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(2025). <i>Emergent physics of rotating quantum impurities in many-body environments</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:19048\">https://doi.org/10.15479/at:ista:19048</a>","ieee":"M. Maslov, “Emergent physics of rotating quantum impurities in many-body environments,” Institute of Science and Technology Austria, 2025.","chicago":"Maslov, Mikhail. “Emergent Physics of Rotating Quantum Impurities in Many-Body Environments.” Institute of Science and Technology Austria, 2025. <a href=\"https://doi.org/10.15479/at:ista:19048\">https://doi.org/10.15479/at:ista:19048</a>.","short":"M. Maslov, Emergent Physics of Rotating Quantum Impurities in Many-Body Environments, Institute of Science and Technology Austria, 2025.","mla":"Maslov, Mikhail. <i>Emergent Physics of Rotating Quantum Impurities in Many-Body Environments</i>. Institute of Science and Technology Austria, 2025, doi:<a href=\"https://doi.org/10.15479/at:ista:19048\">10.15479/at:ista:19048</a>.","ista":"Maslov M. 2025. Emergent physics of rotating quantum impurities in many-body environments. Institute of Science and Technology Austria.","ama":"Maslov M. Emergent physics of rotating quantum impurities in many-body environments. 2025. doi:<a href=\"https://doi.org/10.15479/at:ista:19048\">10.15479/at:ista:19048</a>"},"article_processing_charge":"No","degree_awarded":"PhD","ddc":["539","535","541"],"day":"18","publication_status":"published","corr_author":"1","date_published":"2025-02-18T00:00:00Z","project":[{"name":"Angulon: physics and applications of a new quasiparticle","_id":"2688CF98-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"801770"},{"grant_number":"F100403","name":"Coherent Optical Metrology Beyond Electric-Dipole-Allowed Transitions","_id":"7c040762-9f16-11ee-852c-dd79eeee4ab3"}],"department":[{"_id":"GradSch"},{"_id":"MiLe"}],"_id":"19048","acknowledged_ssus":[{"_id":"CampIT"},{"_id":"E-Lib"},{"_id":"SSU"}],"supervisor":[{"last_name":"Lemeshko","orcid":"0000-0002-6990-7802","first_name":"Mikhail","full_name":"Lemeshko, Mikhail","id":"37CB05FA-F248-11E8-B48F-1D18A9856A87"}],"ec_funded":1,"file_date_updated":"2025-02-18T14:25:59Z","publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:19048","acknowledgement":"I am grateful to the European Research Council (ERC) [10.3030/801770] and Austrian\r\nScience Fund (FWF) [10.55776/F1004] for funding my research and to the Physical\r\nReview journals for publishing it. I also want to thank the VCQ (previously CoQuS) and\r\nIQOQI for organizing wonderful networking events for the physics community in Vienna\r\nand Innsbruck, respectively. Moreover, I thank Austrian Science Fund (FWF) for the\r\ncontinuous support for quantum research.","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Emergent physics of rotating quantum impurities in many-body environments","abstract":[{"lang":"eng","text":"Rotations are found in physics problems at all scales: from spatial motion of celestial bodies, to transitions between quantum states of atoms and molecules. Mathematically, they represent a fundamental class of transformations and symmetries. Unlike spatial displacements, rotational transformations in three-dimensional space  are non-commutative: the result of applying a sequence of rotations depends on the order of these operations. This feature makes the emergent physics that involves rotations rather intricate, but instrumental for studies of highly-interconnected many-body systems. In the presence of an environment, rotational properties of an object change, due to the interaction with particles of the environment. Owing to the complexity of this interaction, it can be engineered to exhibit certain properties of interest. In this Thesis, we examine several scenarios of how the rotational behavior of an impurity can be modified by interactions with its environment."}],"year":"2025","tmp":{"image":"/images/cc_by_nc_sa.png","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"},"alternative_title":["ISTA Thesis"],"month":"02","type":"dissertation"},{"department":[{"_id":"GradSch"},{"_id":"ChWo"}],"_id":"20551","acknowledged_ssus":[{"_id":"CampIT"}],"ec_funded":1,"supervisor":[{"last_name":"Wojtan","orcid":"0000-0001-6646-5546","first_name":"Christopher J","full_name":"Wojtan, Christopher J","id":"3C61F1D2-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Chern","first_name":"Albert","full_name":"Chern, Albert"}],"file_date_updated":"2025-11-10T08:45:05Z","publication_identifier":{"isbn":["978-3-99078-070-1"],"issn":["2663-337X"]},"doi":"10.15479/AT-ISTA-20551","acknowledgement":"Projects contained in this thesis were financially supported in part by the\r\nEuropean Research Council with grants 1. ERC Consolidator Grant 101045083 CoDiNA,\r\nand 2. the European Union’s Horizon 2020 research and innovation programme under grant\r\nagreement No. 638176.","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Symplectic-prequantum structures and dynamics on the codimension-2 shape space","abstract":[{"lang":"eng","text":"The space of codimension-2 shapes, such as curves in 3D and surfaces in 4D, is an infinite-dimensional manifold. This thesis explores geometric structures and dynamics on this space, with emphasis on their implications for physics, particularly hydrodynamics.\r\n\r\nOur investigation ranges from theoretical studies of infinite-dimensional symplectic and prequantum geometry to numerical computation of the time evolution of shapes. The thesis presents four main contributions.\r\n\r\nIn the first part, we introduce implicit representations of codimension-2 shapes using a class of complex-valued functions, and prove that the space of these implicit representations forms a prequantum bundle over the codimension-2 shape space. This reveals a new geometric interpretation of the canonical symplectic structure on the codimension-2 shape space.\r\n\r\nIn the second part, we use implicit representations to develop a simulation method for the dynamics of space curves. To handle chaotic systems such as vortex filaments in hydrodynamics, we exploit the infinite degrees of freedom, hidden in both the configuration and dynamics of implicit representations.\r\n\r\nIn the third part, we introduce new symplectic structures on the space of space curves, which generalize the only previously known symplectic structure on this space, allowing for new Hamiltonian dynamics of space curves.\r\n\r\nIn the fourth part, we apply a symplectic viewpoint to a differential geometric problem with practical applications. We derive a new area formula for spherical polygons via prequantization. "}],"year":"2025","tmp":{"short":"CC BY (4.0)","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)"},"alternative_title":["ISTA Thesis"],"month":"10","type":"dissertation","date_updated":"2026-04-07T12:02:23Z","OA_place":"publisher","status":"public","file":[{"content_type":"application/zip","file_size":72487812,"creator":"sishida","relation":"source_file","date_updated":"2025-11-01T18:26:14Z","checksum":"4eef80afcb67691cbb6549c4756fa534","file_name":"Thesis_tex.zip","file_id":"20583","access_level":"open_access","date_created":"2025-11-01T18:26:14Z"},{"checksum":"1e5a557900bf2dce01966b211b15d0fe","file_name":"Thesis_Sadashige_Ishida_PDFA.pdf","file_id":"20623","access_level":"open_access","date_created":"2025-11-10T08:45:05Z","file_size":8945141,"content_type":"application/pdf","creator":"sishida","relation":"main_file","date_updated":"2025-11-10T08:45:05Z","success":1}],"oa":1,"author":[{"full_name":"Ishida, Sadashige","first_name":"Sadashige","id":"6F7C4B96-A8E9-11E9-A7CA-09ECE5697425","orcid":"0000-0002-3121-3100","last_name":"Ishida"}],"date_created":"2025-10-27T10:28:52Z","publisher":"Institute of Science and Technology Austria","language":[{"iso":"eng"}],"oa_version":"Published Version","related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"12846"},{"status":"public","id":"12431","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","status":"public","id":"17361"},{"relation":"part_of_dissertation","id":"20580","status":"public"}]},"page":"141","has_accepted_license":"1","citation":{"short":"S. Ishida, Symplectic-Prequantum Structures and Dynamics on the Codimension-2 Shape Space, Institute of Science and Technology Austria, 2025.","mla":"Ishida, Sadashige. <i>Symplectic-Prequantum Structures and Dynamics on the Codimension-2 Shape Space</i>. Institute of Science and Technology Austria, 2025, doi:<a href=\"https://doi.org/10.15479/AT-ISTA-20551\">10.15479/AT-ISTA-20551</a>.","ista":"Ishida S. 2025. Symplectic-prequantum structures and dynamics on the codimension-2 shape space. Institute of Science and Technology Austria.","ama":"Ishida S. Symplectic-prequantum structures and dynamics on the codimension-2 shape space. 