[{"abstract":[{"text":"Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibrosis, inflammation, and edema.\r\nHowever, the inherently dynamic nature of the heart imposes strict limits on acquisition\r\ntimes, making high-resolution T1 mapping a persistent challenge. Compressed sensing (CS)\r\napproaches have reduced scan durations by undersampling k-space and reconstructing images from partial data, and recent studies show that jointly optimizing the undersampling\r\npatterns with the reconstruction network can substantially improve performance. Still,\r\nmost current T1 mapping pipelines rely on static, hand-crafted masks that do not exploit\r\nthe full acceleration and accuracy potential. Furthermore, most existing methods do not\r\nlevarage the physical T1 decay model in optimization. In this work, we introduce T1-\r\nPILOT: an end-to-end method that explicitly incorporates the T1 signal relaxation model\r\ninto the sampling–reconstruction framework to guide the learning of non-Cartesian trajectories, cross-frame alignment, and T1 decay estimation. Through extensive experiments\r\non the CMRxRecon dataset, T1-PILOT significantly outperforms several baseline strategies (including learned single-mask and fixed radial or golden-angle sampling schemes),\r\nachieving higher T1 map fidelity at greater acceleration factors. In particular, we observe consistent gains in PSNR and VIF relative to existing methods, along with marked\r\nimprovements in delineating finer myocardial structures. Our results highlight that optimizing sampling trajectories in tandem with the physical relaxation model leads to both\r\nenhanced quantitative accuracy and reduced acquisition times. Code for reproducing all\r\nexperiments and results is available at https://github.com/tamirshor7/T1-PILOT","lang":"eng"}],"OA_type":"gold","status":"public","day":"17","language":[{"iso":"eng"}],"author":[{"first_name":"Tamir","full_name":"Shor, Tamir","last_name":"Shor"},{"full_name":"Freiman, Moti","last_name":"Freiman","first_name":"Moti"},{"full_name":"Baskin, Chaim","last_name":"Baskin","first_name":"Chaim"},{"first_name":"Alexander","orcid":"0000-0001-9699-8730","last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander"}],"publisher":"ML Research Press","citation":{"ista":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. Medical Imaging with Deep Learning. MIDL: Medical Imaging with Deep Learning, PMLR, vol. 315, 1969–1982.","chicago":"Shor, Tamir, Moti Freiman, Chaim Baskin, and Alex M. Bronstein. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” In <i>Medical Imaging with Deep Learning</i>, 315:1969–82. ML Research Press, n.d.","mla":"Shor, Tamir, et al. “T1-PILOT: Physics-Informed Learned Optimized Trajectories for T1 Mapping Acceleration.” <i>Medical Imaging with Deep Learning</i>, vol. 315, ML Research Press, pp. 1969–82.","ieee":"T. Shor, M. Freiman, C. Baskin, and A. M. Bronstein, “T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration,” in <i>Medical Imaging with Deep Learning</i>, Taipei, Taiwan, vol. 315, pp. 1969–1982.","ama":"Shor T, Freiman M, Baskin C, Bronstein AM. T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In: <i>Medical Imaging with Deep Learning</i>. Vol 315. ML Research Press; :1969-1982.","short":"T. Shor, M. Freiman, C. Baskin, A.M. Bronstein, in:, Medical Imaging with Deep Learning, ML Research Press, n.d., pp. 1969–1982.","apa":"Shor, T., Freiman, M., Baskin, C., &#38; Bronstein, A. M. (n.d.). T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration. In <i>Medical Imaging with Deep Learning</i> (Vol. 315, pp. 1969–1982). Taipei, Taiwan: ML Research Press."},"_id":"21949","department":[{"_id":"AlBr"}],"scopus_import":"1","alternative_title":["PMLR"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"month":"03","article_processing_charge":"No","keyword":["Cardiac T1 Mapping","Trajectory Optimization and Reconstruction","PhysicsInformed Deep-Learning"],"title":"T1-PILOT: Physics-informed learned optimized trajectories for T1 mapping acceleration","date_published":"2026-03-17T00:00:00Z","OA_place":"publisher","corr_author":"1","page":"1969-1982","has_accepted_license":"1","oa_version":"Published Version","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"main_file_link":[{"open_access":"1","url":"https://openreview.net/forum?id=nZaPtHbd6N#discussion"}],"ddc":["000"],"quality_controlled":"1","type":"conference","year":"2026","date_updated":"2026-06-08T08:05:24Z","publication":"Medical Imaging with Deep Learning","publication_identifier":{"eissn":["2640-3498"]},"publication_status":"accepted","date_created":"2026-06-07T22:01:36Z","intvolume":"       315","related_material":{"link":[{"url":"https://github.com/tamirshor7/T1-PILOT","relation":"software"}]},"volume":315,"conference":{"start_date":"2026-07-08","name":"MIDL: Medical Imaging with Deep Learning","location":"Taipei, Taiwan","end_date":"2026-07-10"}},{"year":"2026","type":"conference","date_updated":"2026-06-29T06:56:34Z","fulldoi":"https://doi.org/10.4230/LIPIcs.FORC.2026.2","date_created":"2026-06-28T22:01:34Z","file":[{"access_level":"open_access","checksum":"c661f016d3861a1c1b590b87a744d087","file_id":"22149","file_size":1231914,"file_name":"2026_LIPIcsFORC_Kalinin.pdf","date_updated":"2026-06-29T06:55:23Z","success":1,"date_created":"2026-06-29T06:55:23Z","creator":"dernst","content_type":"application/pdf","relation":"main_file"}],"publication_status":"published","publication_identifier":{"eissn":["1868-8969"],"isbn":["9783959774192"]},"publication":"7th Symposium on Foundations of Responsible Computing","intvolume":"       368","conference":{"end_date":"2026-06-05","location":"Cambridge, MA; United States","name":"FORC: Symposium on Foundations of Responsible Computing","start_date":"2026-06-03"},"volume":368,"oa":1,"month":"06","article_processing_charge":"No","title":"Learning rate scheduling with matrix factorization for private training","keyword":["differential privacy","machine learning","matrix factorization"],"doi":"10.4230/LIPIcs.FORC.2026.2","OA_place":"publisher","date_published":"2026-06-01T00:00:00Z","has_accepted_license":"1","corr_author":"1","tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"ddc":["000"],"quality_controlled":"1","oa_version":"Published Version","publisher":"Schloss Dagstuhl - Leibniz-Zentrum für Informatik","researchdata_availability":"no","acknowledgement":"We thank Rasmus Pagh, Christoph Lampert and Jalaj Upadhyay for valuable\r\ncomments on an early draft. We thank Ryan Mckenna for a fruitful discussion on the experiment\r\ndesign. We thank Antti Honkela for sharing insights on learning rate scheduling and DP.\r\nNikita P. Kalinin: Funded in part by the Austrian Science Fund (FWF) [10.55776/COE12].\r\nJoel Daniel Andersson: Funded by the European Union. Views and opinions expressed are however\r\nthose of the author(s) only and do not necessarily reflect those of the European Union or the European\r\nResearch Council Executive Agency. Neither the European Union nor the granting authority can be\r\nheld responsible for them. This project has received funding from the European Research Council\r\n(ERC) under the European Union’s Horizon 2020 research and innovation programme (MoDynStruct,\r\nNo. 101019564). Additional funding by Providentia, a Data Science Distinguished Investigator grant\r\nfrom Novo Nordisk Fonden, with additional support from VILLUM Investigator grant 54451.