--- _id: '13315' abstract: - lang: eng text: How do statistical dependencies in measurement noise influence high-dimensional inference? To answer this, we study the paradigmatic spiked matrix model of principal components analysis (PCA), where a rank-one matrix is corrupted by additive noise. We go beyond the usual independence assumption on the noise entries, by drawing the noise from a low-order polynomial orthogonal matrix ensemble. The resulting noise correlations make the setting relevant for applications but analytically challenging. We provide characterization of the Bayes optimal limits of inference in this model. If the spike is rotation invariant, we show that standard spectral PCA is optimal. However, for more general priors, both PCA and the existing approximate message-passing algorithm (AMP) fall short of achieving the information-theoretic limits, which we compute using the replica method from statistical physics. We thus propose an AMP, inspired by the theory of adaptive Thouless–Anderson–Palmer equations, which is empirically observed to saturate the conjectured theoretical limit. This AMP comes with a rigorous state evolution analysis tracking its performance. Although we focus on specific noise distributions, our methodology can be generalized to a wide class of trace matrix ensembles at the cost of more involved expressions. Finally, despite the seemingly strong assumption of rotation-invariant noise, our theory empirically predicts algorithmic performance on real data, pointing at strong universality properties. acknowledgement: J.B. was funded by the European Union (ERC, CHORAL, project number 101039794). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. M.M. was supported by the 2019 Lopez-Loreta Prize. We would like to thank the reviewers for the insightful comments and, in particular, for suggesting the BAMP-inspired denoisers leading to AMP-AP. article_number: e2302028120 article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Jean full_name: Barbier, Jean last_name: Barbier - first_name: Francesco full_name: Camilli, Francesco last_name: Camilli - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 - first_name: Manuel full_name: Sáenz, Manuel last_name: Sáenz citation: ama: Barbier J, Camilli F, Mondelli M, Sáenz M. Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. 2023;120(30). doi:10.1073/pnas.2302028120 apa: Barbier, J., Camilli, F., Mondelli, M., & Sáenz, M. (2023). Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2302028120 chicago: Barbier, Jean, Francesco Camilli, Marco Mondelli, and Manuel Sáenz. “Fundamental Limits in Structured Principal Component Analysis and How to Reach Them.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2302028120. ieee: J. Barbier, F. Camilli, M. Mondelli, and M. Sáenz, “Fundamental limits in structured principal component analysis and how to reach them,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 30. National Academy of Sciences, 2023. ista: Barbier J, Camilli F, Mondelli M, Sáenz M. 2023. Fundamental limits in structured principal component analysis and how to reach them. Proceedings of the National Academy of Sciences of the United States of America. 120(30), e2302028120. mla: Barbier, Jean, et al. “Fundamental Limits in Structured Principal Component Analysis and How to Reach Them.” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 30, e2302028120, National Academy of Sciences, 2023, doi:10.1073/pnas.2302028120. short: J. Barbier, F. Camilli, M. Mondelli, M. Sáenz, Proceedings of the National Academy of Sciences of the United States of America 120 (2023). date_created: 2023-07-30T22:01:02Z date_published: 2023-07-25T00:00:00Z date_updated: 2023-10-17T11:44:55Z day: '25' ddc: - '000' department: - _id: MaMo doi: 10.1073/pnas.2302028120 external_id: pmid: - '37463204' file: - access_level: open_access checksum: 1fc06228afdb3aa80cf8e7766bcf9dc5 content_type: application/pdf creator: dernst date_created: 2023-07-31T07:30:48Z date_updated: 2023-07-31T07:30:48Z file_id: '13323' file_name: 2023_PNAS_Barbier.pdf file_size: 995933 relation: main_file success: 1 file_date_updated: 2023-07-31T07:30:48Z has_accepted_license: '1' intvolume: ' 120' issue: '30' language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - 1091-6490 publication_status: published publisher: National Academy of Sciences quality_controlled: '1' related_material: link: - relation: software url: https://github.com/fcamilli95/Structured-PCA- scopus_import: '1' status: public title: Fundamental limits in structured principal component analysis and how to reach them tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 120 year: '2023' ... --- _id: '14037' abstract: - lang: eng text: 'Traditionally, nuclear spin is not considered to affect biological processes. Recently, this has changed as isotopic fractionation that deviates from classical mass dependence was reported both in vitro and in vivo. In these cases, the isotopic effect correlates with the nuclear magnetic spin. Here, we show nuclear spin effects using stable oxygen isotopes (16O, 17O, and 18O) in two separate setups: an artificial dioxygen production system and biological aquaporin channels in cells. We observe that oxygen dynamics in chiral environments (in particular its transport) depend on nuclear spin, suggesting future applications for controlled isotope separation to be used, for instance, in NMR. To demonstrate the mechanism behind our findings, we formulate theoretical models based on a nuclear-spin-enhanced switch between electronic spin states. Accounting for the role of nuclear spin in biology can provide insights into the role of quantum effects in living systems and help inspire the development of future biotechnology solutions.' acknowledgement: N.M.-S. acknowledges the support of the Ministry of Energy, Israel, as part of the scholarship program for graduate students in the fields of energy. M.L. acknowledges support by the European Research Council (ERC) Starting Grant No. 801770 (ANGULON). Y.P. acknowledges the support of the Ministry of Innovation, Science and Technology, Israel Grant No. 1001593872. Y.P acknowledges the support of the BSF-NSF 094 Grant No. 2022503. article_number: e2300828120 article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Ofek full_name: Vardi, Ofek last_name: Vardi - first_name: Naama full_name: Maroudas-Sklare, Naama last_name: Maroudas-Sklare - first_name: Yuval full_name: Kolodny, Yuval last_name: Kolodny - first_name: Artem full_name: Volosniev, Artem id: 37D278BC-F248-11E8-B48F-1D18A9856A87 last_name: Volosniev orcid: 0000-0003-0393-5525 - first_name: Amijai full_name: Saragovi, Amijai last_name: Saragovi - first_name: Nir full_name: Galili, Nir last_name: Galili - first_name: Stav full_name: Ferrera, Stav last_name: Ferrera - first_name: Areg full_name: Ghazaryan, Areg id: 4AF46FD6-F248-11E8-B48F-1D18A9856A87 last_name: Ghazaryan orcid: 0000-0001-9666-3543 - first_name: Nir full_name: Yuran, Nir last_name: Yuran - first_name: Hagit P. full_name: Affek, Hagit P. last_name: Affek - first_name: Boaz full_name: Luz, Boaz last_name: Luz - first_name: Yonaton full_name: Goldsmith, Yonaton last_name: Goldsmith - first_name: Nir full_name: Keren, Nir last_name: Keren - first_name: Shira full_name: Yochelis, Shira last_name: Yochelis - first_name: Itay full_name: Halevy, Itay last_name: Halevy - first_name: Mikhail full_name: Lemeshko, Mikhail id: 37CB05FA-F248-11E8-B48F-1D18A9856A87 last_name: Lemeshko orcid: 0000-0002-6990-7802 - first_name: Yossi full_name: Paltiel, Yossi last_name: Paltiel citation: ama: Vardi O, Maroudas-Sklare N, Kolodny Y, et al. Nuclear spin effects in biological processes. Proceedings of the National Academy of Sciences of the United States of America. 2023;120(32). doi:10.1073/pnas.2300828120 apa: Vardi, O., Maroudas-Sklare, N., Kolodny, Y., Volosniev, A., Saragovi, A., Galili, N., … Paltiel, Y. (2023). Nuclear spin effects in biological processes. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences. https://doi.org/10.1073/pnas.2300828120 chicago: Vardi, Ofek, Naama Maroudas-Sklare, Yuval Kolodny, Artem Volosniev, Amijai Saragovi, Nir Galili, Stav Ferrera, et al. “Nuclear Spin Effects in Biological Processes.” Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences, 2023. https://doi.org/10.1073/pnas.2300828120. ieee: O. Vardi et al., “Nuclear spin effects in biological processes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 32. National Academy of Sciences, 2023. ista: Vardi O, Maroudas-Sklare N, Kolodny Y, Volosniev A, Saragovi A, Galili N, Ferrera S, Ghazaryan A, Yuran N, Affek HP, Luz B, Goldsmith Y, Keren N, Yochelis S, Halevy I, Lemeshko M, Paltiel Y. 2023. Nuclear spin effects in biological processes. Proceedings of the National Academy of Sciences of the United States of America. 120(32), e2300828120. mla: Vardi, Ofek, et al. “Nuclear Spin Effects in Biological Processes.” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 32, e2300828120, National Academy of Sciences, 2023, doi:10.1073/pnas.2300828120. short: O. Vardi, N. Maroudas-Sklare, Y. Kolodny, A. Volosniev, A. Saragovi, N. Galili, S. Ferrera, A. Ghazaryan, N. Yuran, H.P. Affek, B. Luz, Y. Goldsmith, N. Keren, S. Yochelis, I. Halevy, M. Lemeshko, Y. Paltiel, Proceedings of the National Academy of Sciences of the United States of America 120 (2023). date_created: 2023-08-13T22:01:12Z date_published: 2023-07-31T00:00:00Z date_updated: 2023-10-17T11:45:25Z day: '31' ddc: - '530' department: - _id: MiLe doi: 10.1073/pnas.2300828120 ec_funded: 1 external_id: pmid: - '37523549' file: - access_level: open_access checksum: a5ed64788a5acef9b9a300a26fa5a177 content_type: application/pdf creator: dernst date_created: 2023-08-14T07:43:45Z date_updated: 2023-08-14T07:43:45Z file_id: '14047' file_name: 2023_PNAS_Vardi.pdf file_size: 1003092 relation: main_file success: 1 file_date_updated: 2023-08-14T07:43:45Z has_accepted_license: '1' intvolume: ' 120' issue: '32' language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 2688CF98-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '801770' name: 'Angulon: physics and applications of a new quasiparticle' publication: Proceedings of the National Academy of Sciences of the United States of America publication_identifier: eissn: - 1091-6490 publication_status: published publisher: National Academy of Sciences quality_controlled: '1' scopus_import: '1' status: public title: Nuclear spin effects in biological processes tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 120 year: '2023' ... --- _id: '12683' abstract: - lang: eng text: We study the eigenvalue trajectories of a time dependent matrix Gt=H+itvv∗ for t≥0, where H is an N×N Hermitian random matrix and v is a unit vector. In particular, we establish that with high probability, an outlier can be distinguished at all times t>1+N−1/3+ϵ, for any ϵ>0. The study of this natural process combines elements of Hermitian and non-Hermitian analysis, and illustrates some aspects of the intrinsic instability of (even weakly) non-Hermitian matrices. acknowledgement: G. Dubach gratefully acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 754411. L. Erdős is supported by ERC Advanced Grant “RMTBeyond” No. 101020331. article_processing_charge: No article_type: original author: - first_name: Guillaume full_name: Dubach, Guillaume id: D5C6A458-10C4-11EA-ABF4-A4B43DDC885E last_name: Dubach orcid: 0000-0001-6892-8137 - first_name: László full_name: Erdös, László id: 4DBD5372-F248-11E8-B48F-1D18A9856A87 last_name: Erdös orcid: 0000-0001-5366-9603 citation: ama: Dubach G, Erdös L. Dynamics of a rank-one perturbation of a Hermitian matrix. Electronic Communications in Probability. 2023;28:1-13. doi:10.1214/23-ECP516 apa: Dubach, G., & Erdös, L. (2023). Dynamics of a rank-one perturbation of a Hermitian matrix. Electronic Communications in Probability. Institute of Mathematical Statistics. https://doi.org/10.1214/23-ECP516 chicago: Dubach, Guillaume, and László Erdös. “Dynamics of a Rank-One Perturbation of a Hermitian Matrix.” Electronic Communications in Probability. Institute of Mathematical Statistics, 2023. https://doi.org/10.1214/23-ECP516. ieee: G. Dubach and L. Erdös, “Dynamics of a rank-one perturbation of a Hermitian matrix,” Electronic Communications in Probability, vol. 28. Institute of Mathematical Statistics, pp. 1–13, 2023. ista: Dubach G, Erdös L. 2023. Dynamics of a rank-one perturbation of a Hermitian matrix. Electronic Communications in Probability. 28, 1–13. mla: Dubach, Guillaume, and László Erdös. “Dynamics of a Rank-One Perturbation of a Hermitian Matrix.” Electronic Communications in Probability, vol. 28, Institute of Mathematical Statistics, 2023, pp. 1–13, doi:10.1214/23-ECP516. short: G. Dubach, L. Erdös, Electronic Communications in Probability 28 (2023) 1–13. date_created: 2023-02-26T23:01:01Z date_published: 2023-02-08T00:00:00Z date_updated: 2023-10-17T12:48:10Z day: '08' ddc: - '510' department: - _id: LaEr doi: 10.1214/23-ECP516 ec_funded: 1 external_id: arxiv: - '2108.13694' isi: - '000950650200005' file: - access_level: open_access checksum: a1c6f0a3e33688fd71309c86a9aad86e content_type: application/pdf creator: dernst date_created: 2023-02-27T09:43:27Z date_updated: 2023-02-27T09:43:27Z file_id: '12692' file_name: 2023_ElectCommProbability_Dubach.pdf file_size: 479105 relation: main_file success: 1 file_date_updated: 2023-02-27T09:43:27Z has_accepted_license: '1' intvolume: ' 28' isi: 1 language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: 1-13 project: - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships - _id: 62796744-2b32-11ec-9570-940b20777f1d call_identifier: H2020 grant_number: '101020331' name: Random matrices beyond Wigner-Dyson-Mehta publication: Electronic Communications in Probability publication_identifier: eissn: - 1083-589X publication_status: published publisher: Institute of Mathematical Statistics quality_controlled: '1' scopus_import: '1' status: public title: Dynamics of a rank-one perturbation of a Hermitian matrix tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 28 year: '2023' ... --- _id: '12761' abstract: - lang: eng text: "We consider the fluctuations of regular functions f of a Wigner matrix W viewed as an entire matrix f (W). Going beyond the well-studied tracial mode, Trf (W), which is equivalent to the customary linear statistics of eigenvalues, we show that Trf (W)A is asymptotically normal for any nontrivial bounded deterministic matrix A. We identify three different and asymptotically independent modes of this fluctuation, corresponding to the tracial part, the traceless diagonal part and the off-diagonal part of f (W) in the entire mesoscopic regime, where we find that the off-diagonal modes fluctuate on a much smaller scale than the tracial mode. As a main motivation to study CLT in such generality on small mesoscopic scales, we determine\r\nthe fluctuations in the eigenstate thermalization hypothesis (Phys. Rev. A 43 (1991) 2046–2049), that is, prove that the eigenfunction overlaps with any deterministic matrix are asymptotically Gaussian after a small spectral averaging. Finally, in the macroscopic regime our result also generalizes (Zh. Mat. Fiz. Anal. Geom. 9 (2013) 536–581, 611, 615) to complex W and to all crossover ensembles in between. The main technical inputs are the recent\r\nmultiresolvent local laws with traceless deterministic matrices from the companion paper (Comm. Math. Phys. 388 (2021) 1005–1048)." acknowledgement: The second author is partially funded by the ERC Advanced Grant “RMTBEYOND” No. 101020331. The third author is supported by Dr. Max Rössler, the Walter Haefner Foundation and the ETH Zürich Foundation. article_processing_charge: No article_type: original author: - first_name: Giorgio full_name: Cipolloni, Giorgio id: 42198EFA-F248-11E8-B48F-1D18A9856A87 last_name: Cipolloni orcid: 0000-0002-4901-7992 - first_name: László full_name: Erdös, László id: 4DBD5372-F248-11E8-B48F-1D18A9856A87 last_name: Erdös orcid: 0000-0001-5366-9603 - first_name: Dominik J full_name: Schröder, Dominik J id: 408ED176-F248-11E8-B48F-1D18A9856A87 last_name: Schröder orcid: 0000-0002-2904-1856 citation: ama: Cipolloni G, Erdös L, Schröder DJ. Functional central limit theorems for Wigner matrices. Annals of Applied Probability. 