[{"conference":{"end_date":"2024-07-27","name":"ICML: International Conference on Machine Learning","location":"Vienna, Austria","start_date":"2024-07-21"},"_id":"18118","month":"09","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-22T22:01:45Z","department":[{"_id":"ChLa"}],"das_tickbox":"0","oa":1,"date_updated":"2026-10-01T09:28:21Z","oa_version":"Published Version","publication":"Proceedings of the 41st International Conference on Machine Learning","intvolume":"       235","publication_status":"published","citation":{"chicago":"Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:58122–39. ML Research Press, 2024.","apa":"Zakerinia, H., Behjati, A., &#38; Lampert, C. (2024). More flexible PAC-Bayesian meta-learning by learning learning algorithms. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 58122–58139). Vienna, Austria: ML Research Press.","short":"H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139.","ieee":"H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning by learning learning algorithms,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 58122–58139.","ista":"Zakerinia H, Behjati A, Lampert C. 2024. More flexible PAC-Bayesian meta-learning by learning learning algorithms. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 58122–58139.","ama":"Zakerinia H, Behjati A, Lampert C. More flexible PAC-Bayesian meta-learning by learning learning algorithms. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:58122-58139.","mla":"Zakerinia, Hossein, et al. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 58122–39."},"volume":235,"year":"2024","researchdata_availability":"no","title":"More flexible PAC-Bayesian meta-learning by learning learning algorithms","abstract":[{"lang":"eng","text":"We introduce a new framework for studying meta-learning methods using PAC-Bayesian theory. Its main advantage over previous work is that it allows for more flexibility in how the transfer of knowledge between tasks is realized. For previous approaches, this could only happen indirectly, by means of learning prior distributions over models. In contrast, the new generalization bounds that we prove express the process of meta-learning much more directly as learning the learning algorithm that should be used for future tasks. The flexibility of our framework makes it suitable to analyze a wide range of meta-learning mechanisms and even design new mechanisms. Other than our theoretical contributions we also show empirically that our framework improves the prediction quality in practical meta-learning mechanisms."}],"quality_controlled":"1","publisher":"ML Research Press","status":"public","ddc":["000"],"language":[{"iso":"eng"}],"date_published":"2024-09-01T00:00:00Z","external_id":{"arxiv":["2402.04054"]},"alternative_title":["PMLR"],"arxiv":1,"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","main_file_link":[{"open_access":"1","url":" https://doi.org/10.48550/arXiv.2402.04054"}],"author":[{"first_name":"Hossein","id":"653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4","full_name":"Zakerinia, Hossein","orcid":"0009-0007-3977-6462","last_name":"Zakerinia"},{"full_name":"Behjati, Amin","first_name":"Amin","last_name":"Behjati"},{"full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887"}],"corr_author":"1","page":"58122-58139","supplementarymaterial":"no","type":"conference","article_processing_charge":"No","day":"01"},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2310.05293"}],"scopus_import":"1","publication_identifier":{"eissn":["1530-2075"],"isbn":["9798350337662"]},"author":[{"last_name":"Kokorin","full_name":"Kokorin, Ilya","first_name":"Ilya"},{"last_name":"Yudov","full_name":"Yudov, Victor","first_name":"Victor"},{"full_name":"Aksenov, Vitaly","first_name":"Vitaly","last_name":"Aksenov"},{"last_name":"Alistarh","orcid":"0000-0003-3650-940X","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"page":"169-179","corr_author":"1","supplementarymaterial":"no","type":"conference","article_processing_charge":"No","day":"08","fulldoi":"https://doi.org/10.1109/IPDPS57955.2024.00023","language":[{"iso":"eng"}],"external_id":{"isi":["001270389600078"],"arxiv":["2310.05293"]},"date_published":"2024-07-08T00:00:00Z","arxiv":1,"doi":"10.1109/IPDPS57955.2024.00023","publication_status":"published","citation":{"ista":"Kokorin I, Yudov V, Aksenov V, Alistarh D-A. 2024. Wait-free trees with asymptotically-efficient range queries. 2024 IEEE International Parallel and Distributed Processing Symposium. IPDPS: International Parallel and Distributed Processing Symposium, 169–179.","mla":"Kokorin, Ilya, et al. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” <i>2024 IEEE International Parallel and Distributed Processing Symposium</i>, IEEE, 2024, pp. 169–79, doi:<a href=\"https://doi.org/10.1109/IPDPS57955.2024.00023\">10.1109/IPDPS57955.2024.00023</a>.","ama":"Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: <i>2024 IEEE International Parallel and Distributed Processing Symposium</i>. IEEE; 2024:169-179. doi:<a href=\"https://doi.org/10.1109/IPDPS57955.2024.00023\">10.1109/IPDPS57955.2024.00023</a>","apa":"Kokorin, I., Yudov, V., Aksenov, V., &#38; Alistarh, D.-A. (2024). Wait-free trees with asymptotically-efficient range queries. In <i>2024 IEEE International Parallel and Distributed Processing Symposium</i> (pp. 169–179). San Francisco, CA, United States: IEEE. <a href=\"https://doi.org/10.1109/IPDPS57955.2024.00023\">https://doi.org/10.1109/IPDPS57955.2024.00023</a>","chicago":"Kokorin, Ilya, Victor Yudov, Vitaly Aksenov, and Dan-Adrian Alistarh. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” In <i>2024 IEEE International Parallel and Distributed Processing Symposium</i>, 169–79. IEEE, 2024. <a href=\"https://doi.org/10.1109/IPDPS57955.2024.00023\">https://doi.org/10.1109/IPDPS57955.2024.00023</a>.","ieee":"I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in <i>2024 IEEE International Parallel and Distributed Processing Symposium</i>, San Francisco, CA, United States, 2024, pp. 169–179.","short":"I. Kokorin, V. Yudov, V. Aksenov, D.-A. Alistarh, in:, 2024 IEEE International Parallel and Distributed Processing Symposium, IEEE, 2024, pp. 169–179."},"year":"2024","researchdata_availability":"no","acknowledgement":"We thank Trevor Brown and Yuanhao Wei for the discussion and anonymous reviewers for helping us to improve the paper. Also, we thank JetBrains and Huawei for their support.","title":"Wait-free trees with asymptotically-efficient range queries","abstract":[{"text":"Tree data structures, such as red-black trees, quad trees, treaps, or tries, are fundamental tools in computer science. A classical problem in concurrency is to obtain expressive, efficient, and scalable versions of practical tree data structures. We are interested in concurrent trees supporting range queries, i.e., queries that involve multiple consecutive data items. Existing implementations with this capability can list keys in a specific range, but do not support aggregate range queries: for instance, if we want to calculate the number of keys in a range, the only choice is to retrieve a whole list and return its size. This is suboptimal: in the sequential setting, one can augment a balanced search tree with counters and, consequently, perform these aggregate requests in logarithmic rather than linear time.In this paper, we propose a generic approach to implement a broad class of range queries on concurrent trees in a way that is wait-free, asymptotically efficient, and practically scalable. The key idea is a new mechanism for maintaining metadata concurrently at tree nodes, which can be seen as a wait-free variant of hand-over-hand locking (which we call hand-over-hand helping). We did a preliminary implementation of the wait-free binary search tree and preliminary experiments have indicated the soundness of our approach.","lang":"eng"}],"quality_controlled":"1","status":"public","publisher":"IEEE","_id":"17332","conference":{"location":"San Francisco, CA, United States","end_date":"2024-05-31","name":"IPDPS: International Parallel and Distributed Processing Symposium","start_date":"2024-05-27"},"month":"07","date_created":"2024-07-28T22:01:11Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","department":[{"_id":"DaAl"}],"das_tickbox":"0","date_updated":"2026-10-01T09:30:18Z","isi":1,"oa":1,"publication":"2024 IEEE International Parallel and Distributed Processing Symposium","oa_version":"Preprint"},{"language":[{"iso":"eng"}],"arxiv":1,"external_id":{"arxiv":["2312.13547"]},"alternative_title":["PMLR"],"date_published":"2024-01-08T00:00:00Z","author":[{"full_name":"Kurtic, Eldar","first_name":"Eldar","id":"47beb3a5-07b5-11eb-9b87-b108ec578218","last_name":"Kurtic"},{"last_name":"Hoefler","full_name":"Hoefler, Torsten","first_name":"Torsten"},{"last_name":"Alistarh","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87"}],"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","main_file_link":[{"url":"https://proceedings.mlr.press/v234/kurtic24a","open_access":"1"}],"supplementarymaterial":"no","corr_author":"1","page":"542-553","article_processing_charge":"No","type":"conference","day":"08","month":"01","conference":{"start_date":"2024-01-03","location":"Hongkong, China","end_date":"2024-01-06","name":"CPAL: Conference on Parsimony and Learning"},"_id":"15011","department":[{"_id":"DaAl"}],"date_created":"2024-02-18T23:01:03Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","oa":1,"date_updated":"2026-10-01T09:31:02Z","das_tickbox":"0","intvolume":"       234","oa_version":"Preprint","publication":"Proceedings of Machine Learning Research","year":"2024","researchdata_availability":"no","volume":234,"publication_status":"published","citation":{"apa":"Kurtic, E., Hoefler, T., &#38; Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In <i>Proceedings of Machine Learning Research</i> (Vol. 234, pp. 542–553). Hongkong, China: ML Research Press.","chicago":"Kurtic, Eldar, Torsten Hoefler, and Dan-Adrian Alistarh. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” In <i>Proceedings of Machine Learning Research</i>, 234:542–53. ML Research Press, 2024.","ieee":"E. Kurtic, T. Hoefler, and D.-A. Alistarh, “How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark,” in <i>Proceedings of Machine Learning Research</i>, Hongkong, China, 2024, vol. 234, pp. 542–553.","short":"E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553.","ista":"Kurtic E, Hoefler T, Alistarh D-A. 2024. How to prune your language model: Recovering accuracy on the ‘Sparsity May Cry’ benchmark. Proceedings of Machine Learning Research. CPAL: Conference on Parsimony and Learning, PMLR, vol. 234, 542–553.","mla":"Kurtic, Eldar, et al. “How to Prune Your Language Model: Recovering Accuracy on the ‘Sparsity May Cry’ Benchmark.” <i>Proceedings of Machine Learning Research</i>, vol. 234, ML Research Press, 2024, pp. 542–53.","ama":"Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: <i>Proceedings of Machine Learning Research</i>. Vol 234. ML Research Press; 2024:542-553."