ista":"McCarthy M, Meier F, Fatichi S, Stocker BD, Shaw TE, Miles E, Dussaillant I, Pellicciotti F. 2022. Glacier contributions to river discharge during the current Chilean megadrought. Earth’s Future. 10(10), e2022EF002852.","chicago":"McCarthy, Michael, Fabienne Meier, Simone Fatichi, Benjamin D. Stocker, Thomas E. Shaw, Evan Miles, Inés Dussaillant, and Francesca Pellicciotti. “Glacier Contributions to River Discharge during the Current Chilean Megadrought.” <i>Earth’s Future</i>. American Geophysical Union, 2022. <a href=\"https://doi.org/10.1029/2022ef002852\">https://doi.org/10.1029/2022ef002852</a>.","short":"M. McCarthy, F. Meier, S. Fatichi, B.D. Stocker, T.E. Shaw, E. Miles, I. Dussaillant, F. Pellicciotti, Earth’s Future 10 (2022).","ieee":"M. McCarthy <i>et al.</i>, “Glacier contributions to river discharge during the current Chilean megadrought,” <i>Earth’s Future</i>, vol. 10, no. 10. American Geophysical Union, 2022.","ama":"McCarthy M, Meier F, Fatichi S, et al. Glacier contributions to river discharge during the current Chilean megadrought. <i>Earth’s Future</i>. 2022;10(10). doi:<a href=\"https://doi.org/10.1029/2022ef002852\">10.1029/2022ef002852</a>"},"day":"01","extern":"1","author":[{"full_name":"McCarthy, Michael","first_name":"Michael","last_name":"McCarthy"},{"last_name":"Meier","first_name":"Fabienne","full_name":"Meier, Fabienne"},{"full_name":"Fatichi, Simone","last_name":"Fatichi","first_name":"Simone"},{"last_name":"Stocker","first_name":"Benjamin D.","full_name":"Stocker, Benjamin D."},{"full_name":"Shaw, Thomas E.","first_name":"Thomas E.","last_name":"Shaw"},{"first_name":"Evan","last_name":"Miles","full_name":"Miles, Evan"},{"last_name":"Dussaillant","first_name":"Inés","full_name":"Dussaillant, Inés"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"}],"publisher":"American Geophysical Union","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Glacier contributions to river discharge during the current Chilean megadrought","date_created":"2023-02-20T08:09:49Z","publication":"Earth's Future","type":"journal_article","language":[{"iso":"eng"}],"article_processing_charge":"No","article_type":"original","quality_controlled":"1","keyword":["Earth and Planetary Sciences (miscellaneous)","General Environmental Science"],"volume":10},{"intvolume":"        17","citation":{"mla":"Shaw, T. E., et al. “Multi-Decadal Monsoon Characteristics and Glacier Response in High Mountain Asia.” <i>Environmental Research Letters</i>, vol. 17, no. 10, 104001, IOP Publishing, 2022, doi:<a href=\"https://doi.org/10.1088/1748-9326/ac9008\">10.1088/1748-9326/ac9008</a>.","ista":"Shaw TE, Miles ES, Chen D, Jouberton A, Kneib M, Fugger S, Ou T, Lai H-W, Fujita K, Yang W, Fatichi S, Pellicciotti F. 2022. Multi-decadal monsoon characteristics and glacier response in High Mountain Asia. Environmental Research Letters. 17(10), 104001.","apa":"Shaw, T. E., Miles, E. S., Chen, D., Jouberton, A., Kneib, M., Fugger, S., … Pellicciotti, F. (2022). Multi-decadal monsoon characteristics and glacier response in High Mountain Asia. <i>Environmental Research Letters</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1748-9326/ac9008\">https://doi.org/10.1088/1748-9326/ac9008</a>","chicago":"Shaw, T E, E S Miles, D Chen, A Jouberton, M Kneib, S Fugger, T Ou, et al. “Multi-Decadal Monsoon Characteristics and Glacier Response in High Mountain Asia.” <i>Environmental Research Letters</i>. IOP Publishing, 2022. <a href=\"https://doi.org/10.1088/1748-9326/ac9008\">https://doi.org/10.1088/1748-9326/ac9008</a>.","ieee":"T. E. Shaw <i>et al.</i>, “Multi-decadal monsoon characteristics and glacier response in High Mountain Asia,” <i>Environmental Research Letters</i>, vol. 17, no. 10. IOP Publishing, 2022.","ama":"Shaw TE, Miles ES, Chen D, et al. Multi-decadal monsoon characteristics and glacier response in High Mountain Asia. <i>Environmental Research Letters</i>. 2022;17(10). doi:<a href=\"https://doi.org/10.1088/1748-9326/ac9008\">10.1088/1748-9326/ac9008</a>","short":"T.E. Shaw, E.S. Miles, D. Chen, A. Jouberton, M. Kneib, S. Fugger, T. Ou, H.-W. Lai, K. Fujita, W. Yang, S. Fatichi, F. Pellicciotti, Environmental Research Letters 17 (2022)."},"main_file_link":[{"url":"https://doi.org/10.1088/1748-9326/ac9008","open_access":"1"}],"scopus_import":"1","date_published":"2022-09-16T00:00:00Z","oa_version":"Published Version","publication_status":"published","doi":"10.1088/1748-9326/ac9008","month":"09","publication_identifier":{"issn":["1748-9326"]},"oa":1,"status":"public","_id":"12576","article_number":"104001","year":"2022","date_updated":"2023-02-28T13:53:16Z","issue":"10","abstract":[{"lang":"eng","text":"Glacier health across High Mountain Asia (HMA) is highly heterogeneous and strongly governed by regional climate, which is variably influenced by monsoon dynamics and the westerlies. We explore four decades of glacier energy and mass balance at three climatically distinct sites across HMA by utilising a detailed land surface model driven by bias-corrected Weather Research and Forecasting meteorological forcing. All three glaciers have experienced long-term mass losses (ranging from −0.04 ± 0.09 to −0.59 ± 0.20 m w.e. a<jats:sup>−1</jats:sup>) consistent with widespread warming across the region. However, complex and contrasting responses of glacier energy and mass balance to the patterns of the Indian Summer Monsoon were evident, largely driven by the role snowfall timing, amount and phase. A later monsoon onset generates less total snowfall to the glacier in the southeastern Tibetan Plateau during May–June, augmenting net shortwave radiation and affecting annual mass balance (−0.5 m w.e. on average compared to early onset years). Conversely, timing of the monsoon’s arrival has limited impact for the Nepalese Himalaya which is more strongly governed by the temperature and snowfall amount during the core monsoon season. In the arid central Tibetan Plateau, a later monsoon arrival results in a 40 mm (58%) increase of May–June snowfall on average compared to early onset years, likely driven by the greater interaction of westerly storm events. Meanwhile, a late monsoon cessation at this site sees an average 200 mm (192%) increase in late summer precipitation due to monsoonal storms. A trend towards weaker intensity monsoon conditions in recent decades, combined with long-term warming patterns, has produced predominantly negative glacier mass balances for all sites (up to 1 m w.e. more mass loss in the Nepalese Himalaya compared to strong monsoon intensity years) but sub-regional variability in monsoon timing can additionally complicate this response."}],"volume":17,"quality_controlled":"1","keyword":["Public Health","Environmental and Occupational Health","General Environmental Science","Renewable Energy","Sustainability and the Environment"],"article_type":"letter_note","article_processing_charge":"No","language":[{"iso":"eng"}],"type":"journal_article","date_created":"2023-02-20T08:09:56Z","publication":"Environmental Research Letters","title":"Multi-decadal monsoon characteristics and glacier response in High Mountain Asia","publisher":"IOP Publishing","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Shaw, T E","first_name":"T E","last_name":"Shaw"},{"first_name":"E S","last_name":"Miles","full_name":"Miles, E S"},{"last_name":"Chen","first_name":"D","full_name":"Chen, D"},{"full_name":"Jouberton, A","last_name":"Jouberton","first_name":"A"},{"full_name":"Kneib, M","first_name":"M","last_name":"Kneib"},{"first_name":"S","last_name":"Fugger","full_name":"Fugger, S"},{"first_name":"T","last_name":"Ou","full_name":"Ou, T"},{"first_name":"H-W","last_name":"Lai","full_name":"Lai, H-W"},{"full_name":"Fujita, K","first_name":"K","last_name":"Fujita"},{"first_name":"W","last_name":"Yang","full_name":"Yang, W"},{"full_name":"Fatichi, S","last_name":"Fatichi","first_name":"S"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"}],"day":"16","extern":"1"},{"quality_controlled":"1","keyword":["Earth-Surface