2025. doi:<a href=\"https://doi.org/10.15479/AT-ISTA-20551\">10.15479/AT-ISTA-20551</a>","ieee":"S. Ishida, “Symplectic-prequantum structures and dynamics on the codimension-2 shape space,” Institute of Science and Technology Austria, 2025.","chicago":"Ishida, Sadashige. “Symplectic-Prequantum Structures and Dynamics on the Codimension-2 Shape Space.” Institute of Science and Technology Austria, 2025. <a href=\"https://doi.org/10.15479/AT-ISTA-20551\">https://doi.org/10.15479/AT-ISTA-20551</a>.","apa":"Ishida, S. (2025). <i>Symplectic-prequantum structures and dynamics on the codimension-2 shape space</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT-ISTA-20551\">https://doi.org/10.15479/AT-ISTA-20551</a>"},"article_processing_charge":"No","ddc":["516"],"publication_status":"published","day":"31","degree_awarded":"PhD","corr_author":"1","project":[{"name":"Big Splash: Efficient Simulation of Natural Phenomena at Extremely Large Scales","_id":"2533E772-B435-11E9-9278-68D0E5697425","grant_number":"638176","call_identifier":"H2020"},{"grant_number":"101045083","name":"Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena","_id":"34bc2376-11ca-11ed-8bc3-9a3b3961a088"}],"date_published":"2025-10-31T00:00:00Z"},{"oa":1,"author":[{"last_name":"Modoranu","first_name":"Ionut-Vlad","full_name":"Modoranu, Ionut-Vlad","id":"449f7a18-f128-11eb-9611-9b430c0c6333"},{"id":"dd546b39-0804-11ed-9c55-ef075c39778d","first_name":"Mher","full_name":"Safaryan, Mher","last_name":"Safaryan"},{"full_name":"Malinovsky, Grigory","first_name":"Grigory","last_name":"Malinovsky"},{"id":"47beb3a5-07b5-11eb-9b87-b108ec578218","first_name":"Eldar","full_name":"Kurtic, Eldar","last_name":"Kurtic"},{"last_name":"Robert","id":"de632733-1457-11f0-ae22-b5914b8c1c41","first_name":"Thomas","full_name":"Robert, Thomas"},{"last_name":"Richtárik","full_name":"Richtárik, Peter","first_name":"Peter"},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"status":"public","intvolume":"        37","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2405.15593"}],"OA_place":"repository","date_updated":"2025-05-14T11:32:52Z","arxiv":1,"oa_version":"Preprint","related_material":{"link":[{"url":"https://github.com/IST-DASLab/MicroAdam","relation":"software"}]},"language":[{"iso":"eng"}],"publication":"38th Conference on Neural Information Processing Systems","publisher":"Neural Information Processing Systems Foundation","date_created":"2025-04-06T22:01:32Z","date_published":"2024-12-20T00:00:00Z","project":[{"name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c","call_identifier":"H2020","grant_number":"101034413"}],"corr_author":"1","OA_type":"green","volume":37,"publication_status":"published","day":"20","article_processing_charge":"No","citation":{"chicago":"Modoranu, Ionut-Vlad, Mher Safaryan, Grigory Malinovsky, Eldar Kurtic, Thomas Robert, Peter Richtárik, and Dan-Adrian Alistarh. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” In <i>38th Conference on Neural Information Processing Systems</i>, Vol. 37. Neural Information Processing Systems Foundation, 2024.","ieee":"I.-V. Modoranu <i>et al.</i>, “MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence,” in <i>38th Conference on Neural Information Processing Systems</i>, 2024, vol. 37.","apa":"Modoranu, I.-V., Safaryan, M., Malinovsky, G., Kurtic, E., Robert, T., Richtárik, P., &#38; Alistarh, D.-A. (2024). MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In <i>38th Conference on Neural Information Processing Systems</i> (Vol. 37). Neural Information Processing Systems Foundation.","short":"I.-V. Modoranu, M. Safaryan, G. Malinovsky, E. Kurtic, T. Robert, P. Richtárik, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.","mla":"Modoranu, Ionut-Vlad, et al. “MICROADAM: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence.” <i>38th Conference on Neural Information Processing Systems</i>, vol. 37, Neural Information Processing Systems Foundation, 2024.","ama":"Modoranu I-V, Safaryan M, Malinovsky G, et al. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. In: <i>38th Conference on Neural Information Processing Systems</i>. Vol 37. Neural Information Processing Systems Foundation; 2024.","ista":"Modoranu I-V, Safaryan M, Malinovsky G, Kurtic E, Robert T, Richtárik P, Alistarh D-A. 2024. MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence. 38th Conference on Neural Information Processing Systems. , Advances in Neural Information Processing Systems, vol. 37."},"acknowledged_ssus":[{"_id":"CampIT"}],"_id":"19510","department":[{"_id":"DaAl"}],"scopus_import":"1","publication_identifier":{"issn":["1049-5258"]},"ec_funded":1,"abstract":[{"lang":"eng","text":"We propose a new variant of the Adam optimizer [Kingma and Ba, 2014] called\r\nMICROADAM that specifically minimizes memory overheads, while maintaining\r\ntheoretical convergence guarantees. We achieve this by compressing the gradient\r\ninformation before it is fed into the optimizer state, thereby reducing its memory\r\nfootprint significantly. We control the resulting compression error via a novel\r\ninstance of the classical error feedback mechanism from distributed optimization [Seide et al., 2014, Alistarh et al., 2018, Karimireddy et al., 2019] in which\r\nthe error correction information is itself compressed to allow for practical memory\r\ngains. We prove that the resulting approach maintains theoretical convergence\r\nguarantees competitive to those of AMSGrad, while providing good practical performance. Specifically, we show that MICROADAM can be implemented efficiently\r\non GPUs: on both million-scale (BERT) and billion-scale (LLaMA) models, MICROADAM provides practical convergence competitive to that of the uncompressed\r\nAdam baseline, with lower memory usage and similar running time. Our code is\r\navailable at https://github.com/IST-DASLab/MicroAdam."}],"title":"MICROADAM: Accurate adaptive optimization with low space overhead and provable convergence","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","acknowledgement":"The authors thank Razvan Pascanu, Mahdi Nikdan and Soroush Tabesh 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. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 101034413.","type":"conference","month":"12","alternative_title":["Advances in Neural Information Processing Systems"],"year":"2024","external_id":{"arxiv":["2405.15593"]}},{"volume":37,"OA_type":"green","article_processing_charge":"No","citation":{"ama":"Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In: <i>38th Conference on Neural Information Processing Systems</i>. Vol 37. Neural Information Processing Systems Foundation; 2024.","ista":"Wu D, Modoranu I-V, Safaryan M, Kuznedelev D, Alistarh D-A. 2024. The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.","mla":"Wu, Diyuan, et al. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” <i>38th Conference on Neural Information Processing Systems</i>, vol. 37, Neural Information Processing Systems Foundation, 2024.","short":"D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, D.-A. Alistarh, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.","apa":"Wu, D., Modoranu, I.-V., Safaryan, M., Kuznedelev, D., &#38; Alistarh, D.-A. (2024). The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information. In <i>38th Conference on Neural Information Processing Systems</i> (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.","ieee":"D. Wu, I.-V. Modoranu, M. Safaryan, D. Kuznedelev, and D.-A. Alistarh, “The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information,” in <i>38th Conference on Neural Information Processing Systems</i>, Vancouver, Canada, 2024, vol. 37.","chicago":"Wu, Diyuan, Ionut-Vlad Modoranu, Mher Safaryan, Denis Kuznedelev, and Dan-Adrian Alistarh. “The Iterative Optimal Brain Surgeon: Faster Sparse Recovery by Leveraging Second-Order Information.” In <i>38th Conference on Neural Information Processing Systems</i>, Vol. 37. Neural Information Processing Systems Foundation, 2024."