--- APC_amount: 6468 EUR DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '14793' abstract: - lang: eng text: Superconductor/semiconductor hybrid devices have attracted increasing interest in the past years. Superconducting electronics aims to complement semiconductor technology, while hybrid architectures are at the forefront of new ideas such as topological superconductivity and protected qubits. In this work, we engineer the induced superconductivity in two-dimensional germanium hole gas by varying the distance between the quantum well and the aluminum. We demonstrate a hard superconducting gap and realize an electrically and flux tunable superconducting diode using a superconducting quantum interference device (SQUID). This allows to tune the current phase relation (CPR), to a regime where single Cooper pair tunneling is suppressed, creating a sin(2y) CPR. Shapiro experiments complement this interpretation and the microwave drive allows to create a diode with ≈ 100% efficiency. The reported results open up the path towards integration of spin qubit devices, microwave resonators and (protected) superconducting qubits on the same silicon technology compatible platform. acknowledged_ssus: - _id: M-Shop - _id: NanoFab acknowledgement: "We acknowledge Alexander Brinkmann, Alessandro Crippa, Francesco Giazotto, Andrew Higginbotham, Andrea Iorio, Giordano Scappucci, Christian Schonenberger, and Lukas Splitthoff for helpful discussions. We thank Marcel Verheijen for the support in the TEM analysis. This research and related results were made possible with the support of the NOMIS\r\nFoundation. It was supported by the Scientific Service Units of ISTA through resources provided by the MIBA Machine Shop and the nanofabrication facility, the European Union’s Horizon 2020 research andinnovation programme under Grant Agreement No 862046, the HORIZONRIA\r\n101069515 project, the European Innovation Council Pathfinder grant no. 101115315 (QuKiT), and the FWF Projects #P-32235, #P-36507 and #F-8606. For the purpose of open access, the authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. R.S.S. acknowledges Spanish CM “Talento Program\"\r\nProject No. 2022-T1/IND-24070. J.J. acknowledges European Research Council TOCINA 834290." article_number: '169' article_processing_charge: Yes article_type: original author: - first_name: Marco full_name: Valentini, Marco id: C0BB2FAC-D767-11E9-B658-BC13E6697425 last_name: Valentini - first_name: Oliver full_name: Sagi, Oliver id: 71616374-A8E9-11E9-A7CA-09ECE5697425 last_name: Sagi - first_name: Levon full_name: Baghumyan, Levon id: 7aa1f788-b527-11ee-aa9e-e6111a79e0c7 last_name: Baghumyan - first_name: Thijs full_name: de Gijsel, Thijs id: a0ece13c-b527-11ee-929d-bad130106eee last_name: de Gijsel - first_name: Jason full_name: Jung, Jason id: 4C9ACE7A-F248-11E8-B48F-1D18A9856A87 last_name: Jung - first_name: Stefano full_name: Calcaterra, Stefano last_name: Calcaterra - first_name: Andrea full_name: Ballabio, Andrea last_name: Ballabio - first_name: Juan L full_name: Aguilera Servin, Juan L id: 2A67C376-F248-11E8-B48F-1D18A9856A87 last_name: Aguilera Servin orcid: 0000-0002-2862-8372 - first_name: Kushagra full_name: Aggarwal, Kushagra id: b22ab905-3539-11eb-84c3-fc159dcd79cb last_name: Aggarwal orcid: 0000-0001-9985-9293 - first_name: Marian full_name: Janik, Marian id: 396A1950-F248-11E8-B48F-1D18A9856A87 last_name: Janik orcid: 0009-0003-9037-8831 - first_name: Thomas full_name: Adletzberger, Thomas id: 38756BB2-F248-11E8-B48F-1D18A9856A87 last_name: Adletzberger - first_name: Rubén full_name: Seoane Souto, Rubén last_name: Seoane Souto - first_name: Martin full_name: Leijnse, Martin last_name: Leijnse - first_name: Jeroen full_name: Danon, Jeroen last_name: Danon - first_name: Constantin full_name: Schrade, Constantin last_name: Schrade - first_name: Erik full_name: Bakkers, Erik last_name: Bakkers - first_name: Daniel full_name: Chrastina, Daniel last_name: Chrastina - first_name: Giovanni full_name: Isella, Giovanni last_name: Isella - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X citation: ama: Valentini M, Sagi O, Baghumyan L, et al. Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium. Nature Communications. 2024;15. doi:10.1038/s41467-023-44114-0 apa: Valentini, M., Sagi, O., Baghumyan, L., de Gijsel, T., Jung, J., Calcaterra, S., … Katsaros, G. (2024). Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-023-44114-0 chicago: Valentini, Marco, Oliver Sagi, Levon Baghumyan, Thijs de Gijsel, Jason Jung, Stefano Calcaterra, Andrea Ballabio, et al. “Parity-Conserving Cooper-Pair Transport and Ideal Superconducting Diode in Planar Germanium.” Nature Communications. Springer Nature, 2024. https://doi.org/10.1038/s41467-023-44114-0. ieee: M. Valentini et al., “Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium,” Nature Communications, vol. 15. Springer Nature, 2024. ista: Valentini M, Sagi O, Baghumyan L, de Gijsel T, Jung J, Calcaterra S, Ballabio A, Aguilera Servin JL, Aggarwal K, Janik M, Adletzberger T, Seoane Souto R, Leijnse M, Danon J, Schrade C, Bakkers E, Chrastina D, Isella G, Katsaros G. 2024. Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium. Nature Communications. 15, 169. mla: Valentini, Marco, et al. “Parity-Conserving Cooper-Pair Transport and Ideal Superconducting Diode in Planar Germanium.” Nature Communications, vol. 15, 169, Springer Nature, 2024, doi:10.1038/s41467-023-44114-0. short: M. Valentini, O. Sagi, L. Baghumyan, T. de Gijsel, J. Jung, S. Calcaterra, A. Ballabio, J.L. Aguilera Servin, K. Aggarwal, M. Janik, T. Adletzberger, R. Seoane Souto, M. Leijnse, J. Danon, C. Schrade, E. Bakkers, D. Chrastina, G. Isella, G. Katsaros, Nature Communications 15 (2024). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: All experimental data included in this work are available at https://zenodo.org/records/10119346. date_created: 2024-01-14T23:00:56Z date_published: 2024-01-02T00:00:00Z date_updated: 2026-10-02T10:23:53Z day: '02' ddc: - '530' department: - _id: GeKa doi: 10.1038/s41467-023-44114-0 ec_funded: 1 external_id: isi: - '001142794000839' pmid: - '38167818' file: - access_level: open_access checksum: ef79173b45eeaf984ffa61ef2f8a52ab content_type: application/pdf creator: dernst date_created: 2024-01-17T11:03:00Z date_updated: 2024-01-17T11:03:00Z file_id: '14825' file_name: 2024_NatureComm_Valentini.pdf file_size: 2336595 relation: main_file success: 1 file_date_updated: 2024-01-17T11:03:00Z fulldoi: https://doi.org/10.1038/s41467-023-44114-0 has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng month: '01' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 237E5020-32DE-11EA-91FC-C7463DDC885E call_identifier: H2020 grant_number: '862046' name: TOPOLOGICALLY PROTECTED AND SCALABLE QUANTUM BITS - _id: 34c0acea-11ca-11ed-8bc3-8775e10fd452 grant_number: '101069515' name: Integrated Germanium Quantum Technology - _id: bdc2ca30-d553-11ed-ba76-cf164a5bb811 grant_number: '101115315' name: Quantum bits with Kitaev Transmons - _id: 237B3DA4-32DE-11EA-91FC-C7463DDC885E call_identifier: FWF grant_number: P32235 name: Towards scalable hut wire quantum devices - _id: bd8bd29e-d553-11ed-ba76-f0070d4b237a grant_number: P36507 name: Merging spin and superconducting qubits in planar Ge - _id: 34a66131-11ca-11ed-8bc3-a31681c6b03e grant_number: F8606 name: 'Center for Correlated Quantum Materials and Solid State Quantum Systems: Conventional and unconventional topological superconductors' - _id: 3AC91DDA-15DF-11EA-824D-93A3E7B544D1 call_identifier: FWF name: FWF Open Access Fund publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '13312' relation: earlier_version status: public - id: '23036' relation: research_data status: public researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18902' acknowledgement: 'MZ is supported by National Science Center, Poland, 2021/42/E/NZ2/00188, the Polish National Agency for Academic Exchange, and by a grant from the Priority Research Area DigiWorld under the Strategic Programme Excellence Initiative at Jagiellonian University. Work in JB’s lab is supported by the