[{"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"oa_version":"Published Version","ec_funded":1,"title":"Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons","date_created":"2023-07-09T22:01:12Z","citation":{"ama":"Eguchi K, Le Monnier E, Shigemoto R. Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons. The Journal of Neuroscience. 2023;43(23):4197-4216. doi:10.1523/JNEUROSCI.1514-22.2023","mla":"Eguchi, Kohgaku, et al. “Nanoscale Phosphoinositide Distribution on Cell Membranes of Mouse Cerebellar Neurons.” The Journal of Neuroscience, vol. 43, no. 23, Society for Neuroscience, 2023, pp. 4197–216, doi:10.1523/JNEUROSCI.1514-22.2023.","short":"K. Eguchi, E. Le Monnier, R. Shigemoto, The Journal of Neuroscience 43 (2023) 4197–4216.","chicago":"Eguchi, Kohgaku, Elodie Le Monnier, and Ryuichi Shigemoto. “Nanoscale Phosphoinositide Distribution on Cell Membranes of Mouse Cerebellar Neurons.” The Journal of Neuroscience. Society for Neuroscience, 2023. https://doi.org/10.1523/JNEUROSCI.1514-22.2023.","ista":"Eguchi K, Le Monnier E, Shigemoto R. 2023. Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons. The Journal of Neuroscience. 43(23), 4197–4216.","apa":"Eguchi, K., Le Monnier, E., & Shigemoto, R. (2023). Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons. The Journal of Neuroscience. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.1514-22.2023","ieee":"K. Eguchi, E. Le Monnier, and R. Shigemoto, “Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons,” The Journal of Neuroscience, vol. 43, no. 23. Society for Neuroscience, pp. 4197–4216, 2023."},"project":[{"_id":"2659CC84-B435-11E9-9278-68D0E5697425","name":"Ultrastructural analysis of phosphoinositides in nerve terminals: distribution, dynamics and physiological roles in synaptic transmission","grant_number":"793482","call_identifier":"H2020"},{"call_identifier":"H2020","grant_number":"694539","name":"In situ analysis of single channel subunit composition in neurons: physiological implication in synaptic plasticity and behaviour","_id":"25CA28EA-B435-11E9-9278-68D0E5697425"}],"file":[{"success":1,"date_updated":"2023-07-10T09:04:58Z","content_type":"application/pdf","checksum":"70b2141870e0bf1c94fd343e18fdbc32","file_id":"13205","creator":"alisjak","file_name":"2023_JN_Eguchi.pdf","access_level":"open_access","relation":"main_file","file_size":7794425,"date_created":"2023-07-10T09:04:58Z"}],"abstract":[{"lang":"eng","text":"Phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2) plays an essential role in neuronal activities through interaction with various proteins involved in signaling at membranes. However, the distribution pattern of PI(4,5)P2 and the association with these proteins on the neuronal cell membranes remain elusive. In this study, we established a method for visualizing PI(4,5)P2 by SDS-digested freeze-fracture replica labeling (SDS-FRL) to investigate the quantitative nanoscale distribution of PI(4,5)P2 in cryo-fixed brain. We demonstrate that PI(4,5)P2 forms tiny clusters with a mean size of ∼1000 nm2 rather than randomly distributed in cerebellar neuronal membranes in male C57BL/6J mice. These clusters show preferential accumulation in specific membrane compartments of different cell types, in particular, in Purkinje cell (PC) spines and granule cell (GC) presynaptic active zones. Furthermore, we revealed extensive association of PI(4,5)P2 with CaV2.1 and GIRK3 across different membrane compartments, whereas its association with mGluR1α was compartment specific. These results suggest that our SDS-FRL method provides valuable insights into the physiological functions of PI(4,5)P2 in neurons."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-07-10T09:04:58Z","doi":"10.1523/JNEUROSCI.1514-22.2023","acknowledged_ssus":[{"_id":"EM-Fac"}],"page":"4197-4216","oa":1,"article_type":"original","publication":"The Journal of Neuroscience","intvolume":" 43","issue":"23","year":"2023","publication_status":"published","department":[{"_id":"RySh"}],"status":"public","ddc":["570"],"publication_identifier":{"issn":["0270-6474"],"eissn":["1529-2401"]},"_id":"13202","quality_controlled":"1","article_processing_charge":"No","acknowledgement":"This work was supported by The Institute of Science and Technology (IST) Austria, the European Union's Horizon 2020 Research and Innovation Program under the Marie Skłodowska-Curie Grant Agreement No. 793482 (to K.E.) and by the European Research Council (ERC) Grant Agreement No. 694539 (to R.S.). We thank Nicoleta Condruz (IST Austria, Klosterneuburg, Austria) for technical assistance with sample preparation, the Electron Microscopy Facility of IST Austria (Klosterneuburg, Austria) for technical support with EM works, Natalia Baranova (University of Vienna, Vienna, Austria) and Martin Loose (IST Austria, Klosterneuburg, Austria) for advice on liposome preparation, and Yugo Fukazawa (University of Fukui, Fukui, Japan) for comments.","pmid":1,"type":"journal_article","scopus_import":"1","volume":43,"has_accepted_license":"1","publisher":"Society for Neuroscience","author":[{"last_name":"Eguchi","id":"2B7846DC-F248-11E8-B48F-1D18A9856A87","full_name":"Eguchi, Kohgaku","first_name":"Kohgaku","orcid":"0000-0002-6170-2546"},{"last_name":"Le Monnier","id":"3B59276A-F248-11E8-B48F-1D18A9856A87","full_name":"Le