},"day":"20","publication_status":"published","date_published":"2024-12-20T00:00:00Z","project":[{"grant_number":"101034413","call_identifier":"H2020","name":"IST-BRIDGE: International postdoctoral program","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c"}],"corr_author":"1","quality_controlled":"1","intvolume":"        37","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2408.17163","open_access":"1"}],"date_updated":"2025-05-14T11:37:10Z","OA_place":"repository","oa":1,"author":[{"first_name":"Diyuan","full_name":"Wu, Diyuan","id":"1a5914c2-896a-11ed-bdf8-fb80621a0635","last_name":"Wu"},{"full_name":"Modoranu, Ionut-Vlad","first_name":"Ionut-Vlad","id":"449f7a18-f128-11eb-9611-9b430c0c6333","last_name":"Modoranu"},{"last_name":"Safaryan","id":"dd546b39-0804-11ed-9c55-ef075c39778d","first_name":"Mher","full_name":"Safaryan, Mher"},{"full_name":"Kuznedelev, Denis","first_name":"Denis","last_name":"Kuznedelev"},{"full_name":"Alistarh, Dan-Adrian","first_name":"Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-3650-940X","last_name":"Alistarh"}],"status":"public","publication":"38th Conference on Neural Information Processing Systems","language":[{"iso":"eng"}],"publisher":"Neural Information Processing Systems Foundation","date_created":"2025-04-06T22:01:32Z","oa_version":"Preprint","arxiv":1,"acknowledgement":"The authors thank the anonymous NeurIPS reviewers for their useful comments and feedback, the IT department from the Institute of Science and Technology Austria for the hardware support, and Weights and Biases for the infrastructure to track all our experiments. Mher Safaryan has received funding from the European Union’s Horizon 2020 research and innovation program under the Maria Skłodowska-Curie grant agreement No 101034413.","abstract":[{"text":"The rising footprint of machine learning has led to a focus on imposing model\r\nsparsity as a means of reducing computational and memory costs. For deep neural\r\nnetworks (DNNs), the state-of-the-art accuracy-vs-sparsity is achieved by heuristics\r\ninspired by the classical Optimal Brain Surgeon (OBS) framework [LeCun et al.,\r\n1989, Hassibi and Stork, 1992, Hassibi et al., 1993], which leverages loss curvature\r\ninformation to make better pruning decisions. Yet, these results still lack a solid\r\ntheoretical understanding, and it is unclear whether they can be improved by\r\nleveraging connections to the wealth of work on sparse recovery algorithms. In this\r\npaper, we draw new connections between these two areas and present new sparse\r\nrecovery algorithms inspired by the OBS framework that comes with theoretical\r\nguarantees under reasonable assumptions and have strong practical performance.\r\nSpecifically, our work starts from the observation that we can leverage curvature\r\ninformation in OBS-like fashion upon the projection step of classic iterative sparse\r\nrecovery algorithms such as IHT. We show for the first time that this leads both\r\nto improved convergence bounds under standard assumptions. Furthermore, we\r\npresent extensions of this approach to the practical task of obtaining accurate sparse\r\nDNNs, and validate it experimentally at scale for Transformer-based models on\r\nvision and language tasks.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"The iterative optimal brain surgeon: Faster sparse recovery by leveraging second-order information","alternative_title":["Advances in Neural Information Processing Systems"],"external_id":{"arxiv":["2408.17163"]},"year":"2024","month":"12","type":"conference","_id":"19518","acknowledged_ssus":[{"_id":"CampIT"}],"department":[{"_id":"DaAl"},{"_id":"MaMo"}],"ec_funded":1,"scopus_import":"1","conference":{"location":"Vancouver, Canada","end_date":"2024-12-15","name":"NeurIPS: Neural Information Processing Systems","start_date":"2024-12-09"},"publication_identifier":{"issn":["1049-5258"]}},{"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."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Error feedback can accurately compress preconditioners","acknowledgement":"The authors thank Adrian Vladu, Razvan Pascanu, Alexandra Peste, Mher Safaryan for their valuable feedback, the IT department from Institute of Science and Technology Austria for the hardware support and Weights and Biases for the infrastructure to track all our experiments.","month":"07","type":"conference","alternative_title":["PMLR"],"external_id":{"arxiv":["2306.06098"]},"year":"2024","_id":"18975","acknowledged_ssus":[{"_id":"CampIT"}],"department":[{"_id":"DaAl"}],"scopus_import":"1","conference":{"end_date":"2024-07-27","location":"Vienna, Austria","name":"ICML: International Conference on Machine Learning","start_date":"2024-07-21"},"publication_identifier":{"eissn":["2640-3498"]},"date_published":"2024-07-30T00:00:00Z","corr_author":"1","volume":235,"OA_type":"green","citation":{"ieee":"I.-V. Modoranu, A. Kalinov, E. Kurtic, E. Frantar, and D.-A. Alistarh, “Error feedback can accurately compress preconditioners,” in <i>41st International Conference on Machine Learning</i>, Vienna, Austria, 2024, vol. 235, pp. 35910–35933.","apa":"Modoranu, I.-V., Kalinov, A., Kurtic, E., Frantar, E., &#38; Alistarh, D.-A. (2024). Error feedback can accurately compress preconditioners. In <i>41st International Conference on Machine Learning</i> (Vol. 235, pp. 35910–35933). Vienna, Austria: ML Research Press.","chicago":"Modoranu, Ionut-Vlad, Aleksei Kalinov, Eldar Kurtic, Elias Frantar, and Dan-Adrian Alistarh. “Error Feedback Can Accurately Compress Preconditioners.” In <i>41st International Conference on Machine Learning</i>, 235:35910–33. ML Research Press, 2024.","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.","ista":"Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. 2024. Error feedback can accurately compress preconditioners. 41st International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 235, 35910–35933.","ama":"Modoranu I-V, Kalinov A, Kurtic E, Frantar E, Alistarh D-A. Error feedback can accurately compress preconditioners. In: <i>41st International Conference on Machine Learning</i>. Vol 235. ML Research Press; 2024:35910-35933.","mla":"Modoranu, Ionut-Vlad, et al. “Error Feedback Can Accurately Compress Preconditioners.” <i>41st International Conference on Machine Learning</i>, vol. 235, ML Research Press, 2024, pp. 35910–33."},"article_processing_charge":"No","day":"30","publication_status":"published","oa":1,"author":[{"id":"449f7a18-f128-11eb-9611-9b430c0c6333","full_name":"Modoranu, Ionut-Vlad","first_name":"Ionut-Vlad","last_name":"Modoranu"},{"last_name":"Kalinov","orcid":"0000-0003-2189-3904","full_name":"Kalinov, Aleksei","first_name":"Aleksei","id":"44b7120e-eb97-11eb-a6c2-e1557aa81d02"},{"last_name":"Kurtic","id":"47beb3a5-07b5-11eb-9b87-b108ec578218","first_name":"Eldar","full_name":"Kurtic, Eldar"},{"last_name":"Frantar","id":"09a8f98d-ec99-11ea-ae11-c063a7b7fe5f","first_name":"Elias","full_name":"Frantar, Elias"},{"first_name":"Dan-Adrian","full_name":"Alistarh, Dan-Adrian","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","last_name":"Alistarh","orcid":"0000-0003-3650-940X"}],"status":"public","intvolume":"       235","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2306.06098","open_access":"1"}],"quality_controlled":"1","date_updated":"2025-01-30T07:54:16Z","OA_place":"repository","oa_version":"Preprint","arxiv":1,"page":"35910-35933","publication":"41st International Conference on Machine Learning","language":[{"iso":"eng"}],"date_created":"2025-01-30T07:53:22Z","publisher":"ML Research Press"},{"related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"12521"},{"relation":"part_of_dissertation","id":"18549","status":"public"}]},"oa_version":"Published Version","page":"181","language":[{"iso":"eng"}],"publisher":"Institute of Science and Technology Austria","date_created":"2024-11-11T08:40:45Z","file":[{"date_updated":"2025-05-11T22:30:04Z","creator":"amrnjava","relation":"source_file","file_size":26870629,"content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","title":"Early stages of sex chromosome evolution","date_created":"2024-11-13T12:15:28Z","embargo_to":"open_access","access_level":"closed","file_id":"18551","checksum":"3e48b163c22114ef5d5371f758668289","file_name":"AMrnjavac_thesis_library.docx"},{"title":"Early stages of sex chromosome evolution","content_type":"application/pdf","file_size":4228766,"creator":"amrnjava","relation":"main_file","date_updated":"2025-05-11T22:30:04Z","file_name":"AMrnjavac_thesis_library.pdf","checksum":"3ead60c1b678e7dcf018043aef3b5db2","file_id":"18552","access_level":"open_access","date_created":"2024-11-13T12:15:54Z","embargo":"2025-05-11"}],"oa":1,"author":[{"last_name":"Mrnjavac","id":"353FAC84-AE61-11E9-8BFC-00D3E5697425","full_name":"Mrnjavac, Andrea","first_name":"Andrea"}],"status":"public","date_updated":"2026-04-07T13:22:45Z","OA_place":"publisher","date_published":"2024-11-11T00:00:00Z","corr_author":"1","article_processing_charge":"No","citation":{"short":"A. Mrnjavac, Early Stages of Sex Chromosome Evolution, Institute of Science and Technology Austria, 2024.","ista":"Mrnjavac A. 2024. Early stages of sex chromosome evolution. Institute of Science and Technology Austria.","ama":"Mrnjavac A. Early stages of sex chromosome evolution. 