\r\n","citation":{"ieee":"N. Kalinin and J. D. Andersson, “Learning rate scheduling with matrix factorization for private training,” in <i>7th Symposium on Foundations of Responsible Computing</i>, Cambridge, MA; United States, 2026, vol. 368.","mla":"Kalinin, Nikita, and Joel D. Andersson. “Learning Rate Scheduling with Matrix Factorization for Private Training.” <i>7th Symposium on Foundations of Responsible Computing</i>, vol. 368, 2:1-2:21, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026, doi:<a href=\"https://doi.org/10.4230/LIPIcs.FORC.2026.2\">10.4230/LIPIcs.FORC.2026.2</a>.","ama":"Kalinin N, Andersson JD. Learning rate scheduling with matrix factorization for private training. In: <i>7th Symposium on Foundations of Responsible Computing</i>. Vol 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik; 2026. doi:<a href=\"https://doi.org/10.4230/LIPIcs.FORC.2026.2\">10.4230/LIPIcs.FORC.2026.2</a>","short":"N. Kalinin, J.D. Andersson, in:, 7th Symposium on Foundations of Responsible Computing, Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026.","apa":"Kalinin, N., &#38; Andersson, J. D. (2026). Learning rate scheduling with matrix factorization for private training. In <i>7th Symposium on Foundations of Responsible Computing</i> (Vol. 368). Cambridge, MA; United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik. <a href=\"https://doi.org/10.4230/LIPIcs.FORC.2026.2\">https://doi.org/10.4230/LIPIcs.FORC.2026.2</a>","ista":"Kalinin N, Andersson JD. 2026. Learning rate scheduling with matrix factorization for private training. 7th Symposium on Foundations of Responsible Computing. FORC: Symposium on Foundations of Responsible Computing, LIPIcs, vol. 368, 2:1-2:21.","chicago":"Kalinin, Nikita, and Joel D Andersson. “Learning Rate Scheduling with Matrix Factorization for Private Training.” In <i>7th Symposium on Foundations of Responsible Computing</i>, Vol. 368. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2026. <a href=\"https://doi.org/10.4230/LIPIcs.FORC.2026.2\">https://doi.org/10.4230/LIPIcs.FORC.2026.2</a>."},"das_tickbox":"0","_id":"22146","supplementarymaterial":"no","department":[{"_id":"ChLa"},{"_id":"GradSch"},{"_id":"MoHe"}],"scopus_import":"1","ec_funded":1,"alternative_title":["LIPIcs"],"article_number":"2:1-2:21","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","arxiv":1,"OA_type":"gold","external_id":{"arxiv":["2511.17994"]},"abstract":[{"lang":"eng","text":"We study differentially private model training with stochastic gradient descent under learning rate scheduling and correlated noise. Although correlated noise, in particular via matrix factorizations, has been shown to improve accuracy, prior theoretical work focused primarily on the prefix-sum workload. That workload assumes a constant learning rate, whereas in practice learning rate schedules are widely used to accelerate training and improve convergence. We close this gap by deriving general upper and lower bounds for a broad class of learning rate schedules in both single- and multi-epoch settings. Building on these results, we propose a learning-rate-aware factorization that achieves improvements over prefix-sum factorizations under both MaxSE and MeanSE error metrics. Our theoretical analysis yields memory-efficient constructions suitable for practical deployment, and experiments on CIFAR-10 and IMDB datasets confirm that schedule-aware factorizations improve accuracy in private training."}],"status":"public","project":[{"grant_number":"101019564","_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","name":"The design and evaluation of modern fully dynamic data structures","call_identifier":"H2020"}],"day":"01","file_date_updated":"2026-06-29T06:55:23Z","author":[{"first_name":"Nikita","last_name":"Kalinin","id":"4b14526e-14d2-11ed-ba64-c14c9553d137","full_name":"Kalinin, Nikita"},{"last_name":"Andersson","id":"4a893819-d954-11f0-89b1-e360bad9ccc5","full_name":"Andersson, Joel D","first_name":"Joel D"}],"language":[{"iso":"eng"}]},{"arxiv":1,"OA_type":"green","external_id":{"arxiv":["2410.22374"]},"abstract":[{"text":"Modern computer systems store vast amounts of personal data, enabling advances in AI and ML but risking user privacy and trust. For privacy reasons, it is sometimes desired for an ML model to forget part of the data it was trained on. In this paper, we introduce a novel unlearning approach based on Forgetting Neural Networks (FNNs), a neuroscience-inspired architecture that explicitly encodes forgetting through multiplicative decay factors. While FNNs had previously been studied as a theoretical construct, we provide the first concrete implementation and demonstrate their effectiveness for targeted unlearning. We propose several variants with per-neuron forgetting factors, including rank-based assignments guided by activation levels, and evaluate them on MNIST and Fashion-MNIST benchmarks. Our method systematically removes information associated with forget sets while preserving performance on retained data. Membership inference attacks confirm the effectiveness of FNN-based unlearning in erasing information about the training data from the neural network. These results establish FNNs as a promising foundation for efficient and interpretable unlearning. ","lang":"eng"}],"status":"public","day":"30","author":[{"first_name":"Amartya","full_name":"Hatua, Amartya","last_name":"Hatua"},{"last_name":"Nguyen","full_name":"Nguyen, Trung","first_name":"Trung"},{"full_name":"Cano Cordoba, Filip","id":"708cad98-e86a-11ef-8098-bdae2d7c6af1","last_name":"Cano Cordoba","orcid":"0000-0002-0783-904X","first_name":"Filip"},{"first_name":"Andrew","last_name":"Sung","full_name":"Sung, Andrew"}],"language":[{"iso":"eng"}],"publisher":"SciTePress","das_tickbox":"1","citation":{"chicago":"Hatua, Amartya, Trung Nguyen, Filip Cano Cordoba, and Andrew Sung. “Machine Unlearning Using Forgetting Neural Networks.” In <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, 2:1536–46. SciTePress, 2026. <a href=\"https://doi.org/10.5220/0014326500004052\">https://doi.org/10.5220/0014326500004052</a>.","ista":"Hatua A, Nguyen T, Cano Cordoba F, Sung A. 2026. Machine unlearning using forgetting neural networks. Proceedings of the 18th International Conference on Agents and Artificial Intelligence. ICAART: International Conference on Agents and Artificial Intelligence vol. 2, 1536–1546.","apa":"Hatua, A., Nguyen, T., Cano Cordoba, F., &#38; Sung, A. (2026). Machine unlearning using forgetting neural networks. In <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i> (Vol. 2, pp. 1536–1546). Marbella, Spain: SciTePress. <a href=\"https://doi.org/10.5220/0014326500004052\">https://doi.org/10.5220/0014326500004052</a>","short":"A. Hatua, T. Nguyen, F. Cano Cordoba, A. Sung, in:, Proceedings of the 18th International Conference on Agents and Artificial Intelligence, SciTePress, 2026, pp. 