Processes","Water Science and Technology"],"volume":16,"article_type":"original","article_processing_charge":"No","language":[{"iso":"eng"}],"type":"journal_article","publication":"The Cryosphere","page":"1697-1718","date_created":"2023-02-20T08:10:09Z","title":"Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: An application to High Mountain Asia","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Copernicus Publications","author":[{"first_name":"Loris","last_name":"Compagno","full_name":"Compagno, Loris"},{"last_name":"Huss","first_name":"Matthias","full_name":"Huss, Matthias"},{"full_name":"Miles, Evan Stewart","first_name":"Evan Stewart","last_name":"Miles"},{"full_name":"McCarthy, Michael James","last_name":"McCarthy","first_name":"Michael James"},{"last_name":"Zekollari","first_name":"Harry","full_name":"Zekollari, Harry"},{"last_name":"Dehecq","first_name":"Amaury","full_name":"Dehecq, Amaury"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70"},{"last_name":"Farinotti","first_name":"Daniel","full_name":"Farinotti, Daniel"}],"extern":"1","day":"05","citation":{"ieee":"L. Compagno <i>et al.</i>, “Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: An application to High Mountain Asia,” <i>The Cryosphere</i>, vol. 16, no. 5. Copernicus Publications, pp. 1697–1718, 2022.","ama":"Compagno L, Huss M, Miles ES, et al. Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: An application to High Mountain Asia. <i>The Cryosphere</i>. 2022;16(5):1697-1718. doi:<a href=\"https://doi.org/10.5194/tc-16-1697-2022\">10.5194/tc-16-1697-2022</a>","short":"L. Compagno, M. Huss, E.S. Miles, M.J. McCarthy, H. Zekollari, A. Dehecq, F. Pellicciotti, D. Farinotti, The Cryosphere 16 (2022) 1697–1718.","chicago":"Compagno, Loris, Matthias Huss, Evan Stewart Miles, Michael James McCarthy, Harry Zekollari, Amaury Dehecq, Francesca Pellicciotti, and Daniel Farinotti. “Modelling Supraglacial Debris-Cover Evolution from the Single-Glacier to the Regional Scale: An Application to High Mountain Asia.” <i>The Cryosphere</i>. Copernicus Publications, 2022. <a href=\"https://doi.org/10.5194/tc-16-1697-2022\">https://doi.org/10.5194/tc-16-1697-2022</a>.","ista":"Compagno L, Huss M, Miles ES, McCarthy MJ, Zekollari H, Dehecq A, Pellicciotti F, Farinotti D. 2022. Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: An application to High Mountain Asia. The Cryosphere. 16(5), 1697–1718.","mla":"Compagno, Loris, et al. “Modelling Supraglacial Debris-Cover Evolution from the Single-Glacier to the Regional Scale: An Application to High Mountain Asia.” <i>The Cryosphere</i>, vol. 16, no. 5, Copernicus Publications, 2022, pp. 1697–718, doi:<a href=\"https://doi.org/10.5194/tc-16-1697-2022\">10.5194/tc-16-1697-2022</a>.","apa":"Compagno, L., Huss, M., Miles, E. S., McCarthy, M. J., Zekollari, H., Dehecq, A., … Farinotti, D. (2022). Modelling supraglacial debris-cover evolution from the single-glacier to the regional scale: An application to High Mountain Asia. <i>The Cryosphere</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/tc-16-1697-2022\">https://doi.org/10.5194/tc-16-1697-2022</a>"},"intvolume":"        16","main_file_link":[{"url":"https://doi.org/10.5194/tc-16-1697-2022","open_access":"1"}],"scopus_import":"1","date_published":"2022-05-05T00:00:00Z","oa_version":"Published Version","doi":"10.5194/tc-16-1697-2022","publication_status":"published","month":"05","oa":1,"publication_identifier":{"issn":["1994-0424"]},"status":"public","_id":"12578","year":"2022","abstract":[{"text":"Currently, about 12 %–13 % of High Mountain Asia’s glacier area is debris-covered, which alters its surface mass balance. However, in regional-scale modelling approaches, debris-covered glaciers are typically treated as clean-ice glaciers, leading to a bias when modelling their future evolution. Here, we present a new approach for modelling debris area and thickness evolution, applicable from single glaciers to the global scale. We derive a parameterization and implement it as a module into the Global Glacier Evolution Model (GloGEMflow), a combined mass-balance ice-flow model. The module is initialized with both glacier-specific observations of the debris' spatial distribution and estimates of debris thickness. These data sets account for the fact that debris can either enhance or reduce surface melt depending on thickness. Our model approach also enables representing the spatiotemporal evolution of debris extent and thickness. We calibrate and evaluate the module on a selected subset of glaciers and apply GloGEMflow using different climate scenarios to project the future evolution of all glaciers in High Mountain Asia until 2100. Explicitly accounting for debris cover has only a minor effect on the projected mass loss, which is in line with previous projections. Despite this small effect, we argue that the improved process representation is of added value when aiming at capturing intra-glacier scales, i.e. spatial mass-balance distribution.\r\nDepending on the climate scenario, the mean debris-cover fraction is expected to increase, while mean debris thickness is projected to show only minor changes, although large local thickening is expected. To isolate the influence of explicitly accounting for supraglacial debris cover, we re-compute glacier evolution without the debris-cover module. We show that glacier geometry, area, volume, and flow velocity evolve differently, especially at the level of individual glaciers. This highlights the importance of accounting for debris cover and its spatiotemporal evolution when projecting future glacier changes.","lang":"eng"}],"issue":"5","date_updated":"2023-02-28T13:47:17Z"},{"main_file_link":[{"url":"https://doi.org/10.5194/tc-16-1631-2022","open_access":"1"}],"scopus_import":"1","intvolume":"        16","citation":{"chicago":"Fugger, Stefan, Catriona L. Fyffe, Simone Fatichi, Evan Miles, Michael McCarthy, Thomas E. Shaw, Baohong Ding, et al. “Understanding Monsoon Controls on the Energy and Mass Balance of Glaciers in the Central and Eastern Himalaya.” <i>The Cryosphere</i>. Copernicus Publications, 2022. <a href=\"https://doi.org/10.5194/tc-16-1631-2022\">https://doi.org/10.5194/tc-16-1631-2022</a>.","mla":"Fugger, Stefan, et al. “Understanding Monsoon Controls on the Energy and Mass Balance of Glaciers in the Central and Eastern Himalaya.” <i>The Cryosphere</i>, vol. 16, no. 5, Copernicus Publications, 2022, pp. 1631–52, doi:<a href=\"https://doi.org/10.5194/tc-16-1631-2022\">10.5194/tc-16-1631-2022</a>.","ista":"Fugger S, Fyffe CL, Fatichi S, Miles E, McCarthy M, Shaw TE, Ding B, Yang W, Wagnon P, Immerzeel W, Liu Q, Pellicciotti F. 2022. Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya. The Cryosphere. 16(5), 1631–1652.","apa":"Fugger, S., Fyffe, C. L., Fatichi, S., Miles, E., McCarthy, M., Shaw, T. E., … Pellicciotti, F. (2022). Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya. <i>The Cryosphere</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/tc-16-1631-2022\">https://doi.org/10.5194/tc-16-1631-2022</a>","short":"S. Fugger, C.L. Fyffe, S. Fatichi, E. Miles, M. McCarthy, T.E. Shaw, B. Ding, W. Yang, P. Wagnon, W. Immerzeel, Q. Liu, F. Pellicciotti, The Cryosphere 16 (2022) 1631–1652.","ieee":"S. Fugger <i>et al.</i>, “Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya,” <i>The Cryosphere</i>, vol. 16, no. 5. Copernicus Publications, pp. 1631–1652, 2022.","ama":"Fugger S, Fyffe CL, Fatichi S, et al. Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya. <i>The Cryosphere</i>. 