},"abstract":[{"text":"Pruning large language models (LLMs) from the BERT family has emerged as a standard compression benchmark, and several pruning methods have been proposed for this task. The recent “Sparsity May Cry” (SMC) benchmark put into question the validity of all existing methods, exhibiting a more complex setup where many known pruning methods appear to fail. We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets, and propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark. First, we perform a cost-vs-benefits analysis of pruning model components, such as the embeddings and the classification head; second, we provide a simple-yet-general way of scaling training, sparsification and learning rate schedules relative to the desired target sparsity; finally, we investigate the importance of proper parametrization for Knowledge Distillation in the context of LLMs. Our simple insights lead to state-of-the-art results, both on classic BERT-pruning benchmarks, as well as on the SMC benchmark, showing that even classic gradual magnitude pruning (GMP) can yield competitive results, with the right approach.","lang":"eng"}],"title":"How to prune your language model: Recovering accuracy on the \"Sparsity May Cry\" benchmark","publisher":"ML Research Press","status":"public","quality_controlled":"1"},{"abstract":[{"text":"Parallel SGD in a shared-memory setting is oft-represented by the popular Hogwild! algorithm, in which lock-free updates are asynchronously performed by multiple computing processes. Unfortunately, scaling Hogwild! to distributed workers is largely unexplored. Specifically, it is unknown if any adaptation of Hogwild! to the popular decentralized multi-GPU setting offers any competitive speedup, either empirically or theoretically. In this work, we investigate the potential of decentralizing Hogwild! by incorporating simultaneously (a) asynchronous local gradient updates on the shared memory of GPUs, and (b) non-blocking asynchronous decentralized federated averaging. A naive direct implementation shows degradation in performance, arising from scheduling overheads and concurrent write conflicts on GPUs. To mitigate these drawbacks, we investigate and propose a new method, based on careful block selection rules, which update only portions of the parameter vectors. Our experiments show that the resulting decentralized training method exhibits improved throughput and competitive accuracy for standard image classification benchmarks on the CIFAR-10, CIFAR-100, and Imagenet datasets. On the theoretical side, we prove that our method guarantees sublinear ergodic convergence rates for non-convex objectives.","lang":"eng"}],"title":"Federated SGD with local asynchrony","status":"public","publisher":"IEEE","quality_controlled":"1","doi":"10.1109/ICDCS60910.2024.00084","researchdata_availability":"yes","year":"2024","citation":{"chicago":"Chatterjee, Bapi, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Federated SGD with Local Asynchrony.” In <i>Proceedings of the 44th International Conference on Distributed Computing Systems</i>, 857–68. IEEE, 2024. <a href=\"https://doi.org/10.1109/ICDCS60910.2024.00084\">https://doi.org/10.1109/ICDCS60910.2024.00084</a>.","apa":"Chatterjee, B., Kungurtsev, V., &#38; Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In <i>Proceedings of the 44th International Conference on Distributed Computing Systems</i> (pp. 857–868). Jersey City, NJ, United States: IEEE. <a href=\"https://doi.org/10.1109/ICDCS60910.2024.00084\">https://doi.org/10.1109/ICDCS60910.2024.00084</a>","short":"B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the 44th International Conference on Distributed Computing Systems, IEEE, 2024, pp. 857–868.","ieee":"B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in <i>Proceedings of the 44th International Conference on Distributed Computing Systems</i>, Jersey City, NJ, United States, 2024, pp. 857–868.","ista":"Chatterjee B, Kungurtsev V, Alistarh D-A. 2024. Federated SGD with local asynchrony. Proceedings of the 44th International Conference on Distributed Computing Systems. ICDCS: International Conference on Distributed Computing Systems, 857–868.","ama":"Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: <i>Proceedings of the 44th International Conference on Distributed Computing Systems</i>. IEEE; 2024:857-868. doi:<a href=\"https://doi.org/10.1109/ICDCS60910.2024.00084\">10.1109/ICDCS60910.2024.00084</a>","mla":"Chatterjee, Bapi, et al. “Federated SGD with Local Asynchrony.” <i>Proceedings of the 44th International Conference on Distributed Computing Systems</i>, IEEE, 2024, pp. 857–68, doi:<a href=\"https://doi.org/10.1109/ICDCS60910.2024.00084\">10.1109/ICDCS60910.2024.00084</a>."},"publication_status":"published","date_updated":"2026-10-01T09:32:24Z","isi":1,"das_tickbox":"0","oa_version":"None","publication":"Proceedings of the 44th International Conference on Distributed Computing Systems","month":"07","_id":"18070","conference":{"start_date":"2024-07-23","end_date":"2024-07-26","name":"ICDCS: International Conference on Distributed Computing Systems","location":"Jersey City, NJ, United States"},"department":[{"_id":"DaAl"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-15T22:01:41Z","article_processing_charge":"No","type":"conference","day":"26","author":[{"full_name":"Chatterjee, Bapi","first_name":"Bapi","id":"3C41A08A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2742-4028","last_name":"Chatterjee"},{"last_name":"Kungurtsev","first_name":"Vyacheslav","full_name":"Kungurtsev, Vyacheslav"},{"last_name":"Alistarh","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian"}],"scopus_import":"1","publication_identifier":{"issn":["1063-6927"],"eissn":["2575-8411"],"isbn":["9798350386059"]},"supplementarymaterial":"no","corr_author":"1","page":"857-868","language":[{"iso":"eng"}],"external_id":{"isi":["001304430200075"]},"date_published":"2024-07-26T00:00:00Z","fulldoi":"https://doi.org/10.1109/ICDCS60910.2024.00084"},{"author":[{"id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"},{"last_name":"Chatterjee","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu"},{"orcid":"0009-0007-5253-9170","last_name":"Karrabi","full_name":"Karrabi, Mehrdad","first_name":"Mehrdad","id":"67638922-f394-11eb-9cf6-f20423e08757"},{"full_name":"Lazarsfeld, John M","id":"17ce3656-183e-11ef-84c3-8932383e1b23","first_name":"John M","last_name":"Lazarsfeld"}],"publication_identifier":{"isbn":["9798400706684"]},"scopus_import":"1","supplementarymaterial":"no","corr_author":"1","page":"40-49","article_processing_charge":"Yes (via OA deal)","type":"conference","day":"17","ddc":["000"],"fulldoi":"https://doi.org/10.1145/3662158.3662768","has_accepted_license":"1","language":[{"iso":"eng"}],"date_published":"2024-06-17T00:00:00Z","doi":"10.1145/3662158.3662768","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"year":"2024","researchdata_availability":"no","publication_status":"published","citation":{"ista":"Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. 2024. Game dynamics and equilibrium computation in the population protocol model. Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. PODC: Symposium on Principles of Distributed Computing, 40–49.","mla":"Alistarh, Dan-Adrian, et al. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” <i>Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing</i>, Association for Computing Machinery, 2024, pp. 40–49, doi:<a href=\"https://doi.org/10.1145/3662158.3662768\">10.1145/3662158.3662768</a>.","ama":"Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: <i>Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing</i>. Association for Computing Machinery; 2024:40-49. doi:<a href=\"https://doi.org/10.1145/3662158.3662768\">10.1145/3662158.3662768</a>","apa":"Alistarh, D.-A., Chatterjee, K., Karrabi, M., &#38; Lazarsfeld, J. M. (2024). Game dynamics and equilibrium computation in the population protocol model. In <i>Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing</i> (pp. 40–49). Nantes, France: Association for Computing Machinery. <a href=\"https://doi.org/10.1145/3662158.3662768\">https://doi.org/10.1145/3662158.3662768</a>","chicago":"Alistarh, Dan-Adrian, Krishnendu Chatterjee, Mehrdad Karrabi, and John M Lazarsfeld. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” In <i>Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing</i>, 40–49. Association for Computing Machinery, 2024. <a href=\"https://doi.org/10.1145/3662158.3662768\">https://doi.org/10.1145/3662158.3662768</a>.","ieee":"D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in <i>Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing</i>, Nantes, France, 2024, pp. 40–49.","short":"D.-A. Alistarh, K. Chatterjee, M. Karrabi, J.M. Lazarsfeld, in:, Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2024, pp. 40–49."},"abstract":[{"text":"We initiate the study of game dynamics in the population protocol model: n agents each maintain a current local strategy and interact in pairs uniformly at random. Upon each interaction, the agents play a two-person game and receive a payoff from an underlying utility function, and they can subsequently update their strategies according to a fixed local algorithm. In this setting, we ask how the distribution over agent strategies evolves over a sequence of interactions, and we introduce a new distributional equilibrium concept to quantify the quality of such distributions. As an initial example, we study a class of repeated prisoner's dilemma games, and we consider a family of simple local update algorithms that yield non-trivial dynamics over the distribution of agent strategies. We show that these dynamics are related to a new class of high-dimensional Ehrenfest random walks, and we derive exact characterizations of their stationary distributions, bounds on their mixing times, and prove their convergence to approximate distributional equilibria. Our results highlight trade-offs between the local state space of each agent, and the convergence rate and approximation factor of the underlying dynamics. Our approach opens the door towards the further characterization of equilibrium computation for other classes of games and dynamics in the population setting.","lang":"eng"}],"title":"Game dynamics and equilibrium computation in the population protocol model","acknowledgement":"This work was supported in part by the ERC-2020-CoG 863818 (FoRM-SMArt) grant. We thank James Aspnes and Thomas Sauerwald for several helpful discussions on Ehrenfest random walks.","status":"public","project":[{"call_identifier":"H2020","grant_number":"863818","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","name":"Formal Methods for Stochastic Models: Algorithms and Applications"}],"publisher":"Association for Computing Machinery","quality_controlled":"1","license":"https://creativecommons.org/licenses/by/4.0/","month":"06","ec_funded":1,"_id":"17329","conference":{"start_date":"2024-06-17","end_date":"2024-06-21","name":"PODC: Symposium on Principles of Distributed Computing","location":"Nantes, France"},"department":[{"_id":"DaAl"},{"_id":"KrCh"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-07-28T22:01:10Z","date_updated":"2026-10-01T09:34:57Z","file":[{"creator":"dernst","file_name":"2024_ACMPODC_Alistarh.pdf","success":1,"file_id":"17335","date_updated":"2024-07-29T07:37:31Z","date_created":"2024-07-29T07:37:31Z","content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_size":750908,"checksum":"65a40437f83373fa79dd999d5287509e"}],"oa":1,"das_tickbox":"0","file_date_updated":"2024-07-29T07:37:31Z","oa_version":"Published Version","publication":"Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing"},{"related_material":{"link":[{"relation":"software","url":"https://github.com/IST-DASLab/SPADE"}],"record":[{"relation":"dissertation_contains","id":"21854","status":"public"}]},"das_tickbox":"0","date_updated":"2026-10-01T09:32:47Z","oa":1,"publication":"Proceedings of the 41st International Conference on Machine Learning","oa_version":"Preprint","intvolume":"       235","_id":"18121","conference":{"start_date":"2024-07-21","location":"Vienna, Austria","name":"ICML: International Conference on Machine Learning","end_date":"2024-07-27"},"month":"09","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-22T22:01:46Z","department":[{"_id":"DaAl"}],"acknowledgement":"The authors would like to thank Stephen Casper and Tony Wang for their feedback on this work, and Eldar Kurtic for his advice on aspects of the project. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing (SciComp). EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35.","abstract":[{"lang":"eng","text":"It is known that sparsity can improve interpretability for deep neural networks. However, existing methods in the area either require networks that are pre-trained with sparsity constraints, or impose sparsity after the fact, altering the network’s general behavior. In this paper, we demonstrate, for the first time, that sparsity can instead be incorporated into the interpretation process itself, as a sample-specific preprocessing step. Unlike previous work, this approach, which we call SPADE, does not place constraints on the trained model and does not affect its behavior during inference on the sample. Given a trained model and a target sample, SPADE uses sample-targeted pruning to provide a \"trace\" of the network’s execution on the sample, reducing the network to the most important connections prior to computing an interpretation. We demonstrate that preprocessing with SPADE significantly increases the accuracy of image saliency maps across several interpretability methods. Additionally, SPADE improves the usefulness of neuron visualizations, aiding humans in reasoning about network behavior. Our code is available at https://github.com/IST-DASLab/SPADE."}],"title":"SPADE: Sparsity-guided debugging for deep neural networks","quality_controlled":"1","status":"public","publisher":"ML Research Press","project":[{"_id":"9B9290DE-BA93-11EA-9121-9846C619BF3A","grant_number":"W1260-N35","name":"Vienna Graduate School on Computational Optimization"}],"citation":{"short":"A.S. Moakhar, E.B. Iofinova, E. Frantar, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 45955–45987.","ieee":"A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.","chicago":"Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:45955–87. ML Research Press, 2024.","apa":"Moakhar, A. S., Iofinova, E. B., Frantar, E., &#38; Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.","ama":"Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:45955-45987.","mla":"Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 45955–87.","ista":"Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. 2024. SPADE: Sparsity-guided debugging for deep neural networks. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 45955–45987."},"publication_status":"published","volume":235,"researchdata_availability":"yes","year":"2024","language":[{"iso":"eng"}],"alternative_title":["PMLR"],"external_id":{"arxiv":["2310.04519"]},"date_published":"2024-09-01T00:00:00Z","arxiv":1,"type":"conference","acknowledged_ssus":[{"_id":"ScienComp"}],"article_processing_charge":"No","day":"01","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.04519","open_access":"1"}],"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","author":[{"last_name":"Moakhar","first_name":"Arshia Soltani","full_name":"Moakhar, Arshia Soltani"},{"last_name":"Iofinova","orcid":"0000-0002-7778-3221","first_name":"Eugenia B","id":"f9a17499-f6e0-11ea-865d-fdf9a3f77117","full_name":"Iofinova, Eugenia B"},{"full_name":"Frantar, Elias","first_name":"Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","last_name":"Frantar"},{"orcid":"0000-0003-3650-940X","last_name":"Alistarh","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"corr_author":"1","page":"45955-45987","supplementarymaterial":"no"},{"language":[{"iso":"eng"}],"arxiv":1,"date_published":"2024-05-15T00:00:00Z","external_id":{"arxiv":["2310.20452"]},"alternative_title":["PMLR"],"article_processing_charge":"No","type":"conference","day":"15","author":[{"full_name":"Islamov, Rustem","first_name":"Rustem","last_name":"Islamov"},{"last_name":"Safaryan","full_name":"Safaryan, Mher","id":"dd546b39-0804-11ed-9c55-ef075c39778d","first_name":"Mher"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2310.20452","open_access":"1"}],"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","supplementarymaterial":"no","corr_author":"1","page":"649-657","date_updated":"2026-10-01T09:33:24Z","oa":1,"das_tickbox":"0","intvolume":"       238","oa_version":"Preprint","publication":"Proceedings of The 27th International Conference on Artificial Intelligence and Statistics","month":"05","_id":"18976","ec_funded":1,"conference":{"start_date":"2024-05-02","end_date":"2024-05-04","name":"AISTATS: Conference on Artificial Intelligence and Statistics","location":"Valencia, Spain"},"department":[{"_id":"DaAl"}],"date_created":"2025-01-30T08:15:49Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","abstract":[{"text":"We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting, where each worker has its own computation and communication speeds, as well as data distribution. In these algorithms, workers compute possibly stale and stochastic gradients associated with their local data at some iteration back in history and then return those gradients to the server without synchronizing with other workers. We present a unified convergence theory for non-convex smooth functions in the heterogeneous regime. The proposed analysis provides convergence for pure asynchronous SGD and its various modifications. Moreover, our theory explains what affects the convergence rate and what can be done to improve the performance of asynchronous algorithms. In particular, we introduce a novel asynchronous method based on worker shuffling. As a by-product of our analysis, we also demonstrate convergence guarantees for gradient-type algorithms such as SGD with random reshuffling and shuffle-once mini-batch SGD. The derived rates match the best-known results for those algorithms, highlighting the tightness of our approach. Finally, our numerical evaluations support theoretical findings and show the good practical performance of our method.","lang":"eng"}],"title":"AsGrad: A sharp unified analysis of asynchronous-SGD algorithms","acknowledgement":"The authors thank all anonymous reviewers for their valuable comments and suggestions on how to improve the manuscript. This work was done when Rustem Islamov was a Master’s student at Institut Polytechnique de Paris (IP Paris) and an intern at Institute of Science and Technology Austria (ISTA). The research of Rustem Islamov was supported by ISTA internship\r\nprogram. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034413.","status":"public","project":[{"grant_number":"101034413","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program"}],"publisher":"ML Research Press","quality_controlled":"1","OA_type":"green","volume":238,"researchdata_availability":"no","year":"2024","citation":{"ama":"Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics</i>. Vol 238. ML Research Press; 2024:649-657.","mla":"Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics</i>, vol. 238, ML Research Press, 2024, pp. 649–57.","ista":"Islamov R, Safaryan M, Alistarh D-A. 2024. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 238, 649–657.","short":"R. Islamov, M. Safaryan, D.-A. Alistarh, in:, Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 649–657.","ieee":"R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics</i>, Valencia, Spain, 2024, vol. 238, pp. 649–657.","chicago":"Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics</i>, 238:649–57. ML Research Press, 2024.","apa":"Islamov, R., Safaryan, M., &#38; Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In <i>Proceedings of The 27th International Conference on Artificial Intelligence and Statistics</i> (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press."},"OA_place":"repository","publication_status":"published"},{"language":[{"iso":"eng"}],"date_published":"2024-09-01T00:00:00Z","alternative_title":["PMLR"],"external_id":{"arxiv":["2401.06118"]},"arxiv":1,"main_file_link":[{"url":" https://doi.org/10.48550/arXiv.2401.06118","open_access":"1"}],"scopus_import":"1","publication_identifier":{"eissn":["2640-3498"]},"author":[{"last_name":"Egiazarian","first_name":"Vage","full_name":"Egiazarian, Vage"},{"last_name":"Panferov","full_name":"Panferov, Andrei","id":"2c18daae-4dbe-11ef-8491-98ce2d960f09","first_name":"Andrei"},{"last_name":"Kuznedelev","first_name":"Denis","full_name":"Kuznedelev, Denis"},{"id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","first_name":"Elias","full_name":"Frantar, Elias","last_name":"Frantar"},{"first_name":"Artem","full_name":"Babenko, Artem","last_name":"Babenko"},{"last_name":"Alistarh","orcid":"0000-0003-3650-940X","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian"}],"page":"12284-12303","corr_author":"1","supplementarymaterial":"no","type":"conference","article_processing_charge":"No","day":"01","_id":"18113","conference":{"name":"ICML: International Conference on Machine Learning","end_date":"2024-07-27","location":"Vienna, Austria","start_date":"2024-07-21"},"month":"09","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-09-22T22:01:43Z","department":[{"_id":"DaAl"},{"_id":"GradSch"}],"das_tickbox":"0","date_updated":"2026-10-01T09:34:19Z","oa":1,"oa_version":"Preprint","publication":"Proceedings of the 41st International Conference on Machine Learning","intvolume":"       235","citation":{"short":"V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 12284–12303.","ieee":"V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.","chicago":"Egiazarian, Vage, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, and Dan-Adrian Alistarh. “Extreme Compression of Large Language Models via Additive Quantization.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:12284–303. ML Research Press, 2024.","apa":"Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., &#38; Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.","ama":"Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:12284-12303.","mla":"Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 12284–303.","ista":"Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. 2024. Extreme compression of large language models via additive quantization. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 12284–12303."