Monnier, Elodie","first_name":"Elodie"},{"orcid":"0000-0001-8761-9444","first_name":"Ryuichi","full_name":"Shigemoto, Ryuichi","id":"499F3ABC-F248-11E8-B48F-1D18A9856A87","last_name":"Shigemoto"}],"month":"06","isi":1,"date_published":"2023-06-07T00:00:00Z","external_id":{"pmid":["37160366"],"isi":["001020132100005"]},"language":[{"iso":"eng"}],"date_updated":"2023-10-18T07:12:47Z","day":"07","license":"https://creativecommons.org/licenses/by/4.0/"},{"day":"16","date_updated":"2023-10-18T06:54:30Z","external_id":{"arxiv":["2007.14182"]},"date_published":"2023-02-16T00:00:00Z","language":[{"iso":"eng"}],"month":"02","author":[{"first_name":"Dante","last_name":"Bonolis","id":"6A459894-5FDD-11E9-AF35-BB24E6697425","full_name":"Bonolis, Dante"},{"last_name":"Browning","full_name":"Browning, Timothy D","id":"35827D50-F248-11E8-B48F-1D18A9856A87","first_name":"Timothy D","orcid":"0000-0002-8314-0177"}],"publisher":"Scuola Normale Superiore - Edizioni della Normale","scopus_import":"1","type":"journal_article","volume":24,"quality_controlled":"1","article_processing_charge":"No","_id":"12916","publication_identifier":{"eissn":["2036-2145"],"issn":["0391-173X"]},"publication_status":"published","status":"public","department":[{"_id":"TiBr"}],"year":"2023","issue":"1","publication":"Annali della Scuola Normale Superiore di Pisa - Classe di Scienze","intvolume":" 24","page":"173-204","oa":1,"article_type":"original","doi":"10.2422/2036-2145.202010_018","abstract":[{"lang":"eng","text":"We apply a variant of the square-sieve to produce an upper bound for the number of rational points of bounded height on a family of surfaces that admit a fibration over P1 whose general fibre is a hyperelliptic curve. The implied constant does not depend on the coefficients of the polynomial defining the surface.\r\n"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2023-05-07T22:01:04Z","title":"Uniform bounds for rational points on hyperelliptic fibrations","citation":{"ama":"Bonolis D, Browning TD. Uniform bounds for rational points on hyperelliptic fibrations. Annali della Scuola Normale Superiore di Pisa - Classe di Scienze. 2023;24(1):173-204. doi:10.2422/2036-2145.202010_018","mla":"Bonolis, Dante, and Timothy D. Browning. “Uniform Bounds for Rational Points on Hyperelliptic Fibrations.” Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze, vol. 24, no. 1, Scuola Normale Superiore - Edizioni della Normale, 2023, pp. 173–204, doi:10.2422/2036-2145.202010_018.","short":"D. Bonolis, T.D. Browning, Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze 24 (2023) 173–204.","ista":"Bonolis D, Browning TD. 2023. Uniform bounds for rational points on hyperelliptic fibrations. Annali della Scuola Normale Superiore di Pisa - Classe di Scienze. 24(1), 173–204.","chicago":"Bonolis, Dante, and Timothy D Browning. “Uniform Bounds for Rational Points on Hyperelliptic Fibrations.” Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze. Scuola Normale Superiore - Edizioni della Normale, 2023. https://doi.org/10.2422/2036-2145.202010_018.","ieee":"D. Bonolis and T. D. Browning, “Uniform bounds for rational points on hyperelliptic fibrations,” Annali della Scuola Normale Superiore di Pisa - Classe di Scienze, vol. 24, no. 1. Scuola Normale Superiore - Edizioni della Normale, pp. 173–204, 2023.","apa":"Bonolis, D., & Browning, T. D. (2023). Uniform bounds for rational points on hyperelliptic fibrations. Annali Della Scuola Normale Superiore Di Pisa - Classe Di Scienze. Scuola Normale Superiore - Edizioni della Normale. https://doi.org/10.2422/2036-2145.202010_018"},"oa_version":"Preprint","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2007.14182","open_access":"1"}]},{"author":[{"id":"C7610134-B532-11EA-BD9F-F5753DDC885E","full_name":"Confavreux, Basile J","last_name":"Confavreux","first_name":"Basile J"}],"publisher":"Institute of Science and Technology Austria","has_accepted_license":"1","type":"dissertation","article_processing_charge":"No","supervisor":[{"first_name":"Tim P","orcid":"0000-0003-3295-6181","last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P"}],"_id":"14422","publication_identifier":{"issn":["2663 - 337X"]},"ddc":["610"],"license":"https://creativecommons.org/licenses/by-nc-sa/4.0/","day":"12","date_updated":"2023-10-18T09:20:56Z","language":[{"iso":"eng"}],"date_published":"2023-10-12T00:00:00Z","month":"10","page":"148","degree_awarded":"PhD","doi":"10.15479/at:ista:14422","file_date_updated":"2023-10-18T07:56:08Z","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","abstract":[{"text":"Animals exhibit a remarkable ability to learn and remember new behaviors, skills, and associations throughout their lifetime. These capabilities are made possible thanks to a variety of\r\nchanges in the brain throughout adulthood, regrouped under the term \"plasticity\". Some cells\r\nin the brain —neurons— and specifically changes in the connections between neurons, the\r\nsynapses, were shown to be crucial for the formation, selection, and consolidation of memories\r\nfrom past experiences. These ongoing changes of synapses across time are called synaptic\r\nplasticity. Understanding how a myriad of biochemical processes operating at individual\r\nsynapses can somehow work in concert to give rise to meaningful changes in behavior is a\r\nfascinating problem and an active area of research.