2022;16(5):1631-1652. doi:<a href=\"https://doi.org/10.5194/tc-16-1631-2022\">10.5194/tc-16-1631-2022</a>"},"publication_status":"published","doi":"10.5194/tc-16-1631-2022","oa_version":"Published Version","date_published":"2022-05-05T00:00:00Z","status":"public","oa":1,"publication_identifier":{"issn":["1994-0424"]},"month":"05","date_updated":"2023-02-28T13:45:01Z","abstract":[{"text":"The Indian and East Asian summer monsoons shape the melt and accumulation patterns of glaciers in High Mountain Asia in complex ways due to the interaction of persistent cloud cover, large temperature ranges, high atmospheric water content and high precipitation rates. Glacier energy- and mass-balance modelling using in situ measurements offers insights into the ways in which surface processes are shaped by climatic regimes. In this study, we use a full energy- and mass-balance model and seven on-glacier automatic weather station datasets from different parts of the Central and Eastern Himalaya to investigate how monsoon conditions influence the glacier surface energy and mass balance. In particular, we look at how debris-covered and debris-free glaciers respond differently to monsoonal conditions.\r\nThe radiation budget primarily controls the melt of clean-ice glaciers, but turbulent fluxes play an important role in modulating the melt energy on debris-covered glaciers. The sensible heat flux decreases during core monsoon, but the latent heat flux cools the surface due to evaporation of liquid water. This interplay of radiative and turbulent fluxes causes debris-covered glacier melt rates to stay almost constant through the different phases of the monsoon. Ice melt under thin debris, on the other hand, is amplified by both the dark surface and the turbulent fluxes, which intensify melt during monsoon through surface heating and condensation.\r\nPre-monsoon snow cover can considerably delay melt onset and have a strong impact on the seasonal mass balance. Intermittent monsoon snow cover lowers the melt rates at high elevation. This work is fundamental to the understanding of the present and future Himalayan cryosphere and water budget, while informing and motivating further glacier- and catchment-scale research using process-based models.","lang":"eng"}],"issue":"5","year":"2022","_id":"12579","article_processing_charge":"No","article_type":"original","volume":16,"quality_controlled":"1","keyword":["Earth-Surface Processes","Water Science and Technology"],"page":"1631-1652","date_created":"2023-02-20T08:10:16Z","publication":"The Cryosphere","type":"journal_article","language":[{"iso":"eng"}],"publisher":"Copernicus Publications","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Understanding monsoon controls on the energy and mass balance of glaciers in the Central and Eastern Himalaya","day":"05","extern":"1","author":[{"full_name":"Fugger, Stefan","last_name":"Fugger","first_name":"Stefan"},{"full_name":"Fyffe, Catriona L.","first_name":"Catriona L.","last_name":"Fyffe"},{"first_name":"Simone","last_name":"Fatichi","full_name":"Fatichi, Simone"},{"full_name":"Miles, Evan","first_name":"Evan","last_name":"Miles"},{"full_name":"McCarthy, Michael","first_name":"Michael","last_name":"McCarthy"},{"full_name":"Shaw, Thomas E.","first_name":"Thomas E.","last_name":"Shaw"},{"last_name":"Ding","first_name":"Baohong","full_name":"Ding, Baohong"},{"full_name":"Yang, Wei","first_name":"Wei","last_name":"Yang"},{"first_name":"Patrick","last_name":"Wagnon","full_name":"Wagnon, Patrick"},{"first_name":"Walter","last_name":"Immerzeel","full_name":"Immerzeel, Walter"},{"full_name":"Liu, Qiao","first_name":"Qiao","last_name":"Liu"},{"full_name":"Pellicciotti, Francesca","first_name":"Francesca","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","last_name":"Pellicciotti"}]},{"author":[{"full_name":"Orr, Andrew","first_name":"Andrew","last_name":"Orr"},{"first_name":"Bashir","last_name":"Ahmad","full_name":"Ahmad, Bashir"},{"first_name":"Undala","last_name":"Alam","full_name":"Alam, Undala"},{"full_name":"Appadurai, ArivudaiNambi","first_name":"ArivudaiNambi","last_name":"Appadurai"},{"last_name":"Bharucha","first_name":"Zareen P.","full_name":"Bharucha, Zareen P."},{"full_name":"Biemans, Hester","first_name":"Hester","last_name":"Biemans"},{"last_name":"Bolch","first_name":"Tobias","full_name":"Bolch, Tobias"},{"full_name":"Chaulagain, Narayan P.","first_name":"Narayan P.","last_name":"Chaulagain"},{"full_name":"Dhaubanjar, Sanita","first_name":"Sanita","last_name":"Dhaubanjar"},{"full_name":"Dimri, A. P.","last_name":"Dimri","first_name":"A. P."},{"first_name":"Harry","last_name":"Dixon","full_name":"Dixon, Harry"},{"full_name":"Fowler, Hayley J.","first_name":"Hayley J.","last_name":"Fowler"},{"full_name":"Gioli, Giovanna","first_name":"Giovanna","last_name":"Gioli"},{"last_name":"Halvorson","first_name":"Sarah J.","full_name":"Halvorson, Sarah J."},{"last_name":"Hussain","first_name":"Abid","full_name":"Hussain, Abid"},{"first_name":"Ghulam","last_name":"Jeelani","full_name":"Jeelani, Ghulam"},{"first_name":"Simi","last_name":"Kamal","full_name":"Kamal, Simi"},{"last_name":"Khalid","first_name":"Imran S.","full_name":"Khalid, Imran S."},{"first_name":"Shiyin","last_name":"Liu","full_name":"Liu, Shiyin"},{"last_name":"Lutz","first_name":"Arthur","full_name":"Lutz, Arthur"},{"full_name":"Mehra, Meeta K.","first_name":"Meeta K.","last_name":"Mehra"},{"full_name":"Miles, Evan","first_name":"Evan","last_name":"Miles"},{"last_name":"Momblanch","first_name":"Andrea","full_name":"Momblanch, Andrea"},{"full_name":"Muccione, Veruska","last_name":"Muccione","first_name":"Veruska"},{"last_name":"Mukherji","first_name":"Aditi","full_name":"Mukherji, Aditi"},{"full_name":"Mustafa, Daanish","first_name":"Daanish","last_name":"Mustafa"},{"full_name":"Najmuddin, Omaid","first_name":"Omaid","last_name":"Najmuddin"},{"full_name":"Nasimi, Mohammad N.","first_name":"Mohammad N.","last_name":"Nasimi"},{"first_name":"Marcus","last_name":"Nüsser","full_name":"Nüsser, Marcus"},{"first_name":"Vishnu P.","last_name":"Pandey","full_name":"Pandey, Vishnu P."},{"last_name":"Parveen","first_name":"Sitara","full_name":"Parveen, Sitara"},{"full_name":"Pellicciotti, Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","first_name":"Francesca"},{"last_name":"Pollino","first_name":"Carmel","full_name":"Pollino, Carmel"},{"first_name":"Emily","last_name":"Potter","full_name":"Potter, Emily"},{"first_name":"Mohammad R.","last_name":"Qazizada","full_name":"Qazizada, Mohammad R."},{"full_name":"Ray, Saon","first_name":"Saon","last_name":"Ray"},{"full_name":"Romshoo, Shakil","first_name":"Shakil","last_name":"Romshoo"},{"first_name":"Syamal K.","last_name":"Sarkar","full_name":"Sarkar, Syamal K."},{"first_name":"Amiera","last_name":"Sawas","full_name":"Sawas, Amiera"},{"full_name":"Sen, Sumit","last_name":"Sen","first_name":"Sumit"},{"first_name":"Attaullah","last_name":"Shah","full_name":"Shah, Attaullah"},{"first_name":"M. Azeem Ali","last_name":"Shah","full_name":"Shah, M. Azeem Ali"},{"full_name":"Shea, Joseph M.","first_name":"Joseph M.","last_name":"Shea"},{"full_name":"Sheikh, Ali T.","first_name":"Ali T.","last_name":"Sheikh"},{"full_name":"Shrestha, Arun B.","last_name":"Shrestha","first_name":"Arun B."