--- _id: '18118' 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. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Hossein full_name: Zakerinia, Hossein id: 653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4 last_name: Zakerinia orcid: 0009-0007-3977-6462 - first_name: Amin full_name: Behjati, Amin last_name: Behjati - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Zakerinia H, Behjati A, Lampert C. More flexible PAC-Bayesian meta-learning by learning learning algorithms. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:58122-58139.' apa: 'Zakerinia, H., Behjati, A., & Lampert, C. (2024). More flexible PAC-Bayesian meta-learning by learning learning algorithms. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 58122–58139). Vienna, Austria: ML Research Press.' chicago: Zakerinia, Hossein, Amin Behjati, and Christoph Lampert. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” In Proceedings of the 41st International Conference on Machine Learning, 235:58122–39. ML Research Press, 2024. ieee: H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning by learning learning algorithms,” in Proceedings of the 41st International Conference on Machine Learning, 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.' mla: Zakerinia, Hossein, et al. “More Flexible PAC-Bayesian Meta-Learning by Learning Learning Algorithms.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 58122–39. short: H. Zakerinia, A. Behjati, C. Lampert, in:, Proceedings of the 41st International Conference on Machine Learning, ML Research Press, 2024, pp. 58122–58139. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' das_tickbox: '0' date_created: 2024-09-22T22:01:45Z date_published: 2024-09-01T00:00:00Z date_updated: 2026-10-01T09:28:21Z day: '01' ddc: - '000' department: - _id: ChLa external_id: arxiv: - '2402.04054' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2402.04054' month: '09' oa: 1 oa_version: Published Version page: 58122-58139 publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: More flexible PAC-Bayesian meta-learning by learning learning algorithms type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 235 year: '2024' ... --- _id: '17332' abstract: - lang: eng 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.' 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. article_processing_charge: No arxiv: 1 author: - first_name: Ilya full_name: Kokorin, Ilya last_name: Kokorin - first_name: Victor full_name: Yudov, Victor last_name: Yudov - first_name: Vitaly full_name: Aksenov, Vitaly last_name: Aksenov - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Kokorin I, Yudov V, Aksenov V, Alistarh D-A. Wait-free trees with asymptotically-efficient range queries. In: 2024 IEEE International Parallel and Distributed Processing Symposium. IEEE; 2024:169-179. doi:10.1109/IPDPS57955.2024.00023' apa: 'Kokorin, I., Yudov, V., Aksenov, V., & Alistarh, D.-A. (2024). Wait-free trees with asymptotically-efficient range queries. In 2024 IEEE International Parallel and Distributed Processing Symposium (pp. 169–179). San Francisco, CA, United States: IEEE. https://doi.org/10.1109/IPDPS57955.2024.00023' chicago: Kokorin, Ilya, Victor Yudov, Vitaly Aksenov, and Dan-Adrian Alistarh. “Wait-Free Trees with Asymptotically-Efficient Range Queries.” In 2024 IEEE International Parallel and Distributed Processing Symposium, 169–79. IEEE, 2024. https://doi.org/10.1109/IPDPS57955.2024.00023. ieee: I. Kokorin, V. Yudov, V. Aksenov, and D.-A. Alistarh, “Wait-free trees with asymptotically-efficient range queries,” in 2024 IEEE International Parallel and Distributed Processing Symposium, San Francisco, CA, United States, 2024, pp. 169–179. 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.” 2024 IEEE International Parallel and Distributed Processing Symposium, IEEE, 2024, pp. 169–79, doi:10.1109/IPDPS57955.2024.00023. short: I. Kokorin, V. Yudov, V. Aksenov, D.-A. Alistarh, in:, 2024 IEEE International Parallel and Distributed Processing Symposium, IEEE, 2024, pp. 169–179. conference: end_date: 2024-05-31 location: San Francisco, CA, United States name: 'IPDPS: International Parallel and Distributed Processing Symposium' start_date: 2024-05-27 corr_author: '1' das_tickbox: '0' date_created: 2024-07-28T22:01:11Z date_published: 2024-07-08T00:00:00Z date_updated: 2026-10-01T09:30:18Z day: '08' department: - _id: DaAl doi: 10.1109/IPDPS57955.2024.00023 external_id: arxiv: - '2310.05293' isi: - '001270389600078' fulldoi: https://doi.org/10.1109/IPDPS57955.2024.00023 isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.05293 month: '07' oa: 1 oa_version: Preprint page: 169-179 publication: 2024 IEEE International Parallel and Distributed Processing Symposium publication_identifier: eissn: - 1530-2075 isbn: - '9798350337662' publication_status: published publisher: IEEE quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Wait-free trees with asymptotically-efficient range queries type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '15011' abstract: - lang: eng 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. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Torsten full_name: Hoefler, Torsten last_name: Hoefler - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Kurtic E, Hoefler T, Alistarh D-A. How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In: Proceedings of Machine Learning Research. Vol 234. ML Research Press; 2024:542-553.' apa: 'Kurtic, E., Hoefler, T., & Alistarh, D.-A. (2024). How to prune your language model: Recovering accuracy on the “Sparsity May Cry” benchmark. In Proceedings of Machine Learning Research (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 Proceedings of Machine Learning Research, 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 Proceedings of Machine Learning Research, Hongkong, China, 2024, vol. 234, 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.” Proceedings of Machine Learning Research, vol. 234, ML Research Press, 2024, pp. 542–53.' short: E. Kurtic, T. Hoefler, D.