2024. doi:<a href=\"https://doi.org/10.15479/at:ista:18531\">10.15479/at:ista:18531</a>","mla":"Mrnjavac, Andrea. <i>Early Stages of Sex Chromosome Evolution</i>. Institute of Science and Technology Austria, 2024, doi:<a href=\"https://doi.org/10.15479/at:ista:18531\">10.15479/at:ista:18531</a>.","apa":"Mrnjavac, A. (2024). <i>Early stages of sex chromosome evolution</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:18531\">https://doi.org/10.15479/at:ista:18531</a>","ieee":"A. Mrnjavac, “Early stages of sex chromosome evolution,” Institute of Science and Technology Austria, 2024.","chicago":"Mrnjavac, Andrea. “Early Stages of Sex Chromosome Evolution.” Institute of Science and Technology Austria, 2024. <a href=\"https://doi.org/10.15479/at:ista:18531\">https://doi.org/10.15479/at:ista:18531</a>."},"degree_awarded":"PhD","publication_status":"published","day":"11","ddc":["576"],"has_accepted_license":"1","publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:18531","supervisor":[{"last_name":"Vicoso","orcid":"0000-0002-4579-8306","full_name":"Vicoso, Beatriz","first_name":"Beatriz","id":"49E1C5C6-F248-11E8-B48F-1D18A9856A87"}],"file_date_updated":"2025-05-11T22:30:04Z","OA_embargo":"6","keyword":["Sex chromosomes","evolution","selection","sheltering"],"_id":"18531","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"CampIT"}],"department":[{"_id":"GradSch"},{"_id":"BeVi"}],"month":"11","type":"dissertation","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"alternative_title":["ISTA Thesis"],"year":"2024","abstract":[{"lang":"eng","text":"Sex chromosomes and autosomes exhibit very different evolutionary dynamics.\r\nThe Y chromosome usually degenerates, leaving many X-linked loci hemizygous in\r\nmales. Since recessive X-linked mutations are always exposed to selection in males,\r\nselection is more efficient on the X chromosome than on autosomes on recessive\r\nmutations, leading to faster adaptation on the X chromosome than other genomic\r\nregions, if beneficial mutations are on average recessive (known as the Faster-X\r\neffect). In the presence of the functional, but non-recombining gametolog on the Y (as\r\nis often the case in young non-recombining regions), recessive mutations are\r\nsheltered from selection on the X chromosome. We model this scenario and show that\r\nthe efficiency of selection is reduced on diploid X loci due to sheltering by the Y\r\nchromosome. Reduced efficiency of selection leads to slower adaptation and\r\nincreased accumulation of deleterious mutations (Slower-X effect). We extended this\r\nmodel to explore the effect of sex-specific selection on degeneration of sex\r\nchromosomes, showing theoretically that male-limited genes degenerate on the X\r\nchromosome and female-biased genes degenerate on the Y chromosome. This\r\nprediction depends on the effective population size and the mutation rate, explaining\r\nthe variety of sex chromosome degeneration patterns observed in nature.\r\nTo test for direct evidence of a Slower-X (or Slower-Z) effect, we analyzed the\r\nZW sex chromosomes of the flatworm Schistosoma japonicum, which have a very\r\nyoung non-recombining region with non-degenerated W. Diploid Z-linked genes have\r\nhigher ratios of non-synonymous to synonymous polymorphisms than autosomal\r\ngenes, supporting reduced efficiency of selection on the diploid Z region. These results\r\nprovide evidence of sheltering by the W chromosome, a mechanism that could\r\ncontribute to Z (X) chromosome degeneration, and illustrate contrasting evolutionary\r\npatterns in old and young sex chromosome regions. In addition, genes with sexspecific patterns of expression show opposite patterns of selection in the young\r\n(diploid) and old (hemizygous) Z, showing the complex manner in which sex-specific selection shapes the evolutionary patterns of sex chromosomes. "}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Early stages of sex chromosome evolution","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/"},{"abstract":[{"text":"Mutation rates represent the net result of complex interactions among various\r\ncellular processes and can dramatically influence the evolutionary fate of\r\nmicrobial populations. However, many popular techniques used to study\r\nmutations are subject to the confounding effects of heredity and the subtleties\r\nof adaptation to selection, all of which make it difficult to observe any dynamic\r\nresponses of mutation rates to fitness challenges. Furthermore, in spite of the\r\nubiquity of quorum sensing systems across the bacterial domain and relevance\r\nfor many physiological behaviors, the effects of such mechanisms on mutation\r\nrate and adaptation remain poorly understood. In the following work, I\r\npresent the development of a microfluidic droplet-based method to measure\r\nsingle base-pair mutation rates in growing populations of the bacterium\r\nEscherichia coli. I use this method to observe a stress-induced increase in\r\nmutation rate that is mediated by luxS, a highly conserved bacterial quorum\r\nsensing component. I also show that the aforementioned increase in mutation\r\nrate, and its associated control by luxS, corresponds to a higher degree of\r\nadaptability under competitive environments.","lang":"eng"}],"title":"Adaptive mutation in E. coli modulated by luxS","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","type":"dissertation","month":"11","alternative_title":["ISTA Thesis"],"year":"2023","keyword":["microfluidics","miceobiology","mutations","quorum sensing"],"acknowledged_ssus":[{"_id":"LifeSc"},{"_id":"Bio"},{"_id":"CampIT"}],"_id":"14641","department":[{"_id":"GradSch"},{"_id":"BjHo"}],"publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/at:ista:14641","file_date_updated":"2025-07-17T11:20:25Z","supervisor":[{"orcid":"0000-0003-2057-2754","last_name":"Hof","full_name":"Hof, Björn","first_name":"Björn","id":"3A374330-F248-11E8-B48F-1D18A9856A87"}],"ec_funded":1,"has_accepted_license":"1","date_published":"2023-11-30T00:00:00Z","project":[{"name":"International IST Doctoral Program","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","grant_number":"665385","call_identifier":"H2020"}],"corr_author":"1","day":"30","degree_awarded":"PhD","publication_status":"published","ddc":["570"],"article_processing_charge":"No","citation":{"short":"M. Hennessey-Wesen, Adaptive Mutation in E. Coli Modulated by LuxS, Institute of Science and Technology Austria, 2023.","mla":"Hennessey-Wesen, Mike. <i>Adaptive Mutation in E. Coli Modulated by LuxS</i>. Institute of Science and Technology Austria, 2023, doi:<a href=\"https://doi.org/10.15479/at:ista:14641\">10.15479/at:ista:14641</a>.","ista":"Hennessey-Wesen M. 2023. Adaptive mutation in E. coli modulated by luxS. Institute of Science and Technology Austria.","ama":"Hennessey-Wesen M. Adaptive mutation in E. coli modulated by luxS. 2023. doi:<a href=\"https://doi.org/10.15479/at:ista:14641\">10.15479/at:ista:14641</a>","apa":"Hennessey-Wesen, M. (2023). <i>Adaptive mutation in E. coli modulated by luxS</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:14641\">https://doi.org/10.15479/at:ista:14641</a>","ieee":"M. Hennessey-Wesen, “Adaptive mutation in E. coli modulated by luxS,” Institute of Science and Technology Austria, 2023.","chicago":"Hennessey-Wesen, Mike. “Adaptive Mutation in E. Coli Modulated by LuxS.” Institute of Science and Technology Austria, 2023. <a href=\"https://doi.org/10.15479/at:ista:14641\">https://doi.org/10.15479/at:ista:14641</a>."