1536–1546.","ieee":"A. Hatua, T. Nguyen, F. Cano Cordoba, and A. Sung, “Machine unlearning using forgetting neural networks,” in <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, Marbella, Spain, 2026, vol. 2, pp. 1536–1546.","mla":"Hatua, Amartya, et al. “Machine Unlearning Using Forgetting Neural Networks.” <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>, vol. 2, SciTePress, 2026, pp. 1536–46, doi:<a href=\"https://doi.org/10.5220/0014326500004052\">10.5220/0014326500004052</a>.","ama":"Hatua A, Nguyen T, Cano Cordoba F, Sung A. Machine unlearning using forgetting neural networks. In: <i>Proceedings of the 18th International Conference on Agents and Artificial Intelligence</i>. Vol 2. SciTePress; 2026:1536-1546. doi:<a href=\"https://doi.org/10.5220/0014326500004052\">10.5220/0014326500004052</a>"},"scopus_import":"1","_id":"22294","department":[{"_id":"ToHe"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"month":"06","article_processing_charge":"No","keyword":["Machine Unlearning","Neuroscience-Inspired Machine Learning","Membership Inference Attacks"],"title":"Machine unlearning using forgetting neural networks","doi":"10.5220/0014326500004052","OA_place":"repository","date_published":"2026-06-30T00:00:00Z","page":"1536-1546","quality_controlled":"1","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2410.22374","open_access":"1"}],"oa_version":"Preprint","year":"2026","type":"conference","date_updated":"2026-07-16T09:02:53Z","fulldoi":"https://doi.org/10.5220/0014326500004052","date_created":"2026-07-13T09:46:46Z","publication":"Proceedings of the 18th International Conference on Agents and Artificial Intelligence","publication_status":"published","publication_identifier":{"eissn":["2184-433X"],"isbn":["9789897587962"]},"intvolume":"         2","conference":{"start_date":"2026-03-05","name":"ICAART: International Conference on Agents and Artificial Intelligence","end_date":"2026-03-08","location":"Marbella, Spain"},"volume":2},{"acknowledged_ssus":[{"_id":"PreCl"},{"_id":"Bio"},{"_id":"LifeSc"},{"_id":"M-Shop"}],"date_updated":"2026-04-07T12:37:58Z","fulldoi":"https://doi.org/10.15479/AT-ISTA-19456","date_created":"2025-03-25T11:22:38Z","file":[{"date_updated":"2025-09-30T22:30:02Z","file_name":"2025_Thesis_Cumpelik_corrections_PDFA.pdf","date_created":"2025-03-25T11:07:55Z","access_level":"open_access","embargo":"2025-09-30","file_id":"19457","file_size":11869040,"checksum":"1c7573303d8e5f6da3eb03d59055390f","relation":"main_file","creator":"acumpeli","content_type":"application/pdf"},{"file_size":20436467,"file_id":"19458","checksum":"b93265ebd9a53f7a14100d0d48b4ff5b","access_level":"closed","embargo_to":"open_access","date_created":"2025-03-25T11:08:05Z","date_updated":"2025-09-30T22:30:02Z","file_name":"2025_Thesis_Cumpelik_corrections.docx","content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","creator":"acumpeli","relation":"source_file"}],"publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-056-5"]},"publication_status":"published","year":"2025","type":"dissertation","page":"96","has_accepted_license":"1","corr_author":"1","ddc":["612"],"OA_embargo":"6 months","oa_version":"Published Version","doi":"10.15479/AT-ISTA-19456","OA_place":"publisher","date_published":"2025-02-18T00:00:00Z","article_processing_charge":"No","keyword":["neuroscience","decision making","learning","cognitive flexibility","medial prefrontal cortex","hippocampus","electrophysiology"],"title":"The role of prefrontal spatial coding in supporting a contextual association task","oa":1,"supervisor":[{"first_name":"Jozsef L","orcid":"0000-0002-5193-4036","last_name":"Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","full_name":"Csicsvari, Jozsef L"}],"month":"02","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","alternative_title":["ISTA Thesis"],"citation":{"short":"A.D. Cumpelik, The Role of Prefrontal Spatial Coding in Supporting a Contextual Association Task, Institute of Science and Technology Austria, 2025.","apa":"Cumpelik, A. D. (2025). <i>The role of prefrontal spatial coding in supporting a contextual association task</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-19456\">https://doi.org/10.15479/AT-ISTA-19456</a>","ama":"Cumpelik AD. The role of prefrontal spatial coding in supporting a contextual association task. 2025. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-19456\">10.15479/AT-ISTA-19456</a>","mla":"Cumpelik, Andrea D. <i>The Role of Prefrontal Spatial Coding in Supporting a Contextual Association Task</i>. Institute of Science and Technology Austria, 2025, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-19456\">10.15479/AT-ISTA-19456</a>.","ieee":"A. D. Cumpelik, “The role of prefrontal spatial coding in supporting a contextual association task,” Institute of Science and Technology Austria, 2025.","chicago":"Cumpelik, Andrea D. “The Role of Prefrontal Spatial Coding in Supporting a Contextual Association Task.” Institute of Science and Technology Austria, 2025. <a href=\"https://doi.org/10.15479/AT-ISTA-19456\">https://doi.org/10.15479/AT-ISTA-19456</a>.","ista":"Cumpelik AD. 2025. The role of prefrontal spatial coding in supporting a contextual association task. Institute of Science and Technology Austria."},"degree_awarded":"PhD","_id":"19456","department":[{"_id":"GradSch"},{"_id":"JoCs"}],"publisher":"Institute of Science and Technology Austria","day":"18","file_date_updated":"2025-09-30T22:30:02Z","author":[{"orcid":"0000-0003-1727-6612","first_name":"Andrea D","full_name":"Cumpelik, Andrea D","id":"3F158B32-F248-11E8-B48F-1D18A9856A87","last_name":"Cumpelik"}],"language":[{"iso":"eng"}],"status":"public","abstract":[{"lang":"eng","text":"Making decisions requires flexibly adapting to changing environments, a process that\r\ndepends on accurately interpreting current contingencies and integrating them with\r\npast experience. Two brain regions are particularly critical for this process, the medial\r\nprefrontal cortex (mPFC) and the hippocampus. Using contextual information from the\r\nhippocampus, the mPFC selects relevant cognitive frameworks and suppresses\r\nirrelevant ones to guide appropriate actions. Several studies have shown that some\r\nmPFC pyramidal neurons become spatially tuned when spatial information is required\r\nto guide goal-directed behavior. However, the role of prefrontal spatial representations\r\nin learning and decision making is not well understood. This work aims to characterize\r\nthe role of mPFC spatial tuning in supporting a contextual association task. Rats were\r\ntrained to learn two cue–location associations on a radial arm maze over multiple days,\r\nwhile we simultaneously recorded from dorsal CA1 of the hippocampus and the\r\nprelimbic area of the mPFC. We describe a subset of spatially tuned hippocampal and\r\nprefrontal pyramidal neurons that “flicker” between multiple spatial representations on\r\ndifferent trials, suggesting dynamic, context-dependent coding. This flickering may\r\nprovide a substrate for how the network reorganizes in response to task demands,\r\nlikely by enabling the flexible evaluation of competing