},"publication_status":"published","year":"2024","researchdata_availability":"no","volume":235,"acknowledgement":"Authors would like to thank Ruslan Svirschevski for his help in solving technical issues with AQLM and baselines. We also thank Tim Dettmers for helpful discussions on the structure of weights in modern LLMs and size-accuracy trade-offs. The authors would also like to thank Daniil Pavlov for his assistance with CPU benchmarking. Finally, authors would like to thank the communities of ML enthusiasts known as LocalLLaMA5 and Petals community on discord6\r\nfor the crowd wisdom about running LLMs on consumer devices. Egiazarian Vage and Denis Kuznedelev and Andrei Panferov were supported by the grant for research centers in the field of AI provided by the Analytical Center for the Government of the Russian Federation (ACRF) in\r\naccordance with the agreement on the provision of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement with HSE University No. 70-2021-00139.","title":"Extreme compression of large language models via additive quantization","abstract":[{"lang":"eng","text":"The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In this paper, we revisit the problem of “extreme” LLM compression—defined as targeting extremely low bit counts, such as 2 to 3 bits per parameter—from the point of view of classic methods in Multi-Codebook Quantization (MCQ). Our algorithm, called AQLM, generalizes the classic Additive Quantization (AQ) approach for information retrieval to advance the state-of-the-art in LLM compression, via two innovations: 1) learned additive quantization of weight matrices in input-adaptive fashion, and 2) joint optimization of codebook parameters across each transformer blocks. Broadly, AQLM is the first scheme that is Pareto optimal in terms of accuracy-vs-model-size when compressing to less than 3 bits per parameter, and significantly improves upon all known schemes in the extreme compression (2bit) regime. In addition, AQLM is practical: we provide fast GPU and CPU implementations of AQLM for token generation, which enable us to match or outperform optimized FP16 implementations for speed, while executing in a much smaller memory footprint."}],"quality_controlled":"1","status":"public","publisher":"ML Research Press"},{"day":"30","acknowledged_ssus":[{"_id":"CampIT"}],"article_processing_charge":"No","type":"conference","supplementarymaterial":"no","corr_author":"1","page":"35910-35933","author":[{"first_name":"Ionut-Vlad","id":"449f7a18-f128-11eb-9611-9b430c0c6333","full_name":"Modoranu, Ionut-Vlad","last_name":"Modoranu"},{"id":"44b7120e-eb97-11eb-a6c2-e1557aa81d02","first_name":"Aleksei","full_name":"Kalinov, Aleksei","orcid":"0000-0003-2189-3904","last_name":"Kalinov"},{"id":"47beb3a5-07b5-11eb-9b87-b108ec578218","first_name":"Eldar","full_name":"Kurtic, Eldar","last_name":"Kurtic"},{"full_name":"Frantar, Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","first_name":"Elias","last_name":"Frantar"},{"full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2306.06098"}],"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","arxiv":1,"external_id":{"arxiv":["2306.06098"]},"alternative_title":["PMLR"],"date_published":"2024-07-30T00:00:00Z","language":[{"iso":"eng"}],"status":"public","publisher":"ML Research Press","quality_controlled":"1","abstract":[{"text":"Leveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression before it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99% sparsity without accuracy loss, effectively removing the memory overhead of fullmatrix preconditioners such as GGT and M-FAC.","lang":"eng"}],"title":"Error feedback can accurately compress preconditioners","acknowledgement":"The authors thank Adrian Vladu, Razvan Pascanu, Alexandra Peste, Mher Safaryan for their valuable feedback, the IT department from Institute of Science and Technology Austria for the hardware support and Weights and Biases for the infrastructure to track all our experiments.","volume":235,"researchdata_availability":"yes","year":"2024","OA_place":"repository","citation":{"ista":"Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback can accurately compress preconditioners. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 35910–35933.","ama":"Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: <i>41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:35910-35933.","mla":"Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” <i>41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 35910–33.","chicago":"Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In <i>41st International Conference on Machine Learning</i>, 235:35910–33. ML Research Press, 2024.","apa":"Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., &#38; Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In <i>41st International Conference on Machine Learning</i> (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.","short":"I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, D.-A. Alistarh, in:, 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 35910–35933.","ieee":"I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in <i>41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 35910–35933."},"publication_status":"published","OA_type":"green","intvolume":"       235","publication":"41st International Conference on Machine Learning","oa_version":"Preprint","date_updated":"2026-10-01T09:33:07Z","oa":1,"das_tickbox":"0","department":[{"_id":"DaAl"}],"date_created":"2025-01-30T07:53:22Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"07","_id":"18975","conference":{"end_date":"2024-07-27","name":"ICML: International Conference on Machine Learning","location":"Vienna, Austria","start_date":"2024-07-21"}},{"arxiv":1,"external_id":{"arxiv":["2306.03078"]},"date_published":"2024-05-15T00:00:00Z","language":[{"iso":"eng"}],"supplementarymaterial":"no","author":[{"full_name":"Dettmers, Tim","first_name":"Tim","last_name":"Dettmers"},{"last_name":"Svirschevski","first_name":"Ruslan A.","full_name":"Svirschevski, Ruslan A."},{"last_name":"Egiazarian","first_name":"Vage","full_name":"Egiazarian, Vage"},{"first_name":"Denis","full_name":"Kuznedelev, Denis","last_name":"Kuznedelev"},{"full_name":"Frantar, Elias","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","first_name":"Elias","last_name":"Frantar"},{"last_name":"Ashkboos","first_name":"Saleh","full_name":"Ashkboos, Saleh"},{"full_name":"Borzunov, Alexander","first_name":"Alexander","last_name":"Borzunov"},{"first_name":"Torsten","full_name":"Hoefler, Torsten","last_name":"Hoefler"},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2306.03078"}],"scopus_import":"1","day":"15","article_processing_charge":"No","type":"conference","department":[{"_id":"DaAl"}],"date_created":"2025-01-30T08:26:59Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"05","_id":"18977","conference":{"name":"ICLR: International Conference on Learning Representations","end_date":"2024-05-11","location":"Vienna, Austria","start_date":"2024-05-07"},"publication":"12th International Conference on Learning Representations","oa_version":"Preprint","date_updated":"2026-10-01T09:33:44Z","oa":1,"das_tickbox":"0","researchdata_availability":"no","year":"2024","citation":{"apa":"Dettmers, T., Svirschevski, R. A., Egiazarian, V., Kuznedelev, D., Frantar, E., Ashkboos, S., … Alistarh, D.-A. (2024). SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In <i>12th International Conference on Learning Representations</i>. Vienna, Austria: OpenReview.","chicago":"Dettmers, Tim, Ruslan A. Svirschevski, Vage Egiazarian, Denis Kuznedelev, Elias Frantar, Saleh Ashkboos, Alexander Borzunov, Torsten Hoefler, and Dan-Adrian Alistarh. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” In <i>12th International Conference on Learning Representations</i>. OpenReview, 2024.","ieee":"T. Dettmers <i>et al.</i>, “SpQR: A sparse-quantized representation for near-lossless LLM weight compression,” in <i>12th International Conference on Learning Representations</i>, Vienna, Austria, 2024.","short":"T. Dettmers, R.A. Svirschevski, V. Egiazarian, D. Kuznedelev, E. Frantar, S. Ashkboos, A. Borzunov, T. Hoefler, D.-A. Alistarh, in:, 12th International Conference on Learning Representations, OpenReview, 2024.","ista":"Dettmers T, Svirschevski RA, Egiazarian V, Kuznedelev D, Frantar E, Ashkboos S, Borzunov A, Hoefler T, Alistarh D-A. 2024. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. 12th International Conference on Learning Representations. ICLR: International Conference on Learning Representations.","mla":"Dettmers, Tim, et al. “SpQR: A Sparse-Quantized Representation for near-Lossless LLM Weight Compression.” <i>12th International Conference on Learning Representations</i>, OpenReview, 2024.","ama":"Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: <i>12th International Conference on Learning Representations</i>. OpenReview; 2024."},"OA_place":"repository","publication_status":"published","OA_type":"green","status":"public","publisher":"OpenReview","quality_controlled":"1","abstract":[{"text":"Recent advances in large language model (LLM) pretraining have led to high-quality LLMs with impressive abilities. By compressing such LLMs via quantization to 3-4 bits per parameter, they can fit into memory-limited devices such as laptops and mobile phones, enabling personalized use. Quantizing models to 3-4 bits per parameter can lead to moderate to high accuracy losses, especially for smaller models (1-10B parameters), which are suitable for edge deployment. To address this accuracy issue, we introduce the Sparse-Quantized Representation (SpQR), a new compressed format and quantization technique that enables for the first time \\emph{near-lossless} compression of LLMs across model scales while reaching similar compression levels to previous methods. SpQR works by identifying and isolating \\emph{outlier weights}, which cause particularly large quantization errors, and storing them in higher precision while compressing all other weights to 3-4 bits, and achieves relative accuracy losses of less than \r\n in perplexity for highly-accurate LLaMA and Falcon LLMs. This makes it possible to run a 33B parameter LLM on a single 24 GB consumer GPU without performance degradation at 15% speedup, thus making powerful LLMs available to consumers without any downsides. SpQR comes with efficient algorithms for both encoding weights into its format, as well as decoding them efficiently at runtime. Specifically, we provide an efficient GPU inference algorithm for SpQR, which yields faster inference than 16-bit baselines at similar accuracy while enabling memory compression gains of more than 4x.","lang":"eng"}],"title":"SpQR: A sparse-quantized representation for near-lossless LLM weight compression","acknowledgement":"Denis Kuznedelev acknowledges the support from the Russian Ministry of Science and Higher\r\nEducation, grant No. 075-10-2021-068. Ruslan Svirschevski and Vage Egiazarian and Denis\r\nKuznedelev were supported by the grant for research centers in the field of AI provided by the\r\nAnalytical Center for the Government of the Russian Federation (ACRF) in accordance with the\r\nagreement on the provision of subsidies (identifier of the agreement 000000D730321P5Q0002) and the agreement with HSE University No. 70-2021-00139."},{"status":"public","publisher":"ML Research Press","quality_controlled":"1","title":"Communication-efficient federated learning with data and client heterogeneity","abstract":[{"lang":"eng","text":"Federated Learning (FL) enables large-scale distributed training of machine learning models, while still allowing individual nodes to maintain data locally. However, executing FL at scale comes with inherent practical challenges: 1) heterogeneity of the local node data distributions, 2) heterogeneity of node computational speeds (asynchrony), but also 3) constraints in the amount of communication between the clients and the server. In this work, we present the first variant of the classic federated averaging (FedAvg) algorithm which, at the same time, supports data heterogeneity, partial client asynchrony, and communication compression. Our algorithm comes with a novel, rigorous analysis showing that, in spite of these system relaxations, it can provide similar convergence to FedAvg in interesting parameter regimes. Experimental results in the rigorous LEAF benchmark on setups of up to 300 nodes show that our algorithm ensures fast convergence for standard federated tasks, improving upon prior quantized and asynchronous approaches."