},"author":[{"id":"3F338C72-F248-11E8-B48F-1D18A9856A87","full_name":"Hennessey-Wesen, Mike","first_name":"Mike","last_name":"Hennessey-Wesen"}],"file":[{"checksum":"4127c285b34f4bf7fb31ef24f9d14c25","file_name":"mike_thesis_v06-12-2023.odt","file_id":"14648","embargo_to":"open_access","access_level":"closed","date_created":"2023-12-06T13:13:26Z","file_size":46405919,"content_type":"application/vnd.oasis.opendocument.text","creator":"mhenness","relation":"source_file","date_updated":"2024-11-30T23:30:05Z"},{"file_size":21282155,"content_type":"application/pdf","creator":"mhenness","relation":"main_file","date_updated":"2025-07-17T11:20:25Z","checksum":"f5203a61eddaf35235bbc51904d73982","file_name":"mike_thesis_v06-12-2023.pdf","file_id":"14649","embargo_to":"open_access","access_level":"closed","embargo":"2026-07-18","date_created":"2023-12-06T13:14:15Z"},{"access_level":"closed","embargo_to":"open_access","date_created":"2025-05-20T12:59:12Z","checksum":"902102d26d30e74dbd6cdd70a65820c3","file_name":"2023_Hennessey_Michael_Thesis_print.pdf","file_id":"19720","description":"for printing purposes only","relation":"other","creator":"cchlebak","date_updated":"2025-05-20T22:31:34Z","file_size":45847968,"content_type":"application/pdf","title":"Print version"}],"status":"public","OA_place":"publisher","date_updated":"2026-04-07T13:29:59Z","page":"104","oa_version":"Published Version","language":[{"iso":"eng"}],"publisher":"Institute of Science and Technology Austria","date_created":"2023-12-04T13:17:37Z"},{"language":[{"iso":"eng"}],"date_created":"2023-03-08T15:19:45Z","publisher":"Institute of Science and Technology Austria","oa_version":"Published Version","page":"178","date_updated":"2026-04-07T13:25:15Z","OA_place":"publisher","file":[{"file_id":"12717","checksum":"6c6d9cc2c4cdacb74e6b1047a34d7332","file_name":"Burnett_Thesis_2023.docx","date_created":"2023-03-08T15:08:46Z","access_level":"closed","file_size":23029260,"content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","date_updated":"2023-03-08T15:08:46Z","creator":"lburnett","relation":"source_file"},{"date_created":"2023-03-08T15:08:46Z","access_level":"open_access","file_id":"12718","checksum":"cebc77705288bf4382db9b3541483cd0","file_name":"Burnett_Thesis_2023_pdfA.pdf","success":1,"date_updated":"2023-03-08T15:08:46Z","relation":"main_file","creator":"lburnett","content_type":"application/pdf","file_size":11959869}],"oa":1,"author":[{"first_name":"Laura","full_name":"Burnett, Laura","id":"3B717F68-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8937-410X","last_name":"Burnett"}],"status":"public","article_processing_charge":"No","citation":{"ista":"Burnett L. 2023. To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism. Institute of Science and Technology Austria.","ama":"Burnett L. To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism. 2023. doi:<a href=\"https://doi.org/10.15479/at:ista:12716\">10.15479/at:ista:12716</a>","mla":"Burnett, Laura. <i>To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism</i>. Institute of Science and Technology Austria, 2023, doi:<a href=\"https://doi.org/10.15479/at:ista:12716\">10.15479/at:ista:12716</a>.","short":"L. Burnett, To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism, Institute of Science and Technology Austria, 2023.","chicago":"Burnett, Laura. “To Flee, or Not to Flee? Using Innate Defensive Behaviours to Investigate Rapid Perceptual Decision-Making through Subcortical Circuits in Mouse Models of Autism.” Institute of Science and Technology Austria, 2023. <a href=\"https://doi.org/10.15479/at:ista:12716\">https://doi.org/10.15479/at:ista:12716</a>.","apa":"Burnett, L. (2023). <i>To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/at:ista:12716\">https://doi.org/10.15479/at:ista:12716</a>","ieee":"L. Burnett, “To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism,” Institute of Science and Technology Austria, 2023."},"publication_status":"published","day":"10","ddc":["599","573"],"degree_awarded":"PhD","date_published":"2023-03-10T00:00:00Z","project":[{"call_identifier":"H2020","grant_number":"756502","name":"Circuits of Visual Attention","_id":"2634E9D2-B435-11E9-9278-68D0E5697425"}],"corr_author":"1","has_accepted_license":"1","ec_funded":1,"supervisor":[{"id":"2BD278E6-F248-11E8-B48F-1D18A9856A87","full_name":"Jösch, Maximilian A","first_name":"Maximilian A","last_name":"Jösch","orcid":"0000-0002-3937-1330"}],"file_date_updated":"2023-03-08T15:08:46Z","doi":"10.15479/at:ista:12716","publication_identifier":{"issn":["2663-337X"]},"_id":"12716","acknowledged_ssus":[{"_id":"PreCl"},{"_id":"Bio"},{"_id":"LifeSc"},{"_id":"M-Shop"},{"_id":"CampIT"}],"department":[{"_id":"GradSch"},{"_id":"MaJö"}],"alternative_title":["ISTA Thesis"],"year":"2023","month":"03","type":"dissertation","abstract":[{"text":"The process of detecting and evaluating sensory information to guide behaviour is termed perceptual decision-making (PDM), and is critical for the ability of an organism to interact with its external world. Individuals with autism, a neurodevelopmental condition primarily characterised by social and communication difficulties, frequently exhibit altered sensory processing and PDM difficulties are widely reported. Recent technological advancements have pushed forward our understanding of the genetic changes accompanying this condition, however our understanding of how these mutations affect the function of specific neuronal circuits and bring about the corresponding behavioural changes remains limited. Here, we use an innate PDM task, the looming avoidance response (LAR) paradigm, to identify a convergent behavioural abnormality across three molecularly distinct genetic mouse models of autism (Cul3, Setd5 and Ptchd1). Although mutant mice can rapidly detect threatening visual stimuli, their responses are consistently delayed, requiring longer to initiate an appropriate response than their wild-type siblings. Mutant animals show abnormal adaptation in both their stimulus- evoked escape responses and exploratory dynamics following repeated stimulus presentations. Similarly delayed behavioural responses are observed in wild-type animals when faced with more ambiguous threats, suggesting the mutant phenotype could arise from a dysfunction in the flexible control of this PDM process.\r\nOur knowledge of the core neuronal circuitry mediating the LAR facilitated a detailed dissection of the neuronal mechanisms underlying the behavioural impairment. In vivo extracellular recording revealed that visual responses were unaffected within a key brain region for the rapid processing of visual threats, the superior colliculus (SC), indicating that the behavioural delay was unlikely to originate from sensory impairments. Delayed behavioural responses were recapitulated in the Setd5 model following optogenetic stimulation of the excitatory output neurons of the SC, which are known to mediate escape initiation through the activation of cells in the underlying dorsal periaqueductal grey (dPAG). In vitro patch-clamp recordings of dPAG cells uncovered a stark hypoexcitability phenotype in two out of the three genetic models investigated (Setd5 and Ptchd1), that in Setd5, is mediated by the misregulation of voltage-gated potassium channels. Overall, our results show that the ability to use visual information to drive efficient escape responses is impaired in three diverse genetic mouse models of autism and that, in one of the models studied, this behavioural delay likely originates from differences in the intrinsic excitability of a key subcortical node, the dPAG. Furthermore, this work showcases the use of an innate behavioural paradigm to mechanistically dissect PDM processes in autism.","lang":"eng"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"To flee, or not to flee? Using innate defensive behaviours to investigate rapid perceptual decision-making through subcortical circuits in mouse models of autism"},{"abstract":[{"text":"Deep learning is best known for its empirical success across a wide range of applications\r\nspanning computer vision, natural language processing and speech. Of equal significance,\r\nthough perhaps less known, are its ramifications for learning theory: deep networks have\r\nbeen observed to perform surprisingly well in the high-capacity regime, aka the overfitting\r\nor underspecified regime. Classically, this regime on the far right of the bias-variance curve\r\nis associated with poor generalisation; however, recent experiments with deep networks\r\nchallenge this view.