2023;33(1):447-489. doi:10.1214/22-AAP1820 apa: Cipolloni, G., Erdös, L., & Schröder, D. J. (2023). Functional central limit theorems for Wigner matrices. Annals of Applied Probability. Institute of Mathematical Statistics. https://doi.org/10.1214/22-AAP1820 chicago: Cipolloni, Giorgio, László Erdös, and Dominik J Schröder. “Functional Central Limit Theorems for Wigner Matrices.” Annals of Applied Probability. Institute of Mathematical Statistics, 2023. https://doi.org/10.1214/22-AAP1820. ieee: G. Cipolloni, L. Erdös, and D. J. Schröder, “Functional central limit theorems for Wigner matrices,” Annals of Applied Probability, vol. 33, no. 1. Institute of Mathematical Statistics, pp. 447–489, 2023. ista: Cipolloni G, Erdös L, Schröder DJ. 2023. Functional central limit theorems for Wigner matrices. Annals of Applied Probability. 33(1), 447–489. mla: Cipolloni, Giorgio, et al. “Functional Central Limit Theorems for Wigner Matrices.” Annals of Applied Probability, vol. 33, no. 1, Institute of Mathematical Statistics, 2023, pp. 447–89, doi:10.1214/22-AAP1820. short: G. Cipolloni, L. Erdös, D.J. Schröder, Annals of Applied Probability 33 (2023) 447–489. date_created: 2023-03-26T22:01:08Z date_published: 2023-02-01T00:00:00Z date_updated: 2023-10-17T12:48:52Z day: '01' department: - _id: LaEr doi: 10.1214/22-AAP1820 ec_funded: 1 external_id: arxiv: - '2012.13218' isi: - '000946432400015' intvolume: ' 33' isi: 1 issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2012.13218 month: '02' oa: 1 oa_version: Preprint page: 447-489 project: - _id: 62796744-2b32-11ec-9570-940b20777f1d call_identifier: H2020 grant_number: '101020331' name: Random matrices beyond Wigner-Dyson-Mehta publication: Annals of Applied Probability publication_identifier: issn: - 1050-5164 publication_status: published publisher: Institute of Mathematical Statistics quality_controlled: '1' scopus_import: '1' status: public title: Functional central limit theorems for Wigner matrices type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 33 year: '2023' ... --- _id: '8682' abstract: - lang: eng text: It is known that the Brauer--Manin obstruction to the Hasse principle is vacuous for smooth Fano hypersurfaces of dimension at least 3 over any number field. Moreover, for such varieties it follows from a general conjecture of Colliot-Thélène that the Brauer--Manin obstruction to the Hasse principle should be the only one, so that the Hasse principle is expected to hold. Working over the field of rational numbers and ordering Fano hypersurfaces of fixed degree and dimension by height, we prove that almost every such hypersurface satisfies the Hasse principle provided that the dimension is at least 3. This proves a conjecture of Poonen and Voloch in every case except for cubic surfaces. article_processing_charge: No article_type: original author: - first_name: Timothy D full_name: Browning, Timothy D id: 35827D50-F248-11E8-B48F-1D18A9856A87 last_name: Browning orcid: 0000-0002-8314-0177 - first_name: Pierre Le full_name: Boudec, Pierre Le last_name: Boudec - first_name: Will full_name: Sawin, Will last_name: Sawin citation: ama: Browning TD, Boudec PL, Sawin W. The Hasse principle for random Fano hypersurfaces. Annals of Mathematics. 2023;197(3):1115-1203. doi:10.4007/annals.2023.197.3.3 apa: Browning, T. D., Boudec, P. L., & Sawin, W. (2023). The Hasse principle for random Fano hypersurfaces. Annals of Mathematics. Princeton University. https://doi.org/10.4007/annals.2023.197.3.3 chicago: Browning, Timothy D, Pierre Le Boudec, and Will Sawin. “The Hasse Principle for Random Fano Hypersurfaces.” Annals of Mathematics. Princeton University, 2023. https://doi.org/10.4007/annals.2023.197.3.3. ieee: T. D. Browning, P. L. Boudec, and W. Sawin, “The Hasse principle for random Fano hypersurfaces,” Annals of Mathematics, vol. 197, no. 3. Princeton University, pp. 1115–1203, 2023. ista: Browning TD, Boudec PL, Sawin W. 2023. The Hasse principle for random Fano hypersurfaces. Annals of Mathematics. 197(3), 1115–1203. mla: Browning, Timothy D., et al. “The Hasse Principle for Random Fano Hypersurfaces.” Annals of Mathematics, vol. 197, no. 3, Princeton University, 2023, pp. 1115–203, doi:10.4007/annals.2023.197.3.3. short: T.D. Browning, P.L. Boudec, W. Sawin, Annals of Mathematics 197 (2023) 1115–1203. date_created: 2020-10-19T14:28:50Z date_published: 2023-05-01T00:00:00Z date_updated: 2023-10-17T12:47:43Z day: '01' department: - _id: TiBr doi: 10.4007/annals.2023.197.3.3 external_id: arxiv: - '2006.02356' isi: - '000966611000003' intvolume: ' 197' isi: 1 issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/2006.02356 month: '05' oa: 1 oa_version: Preprint page: 1115-1203 publication: Annals of Mathematics publication_identifier: issn: - 0003-486X publication_status: published publisher: Princeton University quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/when-is-necessary-sufficient/ status: public title: The Hasse principle for random Fano hypersurfaces type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 197 year: '2023' ... --- _id: '12706' abstract: - lang: eng text: Allometric settings of population dynamics models are appealing due to their parsimonious nature and broad utility when studying system level effects. Here, we parameterise the size-scaled Rosenzweig-MacArthur differential equations to eliminate prey-mass dependency, facilitating an in depth analytic study of the equations which incorporates scaling parameters’ contributions to coexistence. We define the functional response term to match empirical findings, and examine situations where metabolic theory derivations and observation diverge. The dynamical properties of the Rosenzweig-MacArthur system, encompassing the distribution of size-abundance equilibria, the scaling of period and amplitude of population cycling, and relationships between predator and prey abundances, are consistent with empirical observation. Our parameterisation is an accurate minimal model across 15+ orders of mass magnitude. acknowledgement: "This research was supported by an Australian Government Research Training Program\r\n(RTP) Scholarship to JCM (https://www.dese.gov.au), and LB is supported by the Centre de\r\nrecherche sur le vieillissement Fellowship Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript." article_processing_charge: No article_type: original author: - first_name: Jody C. full_name: Mckerral, Jody C. last_name: Mckerral - first_name: Maria full_name: Kleshnina, Maria id: 4E21749C-F248-11E8-B48F-1D18A9856A87 last_name: Kleshnina - first_name: Vladimir full_name: Ejov, Vladimir last_name: Ejov - first_name: Louise full_name: Bartle, Louise last_name: Bartle - first_name: James G. full_name: Mitchell, James G. last_name: Mitchell - first_name: Jerzy A. full_name: Filar, Jerzy A. last_name: Filar citation: ama: Mckerral JC, Kleshnina M, Ejov V, Bartle L, Mitchell JG, Filar JA. Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations. PLoS One. 2023;18(2):e0279838. doi:10.1371/journal.pone.0279838 apa: Mckerral, J. C., Kleshnina, M., Ejov, V., Bartle, L., Mitchell, J. G., & Filar, J. A. (2023). Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0279838 chicago: Mckerral, Jody C., Maria Kleshnina, Vladimir Ejov, Louise Bartle, James G. Mitchell, and Jerzy A. Filar. “Empirical Parameterisation and Dynamical Analysis of the Allometric Rosenzweig-MacArthur Equations.” PLoS One. Public Library of Science, 2023. https://doi.org/10.1371/journal.pone.0279838. ieee: J. C. Mckerral, M. Kleshnina, V. Ejov, L. Bartle, J. G. Mitchell, and J. A. Filar, “Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations,” PLoS One, vol. 18, no. 2. Public Library of Science, p. e0279838, 2023. ista: Mckerral JC, Kleshnina M, Ejov V, Bartle L, Mitchell JG, Filar JA. 2023. Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations. PLoS One. 18(2), e0279838. mla: Mckerral, Jody C., et al. “Empirical Parameterisation and Dynamical Analysis of the Allometric Rosenzweig-MacArthur Equations.” PLoS One, vol. 18, no. 2, Public Library of Science, 2023, p. e0279838, doi:10.1371/journal.pone.0279838. short: J.C. Mckerral, M. Kleshnina, V. Ejov, L. Bartle, J.G. Mitchell, J.A. Filar, PLoS One 18 (2023) e0279838. date_created: 2023-03-05T23:01:05Z date_published: 2023-02-27T00:00:00Z date_updated: 2023-10-17T12:53:30Z day: '27' ddc: - '000' department: - _id: KrCh doi: 10.1371/journal.pone.0279838 external_id: isi: - '000996122900022' pmid: - '36848357' file: - access_level: open_access checksum: 798ed5739a4117b03173e5d56e0534c9 content_type: application/pdf creator: cchlebak date_created: 2023-03-07T10:26:45Z date_updated: 2023-03-07T10:26:45Z file_id: '12712' file_name: 2023_PLOSOne_Mckerral.pdf file_size: 1257003 relation: main_file success: 1 file_date_updated: 2023-03-07T10:26:45Z has_accepted_license: '1' intvolume: ' 18' isi: 1 issue: '2' language: - iso: eng month: '02' oa: 1 oa_version: Published Version page: e0279838 pmid: 1 publication: PLoS One publication_identifier: eissn: - 1932-6203 publication_status: published publisher: Public Library of Science quality_controlled: '1' scopus_import: '1' status: public title: Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 18 year: '2023' ... --- _id: '13202' 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. acknowledged_ssus: - _id: EM-Fac 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. article_processing_charge: No article_type: original author: - first_name: Kohgaku full_name: Eguchi, Kohgaku id: 2B7846DC-F248-11E8-B48F-1D18A9856A87 last_name: Eguchi orcid: 0000-0002-6170-2546 - first_name: Elodie full_name: Le Monnier, Elodie id: 3B59276A-F248-11E8-B48F-1D18A9856A87 last_name: Le Monnier - first_name: Ryuichi full_name: Shigemoto, Ryuichi id: 499F3ABC-F248-11E8-B48F-1D18A9856A87 last_name: Shigemoto orcid: 0000-0001-8761-9444 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 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 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. 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. 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. 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. date_created: 2023-07-09T22:01:12Z date_published: 2023-06-07T00:00:00Z date_updated: 2023-10-18T07:12:47Z day: '07' ddc: - '570' department: - _id: RySh doi: 10.1523/JNEUROSCI.1514-22.2023 ec_funded: 1 external_id: isi: - '001020132100005' pmid: - '37160366' file: - access_level: open_access checksum: 70b2141870e0bf1c94fd343e18fdbc32 content_type: application/pdf creator: alisjak date_created: 2023-07-10T09:04:58Z date_updated: 2023-07-10T09:04:58Z file_id: '13205' file_name: 2023_JN_Eguchi.pdf file_size: 7794425 relation: main_file success: 1 file_date_updated: 2023-07-10T09:04:58Z has_accepted_license: '1' intvolume: ' 43' isi: 1 issue: '23' language: - iso: eng month: '06' oa: 1 oa_version: Published Version page: 4197-4216 pmid: 1 project: - _id: 2659CC84-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '793482' name: 'Ultrastructural analysis of phosphoinositides in nerve terminals: distribution, dynamics and physiological roles in synaptic transmission' - _id: 25CA28EA-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '694539' name: 'In situ analysis of single channel subunit composition in neurons: physiological implication in synaptic plasticity and behaviour' publication: The Journal of Neuroscience publication_identifier: eissn: - 1529-2401 issn: - 0270-6474 publication_status: published publisher: Society for Neuroscience quality_controlled: '1' scopus_import: '1' status: public title: Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 43 year: '2023' ... --- _id: '12916' 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" article_processing_charge: No article_type: original author: - first_name: Dante full_name: Bonolis, Dante id: 6A459894-5FDD-11E9-AF35-BB24E6697425 last_name: Bonolis - first_name: Timothy D full_name: Browning, Timothy D id: 35827D50-F248-11E8-B48F-1D18A9856A87 last_name: Browning orcid: 0000-0002-8314-0177 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 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 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. 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. 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. date_created: 2023-05-07T22:01:04Z date_published: 2023-02-16T00:00:00Z date_updated: 2023-10-18T06:54:30Z day: '16' department: - _id: TiBr doi: 10.2422/2036-2145.202010_018 external_id: arxiv: - '2007.14182' intvolume: ' 24' issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2007.14182 month: '02' oa: 1 oa_version: Preprint page: 173-204 publication: Annali della Scuola Normale Superiore di Pisa - Classe di Scienze publication_identifier: eissn: - 2036-2145 issn: - 0391-173X publication_status: published publisher: Scuola Normale Superiore - Edizioni della Normale quality_controlled: '1' scopus_import: '1' status: public title: Uniform bounds for rational points on hyperelliptic fibrations type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 24 year: '2023' ... --- _id: '14422' abstract: - lang: eng 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" alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Basile J full_name: Confavreux, Basile J id: C7610134-B532-11EA-BD9F-F5753DDC885E last_name: Confavreux citation: ama: 'Confavreux BJ. Synapseek: Meta-learning synaptic plasticity rules. 2023. doi:10.15479/at:ista:14422' 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' 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.' ieee: 'B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.' ista: 'Confavreux BJ. 2023. Synapseek: Meta-learning synaptic plasticity rules. Institute of Science and Technology Austria.' 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.' date_created: 2023-10-12T14:13:25Z date_published: 2023-10-12T00:00:00Z date_updated: 2023-10-18T09:20:56Z day: '12' ddc: - '610' degree_awarded: PhD department: - _id: GradSch - _id: TiVo doi: 10.15479/at:ista:14422 ec_funded: 1 file: - access_level: closed checksum: 7f636555eae7803323df287672fd13ed content_type: application/pdf creator: cchlebak date_created: 2023-10-12T14:53:50Z date_updated: 2023-10-12T14:54:52Z embargo: 2024-10-12 embargo_to: open_access file_id: '14424' file_name: Confavreux_Thesis_2A.pdf file_size: 30599717 relation: main_file - access_level: closed checksum: 725e85946db92290a4583a0de9779e1b content_type: application/x-zip-compressed creator: cchlebak date_created: 2023-10-18T07:38:34Z date_updated: 2023-10-18T07:56:08Z file_id: '14440' file_name: Confavreux Thesis.zip file_size: 68406739 relation: source_file file_date_updated: 2023-10-18T07:56:08Z has_accepted_license: '1' language: - iso: eng license: https://creativecommons.org/licenses/by-nc-sa/4.0/ month: '10' oa_version: Published Version page: '148' project: - _id: 0aacfa84-070f-11eb-9043-d7eb2c709234 call_identifier: H2020 grant_number: '819603' name: Learning the shape of synaptic plasticity rules for neuronal architectures and function through machine learning. publication_identifier: issn: - 2663 - 337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '9633' relation: part_of_dissertation status: public status: public supervisor: - first_name: Tim P full_name: Vogels, Tim P id: CB6FF8D2-008F-11EA-8E08-2637E6697425 last_name: Vogels orcid: 0000-0003-3295-6181 title: 'Synapseek: Meta-learning synaptic plasticity rules' tmp: image: /images/cc_by_nc_sa.png 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) short: CC BY-NC-SA (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '14374' 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." alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Barbara full_name: Roos, Barbara id: 5DA90512-D80F-11E9-8994-2E2EE6697425 last_name: Roos orcid: 0000-0002-9071-5880 citation: ama: Roos B. Boundary superconductivity in BCS theory. 2023. doi:10.15479/at:ista:14374 apa: Roos, B. (2023). Boundary superconductivity in BCS theory. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:14374 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. ista: Roos B. 2023. Boundary superconductivity in BCS theory. Institute of Science and Technology Austria. 