representations. "}]},{"type":"conference","year":"2023","date_updated":"2025-04-15T07:43:52Z","publication":"2023 IEEE International Conference on Robotics and Automation","publication_status":"published","publication_identifier":{"eisbn":["9798350323658"],"issn":["1050-4729"]},"date_created":"2023-05-16T09:14:09Z","fulldoi":"https://doi.org/10.1109/ICRA48891.2023.10160465","file":[{"date_created":"2023-05-16T09:12:05Z","file_name":"Liao2023.pdf","success":1,"date_updated":"2023-05-16T09:12:05Z","checksum":"daeaa67124777d88487f933ea3f77164","file_size":5367986,"file_id":"12977","access_level":"open_access","relation":"main_file","creator":"mpiovarc","content_type":"application/pdf"}],"intvolume":"      2023","isi":1,"volume":2023,"conference":{"start_date":"2023-05-29","name":"ICRA: International Conference on Robotics and Automation","location":"London, United Kingdom","end_date":"2023-06-02"},"oa":1,"month":"07","article_processing_charge":"No","keyword":["reinforcement learning","deposition","control","color","multi-filament"],"title":"Learning deposition policies for fused multi-material 3D printing","doi":"10.1109/ICRA48891.2023.10160465","date_published":"2023-07-04T00:00:00Z","has_accepted_license":"1","page":"12345-12352","oa_version":"Submitted Version","ddc":["004"],"quality_controlled":"1","publisher":"IEEE","citation":{"ama":"Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. Learning deposition policies for fused multi-material 3D printing. In: <i>2023 IEEE International Conference on Robotics and Automation</i>. Vol 2023. IEEE; 2023:12345-12352. doi:<a href=\"https://doi.org/10.1109/ICRA48891.2023.10160465\">10.1109/ICRA48891.2023.10160465</a>","ieee":"K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, and V. Babaei, “Learning deposition policies for fused multi-material 3D printing,” in <i>2023 IEEE International Conference on Robotics and Automation</i>, London, United Kingdom, 2023, vol. 2023, pp. 12345–12352.","mla":"Liao, Kang, et al. “Learning Deposition Policies for Fused Multi-Material 3D Printing.” <i>2023 IEEE International Conference on Robotics and Automation</i>, vol. 2023, IEEE, 2023, pp. 12345–52, doi:<a href=\"https://doi.org/10.1109/ICRA48891.2023.10160465\">10.1109/ICRA48891.2023.10160465</a>.","short":"K. Liao, T. Tricard, M. Piovarci, H.-P. Seidel, V. Babaei, in:, 2023 IEEE International Conference on Robotics and Automation, IEEE, 2023, pp. 12345–12352.","apa":"Liao, K., Tricard, T., Piovarci, M., Seidel, H.-P., &#38; Babaei, V. (2023). Learning deposition policies for fused multi-material 3D printing. In <i>2023 IEEE International Conference on Robotics and Automation</i> (Vol. 2023, pp. 12345–12352). London, United Kingdom: IEEE. <a href=\"https://doi.org/10.1109/ICRA48891.2023.10160465\">https://doi.org/10.1109/ICRA48891.2023.10160465</a>","ista":"Liao K, Tricard T, Piovarci M, Seidel H-P, Babaei V. 2023. Learning deposition policies for fused multi-material 3D printing. 2023 IEEE International Conference on Robotics and Automation. ICRA: International Conference on Robotics and Automation vol. 2023, 12345–12352.","chicago":"Liao, Kang, Thibault Tricard, Michael Piovarci, Hans-Peter Seidel, and Vahid Babaei. “Learning Deposition Policies for Fused Multi-Material 3D Printing.” In <i>2023 IEEE International Conference on Robotics and Automation</i>, 2023:12345–52. IEEE, 2023. <a href=\"https://doi.org/10.1109/ICRA48891.2023.10160465\">https://doi.org/10.1109/ICRA48891.2023.10160465</a>."},"acknowledgement":"This work is graciously supported by FWF Lise Meitner (Grant M 3319). Kang Liao sincerely thank Emiliano Luci, Chunyu Lin, and Yao Zhao for their huge support.","scopus_import":"1","_id":"12976","department":[{"_id":"BeBi"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"isi":["001048371104068"]},"abstract":[{"lang":"eng","text":"3D printing based on continuous deposition of materials, such as filament-based 3D printing, has seen widespread adoption thanks to its versatility in working with a wide range of materials. An important shortcoming of this type of technology is its limited multi-material capabilities. While there are simple hardware designs that enable multi-material printing in principle, the required software is heavily underdeveloped. A typical hardware design fuses together individual materials fed into a single chamber from multiple inlets before they are deposited. This design, however, introduces a time delay between the intended material mixture and its actual deposition. In this work, inspired by diverse path planning research in robotics, we show that this mechanical challenge can be addressed via improved printer control. We propose to formulate the search for optimal multi-material printing policies in a reinforcement\r\nlearning setup. We put forward a simple numerical deposition model that takes into account the non-linear material mixing and delayed material deposition. To validate our system we focus on color fabrication, a problem known for its strict requirements for varying material mixtures at a high spatial frequency. We demonstrate that our learned control policy outperforms state-of-the-art hand-crafted algorithms."}],"status":"public","project":[{"_id":"eb901961-77a9-11ec-83b8-f5c883a62027","name":"Perception-Aware Appearance Fabrication","grant_number":"M03319"}],"file_date_updated":"2023-05-16T09:12:05Z","day":"04","language":[{"iso":"eng"}],"author":[{"first_name":"Kang","last_name":"Liao","full_name":"Liao, Kang"},{"first_name":"Thibault","last_name":"Tricard","full_name":"Tricard, Thibault"},{"orcid":"0000-0002-5062-4474","first_name":"Michael","full_name":"Piovarci, Michael","id":"62E473F4-5C99-11EA-A40E-AF823DDC885E","last_name":"Piovarci"},{"full_name":"Seidel, Hans-Peter","last_name":"Seidel","first_name":"Hans-Peter"},{"last_name":"Babaei","full_name":"Babaei, Vahid","first_name":"Vahid"}]},{"type":"dissertation","year":"2022","date_updated":"2026-04-07T14:19:48Z","publication_status":"published","publication_identifier":{"isbn":["978-3-99078-015-2"],"issn":["2663-337X"]},"date_created":"2022-02-28T13:03:49Z","fulldoi":"https://doi.org/10.15479/at:ista:10799","file":[{"relation":"main_file","content_type":"application/pdf","creator":"nkonstan","date_created":"2022-03-06T11:42:54Z","success":1,"date_updated":"2022-03-06T11:42:54Z","file_name":"thesis.pdf","file_size":4204905,"file_id":"10823","checksum":"626bc523ae8822d20e635d0e2d95182e","access_level":"open_access"},{"file_name":"thesis.zip","date_updated":"2022-03-10T12:11:48Z","date_created":"2022-03-06T11:42:57Z","access_level":"closed","checksum":"e2ca2b88350ac8ea1515b948885cbcb1","file_size":22841103,"file_id":"10824","relation":"source_file","content_type":"application/x-zip-compressed","creator":"nkonstan"}],"related_material":{"record":[{"relation":"part_of_dissertation","id":"10802","status":"public"},{"id":"10803","status":"public","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","id":"6590","status":"public"},{"relation":"part_of_dissertation","id":"8724","status":"public"}]},"oa":1,"supervisor":[{"last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","first_name":"Christoph","orcid":"0000-0001-8622-7887"}],"month":"03","article_processing_charge":"No","keyword":["robustness","fairness","machine learning","PAC learning","adversarial learning"],"title":"Robustness and fairness in machine learning","doi":"10.15479/at:ista:10799","date_published":"2022-03-08T00:00:00Z","OA_place":"publisher","corr_author":"1","has_accepted_license":"1","page":"176","oa_version":"Published Version","ddc":["000"],"publisher":"Institute of Science and Technology Austria","citation":{"chicago":"Konstantinov, Nikola H. “Robustness and Fairness in Machine Learning.