-A. Alistarh, in:, Proceedings of Machine Learning Research, ML Research Press, 2024, pp. 542–553. conference: end_date: 2024-01-06 location: Hongkong, China name: 'CPAL: Conference on Parsimony and Learning' start_date: 2024-01-03 corr_author: '1' das_tickbox: '0' date_created: 2024-02-18T23:01:03Z date_published: 2024-01-08T00:00:00Z date_updated: 2026-10-01T09:31:02Z day: '08' department: - _id: DaAl external_id: arxiv: - '2312.13547' intvolume: ' 234' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.mlr.press/v234/kurtic24a month: '01' oa: 1 oa_version: Preprint page: 542-553 publication: Proceedings of Machine Learning Research publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: 'How to prune your language model: Recovering accuracy on the "Sparsity May Cry" benchmark' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 234 year: '2024' ... --- _id: '18070' abstract: - lang: eng 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. article_processing_charge: No author: - first_name: Bapi full_name: Chatterjee, Bapi id: 3C41A08A-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-2742-4028 - first_name: Vyacheslav full_name: Kungurtsev, Vyacheslav last_name: Kungurtsev - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Chatterjee B, Kungurtsev V, Alistarh D-A. Federated SGD with local asynchrony. In: Proceedings of the 44th International Conference on Distributed Computing Systems. IEEE; 2024:857-868. doi:10.1109/ICDCS60910.2024.00084' apa: 'Chatterjee, B., Kungurtsev, V., & Alistarh, D.-A. (2024). Federated SGD with local asynchrony. In Proceedings of the 44th International Conference on Distributed Computing Systems (pp. 857–868). Jersey City, NJ, United States: IEEE. https://doi.org/10.1109/ICDCS60910.2024.00084' chicago: Chatterjee, Bapi, Vyacheslav Kungurtsev, and Dan-Adrian Alistarh. “Federated SGD with Local Asynchrony.” In Proceedings of the 44th International Conference on Distributed Computing Systems, 857–68. IEEE, 2024. https://doi.org/10.1109/ICDCS60910.2024.00084. ieee: B. Chatterjee, V. Kungurtsev, and D.-A. Alistarh, “Federated SGD with local asynchrony,” in Proceedings of the 44th International Conference on Distributed Computing Systems, 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.' mla: Chatterjee, Bapi, et al. “Federated SGD with Local Asynchrony.” Proceedings of the 44th International Conference on Distributed Computing Systems, IEEE, 2024, pp. 857–68, doi:10.1109/ICDCS60910.2024.00084. short: B. Chatterjee, V. Kungurtsev, D.-A. Alistarh, in:, Proceedings of the 44th International Conference on Distributed Computing Systems, IEEE, 2024, pp. 857–868. conference: end_date: 2024-07-26 location: Jersey City, NJ, United States name: 'ICDCS: International Conference on Distributed Computing Systems' start_date: 2024-07-23 corr_author: '1' das_tickbox: '0' date_created: 2024-09-15T22:01:41Z date_published: 2024-07-26T00:00:00Z date_updated: 2026-10-01T09:32:24Z day: '26' department: - _id: DaAl doi: 10.1109/ICDCS60910.2024.00084 external_id: isi: - '001304430200075' fulldoi: https://doi.org/10.1109/ICDCS60910.2024.00084 isi: 1 language: - iso: eng month: '07' oa_version: None page: 857-868 publication: Proceedings of the 44th International Conference on Distributed Computing Systems publication_identifier: eissn: - 2575-8411 isbn: - '9798350386059' issn: - 1063-6927 publication_status: published publisher: IEEE quality_controlled: '1' researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: no title: Federated SGD with local asynchrony type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '17329' abstract: - lang: eng 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.' 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. article_processing_charge: Yes (via OA deal) author: - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Krishnendu full_name: Chatterjee, Krishnendu id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87 last_name: Chatterjee orcid: 0000-0002-4561-241X - first_name: Mehrdad full_name: Karrabi, Mehrdad id: 67638922-f394-11eb-9cf6-f20423e08757 last_name: Karrabi orcid: 0009-0007-5253-9170 - first_name: John M full_name: Lazarsfeld, John M id: 17ce3656-183e-11ef-84c3-8932383e1b23 last_name: Lazarsfeld citation: ama: 'Alistarh D-A, Chatterjee K, Karrabi M, Lazarsfeld JM. Game dynamics and equilibrium computation in the population protocol model. In: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing. Association for Computing Machinery; 2024:40-49. doi:10.1145/3662158.3662768' apa: 'Alistarh, D.-A., Chatterjee, K., Karrabi, M., & Lazarsfeld, J. M. (2024). Game dynamics and equilibrium computation in the population protocol model. In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing (pp. 40–49). Nantes, France: Association for Computing Machinery. https://doi.org/10.1145/3662158.3662768' chicago: Alistarh, Dan-Adrian, Krishnendu Chatterjee, Mehrdad Karrabi, and John M Lazarsfeld. “Game Dynamics and Equilibrium Computation in the Population Protocol Model.” In Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, 40–49. Association for Computing Machinery, 2024. https://doi.org/10.1145/3662158.3662768. ieee: D.-A. Alistarh, K. Chatterjee, M. Karrabi, and J. M. Lazarsfeld, “Game dynamics and equilibrium computation in the population protocol model,” in Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Nantes, France, 2024, pp. 40–49. 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.” Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing, Association for Computing Machinery, 2024, pp. 40–49, doi:10.1145/3662158.3662768. 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. conference: end_date: 2024-06-21 location: Nantes, France name: 'PODC: Symposium on Principles of Distributed Computing' start_date: 2024-06-17 corr_author: '1' das_tickbox: '0' date_created: 2024-07-28T22:01:10Z date_published: 2024-06-17T00:00:00Z date_updated: 2026-10-01T09:34:57Z day: '17' ddc: - '000' department: - _id: DaAl - _id: KrCh doi: 10.1145/3662158.3662768 ec_funded: 1 file: - access_level: open_access checksum: 65a40437f83373fa79dd999d5287509e content_type: application/pdf creator: dernst date_created: 2024-07-29T07:37:31Z date_updated: 2024-07-29T07:37:31Z file_id: '17335' file_name: 2024_ACMPODC_Alistarh.pdf file_size: 750908 relation: main_file success: 1 file_date_updated: 2024-07-29T07:37:31Z fulldoi: https://doi.org/10.1145/3662158.3662768 has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '06' oa: 1 oa_version: Published Version page: 40-49 project: - _id: 0599E47C-7A3F-11EA-A408-12923DDC885E call_identifier: H2020 grant_number: '863818' name: 'Formal Methods for Stochastic Models: Algorithms and Applications' publication: Proceedings of the 43rd Annual ACM Symposium on Principles of Distributed Computing publication_identifier: isbn: - '9798400706684' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Game dynamics and equilibrium computation in the population protocol model tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '18121' 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. acknowledged_ssus: - _id: ScienComp 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. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Arshia Soltani full_name: Moakhar, Arshia Soltani last_name: Moakhar - first_name: Eugenia B full_name: Iofinova, Eugenia B id: f9a17499-f6e0-11ea-865d-fdf9a3f77117 last_name: Iofinova orcid: 0000-0002-7778-3221 - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Moakhar AS, Iofinova EB, Frantar E, Alistarh D-A. SPADE: Sparsity-guided debugging for deep neural networks. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:45955-45987.' apa: 'Moakhar, A. S., Iofinova, E. B., Frantar, E., & Alistarh, D.-A. (2024). SPADE: Sparsity-guided debugging for deep neural networks. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 45955–45987). Vienna, Austria: ML Research Press.' chicago: 'Moakhar, Arshia Soltani, Eugenia B Iofinova, Elias Frantar, and Dan-Adrian Alistarh. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” In Proceedings of the 41st International Conference on Machine Learning, 235:45955–87. ML Research Press, 2024.' ieee: 'A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.' 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.' mla: 'Moakhar, Arshia Soltani, et al. “SPADE: Sparsity-Guided Debugging for Deep Neural Networks.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 45955–87.' 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. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' das_tickbox: '0' date_created: 2024-09-22T22:01:46Z date_published: 2024-09-01T00:00:00Z date_updated: 2026-10-01T09:32:47Z day: '01' department: - _id: DaAl external_id: arxiv: - '2310.04519' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.04519 month: '09' oa: 1 oa_version: Preprint page: 45955-45987 project: - _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A grant_number: W1260-N35 name: Vienna Graduate School on Computational Optimization publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - relation: software url: https://github.com/IST-DASLab/SPADE record: - id: '21854' relation: dissertation_contains status: public researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: no title: 'SPADE: Sparsity-guided debugging for deep neural networks' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18976' abstract: - lang: eng 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. 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." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Rustem full_name: Islamov, Rustem last_name: Islamov - first_name: Mher full_name: Safaryan, Mher id: dd546b39-0804-11ed-9c55-ef075c39778d last_name: Safaryan - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Islamov R, Safaryan M, Alistarh D-A. AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:649-657.' apa: 'Islamov, R., Safaryan, M., & Alistarh, D.-A. (2024). AsGrad: A sharp unified analysis of asynchronous-SGD algorithms. In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 649–657). Valencia, Spain: ML Research Press.' chicago: 'Islamov, Rustem, Mher Safaryan, and Dan-Adrian Alistarh. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” In Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, 238:649–57. ML Research Press, 2024.' ieee: 'R. Islamov, M. Safaryan, and D.-A. Alistarh, “AsGrad: A sharp unified analysis of asynchronous-SGD algorithms,” in Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 649–657.' 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.' mla: 'Islamov, Rustem, et al. “AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms.” Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 649–57.' 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. conference: end_date: 2024-05-04 location: Valencia, Spain name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2024-05-02 corr_author: '1' das_tickbox: '0' date_created: 2025-01-30T08:15:49Z date_published: 2024-05-15T00:00:00Z date_updated: 2026-10-01T09:33:24Z day: '15' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2310.20452' intvolume: ' 238' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2310.20452 month: '05' oa: 1 oa_version: Preprint page: 649-657 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: 'AsGrad: A sharp unified analysis of asynchronous-SGD algorithms' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 238 year: '2024' ... --- _id: '18113' 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.' 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." alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Vage full_name: Egiazarian, Vage last_name: Egiazarian - first_name: Andrei full_name: Panferov, Andrei id: 2c18daae-4dbe-11ef-8491-98ce2d960f09 last_name: Panferov - first_name: Denis full_name: Kuznedelev, Denis last_name: Kuznedelev - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Artem full_name: Babenko, Artem last_name: Babenko - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Egiazarian V, Panferov A, Kuznedelev D, Frantar E, Babenko A, Alistarh D-A. Extreme compression of large language models via additive quantization. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:12284-12303.' apa: 'Egiazarian, V., Panferov, A., Kuznedelev, D., Frantar, E., Babenko, A., & Alistarh, D.-A. (2024). Extreme compression of large language models via additive quantization. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 12284–12303). Vienna, Austria: ML Research Press.' 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 Proceedings of the 41st International Conference on Machine Learning, 235:12284–303. ML Research Press, 2024. 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 Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303. 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.' mla: Egiazarian, Vage, et al. “Extreme Compression of Large Language Models via Additive Quantization.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 12284–303. 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. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' das_tickbox: '0' date_created: 2024-09-22T22:01:43Z date_published: 2024-09-01T00:00:00Z date_updated: 2026-10-01T09:34:19Z day: '01' department: - _id: DaAl - _id: GradSch external_id: arxiv: - '2401.06118' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: ' https://doi.org/10.48550/arXiv.2401.06118' month: '09' oa: 1 oa_version: Preprint page: 12284-12303 publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Extreme compression of large language models via additive quantization type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18975' abstract: - lang: eng 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. acknowledged_ssus: - _id: CampIT 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. alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Ionut-Vlad full_name: Modoranu, Ionut-Vlad id: 449f7a18-f128-11eb-9611-9b430c0c6333 last_name: Modoranu - first_name: Aleksei full_name: Kalinov, Aleksei id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02 last_name: Kalinov orcid: 0000-0003-2189-3904 - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:35910-35933.' apa: 'Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., & Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In 41st International Conference on Machine Learning (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.' chicago: Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In 41st International Conference on Machine Learning, 235:35910–33. ML Research Press, 2024. ieee: I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 35910–35933. 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.' mla: Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 35910–33. 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. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' das_tickbox: '0' date_created: 2025-01-30T07:53:22Z date_published: 2024-07-30T00:00:00Z date_updated: 2026-10-01T09:33:07Z day: '30' department: - _id: DaAl external_id: arxiv: - '2306.06098' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2306.06098 month: '07' oa: 1 oa_version: Preprint page: 35910-35933 publication: 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: no title: Error feedback can accurately compress preconditioners type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 235 year: '2024' ... --- OA_place: repository OA_type: green _id: '18977' abstract: - lang: eng 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." 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." article_processing_charge: No arxiv: 1 author: - first_name: Tim full_name: Dettmers, Tim last_name: Dettmers - first_name: Ruslan A. full_name: Svirschevski, Ruslan A. last_name: Svirschevski - first_name: Vage full_name: Egiazarian, Vage last_name: Egiazarian - first_name: Denis full_name: Kuznedelev, Denis last_name: Kuznedelev - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Saleh full_name: Ashkboos, Saleh last_name: Ashkboos - first_name: Alexander full_name: Borzunov, Alexander last_name: Borzunov - first_name: Torsten full_name: Hoefler, Torsten last_name: Hoefler - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Dettmers T, Svirschevski RA, Egiazarian V, et al. SpQR: A sparse-quantized representation for near-lossless LLM weight compression. In: 12th International Conference on Learning Representations. OpenReview; 2024.' 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 12th International Conference on Learning Representations. 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 12th International Conference on Learning Representations. OpenReview, 2024.' ieee: 'T. Dettmers et al., “SpQR: A sparse-quantized representation for near-lossless LLM weight compression,” in 12th International Conference on Learning Representations, Vienna, Austria, 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.” 12th International Conference on Learning Representations, OpenReview, 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. conference: end_date: 2024-05-11 location: Vienna, Austria name: 'ICLR: International Conference on Learning Representations' start_date: 2024-05-07 das_tickbox: '0' date_created: 2025-01-30T08:26:59Z date_published: 2024-05-15T00:00:00Z date_updated: 2026-10-01T09:33:44Z day: '15' department: - _id: DaAl external_id: arxiv: - '2306.03078' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2306.03078 month: '05' oa: 1 oa_version: Preprint publication: 12th International Conference on Learning Representations publication_status: published publisher: OpenReview quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: 'SpQR: A sparse-quantized representation for near-lossless LLM weight compression' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '17093' 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.' alternative_title: - PMLR article_processing_charge: No arxiv: 1 author: - first_name: Hossein full_name: Zakerinia, Hossein id: 653bd8b6-f394-11eb-9cf6-c0bbf6cd78d4 last_name: Zakerinia orcid: 0009-0007-3977-6462 - first_name: Shayan full_name: Talaei, Shayan last_name: Talaei - first_name: Giorgi full_name: Nadiradze, Giorgi id: 3279A00C-F248-11E8-B48F-1D18A9856A87 last_name: Nadiradze orcid: 0000-0001-5634-0731 - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Zakerinia H, Talaei S, Nadiradze G, Alistarh D-A. Communication-efficient federated learning with data and client heterogeneity. In: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics. Vol 238. ML Research Press; 2024:3448-3456.' apa: 'Zakerinia, H., Talaei, S., Nadiradze, G., & Alistarh, D.-A. (2024). Communication-efficient federated learning with data and client heterogeneity. In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (Vol. 238, pp. 3448–3456). Valencia, Spain: ML Research Press.' chicago: Zakerinia, Hossein, Shayan Talaei, Giorgi Nadiradze, and Dan-Adrian Alistarh. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” In Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, 238:3448–56. ML Research Press, 2024. ieee: H. Zakerinia, S. Talaei, G. Nadiradze, and D.-A. Alistarh, “Communication-efficient federated learning with data and client heterogeneity,” in Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, Valencia, Spain, 2024, vol. 238, pp. 3448–3456. 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.' mla: Zakerinia, Hossein, et al. “Communication-Efficient Federated Learning with Data and Client Heterogeneity.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics, vol. 238, ML Research Press, 2024, pp. 3448–56. 