\r\n\r\nThis thesis is devoted to investigating various aspects of underspecification in deep learning.\r\nFirst, we argue that deep learning models are underspecified on two levels: a) any given\r\ntraining dataset can be fit by many different functions, and b) any given function can be\r\nexpressed by many different parameter configurations. We refer to the second kind of\r\nunderspecification as parameterisation redundancy and we precisely characterise its extent.\r\nSecond, we characterise the implicit criteria (the inductive bias) that guide learning in the\r\nunderspecified regime. Specifically, we consider a nonlinear but tractable classification\r\nsetting, and show that given the choice, neural networks learn classifiers with a large margin.\r\nThird, we consider learning scenarios where the inductive bias is not by itself sufficient to\r\ndeal with underspecification. We then study different ways of ‘tightening the specification’: i)\r\nIn the setting of representation learning with variational autoencoders, we propose a hand-\r\ncrafted regulariser based on mutual information. ii) In the setting of binary classification, we\r\nconsider soft-label (real-valued) supervision. We derive a generalisation bound for linear\r\nnetworks supervised in this way and verify that soft labels facilitate fast learning. Finally, we\r\nexplore an application of soft-label supervision to the training of multi-exit models.","lang":"eng"}],"title":"Underspecification in deep learning","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","type":"dissertation","month":"05","alternative_title":["ISTA Thesis"],"year":"2021","_id":"9418","acknowledged_ssus":[{"_id":"ScienComp"},{"_id":"CampIT"},{"_id":"E-Lib"}],"department":[{"_id":"GradSch"},{"_id":"ChLa"}],"publication_identifier":{"issn":["2663-337X"]},"doi":"10.15479/AT:ISTA:9418","file_date_updated":"2021-05-24T11:56:02Z","supervisor":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","first_name":"Christoph","last_name":"Lampert","orcid":"0000-0001-8622-7887"}],"has_accepted_license":"1","date_published":"2021-05-30T00:00:00Z","corr_author":"1","ddc":["000"],"degree_awarded":"PhD","day":"30","publication_status":"published","citation":{"ieee":"M. Phuong, “Underspecification in deep learning,” Institute of Science and Technology Austria, 2021.","apa":"Phuong, M. (2021). <i>Underspecification in deep learning</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:9418\">https://doi.org/10.15479/AT:ISTA:9418</a>","chicago":"Phuong, Mary. “Underspecification in Deep Learning.” Institute of Science and Technology Austria, 2021. <a href=\"https://doi.org/10.15479/AT:ISTA:9418\">https://doi.org/10.15479/AT:ISTA:9418</a>.","mla":"Phuong, Mary. <i>Underspecification in Deep Learning</i>. Institute of Science and Technology Austria, 2021, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9418\">10.15479/AT:ISTA:9418</a>.","ista":"Phuong M. 2021. Underspecification in deep learning. Institute of Science and Technology Austria.","ama":"Phuong M. Underspecification in deep learning. 2021. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:9418\">10.15479/AT:ISTA:9418</a>","short":"M. Phuong, Underspecification in Deep Learning, Institute of Science and Technology Austria, 2021."},"article_processing_charge":"No","author":[{"last_name":"Bui Thi Mai","id":"3EC6EE64-F248-11E8-B48F-1D18A9856A87","first_name":"Phuong","full_name":"Bui Thi Mai, Phuong"}],"oa":1,"file":[{"date_created":"2021-05-24T11:22:29Z","access_level":"open_access","file_id":"9419","checksum":"4f0abe64114cfed264f9d36e8d1197e3","file_name":"mph-thesis-v519-pdfimages.pdf","success":1,"date_updated":"2021-05-24T11:22:29Z","relation":"main_file","creator":"bphuong","file_size":2673905,"content_type":"application/pdf"},{"file_size":92995100,"content_type":"application/zip","creator":"bphuong","relation":"source_file","date_updated":"2021-05-24T11:56:02Z","file_name":"thesis.zip","checksum":"f5699e876bc770a9b0df8345a77720a2","file_id":"9420","access_level":"closed","date_created":"2021-05-24T11:56:02Z"}],"status":"public","OA_place":"publisher","date_updated":"2026-04-08T07:01:17Z","page":"125","related_material":{"record":[{"relation":"part_of_dissertation","id":"7435","status":"deleted"},{"id":"7481","status":"public","relation":"part_of_dissertation"},{"id":"9416","status":"public","relation":"part_of_dissertation"},{"relation":"part_of_dissertation","status":"public","id":"7479"}]},"oa_version":"Published Version","language":[{"iso":"eng"}],"date_created":"2021-05-24T13:06:23Z","publisher":"Institute of Science and Technology Austria"},{"has_accepted_license":"1","citation":{"apa":"Royer, A. (2020). <i>Leveraging structure in Computer Vision tasks for flexible Deep Learning models</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8390\">https://doi.org/10.15479/AT:ISTA:8390</a>","ieee":"A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep Learning models,” Institute of Science and Technology Austria, 2020.","chicago":"Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8390\">https://doi.org/10.15479/AT:ISTA:8390</a>.","ista":"Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. Institute of Science and Technology Austria.","ama":"Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning models. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8390\">10.15479/AT:ISTA:8390</a>","mla":"Royer, Amélie. <i>Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8390\">10.15479/AT:ISTA:8390</a>.","short":"A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020."},"article_processing_charge":"No","degree_awarded":"PhD","ddc":["000"],"day":"14","publication_status":"published","date_published":"2020-09-14T00:00:00Z","corr_author":"1","date_updated":"2026-04-08T07:26:44Z","OA_place":"publisher","file":[{"content_type":"application/pdf","file_size":30224591,"creator":"dernst","relation":"main_file","success":1,"date_updated":"2020-09-14T13:39:14Z","checksum":"c914d2f88846032f3d8507734861b6ee","file_name":"2020_Thesis_Royer.pdf","file_id":"8391","access_level":"open_access","date_created":"2020-09-14T13:39:14Z"},{"file_size":74227627,"content_type":"application/x-zip-compressed","date_updated":"2020-09-14T13:39:17Z","relation":"main_file","creator":"dernst","file_id":"8392","checksum":"ae98fb35d912cff84a89035ae5794d3c","file_name":"thesis_sources.zip","date_created":"2020-09-14T13:39:17Z","access_level":"closed"}],"oa":1,"author":[{"orcid":"0000-0002-8407-0705","last_name":"Royer","id":"3811D890-F248-11E8-B48F-1D18A9856A87","full_name":"Royer, Amélie","first_name":"Amélie"}],"status":"public","language":[{"iso":"eng"}],"date_created":"2020-09-14T13:42:09Z","publisher":"Institute of Science and Technology Austria","oa_version":"Published Version","related_material":{"record":[{"relation":"part_of_dissertation","status":"public","id":"7936"},{"relation":"part_of_dissertation","status":"public","id":"8092"},{"relation":"part_of_dissertation","status":"public","id":"911"},{"relation":"part_of_dissertation","status":"public","id":"8193"},{"relation":"part_of_dissertation","status":"public","id":"7937"}]},"page":"197","acknowledgement":"Last but not least, I would like to acknowledge the support of the IST IT and scientific computing team for helping provide a great work environment.","abstract":[{"lang":"eng","text":"Deep neural networks have established a new standard for data-dependent feature extraction pipelines in the Computer Vision literature. Despite their remarkable performance in the standard supervised learning scenario, i.e. when models are trained with labeled data and tested on samples that follow a similar distribution, neural networks have been shown to struggle with more advanced generalization abilities, such as transferring knowledge across visually different domains, or generalizing to new unseen combinations of known concepts. In this thesis we argue that, in contrast to the usual black-box behavior of neural networks, leveraging more structured internal representations is a promising direction\r\nfor tackling such problems. In particular, we focus on two forms of structure. First, we tackle modularity: We show that (i) compositional architectures are a natural tool for modeling reasoning tasks, in that they efficiently capture their combinatorial nature, which is key for generalizing beyond the compositions seen during training. We investigate