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/at:ista:10799\">https://doi.org/10.15479/at:ista:10799</a>.","ista":"Konstantinov NH. 2022. Robustness and fairness in machine learning. Institute of Science and Technology Austria.","apa":"Konstantinov, N. H. (2022). <i>Robustness and fairness in machine learning</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:10799\">https://doi.org/10.15479/at:ista:10799</a>","short":"N.H. Konstantinov, Robustness and Fairness in Machine Learning, Institute of Science and Technology Austria, 2022.","ama":"Konstantinov NH. Robustness and fairness in machine learning. 2022. doi:<a href=\"https://doi.org/10.15479/at:ista:10799\">10.15479/at:ista:10799</a>","ieee":"N. H. Konstantinov, “Robustness and fairness in machine learning,” Institute of Science and Technology Austria, 2022.","mla":"Konstantinov, Nikola H. <i>Robustness and Fairness in Machine Learning</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/at:ista:10799\">10.15479/at:ista:10799</a>."},"department":[{"_id":"GradSch"},{"_id":"ChLa"}],"_id":"10799","degree_awarded":"PhD","ec_funded":1,"alternative_title":["ISTA Thesis"],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","abstract":[{"text":"Because of the increasing popularity of machine learning methods, it is becoming important to understand the impact of learned components on automated decision-making systems and to guarantee that their consequences are beneficial to society. In other words, it is necessary to ensure that machine learning is sufficiently trustworthy to be used in real-world applications. This thesis studies two properties of machine learning models that are highly desirable for the\r\nsake of reliability: robustness and fairness. In the first part of the thesis we study the robustness of learning algorithms to training data corruption. Previous work has shown that machine learning models are vulnerable to a range\r\nof training set issues, varying from label noise through systematic biases to worst-case data manipulations. This is an especially relevant problem from a present perspective, since modern machine learning methods are particularly data hungry and therefore practitioners often have to rely on data collected from various external sources, e.g. from the Internet, from app users or via crowdsourcing. Naturally, such sources vary greatly in the quality and reliability of the\r\ndata they provide. With these considerations in mind, we study the problem of designing machine learning algorithms that are robust to corruptions in data coming from multiple sources. We show that, in contrast to the case of a single dataset with outliers, successful learning within this model is possible both theoretically and practically, even under worst-case data corruptions. The second part of this thesis deals with fairness-aware machine learning. There are multiple areas where machine learning models have shown promising results, but where careful considerations are required, in order to avoid discrimanative decisions taken by such learned components. Ensuring fairness can be particularly challenging, because real-world training datasets are expected to contain various forms of historical bias that may affect the learning process. In this thesis we show that data corruption can indeed render the problem of achieving fairness impossible, by tightly characterizing the theoretical limits of fair learning under worst-case data manipulations. However, assuming access to clean data, we also show how fairness-aware learning can be made practical in contexts beyond binary classification, in particular in the challenging learning to rank setting.","lang":"eng"}],"status":"public","project":[{"grant_number":"665385","name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"file_date_updated":"2022-03-10T12:11:48Z","day":"08","language":[{"iso":"eng"}],"author":[{"first_name":"Nikola H","orcid":"0009-0009-5204-7621","full_name":"Konstantinov, Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87","last_name":"Konstantinov"}]},{"file_date_updated":"2022-07-12T15:08:28Z","day":"01","language":[{"iso":"eng"}],"author":[{"first_name":"Nikola H","orcid":"0009-0009-5204-7621","last_name":"Konstantinov","full_name":"Konstantinov, Nikola H","id":"4B9D76E4-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-4561-241X","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","last_name":"Lampert"}],"status":"public","abstract":[{"lang":"eng","text":"Addressing fairness concerns about machine learning models is a crucial step towards their long-term adoption in real-world automated systems. While many approaches have been developed for training fair models from data, little is known about the robustness of these methods to data corruption. In this work we consider fairness-aware learning under worst-case data manipulations. We show that an adversary can in some situations force any learner to return an overly biased classifier, regardless of the sample size and with or without degrading\r\naccuracy, and that the strength of the excess bias increases for learning problems with underrepresented protected groups in the data. We also prove that our hardness results are tight up to constant factors. To this end, we study two natural learning algorithms that optimize for both accuracy and fairness and show that these algorithms enjoy guarantees that are order-optimal in terms of the corruption ratio and the protected groups frequencies in the large data\r\nlimit."}],"external_id":{"arxiv":["2102.06004"]},"arxiv":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Konstantinov NH, Lampert C. 2022. Fairness-aware PAC learning from corrupted data. Journal of Machine Learning Research. 23, 1–60.","chicago":"Konstantinov, Nikola H, and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” <i>Journal of Machine Learning Research</i>. ML Research Press, 2022.","mla":"Konstantinov, Nikola H., and Christoph Lampert. “Fairness-Aware PAC Learning from Corrupted Data.” <i>Journal of Machine Learning Research</i>, vol. 23, ML Research Press, 2022, pp. 1–60.","ieee":"N. H. Konstantinov and C. Lampert, “Fairness-aware PAC learning from corrupted data,” <i>Journal of Machine Learning Research</i>, vol. 23. ML Research Press, pp. 1–60, 2022.","ama":"Konstantinov NH, Lampert C. Fairness-aware PAC learning from corrupted data. <i>Journal of Machine Learning Research</i>. 2022;23:1-60.","short":"N.H. Konstantinov, C. Lampert, Journal of Machine Learning Research 23 (2022) 1–60.","apa":"Konstantinov, N. H., &#38; Lampert, C. (2022). Fairness-aware PAC learning from corrupted data. <i>Journal of Machine Learning Research</i>. ML Research Press."