}],"volume":238,"researchdata_availability":"no","year":"2024","publication_status":"published","citation":{"short":"H. Zakerinia, S. Talaei, G. Nadiradze, D.-A. Alistarh, in:, Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, ML Research Press, 2024, pp. 3448–3456.","ieee":"H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, Valencia, Spain, 2024, vol. 238, pp. 3448–3456.","chicago":"Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, 238:3448–56. ML Research Press, 2024.","apa":"Zakerinia, H., Talaei, S., Nadiradze, G., &#38; Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i> (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.","ama":"Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>. Vol 238. ML Research Press; 2024:3448-3456.","mla":"Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” <i>Proceedings of the 27th International Conference on Artificial Intelligence and Statistics</i>, vol. 238, ML Research Press, 2024, pp. 3448–56.","ista":"Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. 2024. Communication-efficient federated learning with data and client heterogeneity. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. AISTATS: Conference on Artificial Intelligence and Statistics, PMLR, vol. 238, 3448–3456."},"intvolume":"       238","publication":"Proceedings of the 27th International Conference on Artificial Intelligence and Statistics","oa_version":"Preprint","date_updated":"2026-10-01T09:34:02Z","oa":1,"das_tickbox":"0","department":[{"_id":"DaAl"},{"_id":"ChLa"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-06-02T22:00:57Z","month":"05","_id":"17093","conference":{"start_date":"2024-05-02","location":"Valencia, Spain","name":"AISTATS: Conference on Artificial Intelligence and Statistics","end_date":"2024-05-04"},"day":"01","article_processing_charge":"No","type":"conference","supplementarymaterial":"no","corr_author":"1","page":"3448-3456","author":[{"orcid":"0009-0007-3977-6462","last_name":"Zakerinia","full_name":"Zakerinia, Hossein","first_name":"Hossein","id":"653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4"},{"first_name":"Shayan","full_name":"Talaei, Shayan","last_name":"Talaei"},{"full_name":"Nadiradze, Giorgi","id":"3279A00C-F248-11E8-B48F-1D18A9856A87","first_name":"Giorgi","orcid":"0000-0001-5634-0731","last_name":"Nadiradze"},{"last_name":"Alistarh","orcid":"0000-0003-3650-940X","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","first_name":"Dan-Adrian"}],"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2206.10032"}],"publication_identifier":{"eissn":["2640-3498"]},"scopus_import":"1","arxiv":1,"date_published":"2024-05-01T00:00:00Z","external_id":{"arxiv":["2206.10032"]},"alternative_title":["PMLR"],"language":[{"iso":"eng"}]},{"researchdata_availability":"yes","volume":235,"year":"2024","citation":{"short":"M. Nikdan, S. Tabesh, E. Crncevic, D.-A. Alistarh, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 38187–38206.","ieee":"M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in <i>Proceedings of the 41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.","chicago":"Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In <i>Proceedings of the 41st International Conference on Machine Learning</i>, 235:38187–206. ML Research Press, 2024.","apa":"Nikdan, M., Tabesh, S., Crncevic, E., &#38; Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In <i>Proceedings of the 41st International Conference on Machine Learning</i> (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.","ama":"Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: <i>Proceedings of the 41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:38187-38206.","mla":"Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” <i>Proceedings of the 41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 38187–206.","ista":"Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. 2024. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. Proceedings of the 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning vol. 235, 38187–38206."},"publication_status":"published","abstract":[{"text":"We investigate parameter-efficient fine-tuning (PEFT) methods that can provide good accuracy under limited computational and memory budgets in the context of large language models (LLMs). We present a new PEFT method called Robust Adaptation (RoSA) inspired by robust principal component analysis that jointly trains low-rank\r\n and highly-sparse components on top of a set of fixed pretrained weights to efficiently approximate the performance of a full-fine-tuning (FFT) solution. Across a series of challenging generative tasks such as grade-school math and SQL query generation, which require fine-tuning for good performance, we show that RoSA outperforms LoRA, pure sparse fine-tuning, and alternative hybrid methods at the same parameter budget, and can even recover the performance of FFT on some tasks. We provide system support for RoSA to complement the training algorithm, specifically in the form of sparse GPU kernels which enable memory- and computationally-efficient training, and show that it is also compatible with low-precision base weights, resulting in the first joint representation combining quantization, low-rank and sparse approximations. Our code is available at https://github.com/IST-DASLab/RoSA.","lang":"eng"}],"title":"RoSA: Accurate parameter-efficient fine-tuning via robust adaptation","acknowledgement":"The authors would like to thank Eldar Kurtic for experimental support and useful suggestions throughout the project","publisher":"ML Research Press","status":"public","quality_controlled":"1","month":"09","conference":{"start_date":"2024-07-21","location":"Vienna, Austria","end_date":"2024-07-27","name":"ICML: International Conference on Machine Learning"},"_id":"18117","department":[{"_id":"DaAl"},{"_id":"GradSch"}],"date_created":"2024-09-22T22:01:44Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","oa":1,"date_updated":"2026-10-01T09:34:36Z","related_material":{"link":[{"relation":"software","url":"https://github.com/IST-DASLab/RoSA"}]},"das_tickbox":"0","intvolume":"       235","oa_version":"Preprint","publication":"Proceedings of the 41st International Conference on Machine Learning","author":[{"last_name":"Nikdan","first_name":"Mahdi","id":"66374281-f394-11eb-9cf6-869147deecc0","full_name":"Nikdan, Mahdi"},{"orcid":"0009-0003-4119-6281","last_name":"Tabesh","first_name":"Soroush","id":"06000900-6068-11ef-8d61-c2472ef2e752","full_name":"Tabesh, Soroush"},{"full_name":"Crncevic, Elvir","id":"41888001-440d-11ef-8299-d0e838b8185e","first_name":"Elvir","last_name":"Crncevic"},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"scopus_import":"1","publication_identifier":{"eissn":["2640-3498"]},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2401.04679"}],"supplementarymaterial":"no","page":"38187-38206","corr_author":"1","article_processing_charge":"No","type":"conference","day":"01","language":[{"iso":"eng"}],"arxiv":1,"external_id":{"arxiv":["2401.04679"]},"date_published":"2024-09-01T00:00:00Z"},{"article_processing_charge":"Yes (in subscription journal)","type":"journal_article","day":"04","author":[{"orcid":"0000-0001-7205-2975","last_name":"Brückner","first_name":"David","id":"e1e86031-6537-11eb-953a-f7ab92be508d","full_name":"Brückner, David"},{"last_name":"Broedersz","full_name":"Broedersz, Chase P.","first_name":"Chase P."}],"publication_identifier":{"issn":["0034-4885"],"eissn":["1361-6633"]},"scopus_import":"1","supplementarymaterial":"no","corr_author":"1","article_number":"056601","language":[{"iso":"eng"}],"arxiv":1,"pmid":1,"date_published":"2024-04-04T00:00:00Z","external_id":{"isi":["001196692400001"],"pmid":["38518358"],"arxiv":["2309.00545"]},"ddc":["530"],"fulldoi":"https://doi.org/10.1088/1361-6633/ad36d2","has_accepted_license":"1","title":"Learning dynamical models of single and collective cell migration: a review","abstract":[{"lang":"eng","text":"Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development and immune response to wound healing and cancer metastasis. To understand cell migration from a physical perspective, a broad variety of models for the underlying physical mechanisms that govern cell motility have been developed. A key challenge in the development of such models is how to connect them to experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances in data-driven theoretical approaches that directly connect with experimental data to infer dynamical models of stochastic cell migration. Leveraging advances in nanofabrication, image analysis, and tracking technology, experimental studies now provide unprecedented large datasets on cellular dynamics. In parallel, theoretical efforts have been directed towards integrating such datasets into physical models from the single cell to the tissue scale with the aim of conceptualising the emergent behaviour of cells. We first review how this inference problem has been addressed in both freely migrating and confined cells. Next, we discuss why these dynamics typically take the form of underdamped stochastic equations of motion, and how such equations can be inferred from data. We then review applications of data-driven inference and machine learning approaches to heterogeneity in cell behaviour, subcellular degrees of freedom, and to the collective dynamics of multicellular systems. Across these applications, we emphasise how data-driven methods can be integrated with physical active matter models of migrating cells, and help reveal how underlying molecular mechanisms control cell behaviour. Together, these data-driven approaches are a promising avenue for building physical models of cell migration directly from experimental data, and for providing conceptual links between different length-scales of description."}],"acknowledgement":"This work was supported by the Deutsche Forschungsgemeinschaft (German Research Foundation)—Project-ID 201269156—SFB 1032 (Project B12). D B B was supported by an NOMIS Fellowship and an EMBO Fellowship (ALTF 343-2022). We thank Joachim Rädler, Alexandra Fink, Erwin Frey, Pierre Ronceray, Ricard Alert, Edouard Hannezo, Henrik Flyvbjerg, Ulrich Schwarz, Joshua Shaevitz, Greg Stephens, Andrea Cavagna, Grzegorz Gradziuk, Fridtjof Brauns, Nikolas Claussen, Tom Brandstätter, Johannes Flommersfeld, Christoph Schreiber, Nicolas Arlt, Matthew Schmitt, Joris Messelink, Federico Gnesotto, Federica Mura, Bram Hoogland, Manon Wigbers, Isabella Graf, Jessica Lober, and many others for inspiring discussions. We also thank Claudia Flandoli for the artwork in figures 1, 5, 8 and 9.","issue":"5","status":"public","publisher":"IOP Publishing","project":[{"name":"A mechano-chemical theory for stem cell fate decisions in organoid development","grant_number":"ALTF 343-2022","_id":"34e2a5b5-11ca-11ed-8bc3-b2265616ef0b"}],"quality_controlled":"1","license":"https://creativecommons.org/licenses/by/3.0/","doi":"10.1088/1361-6633/ad36d2","tmp":{"image":"/images/cc_by.png","short":"CC BY (3.0)","legal_code_url":"https://creativecommons.org/licenses/by/3.0/legalcode","name":"Creative Commons Attribution 3.0 Unported (CC BY 3.0)"},"year":"2024","volume":87,"researchdata_availability":"not applicable","publication_status":"published","citation":{"apa":"Brückner, D., &#38; Broedersz, C. P. (2024). Learning dynamical models of single and collective cell migration: a review. <i>Reports on Progress in Physics</i>. IOP Publishing. <a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">https://doi.org/10.1088/1361-6633/ad36d2</a>","chicago":"Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single and Collective Cell Migration: A Review.” <i>Reports on Progress in Physics</i>. IOP Publishing, 2024. <a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">https://doi.org/10.1088/1361-6633/ad36d2</a>.","ieee":"D. Brückner and C. P. Broedersz, “Learning dynamical models of single and collective cell migration: a review,” <i>Reports on Progress in Physics</i>, vol. 87, no. 5. IOP Publishing, 2024.","short":"D. Brückner, C.P. Broedersz, Reports on Progress in Physics 87 (2024).","ista":"Brückner D, Broedersz CP. 2024. Learning dynamical models of single and collective cell migration: a review. Reports on Progress in Physics. 