\r\nHowever, the experimental search for the precise plasticity mechanisms at play in the brain\r\nis daunting, as it is difficult to control and observe synapses during learning. Theoretical\r\napproaches have thus been the default method to probe the plasticity-behavior connection. Such\r\nstudies attempt to extract unifying principles across synapses and model all observed synaptic\r\nchanges using plasticity rules: equations that govern the evolution of synaptic strengths across\r\ntime in neuronal network models. These rules can use many relevant quantities to determine\r\nthe magnitude of synaptic changes, such as the precise timings of pre- and postsynaptic\r\naction potentials, the recent neuronal activity levels, the state of neighboring synapses, etc.\r\nHowever, analytical studies rely heavily on human intuition and are forced to make simplifying\r\nassumptions about plasticity rules.\r\nIn this thesis, we aim to assist and augment human intuition in this search for plasticity rules.\r\nWe explore whether a numerical approach could automatically discover the plasticity rules\r\nthat elicit desired behaviors in large networks of interconnected neurons. This approach is\r\ndubbed meta-learning synaptic plasticity: learning plasticity rules which themselves will make\r\nneuronal networks learn how to solve a desired task. We first write all the potential plasticity\r\nmechanisms to consider using a single expression with adjustable parameters. We then optimize\r\nthese plasticity parameters using evolutionary strategies or Bayesian inference on tasks known\r\nto involve synaptic plasticity, such as familiarity detection and network stabilization.\r\nWe show that these automated approaches are powerful tools, able to complement established\r\nanalytical methods. By comprehensively screening plasticity rules at all synapse types in\r\nrealistic, spiking neuronal network models, we discover entire sets of degenerate plausible\r\nplasticity rules that reliably elicit memory-related behaviors. Our approaches allow for more\r\nrobust experimental predictions, by abstracting out the idiosyncrasies of individual plasticity\r\nrules, and provide fresh insights on synaptic plasticity in spiking network models.\r\n","lang":"eng"}],"project":[{"_id":"0aacfa84-070f-11eb-9043-d7eb2c709234","grant_number":"819603","name":"Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning.","call_identifier":"H2020"}],"file":[{"date_updated":"2023-10-12T14:54:52Z","content_type":"application/pdf","embargo":"2024-10-12","file_id":"14424","checksum":"7f636555eae7803323df287672fd13ed","file_size":30599717,"date_created":"2023-10-12T14:53:50Z","relation":"main_file","file_name":"Confavreux_Thesis_2A.pdf","access_level":"closed","embargo_to":"open_access","creator":"cchlebak"},{"date_updated":"2023-10-18T07:56:08Z","content_type":"application/x-zip-compressed","checksum":"725e85946db92290a4583a0de9779e1b","file_id":"14440","file_name":"Confavreux Thesis.zip","access_level":"closed","creator":"cchlebak","date_created":"2023-10-18T07:38:34Z","file_size":68406739,"relation":"source_file"}],"citation":{"ama":"Confavreux BJ. Synapseek: Meta-learning synaptic plasticity rules. 2023. doi:10.15479/at:ista:14422","mla":"Confavreux, Basile J. Synapseek: Meta-Learning Synaptic Plasticity Rules. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:14422.","short":"B.J. Confavreux, Synapseek: Meta-Learning Synaptic Plasticity Rules, Institute of Science and Technology Austria, 2023.","chicago":"Confavreux, Basile J. “Synapseek: Meta-Learning Synaptic Plasticity Rules.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:14422.","ista":"Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria.","apa":"Confavreux, B. J. (2023). Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14422","ieee":"B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023."},"alternative_title":["ISTA Thesis"],"title":"Synapseek: Meta-learning synaptic plasticity rules","date_created":"2023-10-12T14:13:25Z","related_material":{"record":[{"relation":"part_of_dissertation","id":"9633","status":"public"}]},"ec_funded":1,"oa_version":"Published Version","tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png"},"status":"public","department":[{"_id":"GradSch"},{"_id":"TiVo"}],"publication_status":"published","year":"2023"},{"article_processing_charge":"No","supervisor":[{"first_name":"Robert","orcid":"0000-0002-6781-0521","last_name":"Seiringer","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","full_name":"Seiringer, Robert"}],"_id":"14374","publication_identifier":{"issn":["2663 - 337X"]},"ddc":["515","539"],"author":[{"first_name":"Barbara","orcid":"0000-0002-9071-5880","last_name":"Roos","id":"5DA90512-D80F-11E9-8994-2E2EE6697425","full_name":"Roos, Barbara"}],"publisher":"Institute of Science and Technology Austria","has_accepted_license":"1","type":"dissertation","language":[{"iso":"eng"}],"date_published":"2023-09-30T00:00:00Z","month":"09","day":"30","date_updated":"2023-10-27T10:37:30Z","citation":{"ama":"Roos B. Boundary superconductivity in BCS theory. 2023. doi:10.15479/at:ista:14374","mla":"Roos, Barbara. Boundary Superconductivity in BCS Theory. Institute of Science and Technology Austria, 2023, doi:10.15479/at:ista:14374.","short":"B. Roos, Boundary Superconductivity in BCS Theory, Institute of Science and Technology Austria, 2023.","ista":"Roos B. 2023. Boundary superconductivity in BCS theory. Institute of Science and Technology Austria.","chicago":"Roos, Barbara. “Boundary Superconductivity in BCS Theory.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/at:ista:14374.","ieee":"B. Roos, “Boundary superconductivity in BCS theory,” Institute of Science and Technology Austria, 2023.","apa":"Roos, B. (2023). Boundary superconductivity in BCS theory. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14374"},"alternative_title":["ISTA Thesis"],"title":"Boundary superconductivity in BCS theory","date_created":"2023-09-28T14:23:04Z","related_material":{"record":[{"relation":"part_of_dissertation","id":"13207","status":"public"},{"id":"10850","status":"public","relation":"part_of_dissertation"}]},"ec_funded":1,"oa_version":"Published Version","tmp":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)","short":"CC BY-NC-SA (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode","image":"/images/cc_by_nc_sa.png"},"oa":1,"page":"206","degree_awarded":"PhD","file_date_updated":"2023-10-06T11:38:01Z","doi":"10.15479/at:ista:14374","user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","abstract":[{"lang":"eng","text":"Superconductivity has many important applications ranging from levitating trains over qubits to MRI scanners. The phenomenon is successfully modeled by Bardeen-Cooper-Schrieffer (BCS) theory. From a mathematical perspective, BCS theory has been studied extensively for systems without boundary. However, little is known in the presence of boundaries. With the help of numerical methods physicists observed that the critical temperature may increase in the presence of a boundary. The goal of this thesis is to understand the influence of boundaries on the critical temperature in BCS theory and to give a first rigorous justification of these observations. On the way, we also study two-body Schrödinger operators on domains with boundaries and prove additional results for superconductors without boundary.\r\n\r\nBCS theory is based on a non-linear functional, where the minimizer indicates whether the system is superconducting or in the normal, non-superconducting state. By considering the Hessian of the BCS functional at the normal state, one can analyze whether the normal state is possibly a minimum of the BCS functional and estimate the critical temperature. The Hessian turns out to be a linear operator resembling a Schrödinger operator for two interacting particles, but with more complicated kinetic energy. As a first step, we study the two-body Schrödinger operator in the presence of boundaries.\r\nFor Neumann boundary conditions, we prove that the addition of a boundary can create new eigenvalues, which correspond to the two particles forming a bound state close to the boundary.\r\n\r\nSecond, we need to understand superconductivity in the translation invariant setting. While in three dimensions this has been extensively studied, there is no mathematical literature for the one and two dimensional cases. In dimensions one and two, we compute the weak coupling asymptotics of the critical temperature and the energy gap in the translation invariant setting. We also prove that their ratio is independent of the microscopic details of the model in the weak coupling limit; this property is referred to as universality.\r\n\r\nIn the third part, we study the critical temperature of superconductors in the presence of boundaries. We start by considering the one-dimensional case of a half-line with contact interaction. Then, we generalize the results to generic interactions and half-spaces in one, two and three dimensions. Finally, we compare the critical temperature of a quarter space in two dimensions to the critical temperatures of a half-space and of the full space."}],"project":[{"_id":"25C6DC12-B435-11E9-9278-68D0E5697425","name":"Analysis of quantum many-body systems","grant_number":"694227","call_identifier":"H2020"},{"_id":"bda63fe5-d553-11ed-ba76-a16e3d2f256b","grant_number":"I06427","name":"Mathematical Challenges in BCS Theory of Superconductivity"}],"file":[{"file_id":"14398","checksum":"ef039ffc3de2cb8dee5b14110938e9b6","file_size":2365702,"date_created":"2023-10-06T11:35:56Z","relation":"main_file","file_name":"phd-thesis-draft_pdfa_acrobat.pdf","access_level":"open_access","creator":"broos","date_updated":"2023-10-06T11:35:56Z","content_type":"application/pdf"},{"creator":"broos","access_level":"closed","file_name":"Version5.zip","relation":"source_file","date_created":"2023-10-06T11:38:01Z","file_size":4691734,"checksum":"81dcac33daeefaf0111db52f41bb1fd0","file_id":"14399","content_type":"application/x-zip-compressed","date_updated":"2023-10-06T11:38:01Z"}],"status":"public","department":[{"_id":"GradSch"},{"_id":"RoSe"}],"publication_status":"published","year":"2023"},{"issue":"4","intvolume":" 12","publication":"Journal of Spectral Theory","department":[{"_id":"GradSch"},{"_id":"RoSe"}],"status":"public","publication_status":"published","year":"2023","citation":{"ieee":"C. Hainzl, B. Roos, and R. Seiringer, “Boundary superconductivity in the BCS model,” Journal of Spectral Theory, vol. 12, no. 4. EMS Press, pp. 1507–1540, 2023.","apa":"Hainzl, C., Roos, B., & Seiringer, R. (2023). Boundary superconductivity in the BCS model. Journal of Spectral Theory. EMS Press. https://doi.org/10.4171/JST/439","chicago":"Hainzl, Christian, Barbara Roos, and Robert Seiringer. “Boundary Superconductivity in the BCS Model.” Journal of Spectral Theory. EMS Press, 2023. https://doi.org/10.4171/JST/439.","ista":"Hainzl C, Roos B, Seiringer R. 2023. Boundary superconductivity in the BCS model. Journal of Spectral Theory. 12(4), 1507–1540.","mla":"Hainzl, Christian, et al. “Boundary Superconductivity in the BCS Model.” Journal of Spectral Theory, vol. 12, no. 4, EMS Press, 2023, pp. 1507–1540, doi:10.4171/JST/439.","short":"C. Hainzl, B. Roos, R. Seiringer, Journal of Spectral Theory 12 (2023) 1507–1540.","ama":"Hainzl C, Roos B, Seiringer R. Boundary superconductivity in the BCS model. Journal of Spectral Theory. 