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. conference: end_date: 2024-05-04 location: Valencia, Spain name: 'AISTATS: Conference on Artificial Intelligence and Statistics' start_date: 2024-05-02 corr_author: '1' das_tickbox: '0' date_created: 2024-06-02T22:00:57Z date_published: 2024-05-01T00:00:00Z date_updated: 2026-10-01T09:34:02Z day: '01' department: - _id: DaAl - _id: ChLa external_id: arxiv: - '2206.10032' intvolume: ' 238' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2206.10032 month: '05' oa: 1 oa_version: Preprint page: 3448-3456 publication: Proceedings of the 27th International Conference on Artificial Intelligence and Statistics publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Communication-efficient federated learning with data and client heterogeneity type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 238 year: '2024' ... --- _id: '18117' abstract: - lang: eng 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." acknowledgement: The authors would like to thank Eldar Kurtic for experimental support and useful suggestions throughout the project article_processing_charge: No arxiv: 1 author: - first_name: Mahdi full_name: Nikdan, Mahdi id: 66374281-f394-11eb-9cf6-869147deecc0 last_name: Nikdan - first_name: Soroush full_name: Tabesh, Soroush id: 06000900-6068-11ef-8d61-c2472ef2e752 last_name: Tabesh orcid: 0009-0003-4119-6281 - first_name: Elvir full_name: Crncevic, Elvir id: 41888001-440d-11ef-8299-d0e838b8185e last_name: Crncevic - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Nikdan M, Tabesh S, Crncevic E, Alistarh D-A. RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In: Proceedings of the 41st International Conference on Machine Learning. Vol 235. ML Research Press; 2024:38187-38206.' apa: 'Nikdan, M., Tabesh, S., Crncevic, E., & Alistarh, D.-A. (2024). RoSA: Accurate parameter-efficient fine-tuning via robust adaptation. In Proceedings of the 41st International Conference on Machine Learning (Vol. 235, pp. 38187–38206). Vienna, Austria: ML Research Press.' chicago: 'Nikdan, Mahdi, Soroush Tabesh, Elvir Crncevic, and Dan-Adrian Alistarh. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” In Proceedings of the 41st International Conference on Machine Learning, 235:38187–206. ML Research Press, 2024.' ieee: 'M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.' 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.' mla: 'Nikdan, Mahdi, et al. “RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation.” Proceedings of the 41st International Conference on Machine Learning, vol. 235, ML Research Press, 2024, pp. 38187–206.' 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. conference: end_date: 2024-07-27 location: Vienna, Austria name: 'ICML: International Conference on Machine Learning' start_date: 2024-07-21 corr_author: '1' das_tickbox: '0' date_created: 2024-09-22T22:01:44Z date_published: 2024-09-01T00:00:00Z date_updated: 2026-10-01T09:34:36Z day: '01' department: - _id: DaAl - _id: GradSch external_id: arxiv: - '2401.04679' intvolume: ' 235' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2401.04679 month: '09' oa: 1 oa_version: Preprint page: 38187-38206 publication: Proceedings of the 41st International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' related_material: link: - relation: software url: https://github.com/IST-DASLab/RoSA researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: no title: 'RoSA: Accurate parameter-efficient fine-tuning via robust adaptation' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 235 year: '2024' ... --- _id: '15315' 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. article_number: '056601' article_processing_charge: Yes (in subscription journal) article_type: review arxiv: 1 author: - first_name: David full_name: Brückner, David id: e1e86031-6537-11eb-953a-f7ab92be508d last_name: Brückner orcid: 0000-0001-7205-2975 - first_name: Chase P. full_name: Broedersz, Chase P. last_name: Broedersz citation: ama: 'Brückner D, Broedersz CP. Learning dynamical models of single and collective cell migration: a review. Reports on Progress in Physics. 2024;87(5). doi:10.1088/1361-6633/ad36d2' apa: 'Brückner, D., & Broedersz, C. P. (2024). Learning dynamical models of single and collective cell migration: a review. Reports on Progress in Physics. IOP Publishing. https://doi.org/10.1088/1361-6633/ad36d2' chicago: 'Brückner, David, and Chase P. Broedersz. “Learning Dynamical Models of Single and Collective Cell Migration: A Review.” Reports on Progress in Physics. IOP Publishing, 2024. https://doi.org/10.1088/1361-6633/ad36d2.' ieee: 'D. Brückner and C. P. Broedersz, “Learning dynamical models of single and collective cell migration: a review,” Reports on Progress in Physics, vol. 87, no. 5. IOP Publishing, 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.” Reports on Progress in Physics, vol. 87, no. 5, 056601, IOP Publishing, 2024, doi:10.1088/1361-6633/ad36d2.' short: D. Brückner, C.P. Broedersz, Reports on Progress in Physics 87 (2024). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: No new data were created or analysed in this study. date_created: 2024-04-14T22:01:01Z date_published: 2024-04-04T00:00:00Z date_updated: 2026-10-01T10:09:41Z day: '04' ddc: - '530' department: - _id: EdHa doi: 10.1088/1361-6633/ad36d2 external_id: arxiv: - '2309.00545' isi: - '001196692400001' pmid: - '38518358' file: - access_level: open_access checksum: c5910078230ade20f4dd83592e862a72 content_type: application/pdf creator: dernst date_created: 2024-08-20T11:00:03Z date_updated: 2024-08-20T11:00:03Z file_id: '17451' file_name: 2024_ReportPhysics_Brueckner.pdf file_size: 4376898 relation: main_file success: 1 file_date_updated: 2024-08-20T11:00:03Z fulldoi: https://doi.org/10.1088/1361-6633/ad36d2 has_accepted_license: '1' intvolume: ' 87' isi: 1 issue: '5' language: - iso: eng license: https://creativecommons.org/licenses/by/3.0/ month: '04' oa: 1 oa_version: Published Version pmid: 1 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 publication: Reports on Progress in Physics publication_identifier: eissn: - 1361-6633 issn: - 0034-4885 publication_status: published publisher: IOP Publishing quality_controlled: '1' researchdata_availability: not applicable scopus_import: '1' status: public supplementarymaterial: no title: 'Learning dynamical models of single and collective cell migration: a review' tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode name: Creative Commons Attribution 3.0 Unported (CC BY 3.0) short: CC BY (3.