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. date_created: 2023-09-28T14:23:04Z date_published: 2023-09-30T00:00:00Z date_updated: 2023-10-27T10:37:30Z day: '30' ddc: - '515' - '539' degree_awarded: PhD department: - _id: GradSch - _id: RoSe doi: 10.15479/at:ista:14374 ec_funded: 1 file: - access_level: open_access checksum: ef039ffc3de2cb8dee5b14110938e9b6 content_type: application/pdf creator: broos date_created: 2023-10-06T11:35:56Z date_updated: 2023-10-06T11:35:56Z file_id: '14398' file_name: phd-thesis-draft_pdfa_acrobat.pdf file_size: 2365702 relation: main_file - access_level: closed checksum: 81dcac33daeefaf0111db52f41bb1fd0 content_type: application/x-zip-compressed creator: broos date_created: 2023-10-06T11:38:01Z date_updated: 2023-10-06T11:38:01Z file_id: '14399' file_name: Version5.zip file_size: 4691734 relation: source_file file_date_updated: 2023-10-06T11:38:01Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '206' project: - _id: 25C6DC12-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '694227' name: Analysis of quantum many-body systems - _id: bda63fe5-d553-11ed-ba76-a16e3d2f256b grant_number: I06427 name: Mathematical Challenges in BCS Theory of Superconductivity publication_identifier: issn: - 2663 - 337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '13207' relation: part_of_dissertation status: public - id: '10850' relation: part_of_dissertation status: public status: public supervisor: - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 title: Boundary superconductivity in BCS theory tmp: image: /images/cc_by_nc_sa.png 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) short: CC BY-NC-SA (4.0) type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2023' ... --- _id: '13207' 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. 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 article_type: original author: - first_name: Christian full_name: Hainzl, Christian last_name: Hainzl - first_name: Barbara full_name: Roos, Barbara id: 5DA90512-D80F-11E9-8994-2E2EE6697425 last_name: Roos orcid: 0000-0002-9071-5880 - first_name: Robert full_name: Seiringer, Robert id: 4AFD0470-F248-11E8-B48F-1D18A9856A87 last_name: Seiringer orcid: 0000-0002-6781-0521 citation: 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 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. 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. 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. date_created: 2023-07-10T16:35:45Z date_published: 2023-05-18T00:00:00Z date_updated: 2023-10-27T10:37:29Z day: '18' ddc: - '530' department: - _id: GradSch - _id: RoSe doi: 10.4171/JST/439 ec_funded: 1 external_id: arxiv: - '2201.08090' isi: - '000997933500008' file: - access_level: open_access checksum: 5501da33be010b5c81440438287584d5 content_type: application/pdf creator: alisjak date_created: 2023-07-11T08:19:15Z date_updated: 2023-07-11T08:19:15Z file_id: '13208' file_name: 2023_EMS_Hainzl.pdf file_size: 304619 relation: main_file success: 1 file_date_updated: 2023-07-11T08:19:15Z has_accepted_license: '1' intvolume: ' 12' isi: 1 issue: '4' language: - iso: eng month: '05' oa: 1 oa_version: Published Version page: 1507–1540 project: - _id: 25C6DC12-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '694227' name: Analysis of quantum many-body systems publication: Journal of Spectral Theory publication_identifier: eissn: - 1664-0403 issn: - 1664-039X publication_status: published publisher: EMS Press quality_controlled: '1' related_material: record: - id: '14374' relation: dissertation_contains status: public status: public title: Boundary superconductivity in the BCS model tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 12 year: '2023' ... --- _id: '14452' abstract: - lang: eng 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. acknowledgement: NHB was supported in part by ERC Grants 250152 and 101055327. AV was partly supported by the chaire Modélisation Mathématique et Biodiversité of Veolia Environment—Ecole Polytechnique—Museum National d’Histoire Naturelle—Fondation X. article_number: iyad133 article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 - first_name: Alison M. full_name: Etheridge, Alison M. last_name: Etheridge - first_name: Amandine full_name: Véber, Amandine last_name: Véber citation: ama: Barton NH, Etheridge AM, Véber A. The infinitesimal model with dominance. Genetics. 2023;225(2). doi:10.1093/genetics/iyad133 apa: Barton, N. H., Etheridge, A. M., & Véber, A. (2023). The infinitesimal model with dominance. Genetics. Oxford Academic. https://doi.org/10.1093/genetics/iyad133 chicago: Barton, Nicholas H, Alison M. Etheridge, and Amandine Véber. “The Infinitesimal Model with Dominance.” Genetics. Oxford Academic, 2023. https://doi.org/10.1093/genetics/iyad133. ieee: N. H. Barton, A. M. Etheridge, and A. Véber, “The infinitesimal model with dominance,” Genetics, vol. 225, no. 2. Oxford Academic, 2023. ista: Barton NH, Etheridge AM, Véber A. 2023. The infinitesimal model with dominance. Genetics. 225(2), iyad133. mla: Barton, Nicholas H., et al. “The Infinitesimal Model with Dominance.” Genetics, vol. 225, no. 2, iyad133, Oxford Academic, 2023, doi:10.1093/genetics/iyad133. short: N.H. Barton, A.M. Etheridge, A. Véber, Genetics 225 (2023). date_created: 2023-10-29T23:01:15Z date_published: 2023-10-01T00:00:00Z date_updated: 2023-10-30T13:04:11Z day: '01' ddc: - '570' department: - _id: NiBa doi: 10.1093/genetics/iyad133 ec_funded: 1 external_id: arxiv: - '2211.03515' file: - access_level: open_access checksum: 3f65b1fbe813e2f4dbb5d2b5e891844a content_type: application/pdf creator: dernst date_created: 2023-10-30T12:57:53Z date_updated: 2023-10-30T12:57:53Z file_id: '14469' file_name: 2023_Genetics_Barton.pdf file_size: 1439032 relation: main_file success: 1 file_date_updated: 2023-10-30T12:57:53Z has_accepted_license: '1' intvolume: ' 225' issue: '2' language: - iso: eng month: '10' oa: 1 oa_version: Published Version project: - _id: 25B07788-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '250152' name: Limits to selection in biology and in evolutionary computation - _id: bd6958e0-d553-11ed-ba76-86eba6a76c00 grant_number: '101055327' name: Understanding the evolution of continuous genomes publication: Genetics publication_identifier: eissn: - 1943-2631 issn: - 0016-6731 publication_status: published publisher: Oxford Academic quality_controlled: '1' related_material: record: - id: '12949' relation: research_data status: public scopus_import: '1' status: public title: The infinitesimal model with dominance tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 225 year: '2023' ... --- _id: '12949' abstract: - lang: eng 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 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. article_processing_charge: No author: - first_name: Nicholas H full_name: Barton, Nicholas H id: 4880FE40-F248-11E8-B48F-1D18A9856A87 last_name: Barton orcid: 0000-0002-8548-5240 citation: ama: Barton NH. The infinitesimal model with dominance. 2023. doi: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 chicago: Barton, Nicholas H. “The Infinitesimal Model with Dominance.” Institute of Science and Technology Austria, 2023. https://doi.org/10.15479/AT:ISTA:12949. ieee: N. H. Barton, “The infinitesimal model with dominance.” Institute of Science and Technology Austria, 2023. ista: Barton NH. 2023. The infinitesimal model with dominance, Institute of Science and Technology Austria, 10.15479/AT:ISTA:12949. mla: Barton, Nicholas H. The Infinitesimal Model with Dominance. Institute of Science and Technology Austria, 2023, doi:10.15479/AT:ISTA:12949. short: N.H. Barton, (2023). contributor: - contributor_type: researcher first_name: Amandine last_name: Veber - contributor_type: researcher first_name: Alison last_name: Etheridge date_created: 2023-05-13T09:49:09Z date_published: 2023-05-13T00:00:00Z date_updated: 2023-10-30T13:04:11Z day: '13' ddc: - '576' department: - _id: NiBa doi: 10.15479/AT:ISTA:12949 file: - access_level: open_access checksum: b0ce7d4b1ee7e7265430ceed36fc3336 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:36:33Z date_updated: 2023-05-13T09:36:33Z file_id: '12950' file_name: Neutral identities 16th Jan file_size: 13662 relation: main_file success: 1 - access_level: open_access checksum: ad5035ad4f7d3b150a252c79884f6a83 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:38:17Z date_updated: 2023-05-13T09:38:17Z file_id: '12951' file_name: p, zA, zD, N=30 neutral III file_size: 181619928 relation: main_file success: 1 - access_level: open_access checksum: 62182a1de796256edd6f4223704312ef content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:41:59Z date_updated: 2023-05-13T09:41:59Z file_id: '12952' file_name: p, zA, zD, N=30 neutral IV file_size: 605902074 relation: main_file success: 1 - access_level: open_access checksum: af775dda5c4f6859cb1e5a81ec40a667 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:46:52Z date_updated: 2023-05-13T09:46:52Z file_id: '12953' file_name: p, zA, zD, N=30 selected k=5 file_size: 1018238746 relation: main_file success: 1 - access_level: open_access checksum: af26f3394c387d3ada14b434cd68b1e5 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:42:05Z date_updated: 2023-05-13T09:42:05Z file_id: '12954' file_name: Pairwise F N=30 neutral II file_size: 3197160 relation: main_file success: 1 - access_level: open_access checksum: d5da7dc0e7282dd48222e26d12e34220 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:42:06Z date_updated: 2023-05-13T09:42:06Z file_id: '12955' file_name: Pedigrees N=30 neutral II file_size: 55492 relation: main_file success: 1 - access_level: open_access checksum: 00f386d80677590e29f6235d49cba58d content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:46:06Z date_updated: 2023-05-13T09:46:06Z file_id: '12956' file_name: selected reps N=30 selected k=1,2 300 reps III file_size: 474003467 relation: main_file success: 1 - access_level: open_access checksum: 658cef3eaea6136a4d24da4f074191d7 content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:46:08Z date_updated: 2023-05-13T09:46:08Z file_id: '12957' file_name: Algorithm for caclulating identities.nb file_size: 121209 relation: main_file success: 1 - access_level: open_access checksum: db9b6dddd7a596d974e25f5e78f5c45c content_type: application/octet-stream creator: nbarton date_created: 2023-05-13T09:46:08Z date_updated: 2023-05-13T09:46:08Z file_id: '12958' file_name: Infinitesimal with dominance.nb file_size: 1803898 relation: main_file success: 1 - access_level: open_access checksum: 91f80a9fb58cae8eef2d8bf59fe30189 content_type: text/plain creator: nbarton date_created: 2023-05-16T04:09:08Z date_updated: 2023-05-16T04:09:08Z file_id: '12967' file_name: ReadMe.txt file_size: 990 relation: main_file success: 1 file_date_updated: 2023-05-16T04:09:08Z has_accepted_license: '1' keyword: - Quantitative genetics - infinitesimal model month: '05' oa: 1 oa_version: Published Version project: - _id: bd6958e0-d553-11ed-ba76-86eba6a76c00 grant_number: '101055327' name: Understanding the evolution of continuous genomes publisher: Institute of Science and Technology Austria related_material: record: - id: '14452' relation: used_in_publication status: public status: public title: The infinitesimal model with dominance tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ... --- _id: '14461' 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.' acknowledged_ssus: - _id: ScienComp 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). alternative_title: - PMLR article_processing_charge: No author: - first_name: Ilia full_name: Markov, Ilia id: D0CF4148-C985-11E9-8066-0BDEE5697425 last_name: Markov - first_name: Adrian full_name: Vladu, Adrian last_name: Vladu - first_name: Qi full_name: Guo, Qi last_name: Guo - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: '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.' 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. 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. 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.' 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. 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. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, HI, United States name: 'ICML: International Conference on Machine Learning' start_date: 2023-07-23 date_created: 2023-10-29T23:01:17Z date_published: 2023-07-30T00:00:00Z date_updated: 2023-10-31T09:40:45Z day: '30' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2302.02390' intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2302.02390 month: '07' oa: 1 oa_version: Preprint page: 24020-24044 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Proceedings of the 40th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Quantized distributed training of large models with convergence guarantees type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14462' abstract: - lang: eng 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." 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." alternative_title: - PMLR article_processing_charge: No author: - first_name: Hendrik full_name: Fichtenberger, Hendrik last_name: Fichtenberger - first_name: Monika H full_name: Henzinger, Monika H id: 540c9bbd-f2de-11ec-812d-d04a5be85630 last_name: Henzinger orcid: 0000-0002-5008-6530 - first_name: Jalaj full_name: Upadhyay, Jalaj last_name: Upadhyay citation: ama: 'Fichtenberger H, Henzinger MH, Upadhyay J. Constant matters: Fine-grained error bound on differentially private continual observation. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10072-10092.' 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.' chicago: 'Fichtenberger, Hendrik, Monika H Henzinger, and Jalaj Upadhyay. “Constant Matters: Fine-Grained Error Bound on Differentially Private Continual Observation.” In Proceedings of the 40th International Conference on Machine Learning, 202:10072–92. ML Research Press, 2023.' 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.' 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. ICML: International Conference on Machine Learning, PMLR, vol. 202, 10072–10092.' mla: 'Fichtenberger, Hendrik, et al. “Constant Matters: Fine-Grained Error Bound on Differentially Private Continual Observation.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10072–92.' short: H. Fichtenberger, M.H. Henzinger, J. Upadhyay, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10072–10092. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, HI, United States name: 'ICML: International Conference on Machine Learning' start_date: 2023-07-23 date_created: 2023-10-29T23:01:17Z date_published: 2023-07-30T00:00:00Z date_updated: 2023-10-31T09:54:05Z day: '30' department: - _id: MoHe ec_funded: 1 intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.mlr.press/v202/fichtenberger23a/fichtenberger23a.pdf month: '07' oa: 1 oa_version: Published Version page: 10072-10092 project: - _id: bd9ca328-d553-11ed-ba76-dc4f890cfe62 call_identifier: H2020 grant_number: '101019564' name: The design and evaluation of modern fully dynamic data structures - _id: 34def286-11ca-11ed-8bc3-da5948e1613c grant_number: Z00422 name: Wittgenstein Award - Monika Henzinger - _id: bd9e3a2e-d553-11ed-ba76-8aa684ce17fe grant_number: 'P33775 ' name: Fast Algorithms for a Reactive Network Layer publication: Proceedings of the 40th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'Constant matters: Fine-grained error bound on differentially private continual observation' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14459' abstract: - lang: eng 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. 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). alternative_title: - PMLR article_processing_charge: No author: - first_name: Aleksandr full_name: Shevchenko, Aleksandr id: F2B06EC2-C99E-11E9-89F0-752EE6697425 last_name: Shevchenko - first_name: Kevin full_name: Kögler, Kevin id: 94ec913c-dc85-11ea-9058-e5051ab2428b last_name: Kögler - first_name: Hamed full_name: Hassani, Hamed last_name: Hassani - first_name: Marco full_name: Mondelli, Marco id: 27EB676C-8706-11E9-9510-7717E6697425 last_name: Mondelli orcid: 0000-0002-3242-7020 citation: 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.' 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. 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. 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.' 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. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, HI, United States name: 'ICML: International Conference on Machine Learning' start_date: 2023-07-23 date_created: 2023-10-29T23:01:17Z date_published: 2023-07-30T00:00:00Z date_updated: 2023-10-31T08:52:28Z day: '30' department: - _id: MaMo - _id: DaAl external_id: arxiv: - '2212.13468' intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2212.13468 month: '07' oa: 1 oa_version: Preprint page: 31151-31209 project: - _id: 059876FA-7A3F-11EA-A408-12923DDC885E name: Prix Lopez-Loretta 2019 - Marco Mondelli publication: Proceedings of the 40th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: Fundamental limits of two-layer autoencoders, and achieving them with gradient methods type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14460' abstract: - lang: eng text: We provide an efficient implementation of the backpropagation algorithm, specialized to the case where the weights of the neural network being trained are sparse. Our algorithm is general, as it applies to arbitrary (unstructured) sparsity and common layer types (e.g., convolutional or linear). We provide a fast vectorized implementation on commodity CPUs, and show that it can yield speedups in end-to-end runtime experiments, both in transfer learning using already-sparsified networks, and in training sparse networks from scratch. Thus, our results provide the first support for sparse training on commodity hardware. acknowledgement: 'We would like to thank Elias Frantar for his valuable assistance and support at the outset of this project, and the anonymous ICML and SNN reviewers for very constructive feedback. EI was supported in part by the FWF DK VGSCO, grant agreement number W1260-N35. DA acknowledges generous ERC support, via Starting Grant 805223 ScaleML. ' alternative_title: - PMLR article_processing_charge: No author: - first_name: Mahdi full_name: Nikdan, Mahdi id: 66374281-f394-11eb-9cf6-869147deecc0 last_name: Nikdan - first_name: Tommaso full_name: Pegolotti, Tommaso last_name: Pegolotti - first_name: Eugenia B full_name: Iofinova, Eugenia B id: f9a17499-f6e0-11ea-865d-fdf9a3f77117 last_name: Iofinova orcid: 0000-0002-7778-3221 - first_name: Eldar full_name: Kurtic, Eldar id: 47beb3a5-07b5-11eb-9b87-b108ec578218 last_name: Kurtic - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:26215-26227.' apa: 'Nikdan, M., Pegolotti, T., Iofinova, E. B., Kurtic, E., & Alistarh, D.-A. (2023). SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 26215–26227). Honolulu, Hawaii, HI, United States: ML Research Press.' chicago: 'Nikdan, Mahdi, Tommaso Pegolotti, Eugenia B Iofinova, Eldar Kurtic, and Dan-Adrian Alistarh. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” In Proceedings of the 40th International Conference on Machine Learning, 202:26215–27. ML Research Press, 2023.' ieee: 'M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.' ista: 'Nikdan M, Pegolotti T, Iofinova EB, Kurtic E, Alistarh D-A. 2023. SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 26215–26227.' mla: 'Nikdan, Mahdi, et al. “SparseProp: Efficient Sparse Backpropagation for Faster Training of Neural Networks at the Edge.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 26215–27.' short: M. Nikdan, T. Pegolotti, E.B. Iofinova, E. Kurtic, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 26215–26227. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, HI, United States name: 'ICML: International Conference on Machine Learning' start_date: 2023-07-23 date_created: 2023-10-29T23:01:17Z date_published: 2023-07-30T00:00:00Z date_updated: 2023-10-31T09:33:51Z day: '30' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2302.04852' intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2302.04852 month: '07' oa: 1 oa_version: Preprint page: 26215-26227 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Proceedings of the 40th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14457' abstract: - lang: eng text: "Threshold secret sharing allows a dealer to split a secret s into n shares, such that any t shares allow for reconstructing s, but no t-1 shares reveal any information about s. Leakage-resilient secret sharing requires that the secret remains hidden, even when an adversary additionally obtains a limited amount of leakage from every share. Benhamouda et al. (CRYPTO’18) proved that Shamir’s secret sharing scheme is one bit leakage-resilient for reconstruction threshold t≥0.85n and conjectured that the same holds for t = c.n for any constant 0≤c≤1. Nielsen and Simkin (EUROCRYPT’20) showed that this is the best one can hope for by proving that Shamir’s scheme is not secure against one-bit leakage when t0c.n/log(n).\r\nIn this work, we strengthen the lower bound of Nielsen and Simkin. We consider noisy leakage-resilience, where a random subset of leakages is replaced by uniformly random noise. We prove a lower bound for Shamir’s secret sharing, similar to that of Nielsen and Simkin, which holds even when a constant fraction of leakages is replaced by random noise. To this end, we first prove a lower bound on the share size of any noisy-leakage-resilient sharing scheme. We then use this lower bound to show that there exist universal constants c1, c2, such that for sufficiently large n it holds that Shamir’s secret sharing scheme is not noisy-leakage-resilient for t≤c1.n/log(n), even when a c2 fraction of leakages are replaced by random noise.\r\n\r\n\r\n\r\n" alternative_title: - LNCS article_processing_charge: No author: - first_name: Charlotte full_name: Hoffmann, Charlotte id: 0f78d746-dc7d-11ea-9b2f-83f92091afe7 last_name: Hoffmann orcid: 0000-0003-2027-5549 - first_name: Mark full_name: Simkin, Mark last_name: Simkin citation: ama: 'Hoffmann C, Simkin M. Stronger lower bounds for leakage-resilient secret sharing. In: 8th International Conference on Cryptology and Information Security in Latin America. Vol 14168. Springer Nature; 2023:215-228. doi:10.1007/978-3-031-44469-2_11' apa: 'Hoffmann, C., & Simkin, M. (2023). Stronger lower bounds for leakage-resilient secret sharing. In 8th International Conference on Cryptology and Information Security in Latin America (Vol. 14168, pp. 215–228). Quito, Ecuador: Springer Nature. https://doi.org/10.1007/978-3-031-44469-2_11' chicago: Hoffmann, Charlotte, and Mark Simkin. “Stronger Lower Bounds for Leakage-Resilient Secret Sharing.” In 8th International Conference on Cryptology and Information Security in Latin America, 14168:215–28. Springer Nature, 2023. https://doi.org/10.1007/978-3-031-44469-2_11. ieee: C. Hoffmann and M. Simkin, “Stronger lower bounds for leakage-resilient secret sharing,” in 8th International Conference on Cryptology and Information Security in Latin America, Quito, Ecuador, 2023, vol. 14168, pp. 215–228. ista: 'Hoffmann C, Simkin M. 2023. Stronger lower bounds for leakage-resilient secret sharing. 8th International Conference on Cryptology and Information Security in Latin America. LATINCRYPT: Conference on Cryptology and Information Security in Latin America, LNCS, vol. 14168, 215–228.' mla: Hoffmann, Charlotte, and Mark Simkin. “Stronger Lower Bounds for Leakage-Resilient Secret Sharing.” 