},{"last_name":"Tayal","first_name":"Shresth","full_name":"Tayal, Shresth"},{"last_name":"Tigala","first_name":"Snehlata","full_name":"Tigala, Snehlata"},{"full_name":"Virk, Zeeshan T.","first_name":"Zeeshan T.","last_name":"Virk"},{"last_name":"Wester","first_name":"Philippus","full_name":"Wester, Philippus"},{"full_name":"Wescoat, James L.","first_name":"James L.","last_name":"Wescoat"}],"day":"01","extern":"1","publisher":"American Geophysical Union","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the Top 100 research questions in social and natural sciences","type":"journal_article","language":[{"iso":"eng"}],"date_created":"2023-02-20T08:10:23Z","publication":"Earth's Future","quality_controlled":"1","volume":10,"keyword":["Earth and Planetary Sciences (miscellaneous)","General Environmental Science"],"article_type":"original","article_processing_charge":"No","article_number":"e2021EF002619","_id":"12580","date_updated":"2023-02-28T13:41:50Z","issue":"4","abstract":[{"lang":"eng","text":"River systems originating from the Upper Indus Basin (UIB) are dominated by runoff from snow and glacier melt and summer monsoonal rainfall. These water resources are highly stressed as huge populations of people living in this region depend on them, including for agriculture, domestic use, and energy production. Projections suggest that the UIB region will be affected by considerable (yet poorly quantified) changes to the seasonality and composition of runoff in the future, which are likely to have considerable impacts on these supplies. Given how directly and indirectly communities and ecosystems are dependent on these resources and the growing pressure on them due to ever-increasing demands, the impacts of climate change pose considerable adaptation challenges. The strong linkages between hydroclimate, cryosphere, water resources, and human activities within the UIB suggest that a multi- and inter-disciplinary research approach integrating the social and natural/environmental sciences is critical for successful adaptation to ongoing and future hydrological and climate change. Here we use a horizon scanning technique to identify the Top 100 questions related to the most pressing knowledge gaps and research priorities in social and natural sciences on climate change and water in the UIB. These questions are on the margins of current thinking and investigation and are clustered into 14 themes, covering three overarching topics of “governance, policy, and sustainable solutions”, “socioeconomic processes and livelihoods”, and “integrated Earth System processes”. Raising awareness of these cutting-edge knowledge gaps and opportunities will hopefully encourage researchers, funding bodies, practitioners, and policy makers to address them."}],"year":"2022","month":"04","status":"public","publication_identifier":{"issn":["2328-4277"]},"oa":1,"oa_version":"Published Version","date_published":"2022-04-01T00:00:00Z","publication_status":"published","doi":"10.1029/2021ef002619","citation":{"chicago":"Orr, Andrew, Bashir Ahmad, Undala Alam, ArivudaiNambi Appadurai, Zareen P. Bharucha, Hester Biemans, Tobias Bolch, et al. “Knowledge Priorities on Climate Change and Water in the Upper Indus Basin: A Horizon Scanning Exercise to Identify the Top 100 Research Questions in Social and Natural Sciences.” <i>Earth’s Future</i>. American Geophysical Union, 2022. <a href=\"https://doi.org/10.1029/2021ef002619\">https://doi.org/10.1029/2021ef002619</a>.","mla":"Orr, Andrew, et al. “Knowledge Priorities on Climate Change and Water in the Upper Indus Basin: A Horizon Scanning Exercise to Identify the Top 100 Research Questions in Social and Natural Sciences.” <i>Earth’s Future</i>, vol. 10, no. 4, e2021EF002619, American Geophysical Union, 2022, doi:<a href=\"https://doi.org/10.1029/2021ef002619\">10.1029/2021ef002619</a>.","ista":"Orr A, Ahmad B, Alam U, Appadurai A, Bharucha ZP, Biemans H, Bolch T, Chaulagain NP, Dhaubanjar S, Dimri AP, Dixon H, Fowler HJ, Gioli G, Halvorson SJ, Hussain A, Jeelani G, Kamal S, Khalid IS, Liu S, Lutz A, Mehra MK, Miles E, Momblanch A, Muccione V, Mukherji A, Mustafa D, Najmuddin O, Nasimi MN, Nüsser M, Pandey VP, Parveen S, Pellicciotti F, Pollino C, Potter E, Qazizada MR, Ray S, Romshoo S, Sarkar SK, Sawas A, Sen S, Shah A, Shah MAA, Shea JM, Sheikh AT, Shrestha AB, Tayal S, Tigala S, Virk ZT, Wester P, Wescoat JL. 2022. Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the Top 100 research questions in social and natural sciences. Earth’s Future. 10(4), e2021EF002619.","apa":"Orr, A., Ahmad, B., Alam, U., Appadurai, A., Bharucha, Z. P., Biemans, H., … Wescoat, J. L. (2022). Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the Top 100 research questions in social and natural sciences. <i>Earth’s Future</i>. American Geophysical Union. <a href=\"https://doi.org/10.1029/2021ef002619\">https://doi.org/10.1029/2021ef002619</a>","ieee":"A. Orr <i>et al.</i>, “Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the Top 100 research questions in social and natural sciences,” <i>Earth’s Future</i>, vol. 10, no. 4. American Geophysical Union, 2022.","ama":"Orr A, Ahmad B, Alam U, et al. Knowledge priorities on climate change and water in the Upper Indus Basin: A horizon scanning exercise to identify the Top 100 research questions in social and natural sciences. <i>Earth’s Future</i>. 2022;10(4). doi:<a href=\"https://doi.org/10.1029/2021ef002619\">10.1029/2021ef002619</a>","short":"A. Orr, B. Ahmad, U. Alam, A. Appadurai, Z.P. Bharucha, H. Biemans, T. Bolch, N.P. Chaulagain, S. Dhaubanjar, A.P. Dimri, H. Dixon, H.J. Fowler, G. Gioli, S.J. Halvorson, A. Hussain, G. Jeelani, S. Kamal, I.S. Khalid, S. Liu, A. Lutz, M.K. Mehra, E. Miles, A. Momblanch, V. Muccione, A. Mukherji, D. Mustafa, O. Najmuddin, M.N. Nasimi, M. Nüsser, V.P. Pandey, S. Parveen, F. Pellicciotti, C. Pollino, E. Potter, M.R. Qazizada, S. Ray, S. Romshoo, S.K. Sarkar, A. Sawas, S. Sen, A. Shah, M.A.A. Shah, J.M. Shea, A.T. Sheikh, A.B. Shrestha, S. Tayal, S. Tigala, Z.T. Virk, P. Wester, J.L. Wescoat, Earth’s Future 10 (2022)."},"intvolume":"        10","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1029/2021EF002619"}]},{"article_processing_charge":"No","article_type":"original","volume":10,"quality_controlled":"1","keyword":["Earth-Surface Processes","Geophysics"],"publication":"Earth Surface Dynamics","date_created":"2023-02-20T08:10:30Z","page":"23-42","type":"journal_article","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Copernicus Publications","title":"Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, southeastern Tibetan Plateau","extern":"1","day":"11","author":[{"full_name":"Zhong, Yan","last_name":"Zhong","first_name":"Yan"},{"first_name":"Qiao","last_name":"Liu","full_name":"Liu, Qiao"},{"full_name":"Westoby, Matthew","last_name":"Westoby","first_name":"Matthew"},{"last_name":"Nie","first_name":"Yong","full_name":"Nie, Yong"},{"last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","first_name":"Francesca","full_name":"Pellicciotti, Francesca"},{"last_name":"Zhang","first_name":"Bo","full_name":"Zhang, Bo"},{"last_name":"Cai","first_name":"Jialun","full_name":"Cai, Jialun"},{"full_name":"Liu, Guoxiang","last_name":"Liu","first_name":"Guoxiang"},{"full_name":"Liao, Haijun","last_name":"Liao","first_name":"Haijun"},{"full_name":"Lu, Xuyang","last_name":"Lu","first_name":"Xuyang"}],"scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5194/esurf-10-23-2022"}],"citation":{"short":"Y. Zhong, Q. Liu, M. Westoby, Y. Nie, F. Pellicciotti, B. Zhang, J. Cai, G. Liu, H. Liao, X. Lu, Earth Surface Dynamics 10 (2022) 23–42.","ieee":"Y. Zhong <i>et al.</i>, “Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, southeastern Tibetan Plateau,” <i>Earth Surface Dynamics</i>, vol. 10, no. 1. Copernicus Publications, pp. 23–42, 2022.","ama":"Zhong Y, Liu Q, Westoby M, et al. Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, southeastern Tibetan Plateau. <i>Earth Surface Dynamics</i>. 2022;10(1):23-42. doi:<a href=\"https://doi.org/10.5194/esurf-10-23-2022\">10.5194/esurf-10-23-2022</a>","apa":"Zhong, Y., Liu, Q., Westoby, M., Nie, Y., Pellicciotti, F., Zhang, B., … Lu, X. (2022). Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, southeastern Tibetan Plateau. <i>Earth Surface Dynamics</i>. Copernicus Publications. <a href=\"https://doi.org/10.5194/esurf-10-23-2022\">https://doi.org/10.5194/esurf-10-23-2022</a>","ista":"Zhong Y, Liu Q, Westoby M, Nie Y, Pellicciotti F, Zhang B, Cai J, Liu G, Liao H, Lu X. 2022. Intensified paraglacial slope failures due to accelerating downwasting of a temperate glacier in Mt. Gongga, southeastern Tibetan Plateau. Earth Surface Dynamics. 