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 87 year: '2024' ... --- _id: '15250' 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. acknowledged_ssus: - _id: Bio 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.). article_number: e2319686121 article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Danila full_name: Boytsov, Danila last_name: Boytsov - first_name: Gregor M. full_name: Madej, Gregor M. last_name: Madej - first_name: Georg full_name: Horn, Georg last_name: Horn - first_name: Nadine full_name: Blaha, Nadine last_name: Blaha - first_name: Thomas full_name: Köcher, Thomas last_name: Köcher - first_name: Harald H. full_name: Sitte, Harald H. last_name: Sitte - first_name: Daria E full_name: Siekhaus, Daria E id: 3D224B9E-F248-11E8-B48F-1D18A9856A87 last_name: Siekhaus orcid: 0000-0001-8323-8353 - first_name: Christine full_name: Ziegler, Christine last_name: Ziegler - first_name: Walter full_name: Sandtner, Walter last_name: Sandtner - first_name: Marko full_name: Roblek, Marko id: 3047D808-F248-11E8-B48F-1D18A9856A87 last_name: Roblek orcid: 0000-0001-9588-1389 citation: ama: Boytsov D, Madej GM, Horn G, et al. Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport. Proceedings of the National Academy of Sciences of the United States of America. 2024;121(13). doi:10.1073/pnas.2319686121 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. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2319686121 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.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2319686121. ieee: D. Boytsov et al., “Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport,” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 13. National Academy of Sciences, 2024. 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. mla: Boytsov, Danila, et al. “Orphan Lysosomal Solute Carrier MFSD1 Facilitates Highly Selective Dipeptide Transport.” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 13, e2319686121, National Academy of Sciences, 2024, doi:10.1073/pnas.2319686121. 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). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: All study data are included in the article and/or SI Appendix. date_created: 2024-03-31T22:01:12Z date_published: 2024-03-26T00:00:00Z date_updated: 2026-10-01T09:36:29Z day: '26' ddc: - '570' department: - _id: DaSi doi: 10.1073/pnas.2319686121 external_id: isi: - '001206419000003' pmid: - '38507452' file: - access_level: open_access checksum: 06f9e60b1146a685d58bf33999422fa8 content_type: application/pdf creator: dernst date_created: 2024-04-02T08:54:28Z date_updated: 2024-04-02T08:54:28Z file_id: '15256' file_name: 2024_PNAS_Boytsov.pdf file_size: 2483787 relation: main_file success: 1 file_date_updated: 2024-04-02T08:54:28Z fulldoi: https://doi.org/10.1073/pnas.2319686121 has_accepted_license: '1' intvolume: ' 121' isi: 1 issue: '13' language: - iso: eng month: '03' oa: 1 oa_version: Published Version pmid: 1 publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - 1091-6490 publication_status: published publisher: National Academy of Sciences quality_controlled: '1' researchdata_availability: yes (supplementary data) scopus_import: '1' status: public supplementarymaterial: yes title: Orphan lysosomal solute carrier MFSD1 facilitates highly selective dipeptide transport tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 121 year: '2024' ... --- _id: '15024' abstract: - lang: eng 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. 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. article_number: '064503' article_processing_charge: Yes (in subscription journal) article_type: original arxiv: 1 author: - first_name: Paul full_name: Robin, Paul id: 48c58128-57b0-11ee-9095-dc28fd97fc1d last_name: Robin orcid: 0000-0002-5728-9189 citation: ama: Robin P. Correlation-induced viscous dissipation in concentrated electrolytes. Journal of Chemical Physics. 2024;160(6). doi:10.1063/5.0188215 apa: Robin, P. (2024). Correlation-induced viscous dissipation in concentrated electrolytes. Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0188215 chicago: Robin, Paul. “Correlation-Induced Viscous Dissipation in Concentrated Electrolytes.” Journal of Chemical Physics. AIP Publishing, 2024. https://doi.org/10.1063/5.0188215. ieee: P. Robin, “Correlation-induced viscous dissipation in concentrated electrolytes,” Journal of Chemical Physics, vol. 160, no. 6. AIP Publishing, 2024. ista: Robin P. 2024. Correlation-induced viscous dissipation in concentrated electrolytes. Journal of Chemical Physics. 160(6), 064503. mla: Robin, Paul. “Correlation-Induced Viscous Dissipation in Concentrated Electrolytes.” Journal of Chemical Physics, vol. 160, no. 6, 064503, AIP Publishing, 2024, doi:10.1063/5.0188215. short: P. Robin, Journal of Chemical Physics 160 (2024). corr_author: '1' das_tickbox: '1' 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. date_created: 2024-02-25T23:00:55Z date_published: 2024-02-14T00:00:00Z date_updated: 2026-10-01T10:08:50Z day: '14' ddc: - '540' department: - _id: EdHa doi: 10.1063/5.0188215 ec_funded: 1 external_id: arxiv: - '2311.11784' isi: - '001161104900003' pmid: - '38349632' file: - access_level: open_access checksum: 0a5e0ae70849bce674466fc054390ec0 content_type: application/pdf creator: dernst date_created: 2024-02-27T08:12:52Z date_updated: 2024-02-27T08:12:52Z file_id: '15034' file_name: 2024_JourChemicalPhysics_Robin.pdf file_size: 5452738 relation: main_file success: 1 file_date_updated: 2024-02-27T08:12:52Z fulldoi: https://doi.org/10.1063/5.0188215 has_accepted_license: '1' intvolume: ' 160' isi: 1 issue: '6' language: - iso: eng month: '02' oa: 1 oa_version: Published Version pmid: 1 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Journal of Chemical Physics publication_identifier: eissn: - 1089-7690 issn: - 0021-9606 publication_status: published publisher: AIP Publishing quality_controlled: '1' researchdata_availability: not applicable scopus_import: '1' status: public supplementarymaterial: no title: Correlation-induced viscous dissipation in concentrated electrolytes tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 160 year: '2024' ... --- _id: '17892' 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 −\U0001D6FE. 