2023;12(4):1507–1540. doi:10.4171/JST/439"},"date_created":"2023-07-10T16:35:45Z","title":"Boundary superconductivity in the BCS model","ec_funded":1,"related_material":{"record":[{"status":"public","id":"14374","relation":"dissertation_contains"}]},"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png"},"oa_version":"Published Version","article_type":"original","oa":1,"page":"1507–1540","abstract":[{"lang":"eng","text":"We consider the linear BCS equation, determining the BCS critical temperature, in the presence of a boundary, where Dirichlet boundary conditions are imposed. In the one-dimensional case with point interactions, we prove that the critical temperature is strictly larger than the bulk value, at least at weak coupling. In particular, the Cooper-pair wave function localizes near the boundary, an effect that cannot be modeled by effective Neumann boundary conditions on the order parameter as often imposed in Ginzburg–Landau theory. We also show that the relative shift in critical temperature vanishes if the coupling constant either goes to zero or to infinity."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","file_date_updated":"2023-07-11T08:19:15Z","doi":"10.4171/JST/439","project":[{"_id":"25C6DC12-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Analysis of quantum many-body systems","grant_number":"694227"}],"file":[{"content_type":"application/pdf","date_updated":"2023-07-11T08:19:15Z","success":1,"access_level":"open_access","file_name":"2023_EMS_Hainzl.pdf","creator":"alisjak","file_size":304619,"date_created":"2023-07-11T08:19:15Z","relation":"main_file","checksum":"5501da33be010b5c81440438287584d5","file_id":"13208"}],"language":[{"iso":"eng"}],"date_published":"2023-05-18T00:00:00Z","external_id":{"isi":["000997933500008"],"arxiv":["2201.08090"]},"isi":1,"month":"05","day":"18","date_updated":"2023-10-27T10:37:29Z","acknowledgement":"We thank Egor Babaev for encouraging us to study this problem, and Rupert Frank for many fruitful discussions. scussions. Funding. Funding from the European Union’s Horizon 2020 research and innovation programme under the ERC grant agreement No. 694227 (Barbara Roos and Robert Seiringer) is gratefully acknowledged.","article_processing_charge":"No","quality_controlled":"1","publication_identifier":{"eissn":["1664-0403"],"issn":["1664-039X"]},"_id":"13207","ddc":["530"],"publisher":"EMS Press","author":[{"last_name":"Hainzl","full_name":"Hainzl, Christian","first_name":"Christian"},{"full_name":"Roos, Barbara","id":"5DA90512-D80F-11E9-8994-2E2EE6697425","last_name":"Roos","orcid":"0000-0002-9071-5880","first_name":"Barbara"},{"last_name":"Seiringer","full_name":"Seiringer, Robert","id":"4AFD0470-F248-11E8-B48F-1D18A9856A87","first_name":"Robert","orcid":"0000-0002-6781-0521"}],"has_accepted_license":"1","volume":12,"type":"journal_article"},{"department":[{"_id":"NiBa"}],"status":"public","publication_status":"published","year":"2023","issue":"2","intvolume":" 225","publication":"Genetics","oa":1,"article_type":"original","abstract":[{"text":"The classical infinitesimal model is a simple and robust model for the inheritance of quantitative traits. In this model, a quantitative trait is expressed as the sum of a genetic and an environmental component, and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents’ trait values, and has a variance that is independent of the parental traits. In previous work, we showed that when trait values are determined by the sum of a large number of additive Mendelian factors, each of small effect, one can justify the infinitesimal model as a limit of Mendelian inheritance. In this paper, we show that this result extends to include dominance. We define the model in terms of classical quantities of quantitative genetics, before justifying it as a limit of Mendelian inheritance as the number, M, of underlying loci tends to infinity. As in the additive case, the multivariate normal distribution of trait values across the pedigree can be expressed in terms of variance components in an ancestral population and probabilities of identity by descent determined by the pedigree. Now, with just first-order dominance effects, we require two-, three-, and four-way identities. We also show that, even if we condition on parental trait values, the “shared” and “residual” components of trait values within each family will be asymptotically normally distributed as the number of loci tends to infinity, with an error of order 1/M−−√. We illustrate our results with some numerical examples.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file_date_updated":"2023-10-30T12:57:53Z","doi":"10.1093/genetics/iyad133","project":[{"_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7"},{"grant_number":"101055327","name":"Understanding the evolution of continuous genomes","_id":"bd6958e0-d553-11ed-ba76-86eba6a76c00"}],"file":[{"file_id":"14469","checksum":"3f65b1fbe813e2f4dbb5d2b5e891844a","date_created":"2023-10-30T12:57:53Z","file_size":1439032,"relation":"main_file","file_name":"2023_Genetics_Barton.pdf","access_level":"open_access","creator":"dernst","date_updated":"2023-10-30T12:57:53Z","success":1,"content_type":"application/pdf"}],"citation":{"apa":"Barton, N. H., Etheridge, A. M., & Véber, A. (2023). The infinitesimal model with dominance. Genetics. 