10(1), 23–42.","mla":"Zhong, Yan, et al. “Intensified Paraglacial Slope Failures Due to Accelerating Downwasting of a Temperate Glacier in Mt. Gongga, Southeastern Tibetan Plateau.” <i>Earth Surface Dynamics</i>, vol. 10, no. 1, Copernicus Publications, 2022, pp. 23–42, doi:<a href=\"https://doi.org/10.5194/esurf-10-23-2022\">10.5194/esurf-10-23-2022</a>.","chicago":"Zhong, Yan, Qiao Liu, Matthew Westoby, Yong Nie, Francesca Pellicciotti, Bo Zhang, Jialun Cai, Guoxiang Liu, Haijun Liao, and Xuyang Lu. “Intensified Paraglacial Slope Failures Due to Accelerating Downwasting of a Temperate Glacier in Mt. Gongga, Southeastern Tibetan Plateau.” <i>Earth Surface Dynamics</i>. Copernicus Publications, 2022. <a href=\"https://doi.org/10.5194/esurf-10-23-2022\">https://doi.org/10.5194/esurf-10-23-2022</a>."},"intvolume":"        10","doi":"10.5194/esurf-10-23-2022","publication_status":"published","oa_version":"Published Version","date_published":"2022-01-11T00:00:00Z","status":"public","oa":1,"publication_identifier":{"issn":["2196-632X"]},"month":"01","issue":"1","abstract":[{"lang":"eng","text":"Topographic development via paraglacial slope failure (PSF) represents a complex interplay between geological structure, climate, and glacial denudation. Southeastern Tibet has experienced amongst the highest rates of ice mass loss in High Mountain Asia in recent decades, but few studies have focused on the implications of this mass loss on the stability of paraglacial slopes. We used repeat satellite- and unpiloted aerial vehicle (UAV)-derived imagery between 1990 and 2020 as the basis for mapping PSFs from slopes adjacent to Hailuogou Glacier (HLG), a 5 km long monsoon temperate valley glacier in the Mt. Gongga region. We observed recent lowering of the glacier tongue surface at rates of up to 0.88 m a−1 in the period 2000 to 2016, whilst overall paraglacial bare ground area (PBGA) on glacier-adjacent slopes increased from 0.31 ± 0.27 km2 in 1990 to 1.38 ± 0.06 km2 in 2020. Decadal PBGA expansion rates were ∼ 0.01 km2 a−1, 0.02 km2 a−1, and 0.08 km2 in the periods 1990–2000, 2000–2011, and 2011–2020 respectively, indicating an increasing rate of expansion of PBGA. Three types of PSFs, including rockfalls, sediment-mantled slope slides, and headward gully erosion, were mapped, with a total area of 0.75 ± 0.03 km2 in 2020. South-facing valley slopes (true left of the glacier) exhibited more destabilization (56 % of the total PSF area) than north-facing (true right) valley slopes (44 % of the total PSF area). Deformation of sediment-mantled moraine slopes (mean 1.65–2.63 ± 0.04 cm d−1) and an increase in erosion activity in ice-marginal tributary valleys caused by a drop in local base level (gully headward erosion rates are 0.76–3.39 cm d−1) have occurred in tandem with recent glacier downwasting. We also observe deformation of glacier ice, possibly driven by destabilization of lateral moraine, as has been reported in other deglaciating mountain glacier catchments. The formation, evolution, and future trajectory of PSFs at HLG (as well as other monsoon-dominated deglaciating mountain areas) are related to glacial history, including recent rapid downwasting leading to the exposure of steep, unstable bedrock and moraine slopes, and climatic conditions that promote slope instability, such as very high seasonal precipitation and seasonal temperature fluctuations that are conducive to freeze–thaw and ice segregation processes."}],"date_updated":"2023-02-28T13:38:27Z","year":"2022","_id":"12581"},{"publisher":"IOP Publishing","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Controls on the relative melt rates of debris-covered glacier surfaces","author":[{"full_name":"Miles, E S","last_name":"Miles","first_name":"E S"},{"full_name":"Steiner, J F","first_name":"J F","last_name":"Steiner"},{"first_name":"P","last_name":"Buri","full_name":"Buri, P"},{"last_name":"Immerzeel","first_name":"W W","full_name":"Immerzeel, W W"},{"first_name":"Francesca","last_name":"Pellicciotti","id":"b28f055a-81ea-11ed-b70c-a9fe7f7b0e70","full_name":"Pellicciotti, Francesca"}],"day":"01","extern":"1","volume":17,"quality_controlled":"1","keyword":["Public Health","Environmental and Occupational Health","General Environmental Science","Renewable Energy","Sustainability and the Environment"],"article_processing_charge":"No","article_type":"letter_note","type":"journal_article","language":[{"iso":"eng"}],"date_created":"2023-02-20T08:10:37Z","publication":"Environmental Research Letters","month":"06","status":"public","publication_identifier":{"issn":["1748-9326"]},"oa":1,"article_number":"064004","_id":"12582","date_updated":"2023-02-28T13:34:25Z","abstract":[{"lang":"eng","text":"Supraglacial debris covers 7% of mountain glacier area globally and generally reduces glacier surface melt. Enhanced energy absorption at ice cliffs and supraglacial ponds scattered across the debris surface leads these features to contribute disproportionately to glacier-wide ablation. However, the degree to which cliffs and ponds actually increase melt rates remains unclear, as these features have only been studied in a detailed manner for selected locations, almost exclusively in High Mountain Asia. In this study we model the surface energy balance for debris-covered ice, ice cliffs, and supraglacial ponds with a set of automatic weather station records representing the global prevalence of debris-covered glacier ice. We generate 5000 random sets of values for physical parameters using probability distributions derived from literature, which we use to investigate relative melt rates and to isolate the melt responses of debris, cliffs and ponds to the site-specific meteorological forcing. Modelled sub-debris melt rates are primarily controlled by debris thickness and thermal conductivity. At a reference thickness of 0.1 m, sub-debris melt rates vary considerably, differing by up to a factor of four between sites, mainly attributable to air temperature differences. We find that melt rates for ice cliffs are consistently 2–3× the melt rate for clean glacier ice, but this melt enhancement decays with increasing clean ice melt rates. Energy absorption at supraglacial ponds is dominated by latent heat exchange and is therefore highly sensitive to wind speed and relative humidity, but is generally less than for clean ice. Our results provide reference melt enhancement factors for melt modelling of debris-covered glacier sites, globally, while highlighting the need for direct measurement of debris-covered glacier surface characteristics, physical parameters, and local meteorological conditions at a variety of sites around the world."}],"issue":"6","year":"2022","intvolume":"        17","citation":{"ista":"Miles ES, Steiner JF, Buri P, Immerzeel WW, Pellicciotti F. 2022. Controls on the relative melt rates of debris-covered glacier surfaces. Environmental Research Letters. 17(6), 064004.","apa":"Miles, E. S., Steiner, J. F., Buri, P., Immerzeel, W. W., &#38; Pellicciotti, F. (2022). Controls on the relative melt rates of debris-covered glacier surfaces. <i>Environmental Research Letters</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1748-9326/ac6966\">https://doi.org/10.1088/1748-9326/ac6966</a>","mla":"Miles, E. S., et al. “Controls on the Relative Melt Rates of Debris-Covered Glacier Surfaces.” <i>Environmental Research Letters</i>, vol. 17, no. 6, 064004, IOP Publishing, 2022, doi:<a href=\"https://doi.org/10.1088/1748-9326/ac6966\">10.1088/1748-9326/ac6966</a>.","chicago":"Miles, E S, J F Steiner, P Buri, W W Immerzeel, and Francesca Pellicciotti. “Controls on the Relative Melt Rates of Debris-Covered Glacier Surfaces.” <i>Environmental Research Letters</i>. IOP Publishing, 2022. <a href=\"https://doi.org/10.1088/1748-9326/ac6966\">https://doi.org/10.1088/1748-9326/ac6966</a>.","short":"E.S. Miles, J.F. Steiner, P. Buri, W.W. Immerzeel, F. Pellicciotti, Environmental Research Letters 17 (2022).","ieee":"E. S. Miles, J. F. Steiner, P. Buri, W. W. Immerzeel, and F. Pellicciotti, “Controls on the relative melt rates of debris-covered glacier surfaces,” <i>Environmental Research Letters</i>, vol. 17, no. 6. IOP Publishing, 2022.","ama":"Miles ES, Steiner JF, Buri P, Immerzeel WW, Pellicciotti F. Controls on the relative melt rates of debris-covered glacier surfaces. <i>Environmental Research Letters</i>. 