},"acknowledgement":"The authors thank Eugenia Iofinova and Bernd Prach for providing feedback on early versions of this paper. This publication was made possible by an ETH AI Center postdoctoral fellowship to Nikola Konstantinov.","_id":"10802","scopus_import":"1","department":[{"_id":"ChLa"}],"publisher":"ML Research Press","corr_author":"1","has_accepted_license":"1","page":"1-60","article_type":"original","oa_version":"Published Version","ddc":["004"],"tmp":{"image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"quality_controlled":"1","date_published":"2022-05-01T00:00:00Z","article_processing_charge":"No","keyword":["Fairness","robustness","data poisoning","trustworthy machine learning","PAC learning"],"title":"Fairness-aware PAC learning from corrupted data","oa":1,"month":"05","related_material":{"record":[{"relation":"shorter_version","status":"public","id":"13241"},{"relation":"dissertation_contains","status":"public","id":"10799"}]},"volume":23,"intvolume":"        23","date_updated":"2026-04-07T14:19:48Z","publication_status":"published","publication_identifier":{"issn":["1532-4435"],"eissn":["1533-7928"]},"publication":"Journal of Machine Learning Research","date_created":"2022-02-28T14:05:42Z","file":[{"date_created":"2022-07-12T15:08:28Z","file_name":"2022_JournalMachineLearningResearch_Konstantinov.pdf","success":1,"date_updated":"2022-07-12T15:08:28Z","checksum":"9cac897b54a0ddf3a553a2c33e88cfda","file_size":551862,"file_id":"11570","access_level":"open_access","relation":"main_file","content_type":"application/pdf","creator":"kschuh"}],"type":"journal_article","year":"2022"},{"related_material":{"record":[{"relation":"part_of_dissertation","id":"11366","status":"public"},{"relation":"part_of_dissertation","id":"7808","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"10666"},{"relation":"part_of_dissertation","id":"10667","status":"public"},{"relation":"part_of_dissertation","status":"public","id":"10665"}]},"fulldoi":"https://doi.org/10.15479/at:ista:11362","file":[{"access_level":"closed","file_id":"11378","file_size":13210143,"checksum":"8eefa9c7c10ca7e1a2ccdd731962a645","date_updated":"2022-05-13T12:49:00Z","file_name":"src.zip","date_created":"2022-05-13T12:33:26Z","creator":"mlechner","content_type":"application/zip","relation":"source_file"},{"access_level":"open_access","checksum":"1b9e1e5a9a83ed9d89dad2f5133dc026","file_size":2732536,"file_id":"11382","file_name":"thesis_main-a2.pdf","date_updated":"2022-05-17T15:19:39Z","date_created":"2022-05-16T08:02:28Z","content_type":"application/pdf","creator":"mlechner","relation":"main_file"}],"date_created":"2022-05-12T07:14:01Z","publication_status":"published","publication_identifier":{"isbn":["978-3-99078-017-6"]},"date_updated":"2026-08-19T09:28:05Z","year":"2022","type":"dissertation","tmp":{"name":"Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0)","short":"CC BY-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nd/4.0/legalcode","image":"/image/cc_by_nd.png"},"ddc":["004"],"oa_version":"Published Version","has_accepted_license":"1","page":"124","corr_author":"1","OA_place":"publisher","date_published":"2022-05-12T00:00:00Z","doi":"10.15479/at:ista:11362","title":"Learning verifiable representations","keyword":["neural networks","verification","machine learning"],"article_processing_charge":"No","month":"05","supervisor":[{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","orcid":"0000-0002-2985-7724"}],"oa":1,"license":"https://creativecommons.org/licenses/by-nd/4.0/","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","alternative_title":["ISTA Thesis"],"ec_funded":1,"degree_awarded":"PhD","_id":"11362","department":[{"_id":"GradSch"},{"_id":"ToHe"}],"citation":{"ieee":"M. Lechner, “Learning verifiable representations,” Institute of Science and Technology Austria, 2022.","mla":"Lechner, Mathias. <i>Learning Verifiable Representations</i>. Institute of Science and Technology Austria, 2022, doi:<a href=\"https://doi.org/10.15479/at:ista:11362\">10.15479/at:ista:11362</a>.","ama":"Lechner M. Learning verifiable representations. 2022. doi:<a href=\"https://doi.org/10.15479/at:ista:11362\">10.15479/at:ista:11362</a>","apa":"Lechner, M. (2022). <i>Learning verifiable representations</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:11362\">https://doi.org/10.15479/at:ista:11362</a>","short":"M. Lechner, Learning Verifiable Representations, Institute of Science and Technology Austria, 2022.","ista":"Lechner M. 2022. Learning verifiable representations. Institute of Science and Technology Austria.","chicago":"Lechner, Mathias. “Learning Verifiable Representations.” Institute of Science and Technology Austria, 2022. <a href=\"https://doi.org/10.15479/at:ista:11362\">https://doi.org/10.15479/at:ista:11362</a>."},"publisher":"Institute of Science and Technology Austria","author":[{"first_name":"Mathias","last_name":"Lechner","full_name":"Lechner, Mathias","id":"3DC22916-F248-11E8-B48F-1D18A9856A87"}],"language":[{"iso":"eng"}],"day":"12","file_date_updated":"2022-05-17T15:19:39Z","status":"public","project":[{"grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"Formal methods for the design and analysis of complex systems","call_identifier":"FWF"},{"grant_number":"101020093","name":"Vigilant Algorithmic Monitoring of Software","_id":"62781420-2b32-11ec-9570-8d9b63373d4d","call_identifier":"H2020"}],"abstract":[{"lang":"eng","text":"Deep learning has enabled breakthroughs in challenging computing problems and has emerged as the standard problem-solving tool for computer vision and natural language processing tasks.\r\nOne exception to this trend is safety-critical tasks where robustness and resilience requirements contradict the black-box nature of neural networks. \r\nTo deploy deep learning methods for these tasks, it is vital to provide guarantees on neural network agents' safety and robustness criteria. \r\nThis can be achieved by developing formal verification methods to verify the safety and robustness properties of neural networks.\r\n\r\nOur goal is to design, develop and assess safety verification methods for neural networks to improve their reliability and trustworthiness in real-world applications.\r\nThis thesis establishes techniques for the verification of compressed and adversarially trained models as well as the design of novel neural networks for verifiably safe decision-making.\r\n\r\nFirst, we establish the problem of verifying quantized neural networks. Quantization is a technique that trades numerical precision for the computational efficiency of running a neural network and is widely adopted in industry.\r\nWe show that neglecting the reduced precision when verifying a neural network can lead to wrong conclusions about the robustness and safety of the network, highlighting that novel techniques for quantized network verification are necessary. We introduce several bit-exact verification methods explicitly designed for quantized neural networks and experimentally confirm on realistic networks that the network's robustness and other formal properties are affected by the quantization.