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AV was partly supported by the chaire Modélisation Mathématique et Biodiversité of Veolia Environment—Ecole Polytechnique—Museum National d’Histoire Naturelle—Fondation X.","article_processing_charge":"Yes (in subscription journal)","quality_controlled":"1","publication_identifier":{"eissn":["1943-2631"],"issn":["0016-6731"]},"_id":"14452","article_number":"iyad133","ddc":["570"]},{"citation":{"chicago":"Barton, Nicholas H. “The Infinitesimal Model with Dominance.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/AT:ISTA:12949.","ista":"Barton NH. 2023. The infinitesimal model with dominance, Institute of Science and Technology Austria, 10.15479/AT:ISTA:12949.","apa":"Barton, N. H. (2023). The infinitesimal model with dominance. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:12949","ieee":"N. H. Barton, “The infinitesimal model with dominance.” Institute of Science and Technology Austria, 2023.","ama":"Barton NH. 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In this model, a quantitative trait is expressed as the sum of a genetic and a non-genetic (environmental) component and the genetic component of offspring traits within a family follows a normal distribution around the average of the parents’ trait values, and has a variance that is independent of the trait values of the parents. Although the trait distribution across the whole population can be far from normal, the trait distributions within families are normally distributed with a variance-covariance matrix that is determined entirely by that in the ancestral population and the probabilities of identity determined by the pedigree. Moreover, conditioning on some of the trait values within the pedigree has predictable effects on the mean and variance within and between families. In previous work, Barton et al. (2017), we showed that when trait values are determined by the sum of a large number of Mendelian factors, each of small effect, one can justify the infinitesimal model as limit of Mendelian inheritance. It was also shown that under some forms of epistasis, trait values within a family are still normally distributed."}],"project":[{"_id":"bd6958e0-d553-11ed-ba76-86eba6a76c00","name":"Understanding the evolution of continuous genomes","grant_number":"101055327"}],"file":[{"relation":"main_file","date_created":"2023-05-13T09:36:33Z","file_size":13662,"creator":"nbarton","access_level":"open_access","file_name":"Neutral identities 16th Jan","file_id":"12950","checksum":"b0ce7d4b1ee7e7265430ceed36fc3336","content_type":"application/octet-stream","success":1,"date_updated":"2023-05-13T09:36:33Z"},{"content_type":"application/octet-stream","date_updated":"2023-05-13T09:38:17Z","success":1,"file_size":181619928,"date_created":"2023-05-13T09:38:17Z","relation":"main_file","file_name":"p, zA, zD, N=30 neutral III","access_level":"open_access","creator":"nbarton","file_id":"12951","checksum":"ad5035ad4f7d3b150a252c79884f6a83"},{"creator":"nbarton","access_level":"open_access","file_name":"p, zA, 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States"},"language":[{"iso":"eng"}],"external_id":{"arxiv":["2302.02390"]},"date_published":"2023-07-30T00:00:00Z","month":"07","author":[{"first_name":"Ilia","full_name":"Markov, Ilia","id":"D0CF4148-C985-11E9-8066-0BDEE5697425","last_name":"Markov"},{"full_name":"Vladu, Adrian","last_name":"Vladu","first_name":"Adrian"},{"first_name":"Qi","last_name":"Guo","full_name":"Guo, Qi"},{"first_name":"Dan-Adrian","orcid":"0000-0003-3650-940X","last_name":"Alistarh","id":"4A899BFC-F248-11E8-B48F-1D18A9856A87","full_name":"Alistarh, Dan-Adrian"}],"publisher":"ML Research Press","volume":202,"scopus_import":"1","type":"conference","article_processing_charge":"No","acknowledgement":"The authors gratefully acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 805223 ScaleML), as well as experimental support from the IST Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and Alois Schloegl. AV acknowledges the support of the French Agence Nationale de la Recherche (ANR), under grant ANR-21-CE48-0016 (project COMCOPT), the support of Fondation Hadamard with a PRMO grant, and the support of CNRS with a CoopIntEER IEA grant (project ALFRED).","quality_controlled":"1","_id":"14461","publication_identifier":{"eissn":["2640-3498"]},"status":"public","department":[{"_id":"DaAl"}],"publication_status":"published","year":"2023","intvolume":" 202","publication":"Proceedings of the 40th International Conference on Machine Learning","oa":1,"page":"24020-24044","acknowledged_ssus":[{"_id":"ScienComp"}],"abstract":[{"lang":"eng","text":"Communication-reduction techniques are a popular way to improve scalability in data-parallel training of deep neural networks (DNNs). The recent emergence of large language models such as GPT has created the need for new approaches to exploit data-parallelism. Among these, fully-sharded data parallel (FSDP) training is highly popular, yet it still encounters scalability bottlenecks. One reason is that applying compression techniques to FSDP is challenging: as the vast majority of the communication involves the model’s weights, direct compression alters convergence and leads to accuracy loss. We present QSDP, a variant of FSDP which supports both gradient and weight quantization with theoretical guarantees, is simple to implement and has essentially no overheads. To derive QSDP we prove that a natural modification of SGD achieves convergence even when we only maintain quantized weights, and thus the domain over which we train consists of quantized points and is, therefore, highly non-convex. We validate this approach by training GPT-family models with up to 1.3 billion parameters on a multi-node cluster. Experiments show that QSDP preserves model accuracy, while completely removing the communication bottlenecks of FSDP, providing end-to-end speedups of up to 2.2x."