2022;17(6). doi:<a href=\"https://doi.org/10.1088/1748-9326/ac6966\">10.1088/1748-9326/ac6966</a>"},"scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1088/1748-9326/ac6966"}],"oa_version":"Published Version","date_published":"2022-06-01T00:00:00Z","publication_status":"published","doi":"10.1088/1748-9326/ac6966"},{"tmp":{"short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"day":"12","year":"2022","date_updated":"2023-02-21T08:20:18Z","arxiv":1,"abstract":[{"text":"We present Cross-Client Label Propagation(XCLP), a new method for transductive federated learning. XCLP estimates a data graph jointly from the data of multiple clients and computes labels for the unlabeled data by propagating label information across the graph. To avoid clients having to share their data with anyone, XCLP employs two cryptographically secure protocols: secure Hamming distance computation and secure summation. We demonstrate two distinct applications of XCLP within federated learning. In the first, we use it in a one-shot way to predict labels for unseen test points. In the second, we use it to repeatedly pseudo-label unlabeled training data in a federated semi-supervised setting. Experiments on both real federated and standard benchmark datasets show that in both applications XCLP achieves higher classification accuracy than alternative approaches.","lang":"eng"}],"file":[{"access_level":"open_access","file_id":"12661","creator":"chl","checksum":"7ab20543fd4393f14fb857ce2e4f03c6","date_created":"2023-02-20T08:21:35Z","file_name":"2210.06434.pdf","date_updated":"2023-02-20T08:21:35Z","success":1,"file_size":291893,"content_type":"application/pdf","relation":"main_file"}],"_id":"12660","article_number":"2210.06434","author":[{"full_name":"Scott, Jonathan A","first_name":"Jonathan A","id":"e499926b-f6e0-11ea-865d-9c63db0031e8","last_name":"Scott"},{"full_name":"Yeo, Michelle X","last_name":"Yeo","id":"2D82B818-F248-11E8-B48F-1D18A9856A87","first_name":"Michelle X"},{"last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"}],"title":"Cross-client Label Propagation for transductive federated learning","oa":1,"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"10","date_created":"2023-02-20T08:21:50Z","publication":"arXiv","publication_status":"submitted","has_accepted_license":"1","doi":"10.48550/arXiv.2210.06434","language":[{"iso":"eng"}],"department":[{"_id":"ChLa"}],"date_published":"2022-10-12T00:00:00Z","type":"preprint","oa_version":"Preprint","ddc":["004"],"file_date_updated":"2023-02-20T08:21:35Z","article_processing_charge":"No","external_id":{"arxiv":["2210.06434"]},"citation":{"short":"J.A. Scott, M.X. Yeo, C. Lampert, ArXiv (n.d.).","ieee":"J. A. Scott, M. X. Yeo, and C. Lampert, “Cross-client Label Propagation for transductive federated learning,” <i>arXiv</i>. .","ama":"Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2210.06434\">10.48550/arXiv.2210.06434</a>","apa":"Scott, J. A., Yeo, M. X., &#38; Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2210.06434\">https://doi.org/10.48550/arXiv.2210.06434</a>","mla":"Scott, Jonathan A., et al. “Cross-Client Label Propagation for Transductive Federated Learning.” <i>ArXiv</i>, 2210.06434, doi:<a href=\"https://doi.org/10.48550/arXiv.2210.06434\">10.48550/arXiv.2210.06434</a>.","ista":"Scott JA, Yeo MX, Lampert C. Cross-client Label Propagation for transductive federated learning. arXiv, 2210.06434.","chicago":"Scott, Jonathan A, Michelle X Yeo, and Christoph Lampert. “Cross-Client Label Propagation for Transductive Federated Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2210.06434\">https://doi.org/10.48550/arXiv.2210.06434</a>."}},{"external_id":{"arxiv":["2208.13499"]},"citation":{"apa":"Súkeník, P., &#38; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2208.13499\">https://doi.org/10.48550/arXiv.2208.13499</a>","mla":"Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” <i>ArXiv</i>, 2208.13499, doi:<a href=\"https://doi.org/10.48550/arXiv.2208.13499\">10.48550/arXiv.2208.13499</a>.","ista":"Súkeník P, Lampert C. Generalization in Multi-objective machine learning. arXiv, 2208.13499.","chicago":"Súkeník, Peter, and Christoph Lampert. “Generalization in Multi-Objective Machine Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2208.13499\">https://doi.org/10.48550/arXiv.2208.13499</a>.","ama":"Súkeník P, Lampert C. Generalization in Multi-objective machine learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2208.13499\">10.48550/arXiv.2208.13499</a>","ieee":"P. Súkeník and C. Lampert, “Generalization in Multi-objective machine learning,” <i>arXiv</i>. .","short":"P. Súkeník, C. Lampert, ArXiv (n.d.)."},"ddc":["004"],"main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2208.13499"}],"article_processing_charge":"No","language":[{"iso":"eng"}],"date_published":"2022-08-29T00:00:00Z","department":[{"_id":"ChLa"}],"oa_version":"Preprint","type":"preprint","date_created":"2023-02-20T08:23:06Z","publication":"arXiv","publication_status":"submitted","has_accepted_license":"1","doi":"10.48550/arXiv.2208.13499","month":"08","title":"Generalization in Multi-objective machine learning","oa":1,"status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"12662","article_number":"2208.13499","author":[{"full_name":"Súkeník, Peter","id":"d64d6a8d-eb8e-11eb-b029-96fd216dec3c","last_name":"Súkeník","first_name":"Peter"},{"last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","orcid":"0000-0001-8622-7887","full_name":"Lampert, Christoph"}],"day":"29","year":"2022","date_updated":"2023-02-21T08:24:55Z","arxiv":1,"abstract":[{"lang":"eng","text":"Modern machine learning tasks often require considering not just one but multiple objectives. For example, besides the prediction quality, this could be the efficiency, robustness or fairness of the learned models, or any of their combinations. Multi-objective learning offers a natural framework for handling such problems without having to commit to early trade-offs. Surprisingly, statistical learning theory so far offers almost no insight into the generalization properties of multi-objective learning. In this work, we make first steps to fill this gap: we establish foundational generalization bounds for the multi-objective setting as well as generalization and excess bounds for learning with scalarizations. We also provide the first theoretical analysis of the relation between the Pareto-optimal sets of the true objectives and the Pareto-optimal sets of their empirical approximations from training data. In particular, we show a surprising asymmetry: all Pareto-optimal solutions can be approximated by empirically Pareto-optimal ones, but not vice versa."