\r\n\r\nFurthermore, we perform a case study providing evidence that adversarial training, a standard technique for making neural networks more robust, has detrimental effects on the network's performance. This robustness-accuracy tradeoff has been studied before regarding the accuracy obtained on classification datasets where each data point is independent of all other data points. On the other hand, we investigate the tradeoff empirically in robot learning settings where a both, a high accuracy and a high robustness, are desirable.\r\nOur results suggest that the negative side-effects of adversarial training outweigh its robustness benefits in practice.\r\n\r\nFinally, we consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with systems over the infinite time horizon. Bayesian neural networks are probabilistic models for learning uncertainties in the data and are therefore often used on robotic and healthcare applications where data is inherently stochastic.\r\nWe introduce a method for recalibrating Bayesian neural networks so that they yield probability distributions over safe decisions only.\r\nOur method learns a safety certificate that guarantees safety over the infinite time horizon to determine which decisions are safe in every possible state of the system.\r\nWe demonstrate the effectiveness of our approach on a series of reinforcement learning benchmarks."}]},{"date_created":"2021-07-30T09:34:56Z","fulldoi":"https://doi.org/10.1007/978-1-0716-1522-5_19","publication_status":"published","publication_identifier":{"eisbn":["9781071615225"],"isbn":["9781071615218"]},"publication":"Receptor and Ion Channel Detection in the Brain","date_updated":"2026-09-10T22:31:05Z","year":"2021","place":"New York","type":"book_chapter","volume":169,"related_material":{"record":[{"id":"9562","status":"public","relation":"dissertation_contains"}]},"intvolume":"       169","title":"High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL)","keyword":["Freeze-fracture replica: Deep learning","Immunogold labeling","Integral membrane protein","Electron microscopy"],"article_processing_charge":"No","month":"07","quality_controlled":"1","ddc":["573"],"oa_version":"None","page":"267-283","has_accepted_license":"1","corr_author":"1","date_published":"2021-07-27T00:00:00Z","doi":"10.1007/978-1-0716-1522-5_19","scopus_import":"1","_id":"9756","department":[{"_id":"RySh"},{"_id":"EM-Fac"}],"acknowledgement":"This work was supported by the European Union (European Research Council Advanced grant no. 694539 and Human Brain Project Ref. 720270 to R. S.) and the Austrian Academy of Sciences (DOC fellowship to D.K.).","citation":{"ieee":"W. Kaufmann, D. Kleindienst, H. Harada, and R. Shigemoto, “High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL),” in <i>Receptor and Ion Channel Detection in the Brain</i>, vol. 169, New York: Humana Press, 2021, pp. 267–283.","mla":"Kaufmann, Walter, et al. “High-Resolution Localization and Quantitation of Membrane Proteins by SDS-Digested Freeze-Fracture Replica Labeling (SDS-FRL).” <i>Receptor and Ion Channel Detection in the Brain</i>, vol. 169, Humana Press, 2021, pp. 267–83, doi:<a href=\"https://doi.org/10.1007/978-1-0716-1522-5_19\">10.1007/978-1-0716-1522-5_19</a>.","ama":"Kaufmann W, Kleindienst D, Harada H, Shigemoto R. High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL). In: <i>Receptor and Ion Channel Detection in the Brain</i>. Vol 169. Neuromethods. New York: Humana Press; 2021:267-283. doi:<a href=\"https://doi.org/10.1007/978-1-0716-1522-5_19\">10.1007/978-1-0716-1522-5_19</a>","short":"W. Kaufmann, D. Kleindienst, H. Harada, R. Shigemoto, in:, Receptor and Ion Channel Detection in the Brain, Humana Press, New York, 2021, pp. 267–283.","apa":"Kaufmann, W., Kleindienst, D., Harada, H., &#38; Shigemoto, R. (2021). High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL). In <i>Receptor and Ion Channel Detection in the Brain</i> (Vol. 169, pp. 267–283). New York: Humana Press. <a href=\"https://doi.org/10.1007/978-1-0716-1522-5_19\">https://doi.org/10.1007/978-1-0716-1522-5_19</a>","ista":"Kaufmann W, Kleindienst D, Harada H, Shigemoto R. 2021.High-Resolution localization and quantitation of membrane proteins by SDS-digested freeze-fracture replica labeling (SDS-FRL). In: Receptor and Ion Channel Detection in the Brain. Neuromethods, vol. 169, 267–283.","chicago":"Kaufmann, Walter, David Kleindienst, Harumi Harada, and Ryuichi Shigemoto. “High-Resolution Localization and Quantitation of Membrane Proteins by SDS-Digested Freeze-Fracture Replica Labeling (SDS-FRL).” In <i>Receptor and Ion Channel Detection in the Brain</i>, 169:267–83. Neuromethods. New York: Humana Press, 2021. <a href=\"https://doi.org/10.1007/978-1-0716-1522-5_19\">https://doi.org/10.1007/978-1-0716-1522-5_19</a>."},"das_tickbox":"1","publisher":"Humana Press","series_title":"Neuromethods","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","alternative_title":["Neuromethods"],"ec_funded":1,"abstract":[{"lang":"eng","text":"High-resolution visualization and quantification of membrane proteins contribute to the understanding of their functions and the roles they play in physiological and pathological conditions. Sodium dodecyl sulfate-digested freeze-fracture replica labeling (SDS-FRL) is a powerful electron microscopy method to study quantitatively the two-dimensional distribution of transmembrane proteins and their tightly associated proteins. During treatment with SDS, intracellular organelles and proteins not anchored to the replica are dissolved, whereas integral membrane proteins captured and stabilized by carbon/platinum deposition remain on the replica. Their intra- and extracellular domains become exposed on the surface of the replica, facilitating the accessibility of antibodies and, therefore, providing higher labeling efficiency than those obtained with other immunoelectron microscopy techniques. In this chapter, we describe the protocols of SDS-FRL adapted for mammalian brain samples, and optimization of the SDS treatment to increase the labeling efficiency for quantification of Cav2.1, the alpha subunit of P/Q-type voltage-dependent calcium channels utilizing deep learning algorithms."}],"author":[{"last_name":"Kaufmann","full_name":"Kaufmann, Walter","id":"3F99E422-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-9735-5315","first_name":"Walter"},{"first_name":"David","last_name":"Kleindienst","id":"42E121A4-F248-11E8-B48F-1D18A9856A87","full_name":"Kleindienst, David"},{"first_name":"Harumi","orcid":"0000-0001-7429-7896","full_name":"Harada, Harumi","id":"2E55CDF2-F248-11E8-B48F-1D18A9856A87","last_name":"Harada"},{"full_name":"Shigemoto, Ryuichi","id":"499F3ABC-F248-11E8-B48F-1D18A9856A87","last_name":"Shigemoto","first_name":"Ryuichi","orcid":"0000-0001-8761-9444"}],"language":[{"iso":"eng"}],"day":"27","project":[{"name":"In situ analysis of single channel subunit composition in neurons: physiological implication in synaptic plasticity and behaviour","_id":"25CA28EA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"694539"},{"call_identifier":"H2020","name":"Human Brain Project Specific Grant Agreement 1","_id":"25CBA828-B435-11E9-9278-68D0E5697425","grant_number":"720270"}],"status":"public"},{"day":"23","oa_version":"Preprint","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1906.09609"}],"author":[{"first_name":"S. N.","full_name":"Breton, S. N.","last_name":"Breton"},{"orcid":"0000-0003-0142-4000","first_name":"Lisa Annabelle","last_name":"Bugnet","full_name":"Bugnet, Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501"},{"full_name":"Santos, A. R. G.","last_name":"Santos","first_name":"A. R. G."