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−\U0001D6FE) over time. The correlation function decays algebraically with two distinct spatial regimes characterized by exponents −\U0001D6FE on short distances and −(2/3)⁢(2−\U0001D6FE) 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. article_number: '024404' article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Fabrizio full_name: Olmeda, Fabrizio id: 69dbf5fb-8a76-11ed-866b-fb486d8b5689 last_name: Olmeda - first_name: Steffen full_name: Rulands, Steffen last_name: Rulands citation: ama: Olmeda F, Rulands S. Field theory of enzyme-substrate systems with restricted long-range interactions. Physical Review E. 2024;110(2). doi:10.1103/PhysRevE.110.024404 apa: Olmeda, F., & Rulands, S. (2024). Field theory of enzyme-substrate systems with restricted long-range interactions. Physical Review E. American Physical Society. https://doi.org/10.1103/PhysRevE.110.024404 chicago: Olmeda, Fabrizio, and Steffen Rulands. “Field Theory of Enzyme-Substrate Systems with Restricted Long-Range Interactions.” Physical Review E. American Physical Society, 2024. https://doi.org/10.1103/PhysRevE.110.024404. ieee: F. Olmeda and S. Rulands, “Field theory of enzyme-substrate systems with restricted long-range interactions,” Physical Review E, vol. 110, no. 2. American Physical Society, 2024. ista: Olmeda F, Rulands S. 2024. Field theory of enzyme-substrate systems with restricted long-range interactions. Physical Review E. 110(2), 024404. mla: Olmeda, Fabrizio, and Steffen Rulands. “Field Theory of Enzyme-Substrate Systems with Restricted Long-Range Interactions.” Physical Review E, vol. 110, no. 2, 024404, American Physical Society, 2024, doi:10.1103/PhysRevE.110.024404. short: F. Olmeda, S. Rulands, Physical Review E 110 (2024). corr_author: '1' das_tickbox: '0' date_created: 2024-09-08T22:01:12Z date_published: 2024-08-01T00:00:00Z date_updated: 2026-10-01T10:06:45Z day: '01' ddc: - '530' department: - _id: EdHa doi: 10.1103/PhysRevE.110.024404 ec_funded: 1 external_id: isi: - '001299670100004' pmid: - '39294986' file: - access_level: open_access checksum: 67fc2cc8eee3155e5c3b7380307d8284 content_type: application/pdf creator: dernst date_created: 2024-09-11T05:59:36Z date_updated: 2024-09-11T05:59:36Z file_id: '18053' file_name: 2024_PhysReviewE_Olmeda.pdf file_size: 445696 relation: main_file success: 1 file_date_updated: 2024-09-11T05:59:36Z fulldoi: https://doi.org/10.1103/PhysRevE.110.024404 has_accepted_license: '1' intvolume: ' 110' isi: 1 issue: '2' language: - iso: eng month: '08' oa: 1 oa_version: Published Version pmid: 1 project: - _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c call_identifier: H2020 grant_number: '101034413' name: 'IST-BRIDGE: International postdoctoral program' publication: Physical Review E publication_identifier: eissn: - 2470-0053 issn: - 2470-0045 publication_status: published publisher: American Physical Society quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Field theory of enzyme-substrate systems with restricted long-range interactions tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 110 year: '2024' ... --- _id: '17104' abstract: - lang: eng 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.' 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’. article_number: '097' article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Johannes C. full_name: Krämer, Johannes C. last_name: Krämer - first_name: Edouard B full_name: Hannezo, Edouard B id: 3A9DB764-F248-11E8-B48F-1D18A9856A87 last_name: Hannezo orcid: 0000-0001-6005-1561 - first_name: Gerhard full_name: Gompper, Gerhard last_name: Gompper - first_name: Jens full_name: Elgeti, Jens last_name: Elgeti citation: ama: Krämer JC, Hannezo EB, Gompper G, Elgeti J. Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells. SciPost Physics. 2024;16(4). doi:10.21468/scipostphys.16.4.097 apa: Krämer, J. C., Hannezo, E. B., Gompper, G., & Elgeti, J. (2024). Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells. SciPost Physics. SciPost Foundation. https://doi.org/10.21468/scipostphys.16.4.097 chicago: Krämer, Johannes C., Edouard B Hannezo, Gerhard Gompper, and Jens Elgeti. “Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” SciPost Physics. SciPost Foundation, 2024. https://doi.org/10.21468/scipostphys.16.4.097. ieee: J. C. Krämer, E. B. Hannezo, G. Gompper, and J. Elgeti, “Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells,” SciPost Physics, vol. 16, no. 4. SciPost Foundation, 2024. 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. mla: Krämer, Johannes C., et al. “Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” SciPost Physics, vol. 16, no. 4, 097, SciPost Foundation, 2024, doi:10.21468/scipostphys.16.4.097. short: J.C. Krämer, E.B. Hannezo, G. Gompper, J. Elgeti, SciPost Physics 16 (2024). das_tickbox: '1' 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. date_created: 2024-06-03T08:58:44Z date_published: 2024-04-08T00:00:00Z date_updated: 2026-10-01T09:44:06Z day: '08' ddc: - '530' department: - _id: EdHa doi: 10.21468/scipostphys.16.4.097 external_id: arxiv: - '2310.04272' isi: - '001202370200001' file: - access_level: open_access checksum: 6fdeecd21c166db8dedb927ecc2e6025 content_type: application/pdf creator: dernst date_created: 2024-06-03T11:18:51Z date_updated: 2024-06-03T11:18:51Z file_id: '17109' file_name: 2024_SciPostPhys_Kraemer.pdf file_size: 4973291 relation: main_file success: 1 file_date_updated: 2024-06-03T11:18:51Z fulldoi: https://doi.org/10.21468/scipostphys.16.4.097 has_accepted_license: '1' intvolume: ' 16' isi: 1 issue: '4' language: - iso: eng month: '04' oa: 1 oa_version: Published Version publication: SciPost Physics publication_identifier: issn: - 2542-4653 publication_status: published publisher: SciPost Foundation quality_controlled: '1' related_material: record: - id: '23022' relation: research_data status: public researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Mechanically-driven stem cell separation in tissues caused by proliferating daughter cells tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 16 year: '2024' ... --- OA_type: closed access _id: '18919' 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. article_processing_charge: No article_type: letter_note author: - first_name: Denitsa Rangelova full_name: Baykusheva, Denitsa Rangelova id: 71b4d059-2a03-11ee-914d-dfa3beed6530 last_name: Baykusheva orcid: 0000-0002-7438-1139 citation: ama: Baykusheva DR. Through the slopes of a light-induced phase transition. Nature Physics. 