how to to learn such models, both formally and experimentally, for the task of abstract visual reasoning. Then, we show that (ii) in some settings, modularity allows us to efficiently break down complex tasks into smaller, easier, modules, thereby improving computational efficiency; We study this behavior in the context of generative models for colorization, as well as for small objects detection. Secondly, we investigate the inherently layered structure of representations learned by neural networks, and analyze its role in the context of transfer learning and domain adaptation across visually\r\ndissimilar domains. "}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","title":"Leveraging structure in Computer Vision tasks for flexible Deep Learning models","tmp":{"image":"/images/cc_by_nc_sa.png","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"},"alternative_title":["ISTA Thesis"],"year":"2020","month":"09","type":"dissertation","_id":"8390","acknowledged_ssus":[{"_id":"CampIT"},{"_id":"ScienComp"}],"department":[{"_id":"ChLa"}],"supervisor":[{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Lampert, Christoph","first_name":"Christoph","orcid":"0000-0001-8622-7887","last_name":"Lampert"}],"file_date_updated":"2020-09-14T13:39:17Z","doi":"10.15479/AT:ISTA:8390","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-007-7"]}},{"degree_awarded":"PhD","ddc":["514"],"day":"26","publication_status":"published","citation":{"ieee":"K. Huszár, “Combinatorial width parameters for 3-dimensional manifolds,” Institute of Science and Technology Austria, 2020.","chicago":"Huszár, Kristóf. “Combinatorial Width Parameters for 3-Dimensional Manifolds.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8032\">https://doi.org/10.15479/AT:ISTA:8032</a>.","apa":"Huszár, K. (2020). <i>Combinatorial width parameters for 3-dimensional manifolds</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8032\">https://doi.org/10.15479/AT:ISTA:8032</a>","short":"K. Huszár, Combinatorial Width Parameters for 3-Dimensional Manifolds, Institute of Science and Technology Austria, 2020.","mla":"Huszár, Kristóf. <i>Combinatorial Width Parameters for 3-Dimensional Manifolds</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8032\">10.15479/AT:ISTA:8032</a>.","ama":"Huszár K. Combinatorial width parameters for 3-dimensional manifolds. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8032\">10.15479/AT:ISTA:8032</a>","ista":"Huszár K. 2020. Combinatorial width parameters for 3-dimensional manifolds. Institute of Science and Technology Austria."},"article_processing_charge":"No","date_published":"2020-06-26T00:00:00Z","corr_author":"1","has_accepted_license":"1","language":[{"iso":"eng"}],"publisher":"Institute of Science and Technology Austria","date_created":"2020-06-26T10:00:36Z","page":"xviii+120","related_material":{"record":[{"id":"6556","status":"public","relation":"dissertation_contains"},{"relation":"dissertation_contains","status":"public","id":"7093"}]},"oa_version":"Published Version","OA_place":"publisher","date_updated":"2026-04-08T07:21:28Z","author":[{"last_name":"Huszár","orcid":"0000-0002-5445-5057","id":"33C26278-F248-11E8-B48F-1D18A9856A87","first_name":"Kristóf","full_name":"Huszár, Kristóf"}],"oa":1,"file":[{"file_id":"8034","checksum":"bd8be6e4f1addc863dfcc0fad29ee9c3","file_name":"Kristof_Huszar-Thesis.pdf","date_created":"2020-06-26T10:03:58Z","access_level":"open_access","content_type":"application/pdf","file_size":2637562,"date_updated":"2020-07-14T12:48:08Z","relation":"main_file","creator":"khuszar"},{"checksum":"d5f8456202b32f4a77552ef47a2837d1","file_name":"Kristof_Huszar-Thesis-source.zip","file_id":"8035","access_level":"closed","date_created":"2020-06-26T10:10:06Z","content_type":"application/x-zip-compressed","file_size":7163491,"creator":"khuszar","relation":"source_file","date_updated":"2020-07-14T12:48:08Z"}],"status":"public","alternative_title":["ISTA Thesis"],"tmp":{"short":"CC BY (4.0)","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)"},"year":"2020","type":"dissertation","month":"06","abstract":[{"lang":"eng","text":"Algorithms in computational 3-manifold topology typically take a triangulation as an input and return topological information about the underlying 3-manifold. However, extracting the desired information from a triangulation (e.g., evaluating an invariant) is often computationally very expensive. In recent years this complexity barrier has been successfully tackled in some cases by importing ideas from the theory of parameterized algorithms into the realm of 3-manifolds. Various computationally hard problems were shown to be efficiently solvable for input triangulations that are sufficiently “tree-like.”\r\nIn this thesis we focus on the key combinatorial parameter in the above context: we consider the treewidth of a compact, orientable 3-manifold, i.e., the smallest treewidth of the dual graph of any triangulation thereof. By building on the work of Scharlemann–Thompson and Scharlemann–Schultens–Saito on generalized Heegaard splittings, and on the work of Jaco–Rubinstein on layered triangulations, we establish quantitative relations between the treewidth and classical topological invariants of a 3-manifold. In particular, among other results, we show that the treewidth of a closed, orientable, irreducible, non-Haken 3-manifold is always within a constant factor of its Heegaard genus."}],"title":"Combinatorial width parameters for 3-dimensional manifolds","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","file_date_updated":"2020-07-14T12:48:08Z","supervisor":[{"id":"36690CA2-F248-11E8-B48F-1D18A9856A87","first_name":"Uli","full_name":"Wagner, Uli","orcid":"0000-0002-1494-0568","last_name":"Wagner"},{"first_name":"Jonathan","full_name":"Spreer, Jonathan","last_name":"Spreer"}],"doi":"10.15479/AT:ISTA:8032","publication_identifier":{"issn":["2663-337X"],"isbn":["978-3-99078-006-0"]},"_id":"8032","acknowledged_ssus":[{"_id":"E-Lib"},{"_id":"CampIT"}],"department":[{"_id":"UlWa"}]},{"year":"2020","alternative_title":["ISTA Thesis"],"type":"dissertation","month":"12","acknowledgement":"Also, I would like to express my appreciation and thanks to the Bioimaging facility, LSF, GSO, library, and IT people at IST Austria.","title":"Metabolic regulation of Drosophila macrophage tissue invasion","user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","abstract":[{"lang":"eng","text":"Metabolic adaptation is a critical feature of migrating cells. It tunes the metabolic programs of migrating cells to allow them to efficiently exert their crucial roles in development, inflammatory responses and tumor metastasis. Cell migration through physically challenging contexts requires energy. However, how the metabolic reprogramming that underlies in vivo cell invasion is controlled is still unanswered. In my PhD project, I identify a novel conserved metabolic shift in Drosophila melanogaster immune cells that by modulating their bioenergetic potential controls developmentally programmed tissue invasion. We show that this regulation requires a novel conserved nuclear protein, named Atossa. Atossa enhances the transcription of a set of proteins, including an RNA helicase Porthos and two metabolic enzymes, each of which increases the tissue invasion of leading Drosophila macrophages and can rescue the atossa mutant phenotype. Porthos selectively regulates the translational efficiency of a subset of mRNAs containing a 5’-UTR cis-regulatory TOP-like sequence. These 5’TOPL mRNA targets encode mitochondrial-related proteins, including subunits of mitochondrial oxidative phosphorylation (OXPHOS) components III and V and other metabolic-related proteins. Porthos powers up mitochondrial OXPHOS to engender a sufficient ATP supply, which is required for tissue invasion of leading macrophages. Atossa’s two vertebrate orthologs rescue the invasion defect. In my PhD project, I elucidate that Atossa displays a conserved developmental metabolic control to modulate metabolic capacities and the cellular energy state, through altered transcription and translation, to aid the tissue infiltration of leading cells into energy demanding barriers."