8th International Conference on Cryptology and Information Security in Latin America, vol. 14168, Springer Nature, 2023, pp. 215–28, doi:10.1007/978-3-031-44469-2_11. short: C. Hoffmann, M. Simkin, in:, 8th International Conference on Cryptology and Information Security in Latin America, Springer Nature, 2023, pp. 215–228. conference: end_date: 2023-10-06 location: Quito, Ecuador name: 'LATINCRYPT: Conference on Cryptology and Information Security in Latin America' start_date: 2023-10-03 date_created: 2023-10-29T23:01:16Z date_published: 2023-10-01T00:00:00Z date_updated: 2023-10-31T11:43:12Z day: '01' department: - _id: KrPi doi: 10.1007/978-3-031-44469-2_11 intvolume: ' 14168' language: - iso: eng main_file_link: - open_access: '1' url: https://eprint.iacr.org/2023/1017 month: '10' oa: 1 oa_version: Preprint page: 215-228 publication: 8th International Conference on Cryptology and Information Security in Latin America publication_identifier: eissn: - 1611-3349 isbn: - '9783031444685' issn: - 0302-9743 publication_status: published publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: Stronger lower bounds for leakage-resilient secret sharing type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 14168 year: '2023' ... --- _id: '14458' abstract: - lang: eng text: 'We show for the first time that large-scale generative pretrained transformer (GPT) family models can be pruned to at least 50% sparsity in one-shot, without any retraining, at minimal loss of accuracy. This is achieved via a new pruning method called SparseGPT, specifically designed to work efficiently and accurately on massive GPT-family models. We can execute SparseGPT on the largest available open-source models, OPT-175B and BLOOM-176B, in under 4.5 hours, and can reach 60% unstructured sparsity with negligible increase in perplexity: remarkably, more than 100 billion weights from these models can be ignored at inference time. SparseGPT generalizes to semi-structured (2:4 and 4:8) patterns, and is compatible with weight quantization approaches. The code is available at: https://github.com/IST-DASLab/sparsegpt.' acknowledged_ssus: - _id: ScienComp acknowledgement: The authors gratefully acknowledge funding from the European Research Council (ERC) under the European Union’s Horizon 2020 programme (grant agreement No. 805223 ScaleML), as well as experimental support from Eldar Kurtic, and from the IST Austria IT department, in particular Stefano Elefante, Andrei Hornoiu, and Alois Schloegl. alternative_title: - PMLR article_processing_charge: No author: - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X citation: ama: 'Frantar E, Alistarh D-A. SparseGPT: Massive language models can be accurately pruned in one-shot. In: Proceedings of the 40th International Conference on Machine Learning. Vol 202. ML Research Press; 2023:10323-10337.' apa: 'Frantar, E., & Alistarh, D.-A. (2023). SparseGPT: Massive language models can be accurately pruned in one-shot. In Proceedings of the 40th International Conference on Machine Learning (Vol. 202, pp. 10323–10337). Honolulu, Hawaii, HI, United States: ML Research Press.' chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” In Proceedings of the 40th International Conference on Machine Learning, 202:10323–37. ML Research Press, 2023.' ieee: 'E. Frantar and D.-A. Alistarh, “SparseGPT: Massive language models can be accurately pruned in one-shot,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10323–10337.' ista: 'Frantar E, Alistarh D-A. 2023. SparseGPT: Massive language models can be accurately pruned in one-shot. Proceedings of the 40th International Conference on Machine Learning. ICML: International Conference on Machine Learning, PMLR, vol. 202, 10323–10337.' mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot.” Proceedings of the 40th International Conference on Machine Learning, vol. 202, ML Research Press, 2023, pp. 10323–37.' short: E. Frantar, D.-A. Alistarh, in:, Proceedings of the 40th International Conference on Machine Learning, ML Research Press, 2023, pp. 10323–10337. conference: end_date: 2023-07-29 location: Honolulu, Hawaii, HI, United States name: 'ICML: International Conference on Machine Learning' start_date: 2023-07-23 date_created: 2023-10-29T23:01:16Z date_published: 2023-07-30T00:00:00Z date_updated: 2023-10-31T09:59:42Z day: '30' department: - _id: DaAl ec_funded: 1 external_id: arxiv: - '2301.00774' intvolume: ' 202' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2301.00774 month: '07' oa: 1 oa_version: Preprint page: 10323-10337 project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication: Proceedings of the 40th International Conference on Machine Learning publication_identifier: eissn: - 2640-3498 publication_status: published publisher: ML Research Press quality_controlled: '1' scopus_import: '1' status: public title: 'SparseGPT: Massive language models can be accurately pruned in one-shot' type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 202 year: '2023' ... --- _id: '14451' abstract: - lang: eng text: 'We investigate the potential of Multi-Objective, Deep Reinforcement Learning for stock and cryptocurrency single-asset trading: in particular, we consider a Multi-Objective algorithm which generalizes the reward functions and discount factor (i.e., these components are not specified a priori, but incorporated in the learning process). Firstly, using several important assets (BTCUSD, ETHUSDT, XRPUSDT, AAPL, SPY, NIFTY50), we verify the reward generalization property of the proposed Multi-Objective algorithm, and provide preliminary statistical evidence showing increased predictive stability over the corresponding Single-Objective strategy. Secondly, we show that the Multi-Objective algorithm has a clear edge over the corresponding Single-Objective strategy when the reward mechanism is sparse (i.e., when non-null feedback is infrequent over time). Finally, we discuss the generalization properties with respect to the discount factor. The entirety of our code is provided in open-source format.' acknowledgement: Open access funding provided by Università degli Studi di Trieste within the CRUI-CARE Agreement. Funding was provided by Austrian Science Fund (Grant No. F65), Horizon 2020 (Grant No. 754411) and Österreichische Forschungsförderungsgesellschaft. article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Federico full_name: Cornalba, Federico id: 2CEB641C-A400-11E9-A717-D712E6697425 last_name: Cornalba orcid: 0000-0002-6269-5149 - first_name: Constantin full_name: Disselkamp, Constantin last_name: Disselkamp - first_name: Davide full_name: Scassola, Davide last_name: Scassola - first_name: Christopher full_name: Helf, Christopher last_name: Helf citation: ama: 'Cornalba F, Disselkamp C, Scassola D, Helf C. Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading. Neural Computing and Applications. 2023. doi:10.1007/s00521-023-09033-7' apa: 'Cornalba, F., Disselkamp, C., Scassola, D., & Helf, C. (2023). Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading. Neural Computing and Applications. Springer Nature. https://doi.org/10.1007/s00521-023-09033-7' chicago: 'Cornalba, Federico, Constantin Disselkamp, Davide Scassola, and Christopher Helf. “Multi-Objective Reward Generalization: Improving Performance of Deep Reinforcement Learning for Applications in Single-Asset Trading.” Neural Computing and Applications. Springer Nature, 2023. https://doi.org/10.1007/s00521-023-09033-7.' ieee: 'F. Cornalba, C. Disselkamp, D. Scassola, and C. Helf, “Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading,” Neural Computing and Applications. Springer Nature, 2023.' ista: 'Cornalba F, Disselkamp C, Scassola D, Helf C. 2023. Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading. Neural Computing and Applications.' mla: 'Cornalba, Federico, et al. “Multi-Objective Reward Generalization: Improving Performance of Deep Reinforcement Learning for Applications in Single-Asset Trading.” Neural Computing and Applications, Springer Nature, 2023, doi:10.1007/s00521-023-09033-7.' short: F. Cornalba, C. Disselkamp, D. Scassola, C. Helf, Neural Computing and Applications (2023). date_created: 2023-10-22T22:01:16Z date_published: 2023-10-05T00:00:00Z date_updated: 2023-10-31T10:58:28Z day: '05' department: - _id: JuFi doi: 10.1007/s00521-023-09033-7 ec_funded: 1 external_id: arxiv: - '2203.04579' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1007/s00521-023-09033-7 month: '10' oa: 1 oa_version: Published Version project: - _id: fc31cba2-9c52-11eb-aca3-ff467d239cd2 grant_number: F6504 name: Taming Complexity in Partial Differential Systems - _id: 260C2330-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '754411' name: ISTplus - Postdoctoral Fellowships publication: Neural Computing and Applications publication_identifier: eissn: - 1433-3058 issn: - 0941-0643 publication_status: epub_ahead publisher: Springer Nature quality_controlled: '1' scopus_import: '1' status: public title: 'Multi-objective reward generalization: improving performance of Deep Reinforcement Learning for applications in single-asset trading' type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2023' ...