2024;20(5):684-685. doi:10.1038/s41567-024-02401-7 apa: Baykusheva, D. R. (2024). Through the slopes of a light-induced phase transition. Nature Physics. Springer Nature. https://doi.org/10.1038/s41567-024-02401-7 chicago: Baykusheva, Denitsa Rangelova. “Through the Slopes of a Light-Induced Phase Transition.” Nature Physics. Springer Nature, 2024. https://doi.org/10.1038/s41567-024-02401-7. ieee: D. R. Baykusheva, “Through the slopes of a light-induced phase transition,” Nature Physics, vol. 20, no. 5. Springer Nature, pp. 684–685, 2024. 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.” Nature Physics, vol. 20, no. 5, Springer Nature, 2024, pp. 684–85, doi:10.1038/s41567-024-02401-7. short: D.R. Baykusheva, Nature Physics 20 (2024) 684–685. corr_author: '1' das_tickbox: '0' date_created: 2025-01-27T14:29:20Z date_published: 2024-05-01T00:00:00Z date_updated: 2026-10-01T09:37:00Z day: '01' department: - _id: DeBa doi: 10.1038/s41567-024-02401-7 external_id: isi: - '001162208200002' fulldoi: https://doi.org/10.1038/s41567-024-02401-7 intvolume: ' 20' isi: 1 issue: '5' language: - iso: eng month: '05' oa_version: None page: 684-685 publication: Nature Physics publication_identifier: eissn: - 1745-2481 issn: - 1745-2473 publication_status: published publisher: Springer Nature quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Through the slopes of a light-induced phase transition type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 20 year: '2024' ... --- OA_place: repository OA_type: green _id: '23022' abstract: - lang: eng 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." article_processing_charge: No author: - first_name: Jens full_name: Elgeti, Jens last_name: Elgeti - first_name: Johannes full_name: Krämer, Johannes last_name: Krämer citation: ama: Elgeti J, Krämer J. Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells. 2024. doi:10.5281/zenodo.8410956 apa: Elgeti, J., & Krämer, J. (2024). Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells. Zenodo. https://doi.org/10.5281/zenodo.8410956 chicago: Elgeti, Jens, and Johannes Krämer. “Data for Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells.” Zenodo, 2024. https://doi.org/10.5281/zenodo.8410956. ieee: J. Elgeti and J. Krämer, “Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells.” Zenodo, 2024. ista: Elgeti J, Krämer J. 2024. Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells, Zenodo, 10.5281/zenodo.8410956. mla: Elgeti, Jens, and Johannes Krämer. Data for Mechanically-Driven Stem Cell Separation in Tissues Caused by Proliferating Daughter Cells. Zenodo, 2024, doi:10.5281/zenodo.8410956. short: J. Elgeti, J. Krämer, (2024). date_created: 2026-10-01T09:44:01Z date_published: 2024-03-25T00:00:00Z date_updated: 2026-10-01T09:44:07Z day: '25' ddc: - '530' department: - _id: EdHa doi: 10.5281/zenodo.8410956 fulldoi: https://doi.org/10.5281/zenodo.8410956 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.8410956 month: '03' oa: 1 oa_version: None publisher: Zenodo related_material: record: - id: '17104' relation: used_in_publication status: public status: public title: Data for Mechanically-driven Stem Cell Separation in Tissues caused by Proliferating Daughter Cells type: research_data_reference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- APC_amount: 2570,79 EUR OA_place: publisher OA_type: hybrid _id: '17123' 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. article_number: e2322326121 article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: David full_name: Brückner, David id: e1e86031-6537-11eb-953a-f7ab92be508d last_name: Brückner orcid: 0000-0001-7205-2975 - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: ama: Brückner D, Tkačik G. Information content and optimization of self-organized developmental systems. Proceedings of the National Academy of Sciences of the United States of America. 2024;121(23). doi:10.1073/pnas.2322326121 apa: 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. National Academy of Sciences. https://doi.org/10.1073/pnas.2322326121 chicago: Brückner, David, and Gašper Tkačik. “Information Content and Optimization of Self-Organized Developmental Systems.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2024. https://doi.org/10.1073/pnas.2322326121. ieee: D. Brückner and G. Tkačik, “Information content and optimization of self-organized developmental systems,” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 23. National Academy of Sciences, 2024. 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. mla: Brückner, David, and Gašper Tkačik. “Information Content and Optimization of Self-Organized Developmental Systems.” Proceedings of the National Academy of Sciences of the United States of America, vol. 121, no. 23, e2322326121, National Academy of Sciences, 2024, doi:10.1073/pnas.2322326121. short: D. Brückner, G. Tkačik, Proceedings of the National Academy of Sciences of the United States of America 121 (2024). corr_author: '1' das_tickbox: '1' 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). date_created: 2024-06-09T22:01:02Z date_published: 2024-06-04T00:00:00Z date_updated: 2026-10-01T11:03:59Z day: '04' ddc: - '570' department: - _id: EdHa - _id: GaTk doi: 10.1073/pnas.2322326121 external_id: isi: - '001244835000006' pmid: - '38819997' file: - access_level: open_access checksum: 59797a75db7beb3721ed7a4d14c241f9 content_type: application/pdf creator: dernst date_created: 2024-06-10T10:27:37Z date_updated: 2024-06-10T10:27:37Z file_id: '17130' file_name: 2024_PNAS_Brueckner.pdf file_size: 12329234 relation: main_file success: 1 file_date_updated: 2024-06-10T10:27:37Z fulldoi: https://doi.org/10.1073/pnas.2322326121 has_accepted_license: '1' intvolume: ' 121' isi: 1 issue: '23' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-nd/4.0/ month: '06' oa: 1 oa_version: Published Version pmid: 1 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 publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - 1091-6490 issn: - 0027-8424 publication_status: published publisher: National Academy of Sciences quality_controlled: '1' related_material: link: - relation: software url: https://github.com/dbrueckner/SelforgInformation - description: News on the ISTA website relation: press_release url: https://ista.ac.at/en/news/the-embryo-assembles-itself/ researchdata_availability: not applicable scopus_import: '1' status: public supplementarymaterial: yes title: Information content and optimization of self-organized developmental systems tmp: image: /images/cc_by_nc_nd.png 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) short: CC BY-NC-ND (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 121 year: '2024' ...