87(5), 056601.","mla":"Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single and Collective Cell Migration: A Review.” <i>Reports on Progress in Physics</i>, vol. 87, no. 5, 056601, IOP Publishing, 2024, doi:<a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">10.1088/1361-6633/ad36d2</a>.","ama":"Brückner D, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. <i>Reports on Progress in Physics</i>. 2024;87(5). doi:<a href=\"https://doi.org/10.1088/1361-6633/ad36d2\">10.1088/1361-6633/ad36d2</a>"},"date_updated":"2026-10-01T10:09:41Z","file":[{"file_size":4376898,"checksum":"c5910078230ade20f4dd83592e862a72","relation":"main_file","access_level":"open_access","date_created":"2024-08-20T11:00:03Z","content_type":"application/pdf","success":1,"date_updated":"2024-08-20T11:00:03Z","file_id":"17451","file_name":"2024_ReportPhysics_Brueckner.pdf","creator":"dernst"}],"isi":1,"oa":1,"das_tickbox":"1","file_date_updated":"2024-08-20T11:00:03Z","intvolume":"        87","oa_version":"Published Version","publication":"Reports on Progress in Physics","dataavailabilitystatement":"No new data were created or analysed in this study.","month":"04","_id":"15315","department":[{"_id":"EdHa"}],"article_type":"review","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-04-14T22:01:01Z"},{"day":"26","type":"journal_article","acknowledged_ssus":[{"_id":"Bio"}],"article_processing_charge":"Yes (in subscription journal)","corr_author":"1","article_number":"e2319686121","supplementarymaterial":"yes","scopus_import":"1","publication_identifier":{"eissn":["1091-6490"]},"author":[{"last_name":"Boytsov","first_name":"Danila","full_name":"Boytsov, Danila"},{"full_name":"Madej, Gregor M.","first_name":"Gregor M.","last_name":"Madej"},{"full_name":"Horn, Georg","first_name":"Georg","last_name":"Horn"},{"full_name":"Blaha, Nadine","first_name":"Nadine","last_name":"Blaha"},{"last_name":"Köcher","first_name":"Thomas","full_name":"Köcher, Thomas"},{"last_name":"Sitte","full_name":"Sitte, Harald H.","first_name":"Harald H."},{"last_name":"Siekhaus","orcid":"0000-0001-8323-8353","full_name":"Siekhaus, Daria E","first_name":"Daria E","id":"3D224B9E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Ziegler","full_name":"Ziegler, Christine","first_name":"Christine"},{"first_name":"Walter","full_name":"Sandtner, Walter","last_name":"Sandtner"},{"full_name":"Roblek, Marko","first_name":"Marko","id":"3047D808-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9588-1389","last_name":"Roblek"}],"date_published":"2024-03-26T00:00:00Z","external_id":{"isi":["001206419000003"],"pmid":["38507452"]},"pmid":1,"language":[{"iso":"eng"}],"has_accepted_license":"1","ddc":["570"],"fulldoi":"https://doi.org/10.1073/pnas.2319686121","quality_controlled":"1","issue":"13","status":"public","publisher":"National Academy of Sciences","acknowledgement":"We thank the Metabolomics Facility at Vienna BioCenter Core Facilities, which is a member of the Vienna BioCenter and funded by the City of Vienna through the Vienna Business Agency (shared research facility), for the LC–MS/MS analysis; and the BioImaging Facility at IST Austria for technical support and assistance. The authors want to thank N. Kastner for help with the live  cell  imaging  and  A.  Korošec  for  help  with  flow  cytometry.  This  work  was  supported by the Austrian Science Fund (FWF), grant P 36621- B (to M.R.), grant P 36667 (to W.S.), and core funding from IST Austria (to D.S.).","abstract":[{"lang":"eng","text":"Orphan solute carrier (SLC) represents a group of membrane transporters whose exact functions and substrate specificities are not known. Elucidating the function and regulation of orphan SLC transporters is not only crucial for advancing our knowledge of cellular and molecular biology but can potentially lead to the development of new therapeutic strategies. Here, we provide evidence for the biological function of a ubiquitous orphan lysosomal SLC, the Major Facilitator Superfamily Domain-containing Protein 1 (MFSD1), which has remained phylogenetically unassigned. Targeted metabolomics revealed that dipeptides containing either lysine or arginine residues accumulate in lysosomes of cells lacking MFSD1. Whole-cell patch-clamp electrophysiological recordings of HEK293-cells expressing MFSD1 on the cell surface displayed transport affinities for positively charged dipeptides in the lower mM range, while dipeptides that carry a negative net charge were not transported. This was also true for single amino acids and tripeptides, which MFSD1 failed to transport. Our results identify MFSD1 as a highly selective lysosomal lysine/arginine/histidine-containing dipeptide exporter, which functions as a uniporter."}],"title":"Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport","publication_status":"published","citation":{"ama":"Boytsov D, Madej GM, Horn G, et al. Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2024;121(13). doi:<a href=\"https://doi.org/10.1073/pnas.2319686121\">10.1073/pnas.2319686121</a>","mla":"Boytsov, Danila, et al. “Orphan Lysosomal Solute Carrier MFSD1 Facilitates Highly Selective Dipeptide Transport.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 121, no. 13, e2319686121, National Academy of Sciences, 2024, doi:<a href=\"https://doi.org/10.1073/pnas.2319686121\">10.1073/pnas.2319686121</a>.","ista":"Boytsov D, Madej GM, Horn G, Blaha N, Köcher T, Sitte HH, Siekhaus DE, Ziegler C, Sandtner W, Roblek M. 2024. Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport. Proceedings of the National Academy of Sciences of the United States of America. 121(13), e2319686121.","short":"D. Boytsov, G.M. Madej, G. Horn, N. Blaha, T. Köcher, H.H. Sitte, D.E. Siekhaus, C. Ziegler, W. Sandtner, M. Roblek, Proceedings of the National Academy of Sciences of the United States of America 121 (2024).","ieee":"D. Boytsov <i>et al.</i>, “Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 121, no. 13. National Academy of Sciences, 2024.","chicago":"Boytsov, Danila, Gregor M. Madej, Georg Horn, Nadine Blaha, Thomas Köcher, Harald H. Sitte, Daria E Siekhaus, Christine Ziegler, Walter Sandtner, and Marko Roblek. “Orphan Lysosomal Solute Carrier MFSD1 Facilitates Highly Selective Dipeptide Transport.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2024. <a href=\"https://doi.org/10.1073/pnas.2319686121\">https://doi.org/10.1073/pnas.2319686121</a>.","apa":"Boytsov, D., Madej, G. M., Horn, G., Blaha, N., Köcher, T., Sitte, H. H., … Roblek, M. (2024). Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2319686121\">https://doi.org/10.1073/pnas.2319686121</a>"},"year":"2024","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"researchdata_availability":"yes (supplementary data)","volume":121,"doi":"10.1073/pnas.2319686121","publication":"Proceedings of the National Academy of Sciences of the United States of America","oa_version":"Published Version","file_date_updated":"2024-04-02T08:54:28Z","intvolume":"       121","das_tickbox":"1","date_updated":"2026-10-01T09:36:29Z","isi":1,"file":[{"file_id":"15256","date_updated":"2024-04-02T08:54:28Z","success":1,"file_name":"2024_PNAS_Boytsov.pdf","creator":"dernst","relation":"main_file","file_size":2483787,"checksum":"06f9e60b1146a685d58bf33999422fa8","content_type":"application/pdf","date_created":"2024-04-02T08:54:28Z","access_level":"open_access"}],"oa":1,"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2024-03-31T22:01:12Z","department":[{"_id":"DaSi"}],"article_type":"original","_id":"15250","dataavailabilitystatement":"All study data are included in the article and/or SI Appendix.","month":"03"},{"acknowledgement":"The author thanks Lydéric Bocquet, Baptiste Coquinot, and Mathieu Lizée for fruitful discussions. This project received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 101034413.","title":"Correlation-induced viscous dissipation in concentrated electrolytes","abstract":[{"text":"Electrostatic correlations between ions dissolved in water are known to impact their transport properties in numerous ways, from conductivity to ion selectivity. The effects of these correlations on the solvent itself remain, however, much less clear. In particular, the addition of salt has been consistently reported to affect the solution’s viscosity, but most modeling attempts fail to reproduce experimental data even at moderate salt concentrations. Here, we use an approach based on stochastic density functional theory, which accurately captures charge fluctuations and correlations. We derive a simple analytical expression for the viscosity correction in concentrated electrolytes, by directly linking it to the liquid’s structure factor. Our prediction compares quantitatively to experimental data at all temperatures and all salt concentrations up to the saturation limit. This universal link between the microscopic structure and viscosity allows us to shed light on the nanoscale dynamics of water and ions under highly concentrated and correlated conditions.","lang":"eng"}],"quality_controlled":"1","status":"public","issue":"6","project":[{"name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","grant_number":"101034413","call_identifier":"H2020"}],"publisher":"AIP Publishing","doi":"10.1063/5.0188215","citation":{"ista":"Robin P. 2024. Correlation-induced viscous dissipation in concentrated electrolytes. Journal of Chemical Physics. 160(6), 064503.","ama":"Robin P. Correlation-induced viscous dissipation in concentrated electrolytes. <i>Journal of Chemical Physics</i>. 2024;160(6). doi:<a href=\"https://doi.org/10.1063/5.0188215\">10.1063/5.0188215</a>","mla":"Robin, Paul. “Correlation-Induced Viscous Dissipation in Concentrated Electrolytes.” <i>Journal of Chemical Physics</i>, vol. 160, no. 6, 064503, AIP Publishing, 2024, doi:<a href=\"https://doi.org/10.1063/5.0188215\">10.1063/5.0188215</a>.","chicago":"Robin, Paul. “Correlation-Induced Viscous Dissipation in Concentrated Electrolytes.” <i>Journal of Chemical Physics</i>. AIP Publishing, 2024. <a href=\"https://doi.org/10.1063/5.0188215\">https://doi.org/10.1063/5.0188215</a>.","apa":"Robin, P. (2024). Correlation-induced viscous dissipation in concentrated electrolytes. <i>Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/5.0188215\">https://doi.org/10.1063/5.0188215</a>","short":"P. Robin, Journal of Chemical Physics 160 (2024).","ieee":"P. Robin, “Correlation-induced viscous dissipation in concentrated electrolytes,” <i>Journal of Chemical Physics</i>, vol. 160, no. 6. AIP Publishing, 2024."