}]},{"quality_controlled":"1","volume":162,"external_id":{"arxiv":["2110.05365"]},"article_processing_charge":"No","type":"conference","language":[{"iso":"eng"}],"has_accepted_license":"1","publication":"Proceedings of the 39th International Conference on Machine Learning","date_created":"2023-02-20T08:30:21Z","page":"20697-20743","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ML Research Press","title":"Intriguing properties of input-dependent randomized smoothing","author":[{"first_name":"Peter","id":"d64d6a8d-eb8e-11eb-b029-96fd216dec3c","last_name":"Súkeník","full_name":"Súkeník, Peter"},{"full_name":"Kuvshinov, Aleksei","first_name":"Aleksei","last_name":"Kuvshinov"},{"full_name":"Günnemann, Stephan","last_name":"Günnemann","first_name":"Stephan"}],"file":[{"file_id":"12665","creator":"chl","checksum":"ab8695b1e24fb4fef4f1f9cd63ca8238","access_level":"open_access","file_name":"sukeni-k22a.pdf","date_updated":"2023-02-20T08:30:10Z","date_created":"2023-02-20T08:30:10Z","content_type":"application/pdf","relation":"main_file","success":1,"file_size":8470811}],"day":"19","intvolume":"       162","citation":{"ama":"Súkeník P, Kuvshinov A, Günnemann S. Intriguing properties of input-dependent randomized smoothing. In: <i>Proceedings of the 39th International Conference on Machine Learning</i>. Vol 162. ML Research Press; 2022:20697-20743.","ieee":"P. Súkeník, A. Kuvshinov, and S. Günnemann, “Intriguing properties of input-dependent randomized smoothing,” in <i>Proceedings of the 39th International Conference on Machine Learning</i>, Baltimore, MD, United States, 2022, vol. 162, pp. 20697–20743.","short":"P. Súkeník, A. Kuvshinov, S. Günnemann, in:, Proceedings of the 39th International Conference on Machine Learning, ML Research Press, 2022, pp. 20697–20743.","chicago":"Súkeník, Peter, Aleksei Kuvshinov, and Stephan Günnemann. “Intriguing Properties of Input-Dependent Randomized Smoothing.” In <i>Proceedings of the 39th International Conference on Machine Learning</i>, 162:20697–743. ML Research Press, 2022.","apa":"Súkeník, P., Kuvshinov, A., &#38; Günnemann, S. (2022). Intriguing properties of input-dependent randomized smoothing. In <i>Proceedings of the 39th International Conference on Machine Learning</i> (Vol. 162, pp. 20697–20743). Baltimore, MD, United States: ML Research Press.","mla":"Súkeník, Peter, et al. “Intriguing Properties of Input-Dependent Randomized Smoothing.” <i>Proceedings of the 39th International Conference on Machine Learning</i>, vol. 162, ML Research Press, 2022, pp. 20697–743.","ista":"Súkeník P, Kuvshinov A, Günnemann S. 2022. Intriguing properties of input-dependent randomized smoothing. Proceedings of the 39th International Conference on Machine Learning. International Conference on Machine Learning vol. 162, 20697–20743."},"file_date_updated":"2023-02-20T08:30:10Z","scopus_import":"1","ddc":["004"],"oa_version":"Published Version","date_published":"2022-07-19T00:00:00Z","conference":{"end_date":"2022-07-23","location":"Baltimore, MD, United States","start_date":"2022-07-17","name":"International Conference on Machine Learning"},"publication_status":"published","month":"07","status":"public","oa":1,"_id":"12664","abstract":[{"lang":"eng","text":"Randomized smoothing is currently considered the state-of-the-art method to obtain certifiably robust classifiers. Despite its remarkable performance, the method is associated with various serious problems such as “certified accuracy waterfalls”, certification vs. accuracy trade-off, or even fairness issues. Input-dependent smoothing approaches have been proposed with intention of overcoming these flaws. However, we demonstrate that these methods lack formal guarantees and so the resulting certificates are not justified. We show that in general, the input-dependent smoothing suffers from the curse of dimensionality, forcing the variance function to have low semi-elasticity. On the other hand, we provide a theoretical and practical framework that enables the usage of input-dependent smoothing even in the presence of the curse of dimensionality, under strict restrictions. We present one concrete design of the smoothing variance function and test it on CIFAR10 and MNIST. Our design mitigates some of the problems of classical smoothing and is formally underlined, yet further improvement of the design is still necessary."}],"arxiv":1,"date_updated":"2023-02-23T10:03:47Z","year":"2022"},{"doi":"10.1111/jipb.13422","publication_status":"published","date_published":"2022-12-07T00:00:00Z","oa_version":"Published Version","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1111/jipb.13422"}],"scopus_import":"1","citation":{"mla":"He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” <i>Journal of Integrative Plant Biology</i>, vol. 64, no. 12, Wiley, 2022, pp. 2240–51, doi:<a href=\"https://doi.org/10.1111/jipb.13422\">10.1111/jipb.13422</a>.","ista":"He S, Feng X. 2022. DNA methylation dynamics during germline development. Journal of Integrative Plant Biology. 64(12), 2240–2251.","apa":"He, S., &#38; Feng, X. (2022). DNA methylation dynamics during germline development. <i>Journal of Integrative Plant Biology</i>. Wiley. <a href=\"https://doi.org/10.1111/jipb.13422\">https://doi.org/10.1111/jipb.13422</a>","chicago":"He, Shengbo, and Xiaoqi Feng. “DNA Methylation Dynamics during Germline Development.” <i>Journal of Integrative Plant Biology</i>. Wiley, 2022. <a href=\"https://doi.org/10.1111/jipb.13422\">https://doi.org/10.1111/jipb.13422</a>.","short":"S. He, X. Feng, Journal of Integrative Plant Biology 64 (2022) 2240–2251.","ieee":"S. He and X. Feng, “DNA methylation dynamics during germline development,” <i>Journal of Integrative Plant Biology</i>, vol. 64, no. 12. Wiley, pp. 2240–2251, 2022.","ama":"He S, Feng X. DNA methylation dynamics during germline development. <i>Journal of Integrative Plant Biology</i>. 2022;64(12):2240-2251. doi:<a href=\"https://doi.org/10.1111/jipb.13422\">10.1111/jipb.13422</a>"},"intvolume":"        64","year":"2022","issue":"12","abstract":[{"text":"DNA methylation plays essential homeostatic functions in eukaryotic genomes. In animals, DNA methylation is also developmentally regulated and, in turn, regulates development. In the past two decades, huge research effort has endorsed the understanding that DNA methylation plays a similar role in plant development, especially during sexual reproduction. The power of whole-genome sequencing and cell isolation techniques, as well as bioinformatics tools, have enabled recent studies to reveal dynamic changes in DNA methylation during germline development. Furthermore, the combination of these technological advances with genetics, developmental biology and cell biology tools has revealed functional methylation reprogramming events that control gene and transposon activities in flowering plant germlines. In this review, we discuss the major advances in our knowledge of DNA methylation dynamics during male and female germline development in flowering plants.","lang":"eng"}],"date_updated":"2023-05-08T10:59:00Z","_id":"12670","publication_identifier":{"issn":["1672-9072"],"eissn":["1744-7909"]},"oa":1,"status":"public","month":"12","publication":"Journal of Integrative Plant Biology","date_created":"2023-02-23T09:15:57Z","page":"2240-2251","department":[{"_id":"XiFe"}],"language":[{"iso":"eng"}],"type":"journal_article","article_type":"review","article_processing_charge":"No","quality_controlled":"1","volume":64,"keyword":["Plant Science","General Biochemistry","Genetics and Molecular Biology","Biochemistry"],"external_id":{"pmid":["36478632"]},"extern":"1","day":"07","pmid":1,"author":[{"first_name":"Shengbo","last_name":"He","full_name":"He, Shengbo"},{"full_name":"Feng, Xiaoqi","last_name":"Feng","id":"e0164712-22ee-11ed-b12a-d80fcdf35958","first_name":"Xiaoqi","orcid":"0000-0002-4008-1234"}],"title":"DNA methylation dynamics during germline development","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Wiley"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Springer Nature","title":"Histone H2B.8 compacts flowering plant sperm through chromatin phase