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"_id":"268A44D6-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"805223","name":"Elastic Coordination for Scalable Machine Learning"}],"citation":{"short":"I. Markov, A. Vladu, Q. Guo, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 24020–24044.","mla":"Markov, Ilia, et al. “Quantized Distributed Training of Large Models with Convergence Guarantees.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 24020–44.","ama":"Markov I, Vladu A, Guo Q, Alistarh D-A. Quantized distributed training of large models with convergence guarantees. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:24020-24044.","ieee":"I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.","apa":"Markov, I., Vladu, A., Guo, Q., & Alistarh, D.-A. (2023). Quantized distributed training of large models with convergence guarantees. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 24020–24044). Honolulu, Hawaii, HI, United States: ML Research Press.","chicago":"Markov, Ilia, Adrian Vladu, Qi Guo, and Dan-Adrian Alistarh. “Quantized Distributed Training of Large Models with Convergence Guarantees.” In Proceedings of the 40th International Conference on Machine Learning, 202:24020–44. ML Research Press, 2023.","ista":"Markov I, Vladu A, Guo Q, Alistarh D-A. 2023. Quantized distributed training of large models with convergence guarantees. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 24020–24044."},"alternative_title":["PMLR"],"date_created":"2023-10-29T23:01:17Z","title":"Quantized distributed training of large models with convergence guarantees","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2302.02390","open_access":"1"}],"ec_funded":1,"oa_version":"Preprint"},{"_id":"14462","publication_identifier":{"eissn":["2640-3498"]},"acknowledgement":"This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No.\r\n101019564 “The Design of Modern Fully Dynamic Data Structures (MoDynStruct)” and from the Austrian Science Fund (FWF) project Z 422-N, and project “Fast Algorithms for a Reactive Network Layer (ReactNet)”, P 33775-N, with additional funding from the netidee SCIENCE Stiftung, 2020–2024. 2020–2024. JU’s research was funded by Decanal Research Grant. A part of this work was done when JU was visiting Indian Statistical Institute, Delhi. The authors would like to thank Rajat Bhatia, Aleksandar Nikolov, Shanta Laisharam, Vern Paulsen, Ryan Rogers, Abhradeep Thakurta, and Sarvagya Upadhyay for useful discussions.","article_processing_charge":"No","quality_controlled":"1","volume":202,"type":"conference","scopus_import":"1","author":[{"first_name":"Hendrik","last_name":"Fichtenberger","full_name":"Fichtenberger, Hendrik"},{"orcid":"0000-0002-5008-6530","first_name":"Monika H","full_name":"Henzinger, Monika H","id":"540c9bbd-f2de-11ec-812d-d04a5be85630","last_name":"Henzinger"},{"last_name":"Upadhyay","full_name":"Upadhyay, Jalaj","first_name":"Jalaj"}],"publisher":"ML Research Press","month":"07","language":[{"iso":"eng"}],"conference":{"location":"Honolulu, Hawaii, HI, United States","start_date":"2023-07-23","end_date":"2023-07-29","name":"ICML: International Conference on Machine Learning"},"date_published":"2023-07-30T00:00:00Z","date_updated":"2023-10-31T09:54:05Z","day":"30","main_file_link":[{"open_access":"1","url":"https://proceedings.mlr.press/v202/fichtenberger23a/fichtenberger23a.pdf"}],"ec_funded":1,"oa_version":"Published Version","citation":{"ieee":"H. Fichtenberger, M. H. Henzinger, and J. Upadhyay, “Constant matters: Fine-grained error bound on differentially private continual observation,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10072–10092.","apa":"Fichtenberger, H., Henzinger, M. H., & Upadhyay, J. (2023). Constant matters: Fine-grained error bound on differentially private continual observation. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10072–10092). Honolulu, Hawaii, HI, United States: ML Research Press.","ista":"Fichtenberger H, Henzinger MH, Upadhyay J. 2023. Constant matters: Fine-grained error bound on differentially private continual observation. Proceedings of the 40th International Conference on Machine Learning. 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In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10072-10092."},"alternative_title":["PMLR"],"title":"Constant matters: Fine-grained error bound on differentially private continual observation","date_created":"2023-10-29T23:01:17Z","abstract":[{"text":"We study fine-grained error bounds for differentially private algorithms for counting under continual observation. Our main insight is that the matrix mechanism when using lower-triangular matrices can be used in the continual observation model. More specifically, we give an explicit factorization for the counting matrix Mcount and upper bound the error explicitly. We also give a fine-grained analysis, specifying the exact constant in the upper bound. Our analysis is based on upper and lower bounds of the completely bounded norm (cb-norm) of Mcount\r\n. Along the way, we improve the best-known bound of 28 years by Mathias (SIAM Journal on Matrix Analysis and Applications, 1993) on the cb-norm of Mcount for a large range of the dimension of