},"publication_status":"published","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"researchdata_availability":"not applicable","year":"2024","volume":160,"das_tickbox":"1","isi":1,"file":[{"checksum":"0a5e0ae70849bce674466fc054390ec0","file_size":5452738,"relation":"main_file","access_level":"open_access","content_type":"application/pdf","date_created":"2024-02-27T08:12:52Z","file_id":"15034","date_updated":"2024-02-27T08:12:52Z","success":1,"file_name":"2024_JourChemicalPhysics_Robin.pdf","creator":"dernst"}],"date_updated":"2026-10-01T10:08:50Z","oa":1,"publication":"Journal of Chemical Physics","oa_version":"Published Version","file_date_updated":"2024-02-27T08:12:52Z","intvolume":"       160","ec_funded":1,"_id":"15024","dataavailabilitystatement":"Data sharing is not applicable to this article as no new data were created. Data supporting the findings of this study are accessible in the literature, as indicated throughout the article.","month":"02","date_created":"2024-02-25T23:00:55Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","department":[{"_id":"EdHa"}],"article_type":"original","type":"journal_article","article_processing_charge":"Yes (in subscription journal)","day":"14","publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"scopus_import":"1","author":[{"last_name":"Robin","orcid":"0000-0002-5728-9189","first_name":"Paul","id":"48c58128-57b0-11ee-9095-dc28fd97fc1d","full_name":"Robin, Paul"}],"corr_author":"1","article_number":"064503","supplementarymaterial":"no","language":[{"iso":"eng"}],"date_published":"2024-02-14T00:00:00Z","pmid":1,"external_id":{"isi":["001161104900003"],"pmid":["38349632"],"arxiv":["2311.11784"]},"arxiv":1,"has_accepted_license":"1","ddc":["540"],"fulldoi":"https://doi.org/10.1063/5.0188215"},{"article_type":"original","department":[{"_id":"EdHa"}],"date_created":"2024-09-08T22:01:12Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"08","_id":"17892","ec_funded":1,"intvolume":"       110","file_date_updated":"2024-09-11T05:59:36Z","oa_version":"Published Version","publication":"Physical Review E","oa":1,"date_updated":"2026-10-01T10:06:45Z","file":[{"file_size":445696,"checksum":"67fc2cc8eee3155e5c3b7380307d8284","relation":"main_file","access_level":"open_access","date_created":"2024-09-11T05:59:36Z","content_type":"application/pdf","success":1,"file_id":"18053","date_updated":"2024-09-11T05:59:36Z","file_name":"2024_PhysReviewE_Olmeda.pdf","creator":"dernst"}],"isi":1,"das_tickbox":"0","researchdata_availability":"no","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"year":"2024","volume":110,"citation":{"ista":"Olmeda F, Rulands S. 2024. Field theory of enzyme-substrate systems with restricted long-range interactions. Physical Review E. 110(2), 024404.","ama":"Olmeda F, Rulands S. Field theory of enzyme-substrate systems with restricted long-range interactions. <i>Physical Review E</i>. 2024;110(2). doi:<a href=\"https://doi.org/10.1103/PhysRevE.110.024404\">10.1103/PhysRevE.110.024404</a>","mla":"Olmeda, Fabrizio, and Steffen Rulands. “Field Theory of Enzyme-Substrate Systems with Restricted Long-Range Interactions.” <i>Physical Review E</i>, vol. 110, no. 2, 024404, American Physical Society, 2024, doi:<a href=\"https://doi.org/10.1103/PhysRevE.110.024404\">10.1103/PhysRevE.110.024404</a>.","chicago":"Olmeda, Fabrizio, and Steffen Rulands. “Field Theory of Enzyme-Substrate Systems with Restricted Long-Range Interactions.” <i>Physical Review E</i>. American Physical Society, 2024. <a href=\"https://doi.org/10.1103/PhysRevE.110.024404\">https://doi.org/10.1103/PhysRevE.110.024404</a>.","apa":"Olmeda, F., &#38; Rulands, S. (2024). Field theory of enzyme-substrate systems with restricted long-range interactions. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.110.024404\">https://doi.org/10.1103/PhysRevE.110.024404</a>","short":"F. Olmeda, S. Rulands, Physical Review E 110 (2024).","ieee":"F. Olmeda and S. Rulands, “Field theory of enzyme-substrate systems with restricted long-range interactions,” <i>Physical Review E</i>, vol. 110, no. 2. American Physical Society, 2024."},"publication_status":"published","doi":"10.1103/PhysRevE.110.024404","publisher":"American Physical Society","project":[{"grant_number":"101034413","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program"}],"issue":"2","status":"public","quality_controlled":"1","title":"Field theory of enzyme-substrate systems with restricted long-range interactions","abstract":[{"lang":"eng","text":"Enzyme-substrate kinetics form the basis of many biomolecular processes. The interplay between substrate binding and substrate geometry can give rise to long-range interactions between enzyme binding events. Here we study a general model of enzyme-substrate kinetics with restricted long-range interactions described by an exponent −𝛾. We employ a coherent-state path integral and renormalization group approach to calculate the first moment and two-point correlation function of the enzyme-binding profile. We show that starting from an empty substrate the average occupancy follows a power law with an exponent 1/(1−𝛾) over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents −𝛾 on short distances and −(2/3)⁢(2−𝛾) on long distances. The crossover between both regimes scales inversely with the average substrate occupancy. Our work allows associating experimental measurements of bound enzyme locations with their binding kinetics and the spatial conformation of the substrate."}],"acknowledgement":"We thank F. Piazza, M. Henkel, and F. Jülicher for helpful feedback and the entire Rulands group for fruitful discussions. We thank W. Reik, S. Clark, T. Lohoff, and I. Kafetzopoulos for fruitful discussions about the biological aspects of this work. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (Grant No. 950349). This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant No. 101034413.","fulldoi":"https://doi.org/10.1103/PhysRevE.110.024404","ddc":["530"],"has_accepted_license":"1","pmid":1,"date_published":"2024-08-01T00:00:00Z","external_id":{"pmid":["39294986"],"isi":["001299670100004"]},"language":[{"iso":"eng"}],"supplementarymaterial":"no","article_number":"024404","corr_author":"1","author":[{"id":"69dbf5fb-8a76-11ed-866b-fb486d8b5689","first_name":"Fabrizio","full_name":"Olmeda, Fabrizio","last_name":"Olmeda"},{"full_name":"Rulands, Steffen","first_name":"Steffen","last_name":"Rulands"}],"publication_identifier":{"issn":["2470-0045"],"eissn":["2470-0053"]},"scopus_import":"1","day":"01","article_processing_charge":"Yes (in subscription journal)","type":"journal_article"},{"has_accepted_license":"1","ddc":["530"],"fulldoi":"https://doi.org/10.21468/scipostphys.16.4.097","date_published":"2024-04-08T00:00:00Z","external_id":{"arxiv":["2310.04272"],"isi":["001202370200001"]},"arxiv":1,"language":[{"iso":"eng"}],"article_number":"097","supplementarymaterial":"yes","scopus_import":"1","publication_identifier":{"issn":["2542-4653"]},"author":[{"full_name":"Krämer, Johannes C.","first_name":"Johannes C.","last_name":"Krämer"},{"full_name":"Hannezo, Edouard B","first_name":"Edouard B","id":"3A9DB764-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6005-1561","last_name":"Hannezo"},{"last_name":"Gompper","full_name":"Gompper, Gerhard","first_name":"Gerhard"},{"first_name":"Jens","full_name":"Elgeti, Jens","last_name":"Elgeti"}],"day":"08","type":"journal_article","article_processing_charge":"No","date_created":"2024-06-03T08:58:44Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","department":[{"_id":"EdHa"}],"article_type":"original","_id":"17104","dataavailabilitystatement":"Source code, simulation data, and analysis scripts will be made publicly available on zenodo.org (10.5281/zenodo.8410957) after acceptance of the manuscript.","month":"04","publication":"SciPost Physics","oa_version":"Published Version","file_date_updated":"2024-06-03T11:18:51Z","intvolume":"        16","related_material":{"record":[{"relation":"research_data","id":"23022","status":"public"}]},"das_tickbox":"1","file":[{"access_level":"open_access","content_type":"application/pdf","date_created":"2024-06-03T11:18:51Z","checksum":"6fdeecd21c166db8dedb927ecc2e6025","file_size":4973291,"relation":"main_file","file_name":"2024_SciPostPhys_Kraemer.pdf","creator":"dernst","file_id":"17109","date_updated":"2024-06-03T11:18:51Z","success":1}],"isi":1,"date_updated":"2026-10-01T09:44:06Z","oa":1,"publication_status":"published","citation":{"ista":"Krämer JC, Hannezo EB, Gompper G, Elgeti J. 2024. Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells. SciPost Physics. 16(4), 097.","ama":"Krämer JC, Hannezo EB, Gompper G, Elgeti J. Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells. <i>SciPost Physics</i>. 2024;16(4). doi:<a href=\"https://doi.org/10.21468/scipostphys.16.4.097\">10.21468/scipostphys.16.4.097</a>","mla":"Krämer, Johannes C., et al. “Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” <i>SciPost Physics</i>, vol. 16, no. 4, 097, SciPost Foundation, 2024, doi:<a href=\"https://doi.org/10.21468/scipostphys.16.4.097\">10.21468/scipostphys.16.4.097</a>.","chicago":"Krämer, Johannes C., Edouard B Hannezo, Gerhard Gompper, and Jens Elgeti. “Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” <i>SciPost Physics</i>. SciPost Foundation, 2024. <a href=\"https://doi.org/10.21468/scipostphys.16.4.097\">https://doi.org/10.21468/scipostphys.16.4.097</a>.","apa":"Krämer, J. C., Hannezo, E. B., Gompper, G., &#38; Elgeti, J. (2024). Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells. <i>SciPost Physics</i>. SciPost Foundation. <a href=\"https://doi.org/10.21468/scipostphys.16.4.097\">https://doi.org/10.21468/scipostphys.16.4.097</a>","short":"J.C. Krämer, E.B. Hannezo, G. Gompper, J. Elgeti, SciPost Physics 16 (2024).","ieee":"J. C. Krämer, E. B. Hannezo, G. Gompper, and J. Elgeti, “Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells,” <i>SciPost Physics</i>, vol. 16, no. 4. SciPost Foundation, 2024."},"year":"2024","tmp":{"image":"/images/cc_by.png","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"researchdata_availability":"yes","volume":16,"doi":"10.21468/scipostphys.16.4.097","quality_controlled":"1","status":"public","issue":"4","publisher":"SciPost Foundation","acknowledgement":"JE and JK gratefully acknowledge financial support from the Initiative and Networking Fund (IVF) via the grant number ERC-RA-004. Simulations were performed with computing resources granted by RWTH Aachen University under project ‘rwth0475’.","title":"Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells","abstract":[{"text":"The homeostasis of epithelial tissue relies on a balance between the self-renewal of stem cell populations, cellular differentiation, and loss. Although this balance needs to be tightly regulated to avoid pathologies, such as tumor growth, the regulatory mechanisms, both cell-intrinsic and collective, which ensure tissue steady-state are still poorly understood. Here, we develop a computational model that incorporates basic assumptions of stem cell renewal into distinct populations and mechanical interactions between cells. We find that the model generates unexpected dynamic features: stem cells repel each other in the bulk tissue and are thus found rather isolated, as in a number of in vivo contexts. By mapping the system onto a gas of passive Brownian particles with effective repulsive interactions, that arise from the generated flows of differentiated cells, we show that we can quantitatively describe such stem cell distribution in tissues. The interaction potential between a pair of stem cells decays exponentially with a characteristic length that spans several cell sizes, corresponding to the volume of cells generated per stem cell division. Our findings may help understanding the dynamics of normal and cancerous epithelial tissues.","lang":"eng"}]},{"publisher":"Springer Nature","status":"public","issue":"5","quality_controlled":"1","abstract":[{"lang":"eng","text":"The integration of theory and experiment makes possible tracking the slow evolution of a photodoped Mott insulator to a distinct non-equilibrium metallic phase under the influence of electron-lattice coupling."