separation","pmid":1,"extern":"1","day":"17","author":[{"full_name":"Buttress, Toby","first_name":"Toby","last_name":"Buttress"},{"full_name":"He, Shengbo","last_name":"He","first_name":"Shengbo"},{"last_name":"Wang","first_name":"Liang","full_name":"Wang, Liang"},{"last_name":"Zhou","first_name":"Shaoli","full_name":"Zhou, Shaoli"},{"last_name":"Saalbach","first_name":"Gerhard","full_name":"Saalbach, Gerhard"},{"full_name":"Vickers, Martin","last_name":"Vickers","first_name":"Martin"},{"full_name":"Li, Guohong","last_name":"Li","first_name":"Guohong"},{"full_name":"Li, Pilong","first_name":"Pilong","last_name":"Li"},{"id":"e0164712-22ee-11ed-b12a-d80fcdf35958","last_name":"Feng","first_name":"Xiaoqi","orcid":"0000-0002-4008-1234","full_name":"Feng, Xiaoqi"}],"article_type":"original","article_processing_charge":"No","quality_controlled":"1","volume":611,"external_id":{"pmid":["36323776"]},"publication":"Nature","page":"614-622","date_created":"2023-02-23T09:17:05Z","type":"journal_article","department":[{"_id":"XiFe"}],"language":[{"iso":"eng"}],"status":"public","oa":1,"publication_identifier":{"eissn":["1476-4687"],"issn":["0028-0836"]},"month":"11","issue":"7936","abstract":[{"text":"Sperm chromatin is typically transformed by protamines into a compact and transcriptionally inactive state1,2. Sperm cells of flowering plants lack protamines, yet they have small, transcriptionally active nuclei with chromatin condensed through an unknown mechanism3,4. Here we show that a histone variant, H2B.8, mediates sperm chromatin and nuclear condensation in Arabidopsis thaliana. Loss of H2B.8 causes enlarged sperm nuclei with dispersed chromatin, whereas ectopic expression in somatic cells produces smaller nuclei with aggregated chromatin. This result demonstrates that H2B.8 is sufficient for chromatin condensation. H2B.8 aggregates transcriptionally inactive AT-rich chromatin into phase-separated condensates, which facilitates nuclear compaction without reducing transcription. Reciprocal crosses show that mutation of h2b.8 reduces male transmission, which suggests that H2B.8-mediated sperm compaction is important for fertility. Altogether, our results reveal a new mechanism of nuclear compaction through global aggregation of unexpressed chromatin. We propose that H2B.8 is an evolutionary innovation of flowering plants that achieves nuclear condensation compatible with active transcription.","lang":"eng"}],"date_updated":"2023-05-08T10:59:22Z","year":"2022","_id":"12671","main_file_link":[{"url":"https://doi.org/10.1038/s41586-022-05386-6","open_access":"1"}],"scopus_import":"1","citation":{"ista":"Buttress T, He S, Wang L, Zhou S, Saalbach G, Vickers M, Li G, Li P, Feng X. 2022. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. Nature. 611(7936), 614–622.","apa":"Buttress, T., He, S., Wang, L., Zhou, S., Saalbach, G., Vickers, M., … Feng, X. (2022). Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. <i>Nature</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41586-022-05386-6\">https://doi.org/10.1038/s41586-022-05386-6</a>","mla":"Buttress, Toby, et al. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” <i>Nature</i>, vol. 611, no. 7936, Springer Nature, 2022, pp. 614–22, doi:<a href=\"https://doi.org/10.1038/s41586-022-05386-6\">10.1038/s41586-022-05386-6</a>.","chicago":"Buttress, Toby, Shengbo He, Liang Wang, Shaoli Zhou, Gerhard Saalbach, Martin Vickers, Guohong Li, Pilong Li, and Xiaoqi Feng. “Histone H2B.8 Compacts Flowering Plant Sperm through Chromatin Phase Separation.” <i>Nature</i>. Springer Nature, 2022. <a href=\"https://doi.org/10.1038/s41586-022-05386-6\">https://doi.org/10.1038/s41586-022-05386-6</a>.","ieee":"T. Buttress <i>et al.</i>, “Histone H2B.8 compacts flowering plant sperm through chromatin phase separation,” <i>Nature</i>, vol. 611, no. 7936. Springer Nature, pp. 614–622, 2022.","ama":"Buttress T, He S, Wang L, et al. Histone H2B.8 compacts flowering plant sperm through chromatin phase separation. <i>Nature</i>. 2022;611(7936):614-622. doi:<a href=\"https://doi.org/10.1038/s41586-022-05386-6\">10.1038/s41586-022-05386-6</a>","short":"T. Buttress, S. He, L. Wang, S. Zhou, G. Saalbach, M. Vickers, G. Li, P. Li, X. Feng, Nature 611 (2022) 614–622."},"intvolume":"       611","doi":"10.1038/s41586-022-05386-6","publication_status":"published","oa_version":"Published Version","date_published":"2022-11-17T00:00:00Z"},{"publication_status":"submitted","doi":"10.48550/ARXIV.2209.14368","date_created":"2023-02-24T12:21:40Z","publication":"arXiv","oa_version":"Preprint","type":"preprint","language":[{"iso":"eng"}],"department":[{"_id":"GradSch"},{"_id":"KrCh"}],"date_published":"2022-09-28T00:00:00Z","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2209.14368"}],"article_processing_charge":"No","external_id":{"arxiv":["2209.14368"]},"citation":{"short":"K. Chatterjee, M. Mohammadi, R.J. Saona Urmeneta, ArXiv (n.d.).","ama":"Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">10.48550/ARXIV.2209.14368</a>","ieee":"K. Chatterjee, M. Mohammadi, and R. J. Saona Urmeneta, “Repeated prophet inequality with near-optimal bounds,” <i>arXiv</i>. .","chicago":"Chatterjee, Krishnendu, Mona Mohammadi, and Raimundo J Saona Urmeneta. “Repeated Prophet Inequality with Near-Optimal Bounds.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">https://doi.org/10.48550/ARXIV.2209.14368</a>.","mla":"Chatterjee, Krishnendu, et al. “Repeated Prophet Inequality with Near-Optimal Bounds.” <i>ArXiv</i>, 2209.14368, doi:<a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">10.48550/ARXIV.2209.14368</a>.","apa":"Chatterjee, K., Mohammadi, M., &#38; Saona Urmeneta, R. J. (n.d.). Repeated prophet inequality with near-optimal bounds. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/ARXIV.2209.14368\">https://doi.org/10.48550/ARXIV.2209.14368</a>","ista":"Chatterjee K, Mohammadi M, Saona Urmeneta RJ. Repeated prophet inequality with near-optimal bounds. arXiv, 2209.14368."},"date_updated":"2025-07-14T09:09:51Z","arxiv":1,"acknowledgement":"This research was partially supported by the ERC CoG 863818 (ForM-SMArt) grant.","abstract":[{"lang":"eng","text":"In modern sample-driven Prophet Inequality, an adversary chooses a sequence of n items with values v1,v2,…,vn to be presented to a decision maker (DM). The process follows in two phases. In the first phase (sampling phase), some items, possibly selected at random, are revealed to the DM, but she can never accept them. In the second phase, the DM is presented with the other items in a random order and online fashion. For each item, she must make an irrevocable decision to either accept the item and stop the process or reject the item forever and proceed to the next item. The goal of the DM is to maximize the expected value as compared to a Prophet (or offline algorithm) that has access to all information. In this setting, the sampling phase has no cost and is not part of the optimization process. However, in many scenarios, the samples are obtained as part of the decision-making process.\r\nWe model this aspect as a two-phase Prophet Inequality where an adversary chooses a sequence of 2n items with values v1,v2,…,v2n and the items are randomly ordered. Finally, there are two phases of the Prophet Inequality problem with the first n-items and the rest of the items, respectively. We show that some basic algorithms achieve a ratio of at most 0.450. We present an algorithm that achieves a ratio of at least 0.495. Finally, we show that for every algorithm the ratio it can achieve is at most 0.502. Hence our algorithm is near-optimal."}],"day":"28","year":"2022","article_number":"2209.14368","project":[{"grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications","call_identifier":"H2020"}],"author":[{"full_name":"Chatterjee, Krishnendu","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","last_name":"Chatterjee","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Mohammadi","id":"4363614d-b686-11ed-a7d5-ac9e4a24bc2e","first_name":"Mona","full_name":"Mohammadi, Mona"},{"first_name":"Raimundo J","orcid":"0000-0001-5103-038X","id":"BD1DF4C4-D767-11E9-B658-BC13E6697425","last_name":"Saona Urmeneta","full_name":"Saona Urmeneta, Raimundo J"}],"_id":"12677","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Repeated prophet inequality with near-optimal bounds","oa":1,"month":"09","ec_funded":1}]