Mcount. Furthermore, we are the first to give concrete error bounds for various problems under continual observation such as binary counting, maintaining a histogram, releasing an approximately cut-preserving synthetic graph, many graph-based statistics, and substring and episode counting. Finally, we note that our result can be used to get a fine-grained error bound for non-interactive local learning and the first lower bounds on the additive error for (ϵ,δ)-differentially-private counting under continual observation. Subsequent to this work, Henzinger et al. (SODA, 2023) showed that our factorization also achieves fine-grained mean-squared error.","lang":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"_id":"bd9ca328-d553-11ed-ba76-dc4f890cfe62","name":"The design and evaluation of modern fully dynamic data structures","grant_number":"101019564","call_identifier":"H2020"},{"_id":"34def286-11ca-11ed-8bc3-da5948e1613c","grant_number":"Z00422","name":"Wittgenstein Award - Monika Henzinger"},{"grant_number":"P33775 ","name":"Fast Algorithms for a Reactive Network Layer","_id":"bd9e3a2e-d553-11ed-ba76-8aa684ce17fe"}],"oa":1,"page":"10072-10092","intvolume":" 202","publication":"Proceedings of the 40th International Conference on Machine Learning","year":"2023","status":"public","department":[{"_id":"MoHe"}],"publication_status":"published"},{"status":"public","department":[{"_id":"MaMo"},{"_id":"DaAl"}],"publication_status":"published","year":"2023","intvolume":" 202","publication":"Proceedings of the 40th International Conference on Machine Learning","oa":1,"page":"31151-31209","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","abstract":[{"text":"Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain poorly understood, even in the two-layer setting. In fact, earlier work has considered either linear autoencoders or specific training regimes (leading to vanishing or diverging compression rates). Our paper addresses this gap by focusing on non-linear two-layer autoencoders trained in the challenging proportional regime in which the input dimension scales linearly with the size of the representation. Our results characterize the minimizers of the population risk, and show that such minimizers are achieved by gradient methods; their structure is also unveiled, thus leading to a concise description of the features obtained via training. For the special case of a sign activation function, our analysis establishes the fundamental limits for the lossy compression of Gaussian sources via (shallow) autoencoders. Finally, while the results are proved for Gaussian data, numerical simulations on standard datasets display the universality of the theoretical predictions.","lang":"eng"}],"project":[{"name":"Prix Lopez-Loretta 2019 - Marco Mondelli","_id":"059876FA-7A3F-11EA-A408-12923DDC885E"}],"citation":{"mla":"Shevchenko, Aleksandr, et al. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 31151–209.","short":"A. Shevchenko, K. Kögler, H. Hassani, M. Mondelli, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 31151–31209.","ama":"Shevchenko A, Kögler K, Hassani H, Mondelli M. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:31151-31209.","apa":"Shevchenko, A., Kögler, K., Hassani, H., & Mondelli, M. (2023). Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 31151–31209). Honolulu, Hawaii, HI, United States: ML Research Press.","ieee":"A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.","chicago":"Shevchenko, Aleksandr, Kevin Kögler, Hamed Hassani, and Marco Mondelli. “Fundamental Limits of Two-Layer Autoencoders, and Achieving Them with Gradient Methods.” In Proceedings of the 40th International Conference on Machine Learning, 202:31151–209. ML Research Press, 2023.","ista":"Shevchenko A, Kögler K, Hassani H, Mondelli M. 2023. Fundamental limits of two-layer autoencoders, and achieving them with gradient methods. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 31151–31209."},"alternative_title":["PMLR"],"title":"Fundamental limits of two-layer autoencoders, and achieving them with gradient methods","date_created":"2023-10-29T23:01:17Z","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2212.13468","open_access":"1"}],"oa_version":"Preprint","day":"30","date_updated":"2023-10-31T08:52:28Z","language":[{"iso":"eng"}],"conference":{"name":"ICML: International Conference on Machine Learning","start_date":"2023-07-23","end_date":"2023-07-29","location":"Honolulu, Hawaii, HI, United States"},"date_published":"2023-07-30T00:00:00Z","external_id":{"arxiv":["2212.13468"]},"month":"07","publisher":"ML Research Press","author":[{"first_name":"Aleksandr","full_name":"Shevchenko, Aleksandr","id":"F2B06EC2-C99E-11E9-89F0-752EE6697425","last_name":"Shevchenko"},{"last_name":"Kögler","id":"94ec913c-dc85-11ea-9058-e5051ab2428b","full_name":"Kögler, Kevin","first_name":"Kevin"},{"first_name":"Hamed","full_name":"Hassani, Hamed","last_name":"Hassani"},{"first_name":"Marco","orcid":"0000-0002-3242-7020","last_name":"Mondelli","full_name":"Mondelli, Marco","id":"27EB676C-8706-11E9-9510-7717E6697425"}],"volume":202,"scopus_import":"1","type":"conference","acknowledgement":"Aleksandr Shevchenko, Kevin Kogler and Marco Mondelli are supported by the 2019 Lopez-Loreta Prize. Hamed Hassani acknowledges the support by the NSF CIF award (1910056) and the NSF Institute for CORE Emerging Methods in Data Science (EnCORE).","article_processing_charge":"No","quality_controlled":"1","publication_identifier":{"eissn":["2640-3498"]},"_id":"14459"}]