}],"title":"Through the slopes of a light-induced phase transition","researchdata_availability":"no","volume":20,"year":"2024","citation":{"apa":"Baykusheva, D. R. (2024). Through the slopes of a light-induced phase transition. <i>Nature Physics</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41567-024-02401-7\">https://doi.org/10.1038/s41567-024-02401-7</a>","chicago":"Baykusheva, Denitsa Rangelova. “Through the Slopes of a Light-Induced Phase Transition.” <i>Nature Physics</i>. Springer Nature, 2024. <a href=\"https://doi.org/10.1038/s41567-024-02401-7\">https://doi.org/10.1038/s41567-024-02401-7</a>.","ieee":"D. R. Baykusheva, “Through the slopes of a light-induced phase transition,” <i>Nature Physics</i>, vol. 20, no. 5. Springer Nature, pp. 684–685, 2024.","short":"D.R. Baykusheva, Nature Physics 20 (2024) 684–685.","ista":"Baykusheva DR. 2024. Through the slopes of a light-induced phase transition. Nature Physics. 20(5), 684–685.","mla":"Baykusheva, Denitsa Rangelova. “Through the Slopes of a Light-Induced Phase Transition.” <i>Nature Physics</i>, vol. 20, no. 5, Springer Nature, 2024, pp. 684–85, doi:<a href=\"https://doi.org/10.1038/s41567-024-02401-7\">10.1038/s41567-024-02401-7</a>.","ama":"Baykusheva DR. Through the slopes of a light-induced phase transition. <i>Nature Physics</i>. 2024;20(5):684-685. doi:<a href=\"https://doi.org/10.1038/s41567-024-02401-7\">10.1038/s41567-024-02401-7</a>"},"publication_status":"published","OA_type":"closed access","doi":"10.1038/s41567-024-02401-7","intvolume":"        20","publication":"Nature Physics","oa_version":"None","date_updated":"2026-10-01T09:37:00Z","isi":1,"das_tickbox":"0","article_type":"letter_note","department":[{"_id":"DeBa"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2025-01-27T14:29:20Z","month":"05","_id":"18919","day":"01","article_processing_charge":"No","type":"journal_article","supplementarymaterial":"no","corr_author":"1","page":"684-685","author":[{"last_name":"Baykusheva","orcid":"0000-0002-7438-1139","first_name":"Denitsa Rangelova","id":"71b4d059-2a03-11ee-914d-dfa3beed6530","full_name":"Baykusheva, Denitsa Rangelova"}],"scopus_import":"1","publication_identifier":{"issn":["1745-2473"],"eissn":["1745-2481"]},"external_id":{"isi":["001162208200002"]},"date_published":"2024-05-01T00:00:00Z","language":[{"iso":"eng"}],"fulldoi":"https://doi.org/10.1038/s41567-024-02401-7"},{"citation":{"ieee":"J. Elgeti and J. Krämer, “Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells.” Zenodo, 2024.","short":"J. Elgeti, J. Krämer, (2024).","apa":"Elgeti, J., &#38; Krämer, J. (2024). Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells. Zenodo. <a href=\"https://doi.org/10.5281/zenodo.8410956\">https://doi.org/10.5281/zenodo.8410956</a>","chicago":"Elgeti, Jens, and Johannes Krämer. “Data for Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” Zenodo, 2024. <a href=\"https://doi.org/10.5281/zenodo.8410956\">https://doi.org/10.5281/zenodo.8410956</a>.","mla":"Elgeti, Jens, and Johannes Krämer. <i>Data for Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells</i>. Zenodo, 2024, doi:<a href=\"https://doi.org/10.5281/zenodo.8410956\">10.5281/zenodo.8410956</a>.","ama":"Elgeti J, Krämer J. Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells. 2024. doi:<a href=\"https://doi.org/10.5281/zenodo.8410956\">10.5281/zenodo.8410956</a>","ista":"Elgeti J, Krämer J. 2024. Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells, Zenodo, <a href=\"https://doi.org/10.5281/zenodo.8410956\">10.5281/zenodo.8410956</a>."},"OA_place":"repository","year":"2024","doi":"10.5281/zenodo.8410956","main_file_link":[{"open_access":"1","url":"https://doi.org/10.5281/zenodo.8410956"}],"OA_type":"green","author":[{"last_name":"Elgeti","first_name":"Jens","full_name":"Elgeti, Jens"},{"full_name":"Krämer, Johannes","first_name":"Johannes","last_name":"Krämer"}],"publisher":"Zenodo","day":"25","status":"public","type":"research_data_reference","abstract":[{"text":"Contains animations, data, and source code used for the paper \"Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells\" by the same authors. \r\n\r\n\r\nThis repository contains all the data and analysis scripts to support the manuscript\r\nThe file structure is as follows:\r\n- \"analysis\" contains all the analysis scripts\r\n- \"gfx\" and \"animations\" the figures and supporting animations\r\n- \"1-SCpair\", \"2-SCtissue\", \"3-BDsimulation\", \"4-tissue-stochdiv\", \"6-inertia-effects\" all the source code and data for the respective models.","lang":"eng"}],"article_processing_charge":"No","title":"Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2026-10-01T09:44:01Z","department":[{"_id":"EdHa"}],"_id":"23022","month":"03","fulldoi":"https://doi.org/10.5281/zenodo.8410956","ddc":["530"],"date_published":"2024-03-25T00:00:00Z","oa_version":"None","related_material":{"record":[{"id":"17104","status":"public","relation":"used_in_publication"}]},"oa":1,"date_updated":"2026-10-01T09:44:07Z"},{"language":[{"iso":"eng"}],"date_published":"2024-06-04T00:00:00Z","pmid":1,"external_id":{"pmid":["38819997"],"isi":["001244835000006"]},"fulldoi":"https://doi.org/10.1073/pnas.2322326121","ddc":["570"],"has_accepted_license":"1","article_processing_charge":"Yes (in subscription journal)","type":"journal_article","day":"04","author":[{"last_name":"Brückner","orcid":"0000-0001-7205-2975","first_name":"David","id":"e1e86031-6537-11eb-953a-f7ab92be508d","full_name":"Brückner, David"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"}],"publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"scopus_import":"1","supplementarymaterial":"yes","article_number":"e2322326121","corr_author":"1","oa":1,"isi":1,"file":[{"content_type":"application/pdf","date_created":"2024-06-10T10:27:37Z","access_level":"open_access","relation":"main_file","file_size":12329234,"checksum":"59797a75db7beb3721ed7a4d14c241f9","file_name":"2024_PNAS_Brueckner.pdf","creator":"dernst","file_id":"17130","date_updated":"2024-06-10T10:27:37Z","success":1}],"date_updated":"2026-10-01T11:03:59Z","related_material":{"link":[{"url":"https://github.com/dbrueckner/SelforgInformation","relation":"software"},{"url":"https://ista.ac.at/en/news/the-embryo-assembles-itself/","relation":"press_release","description":"News on the ISTA website"}]},"das_tickbox":"1","intvolume":"       121","file_date_updated":"2024-06-10T10:27:37Z","oa_version":"Published Version","publication":"Proceedings of the National Academy of Sciences of the United States of America","month":"06","dataavailabilitystatement":"There were no data generated as part of this study. Code to perform simulations, evaluate entropy and information quantities, and optimize parameters is available at https://github.com/dbrueckner/SelforgInformation (90).","_id":"17123","APC_amount":"2570,79 EUR","article_type":"original","department":[{"_id":"EdHa"},{"_id":"GaTk"}],"date_created":"2024-06-09T22:01:02Z","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","title":"Information content and optimization of self-organized developmental systems","abstract":[{"lang":"eng","text":"A key feature of many developmental systems is their ability to self-organize spatial patterns of functionally distinct cell fates. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, we lack a principled framework to quantify the performance of such stochastic self-organizing systems. To that end, we introduce an information-theoretic measure for self-organized fate specification during embryonic development. We show that the proposed measure assesses the total information content of fate patterns and decomposes it into interpretable contributions corresponding to the positional and correlational information. By optimizing the proposed measure, our framework provides a normative theory for developmental circuits, which we demonstrate on lateral inhibition, cell type proportioning, and reaction–diffusion models of self-organization. This paves a way toward a classification of developmental systems based on a common information-theoretic language, thereby organizing the zoo of implicated chemical and mechanical signaling processes."}],"acknowledgement":"We thank Wiktor Młynarski, Juraj Majek, Michal Hledík, Fridtjof Brauns, Nikolas Claussen, Benjamin Zoller, Erwin Frey, Thomas Gregor, and Edouard Hannezo for inspiring discussions. D.B.B. was supported by the NOMIS foundation as a NOMIS Fellow and by an European Molecular Biology Organization (EMBO) Postdoctoral Fellowship (ALTF 343-2022). This research was performed in part at the Aspen Center for Physics, which is supported by NSF Grant No. PHY-1607611, and Kavli Institute for Theoretical Physics (KITP) Santa Barbara, supported by NSF Grant No. PHY-1748958 and the Gordon and Betty Moore Foundation Grant No. 2919.02.","publisher":"National Academy of Sciences","project":[{"_id":"34e2a5b5-11ca-11ed-8bc3-b2265616ef0b","grant_number":"ALTF 343-2022","name":"A mechano-chemical theory for stem cell fate decisions in organoid development"}],"issue":"23","status":"public","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","quality_controlled":"1","OA_type":"hybrid","doi":"10.1073/pnas.2322326121","tmp":{"image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)"},"researchdata_availability":"not applicable","year":"2024","volume":121,"publication_status":"published","OA_place":"publisher","citation":{"ama":"Brückner D, Tkačik G. Information content and optimization of self-organized developmental systems. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2024;121(23). doi:<a href=\"https://doi.org/10.1073/pnas.2322326121\">10.1073/pnas.2322326121</a>","mla":"Brückner, David, and Gašper Tkačik. “Information Content and Optimization of Self-Organized Developmental Systems.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 121, no. 23, e2322326121, National Academy of Sciences, 2024, doi:<a href=\"https://doi.org/10.1073/pnas.2322326121\">10.1073/pnas.2322326121</a>.","ista":"Brückner D, Tkačik G. 2024. Information content and optimization of self-organized developmental systems. Proceedings of the National Academy of Sciences of the United States of America. 121(23), e2322326121.","short":"D. Brückner, G. Tkačik, Proceedings of the National Academy of Sciences of the United States of America 121 (2024).","ieee":"D. Brückner and G. Tkačik, “Information content and optimization of self-organized developmental systems,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 121, no. 23. National Academy of Sciences, 2024.","chicago":"Brückner, David, and Gašper Tkačik. “Information Content and Optimization of Self-Organized Developmental Systems.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2024. <a href=\"https://doi.org/10.1073/pnas.2322326121\">https://doi.org/10.1073/pnas.2322326121</a>.","apa":"Brückner, D., &#38; Tkačik, G. (2024). Information content and optimization of self-organized developmental systems. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2322326121\">https://doi.org/10.1073/pnas.2322326121</a>"}}]