}],"file_date_updated":"2021-12-31T23:30:04Z","supervisor":[{"id":"3D224B9E-F248-11E8-B48F-1D18A9856A87","first_name":"Daria E","full_name":"Siekhaus, Daria E","orcid":"0000-0001-8323-8353","last_name":"Siekhaus"}],"doi":"10.15479/AT:ISTA:8983","publication_identifier":{"issn":["2663-337X"]},"department":[{"_id":"DaSi"}],"_id":"8983","acknowledged_ssus":[{"_id":"Bio"},{"_id":"LifeSc"},{"_id":"E-Lib"},{"_id":"CampIT"}],"degree_awarded":"PhD","ddc":["570"],"day":"30","publication_status":"published","citation":{"ieee":"S. Emtenani, “Metabolic regulation of Drosophila macrophage tissue invasion,” Institute of Science and Technology Austria, 2020.","chicago":"Emtenani, Shamsi. “Metabolic Regulation of Drosophila Macrophage Tissue Invasion.” Institute of Science and Technology Austria, 2020. <a href=\"https://doi.org/10.15479/AT:ISTA:8983\">https://doi.org/10.15479/AT:ISTA:8983</a>.","apa":"Emtenani, S. (2020). <i>Metabolic regulation of Drosophila macrophage tissue invasion</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:8983\">https://doi.org/10.15479/AT:ISTA:8983</a>","short":"S. Emtenani, Metabolic Regulation of Drosophila Macrophage Tissue Invasion, Institute of Science and Technology Austria, 2020.","mla":"Emtenani, Shamsi. <i>Metabolic Regulation of Drosophila Macrophage Tissue Invasion</i>. Institute of Science and Technology Austria, 2020, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8983\">10.15479/AT:ISTA:8983</a>.","ista":"Emtenani S. 2020. Metabolic regulation of Drosophila macrophage tissue invasion. Institute of Science and Technology Austria.","ama":"Emtenani S. Metabolic regulation of Drosophila macrophage tissue invasion. 2020. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:8983\">10.15479/AT:ISTA:8983</a>"},"article_processing_charge":"No","corr_author":"1","date_published":"2020-12-30T00:00:00Z","has_accepted_license":"1","publisher":"Institute of Science and Technology Austria","date_created":"2020-12-30T15:41:26Z","language":[{"iso":"eng"}],"page":"141","related_material":{"record":[{"id":"8557","status":"public","relation":"part_of_dissertation"},{"status":"public","id":"6187","relation":"part_of_dissertation"}]},"oa_version":"Published Version","OA_place":"publisher","date_updated":"2026-04-08T07:28:54Z","status":"public","oa":1,"author":[{"full_name":"Emtenani, Shamsi","first_name":"Shamsi","id":"49D32318-F248-11E8-B48F-1D18A9856A87","last_name":"Emtenani","orcid":"0000-0001-6981-6938"}],"file":[{"access_level":"open_access","date_created":"2020-12-30T15:34:01Z","embargo":"2021-12-30","checksum":"ec2797ab7a6f253b35df0572b36d1b43","file_name":"Thesis_Shamsi_Emtenani_pdfA.pdf","file_id":"8984","creator":"semtenan","relation":"main_file","date_updated":"2021-12-31T23:30:04Z","file_size":10848175,"content_type":"application/pdf"},{"creator":"semtenan","relation":"source_file","date_updated":"2021-12-31T23:30:04Z","content_type":"application/pdf","file_size":10073648,"embargo_to":"open_access","access_level":"closed","date_created":"2020-12-30T15:37:36Z","checksum":"cc30e6608a9815414024cf548dff3b3a","file_name":"Thesis_Shamsi_Emtenani_source file.pdf","file_id":"8985"}]},{"type":"journal_article","month":"07","year":"2019","external_id":{"isi":["000484039500018"],"pmid":["31273378"]},"tmp":{"short":"CC BY (4.0)","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)"},"title":"Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","pmid":1,"abstract":[{"lang":"eng","text":"Differentiated sex chromosomes are accompanied by a difference in gene dose between X/Z-specific and autosomal genes. At the transcriptomic level, these sex-linked genes can lead to expression imbalance, or gene dosage can be compensated by epigenetic mechanisms and results into expression level equalization. Schistosoma mansoni has been previously described as a ZW species (i.e., female heterogamety, in opposition to XY male heterogametic species) with a partial dosage compensation, but underlying mechanisms are still unexplored. Here, we combine transcriptomic (RNA-Seq) and epigenetic data (ChIP-Seq against H3K4me3, H3K27me3,andH4K20me1histonemarks) in free larval cercariae and intravertebrate parasitic stages. For the first time, we describe differences in dosage compensation status in ZW females, depending on the parasitic status: free cercariae display global dosage compensation, whereas intravertebrate stages show a partial dosage compensation. We also highlight regional differences of gene expression along the Z chromosome in cercariae, but not in the intravertebrate stages. Finally, we feature a consistent permissive chromatin landscape of the Z chromosome in both sexes and stages. We argue that dosage compensation in schistosomes is characterized by chromatin remodeling mechanisms in the Z-specific region."}],"issue":"7","doi":"10.1093/gbe/evz133","publication_identifier":{"eissn":["1759-6653"]},"scopus_import":"1","file_date_updated":"2020-07-14T12:47:39Z","department":[{"_id":"BeVi"}],"acknowledged_ssus":[{"_id":"CampIT"}],"_id":"6755","date_published":"2019-07-01T00:00:00Z","ddc":["570"],"day":"01","publication_status":"published","article_processing_charge":"No","citation":{"chicago":"Picard, Marion A L, Beatriz Vicoso, David Roquis, Ingo Bulla, Ronaldo C. Augusto, Nathalie Arancibia, Christoph Grunau, Jérôme Boissier, and Céline Cosseau. “Dosage Compensation throughout the Schistosoma Mansoni Lifecycle: Specific Chromatin Landscape of the Z Chromosome.” <i>Genome Biology and Evolution</i>. Oxford University Press, 2019. <a href=\"https://doi.org/10.1093/gbe/evz133\">https://doi.org/10.1093/gbe/evz133</a>.","ieee":"M. A. L. Picard <i>et al.</i>, “Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome,” <i>Genome biology and evolution</i>, vol. 11, no. 7. Oxford University Press, pp. 1909–1922, 2019.","apa":"Picard, M. A. L., Vicoso, B., Roquis, D., Bulla, I., Augusto, R. C., Arancibia, N., … Cosseau, C. (2019). Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome. <i>Genome Biology and Evolution</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/gbe/evz133\">https://doi.org/10.1093/gbe/evz133</a>","ama":"Picard MAL, Vicoso B, Roquis D, et al. Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome. <i>Genome biology and evolution</i>. 2019;11(7):1909-1922. doi:<a href=\"https://doi.org/10.1093/gbe/evz133\">10.1093/gbe/evz133</a>","ista":"Picard MAL, Vicoso B, Roquis D, Bulla I, Augusto RC, Arancibia N, Grunau C, Boissier J, Cosseau C. 2019. Dosage compensation throughout the Schistosoma mansoni lifecycle: Specific chromatin landscape of the Z chromosome. Genome biology and evolution. 11(7), 1909–1922.","mla":"Picard, Marion A. L., et al. “Dosage Compensation throughout the Schistosoma Mansoni Lifecycle: Specific Chromatin Landscape of the Z Chromosome.” <i>Genome Biology and Evolution</i>, vol. 11, no. 7, Oxford University Press, 2019, pp. 1909–22, doi:<a href=\"https://doi.org/10.1093/gbe/evz133\">10.1093/gbe/evz133</a>.","short":"M.A.L. Picard, B. Vicoso, D. Roquis, I. Bulla, R.C. Augusto, N. Arancibia, C. Grunau, J. Boissier, C. Cosseau, Genome Biology and Evolution 11 (2019) 1909–1922."},"volume":11,"has_accepted_license":"1","page":"1909-1922","oa_version":"Published Version","article_type":"original","publisher":"Oxford University Press","date_created":"2019-08-04T21:59:18Z","language":[{"iso":"eng"}],"publication":"Genome biology and evolution","status":"public","oa":1,"author":[{"last_name":"Picard","orcid":"0000-0002-8101-2518","first_name":"Marion A L","full_name":"Picard, Marion A L","id":"2C921A7A-F248-11E8-B48F-1D18A9856A87"},{"id":"49E1C5C6-F248-11E8-B48F-1D18A9856A87","first_name":"Beatriz","full_name":"Vicoso, Beatriz","last_name":"Vicoso","orcid":"0000-0002-4579-8306"},{"full_name":"Roquis, David","first_name":"David","last_name":"Roquis"},{"first_name":"Ingo","full_name":"Bulla, Ingo","last_name":"Bulla"},{"first_name":"Ronaldo C.","full_name":"Augusto, Ronaldo C.","last_name":"Augusto"},{"last_name":"Arancibia","first_name":"Nathalie","full_name":"Arancibia, Nathalie"},{"full_name":"Grunau, Christoph","first_name":"Christoph","last_name":"Grunau"},{"full_name":"Boissier, Jérôme","first_name":"Jérôme","last_name":"Boissier"},{"last_name":"Cosseau","first_name":"Céline","full_name":"Cosseau, Céline"}],"file":[{"date_updated":"2020-07-14T12:47:39Z","relation":"main_file","creator":"dernst","content_type":"application/pdf","file_size":580205,"date_created":"2019-08-05T07:55:02Z","access_level":"open_access","file_id":"6765","file_name":"2019_GenomeBiology_Picard.pdf","checksum":"f9e8f6863a406dcc5a36b2be001c138c"}],"isi":1,"date_updated":"2025-05-14T11:08:50Z","intvolume":"        11","quality_controlled":"1"}]