},{"last_name":"Saux","full_name":"Saux, A. Le","first_name":"A. Le"},{"first_name":"S.","full_name":"Mathur, S.","last_name":"Mathur"},{"first_name":"P. L.","last_name":"Palle","full_name":"Palle, P. L."},{"last_name":"Garcia","full_name":"Garcia, R. A.","first_name":"R. A."}],"doi":"10.48550/arXiv.1906.09609","status":"public","date_published":"2019-06-23T00:00:00Z","article_processing_charge":"No","title":"Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques","keyword":["asteroseismology","rotation","solar-like stars","kepler","machine learning","random forest"],"abstract":[{"text":"For a solar-like star, the surface rotation evolves with time, allowing in principle to estimate the age of a star from its surface rotation period. Here we are interested in measuring surface rotation periods of solar-like stars observed by the NASA mission Kepler. Different methods have been developed to track rotation signals in Kepler photometric light curves: time-frequency analysis based on wavelet techniques, autocorrelation and composite spectrum. We use the learning abilities of random forest classifiers to take decisions during two crucial steps of the analysis. First, given some input parameters, we discriminate the considered Kepler targets between rotating MS stars, non-rotating MS stars, red giants, binaries and pulsators. We then use a second classifier only on the MS rotating targets to decide the best data analysis treatment.","lang":"eng"}],"external_id":{"arxiv":["1906.09609"]},"arxiv":1,"oa":1,"month":"06","extern":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"1906.09609","citation":{"chicago":"Breton, S. N., Lisa Annabelle Bugnet, A. R. G. Santos, A. Le Saux, S. Mathur, P. L. Palle, and R. A. Garcia. “Determining Surface Rotation Periods of Solar-like Stars Observed by the Kepler Mission Using Machine Learning Techniques.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1906.09609\">https://doi.org/10.48550/arXiv.1906.09609</a>.","ista":"Breton SN, Bugnet LA, Santos ARG, Saux AL, Mathur S, Palle PL, Garcia RA. Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques. arXiv, 1906.09609.","short":"S.N. Breton, L.A. Bugnet, A.R.G. Santos, A.L. Saux, S. Mathur, P.L. Palle, R.A. Garcia, ArXiv (n.d.).","apa":"Breton, S. N., Bugnet, L. A., Santos, A. R. G., Saux, A. L., Mathur, S., Palle, P. L., &#38; Garcia, R. A. (n.d.). Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1906.09609\">https://doi.org/10.48550/arXiv.1906.09609</a>","mla":"Breton, S. N., et al. “Determining Surface Rotation Periods of Solar-like Stars Observed by the Kepler Mission Using Machine Learning Techniques.” <i>ArXiv</i>, 1906.09609, doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09609\">10.48550/arXiv.1906.09609</a>.","ieee":"S. N. Breton <i>et al.</i>, “Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques,” <i>arXiv</i>. .","ama":"Breton SN, Bugnet LA, Santos ARG, et al. Determining surface rotation periods of solar-like stars observed by the Kepler mission using machine learning techniques. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09609\">10.48550/arXiv.1906.09609</a>"},"date_updated":"2022-08-22T08:16:53Z","_id":"11627","publication":"arXiv","publication_status":"submitted","date_created":"2022-07-20T11:18:53Z","fulldoi":"https://doi.org/10.48550/arXiv.1906.09609","type":"preprint","year":"2019"},{"date_published":"2019-06-23T00:00:00Z","doi":"10.48550/arXiv.1906.09611","status":"public","oa_version":"Preprint","language":[{"iso":"eng"}],"author":[{"first_name":"A. Le","full_name":"Saux, A. Le","last_name":"Saux"},{"full_name":"Bugnet, Lisa Annabelle","id":"d9edb345-f866-11ec-9b37-d119b5234501","last_name":"Bugnet","first_name":"Lisa Annabelle","orcid":"0000-0003-0142-4000"},{"first_name":"S.","full_name":"Mathur, S.","last_name":"Mathur"},{"full_name":"Breton, S. N.","last_name":"Breton","first_name":"S. N."},{"first_name":"R. A.","last_name":"Garcia","full_name":"Garcia, R. A."}],"main_file_link":[{"url":"https://doi.org/10.48550/arXiv.1906.09611","open_access":"1"}],"day":"23","month":"06","oa":1,"arxiv":1,"abstract":[{"lang":"eng","text":"The second mission of NASA’s Kepler satellite, K2, has collected hundreds of thousands of lightcurves for stars close to the ecliptic plane. This new sample could increase the number of known pulsating stars and then improve our understanding of those stars. For the moment only a few stars have been properly classified and published. In this work, we present a method to automaticly classify K2 pulsating stars using a Machine Learning technique called Random Forest. The objective is to sort out the stars in four classes: red giant (RG), main-sequence Solar-like stars (SL), classical pulsators (PULS) and Other. To do this we use the effective temperatures and the luminosities of the stars as well as the FliPer features, that measures the amount of power contained in the power spectral density. The classifier now retrieves the right classification for more than 80% of the stars."}],"keyword":["asteroseismology - methods","data analysis - thecniques","machine learning - stars","oscillations"],"title":"Automatic classification of K2 pulsating stars using machine learning techniques","external_id":{"arxiv":["1906.09611"]},"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_number":"1906.09611","extern":"1","type":"preprint","year":"2019","_id":"11630","publication_status":"submitted","publication":"arXiv","fulldoi":"https://doi.org/10.48550/arXiv.1906.09611","date_created":"2022-07-21T06:57:10Z","date_updated":"2022-08-22T08:20:29Z","citation":{"ista":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. arXiv, 1906.09611.","chicago":"Saux, A. Le, Lisa Annabelle Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>.","ama":"Saux AL, Bugnet LA, Mathur S, Breton SN, Garcia RA. Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>","ieee":"A. L. Saux, L. A. Bugnet, S. Mathur, S. N. Breton, and R. A. Garcia, “Automatic classification of K2 pulsating stars using machine learning techniques,” <i>arXiv</i>. .","mla":"Saux, A. Le, et al. “Automatic Classification of K2 Pulsating Stars Using Machine Learning Techniques.” <i>ArXiv</i>, 1906.09611, doi:<a href=\"https://doi.org/10.48550/arXiv.1906.09611\">10.48550/arXiv.1906.09611</a>.","apa":"Saux, A. L., Bugnet, L. A., Mathur, S., Breton, S. N., &#38; Garcia, R. A. (n.d.). Automatic classification of K2 pulsating stars using machine learning techniques. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.1906.09611\">https://doi.org/10.48550/arXiv.1906.09611</a>","short":"A.L. Saux, L.A. Bugnet, S. Mathur, S.N. Breton, R.A. Garcia, ArXiv (n.d.)."}}]