Francis Crick Institute, which receives its core funding from Cancer Research UK, the UK Medical Research Council and Wellcome Trust (all under CC001051). Work in the AK lab is supported by ISTA, the European Research Council under Horizon Europe: grant 101044579, and Austrian Science Fund (FWF): F78 (Neural Stem Cell Modulation).' article_number: '929' article_processing_charge: Yes article_type: letter_note author: - first_name: Marcin full_name: Zagorski, Marcin last_name: Zagorski - first_name: Nathalie full_name: Brandenberg, Nathalie last_name: Brandenberg - first_name: Matthias full_name: Lutolf, Matthias last_name: Lutolf - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 - first_name: Mark Tobias full_name: Bollenbach, Mark Tobias id: 3E6DB97A-F248-11E8-B48F-1D18A9856A87 last_name: Bollenbach orcid: 0000-0003-4398-476X - first_name: James full_name: Briscoe, James last_name: Briscoe - first_name: Anna full_name: Kicheva, Anna id: 3959A2A0-F248-11E8-B48F-1D18A9856A87 last_name: Kicheva orcid: 0000-0003-4509-4998 citation: ama: Zagorski M, Brandenberg N, Lutolf M, et al. Assessing the precision of morphogen gradients in neural tube development. Nature Communications. 2024;15. doi:10.1038/s41467-024-45148-8 apa: Zagorski, M., Brandenberg, N., Lutolf, M., Tkačik, G., Bollenbach, M. T., Briscoe, J., & Kicheva, A. (2024). Assessing the precision of morphogen gradients in neural tube development. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-024-45148-8 chicago: Zagorski, Marcin, Nathalie Brandenberg, Matthias Lutolf, Gašper Tkačik, Mark Tobias Bollenbach, James Briscoe, and Anna Kicheva. “Assessing the Precision of Morphogen Gradients in Neural Tube Development.” Nature Communications. Springer Nature, 2024. https://doi.org/10.1038/s41467-024-45148-8. ieee: M. Zagorski et al., “Assessing the precision of morphogen gradients in neural tube development,” Nature Communications, vol. 15. Springer Nature, 2024. ista: Zagorski M, Brandenberg N, Lutolf M, Tkačik G, Bollenbach MT, Briscoe J, Kicheva A. 2024. Assessing the precision of morphogen gradients in neural tube development. Nature Communications. 15, 929. mla: Zagorski, Marcin, et al. “Assessing the Precision of Morphogen Gradients in Neural Tube Development.” Nature Communications, vol. 15, 929, Springer Nature, 2024, doi:10.1038/s41467-024-45148-8. short: M. Zagorski, N. Brandenberg, M. Lutolf, G. Tkačik, M.T. Bollenbach, J. Briscoe, A. Kicheva, Nature Communications 15 (2024). corr_author: '1' das_tickbox: '1' dataavailabilitystatement: Source data for Figs. 1, 2 is provided in the Source data file. All published data is available from us to interested researchers. Source data are provided with this paper. date_created: 2025-01-27T13:01:01Z date_published: 2024-02-01T00:00:00Z date_updated: 2026-10-02T10:12:59Z day: '01' ddc: - '570' department: - _id: GaTk - _id: AnKi doi: 10.1038/s41467-024-45148-8 external_id: isi: - '001156218500022' pmid: - '38302459' file: - access_level: open_access checksum: acf75f2b6fa84a64d1f590dd4a53cbf7 content_type: application/pdf creator: dernst date_created: 2025-01-27T13:04:03Z date_updated: 2025-01-27T13:04:03Z file_id: '18903' file_name: 2024_NatureComm_Zagorski.pdf file_size: 4723831 relation: main_file success: 1 file_date_updated: 2025-01-27T13:04:03Z fulldoi: https://doi.org/10.1038/s41467-024-45148-8 has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng month: '02' oa: 1 oa_version: Published Version pmid: 1 project: - _id: bd7e737f-d553-11ed-ba76-d69ffb5ee3aa grant_number: '101044579' name: Mechanisms of tissue size regulation in spinal cord development - _id: 059DF620-7A3F-11EA-A408-12923DDC885E grant_number: F7802 name: Stem Cell Modulation in Neural Development and Regeneration/ P02-Morphogen control of growth and pattern in the spinal cord publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Assessing the precision of morphogen gradients in neural tube development tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15 year: '2024' ... --- DOAJ_listed: '1' _id: '15320' abstract: - lang: eng text: Josephson diodes are superconducting elements that show an asymmetry in the critical current depending on the direction of the current. Here, we theoretically explore how an alternating current bias can tune the response of such a diode. We show that for slow driving there is always a regime where the system can only carry zero-voltage dc current in one direction, thus effectively behaving as an ideal Josephson diode. Under fast driving, the diode efficiency is also tunable, although the ideal regime cannot be reached in this case. We also investigate the residual dissipation due to the time-dependent current bias and show that it remains small. All our conclusions are solely based on the critical current asymmetry of the junction, and are thus compatible with any Josephson diode. acknowledgement: "We acknowledge support from research grants Spanish CM Talento Program (Project No. 2022-T1/IND-24070), Spanish Ministry of Science, innovation, and Universities through Grant No. PID2022-140552NA-I00, Swedish Research Council under Grant Agreement No. 2020-03412, the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 856526, Nanolund, FWF Project with [82],\r\nand Microsoft Corporation. " article_number: L022002 article_processing_charge: Yes article_type: letter_note author: - first_name: Rubén full_name: Seoane Souto, Rubén last_name: Seoane Souto - first_name: Martin full_name: Leijnse, Martin last_name: Leijnse - first_name: Constantin full_name: Schrade, Constantin last_name: Schrade - first_name: Marco full_name: Valentini, Marco id: C0BB2FAC-D767-11E9-B658-BC13E6697425 last_name: Valentini - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X - first_name: Jeroen full_name: Danon, Jeroen last_name: Danon citation: ama: Seoane Souto R, Leijnse M, Schrade C, Valentini M, Katsaros G, Danon J. Tuning the Josephson diode response with an ac current. Physical Review Research. 2024;6(2). doi:10.1103/PhysRevResearch.6.L022002 apa: Seoane Souto, R., Leijnse, M., Schrade, C., Valentini, M., Katsaros, G., & Danon, J. (2024). Tuning the Josephson diode response with an ac current. Physical Review Research. American Physical Society. https://doi.org/10.1103/PhysRevResearch.6.L022002 chicago: Seoane Souto, Rubén, Martin Leijnse, Constantin Schrade, Marco Valentini, Georgios Katsaros, and Jeroen Danon. “Tuning the Josephson Diode Response with an Ac Current.” Physical Review Research. American Physical Society, 2024. https://doi.org/10.1103/PhysRevResearch.6.L022002. ieee: R. Seoane Souto, M. Leijnse, C. Schrade, M. Valentini, G. Katsaros, and J. Danon, “Tuning the Josephson diode response with an ac current,” Physical Review Research, vol. 6, no. 2. American Physical Society, 2024. ista: Seoane Souto R, Leijnse M, Schrade C, Valentini M, Katsaros G, Danon J. 2024. Tuning the Josephson diode response with an ac current. Physical Review Research. 6(2), L022002. mla: Seoane Souto, Rubén, et al. “Tuning the Josephson Diode Response with an Ac Current.” Physical Review Research, vol. 6, no. 2, L022002, American Physical Society, 2024, doi:10.1103/PhysRevResearch.6.L022002. short: R. Seoane Souto, M. Leijnse, C. Schrade, M. Valentini, G. Katsaros, J. Danon, Physical Review Research 6 (2024). das_tickbox: '0' date_created: 2024-04-14T22:01:02Z date_published: 2024-04-01T00:00:00Z date_updated: 2026-10-02T10:26:37Z day: '01' ddc: - '530' department: - _id: GeKa doi: 10.1103/PhysRevResearch.6.L022002 file: - access_level: open_access checksum: 7b9cb3b17d89f392bd582e30d7a72a29 content_type: application/pdf creator: dernst date_created: 2024-04-17T07:14:53Z date_updated: 2024-04-17T07:14:53Z file_id: '15327' file_name: 2024_PhysReviewResearch_Souto.pdf file_size: 1073544 relation: main_file success: 1 file_date_updated: 2024-04-17T07:14:53Z fulldoi: https://doi.org/10.1103/PhysRevResearch.6.L022002 has_accepted_license: '1' intvolume: ' 6' issue: '2' language: - iso: eng month: '04' oa: 1 oa_version: Published Version publication: Physical Review Research publication_identifier: eissn: - 2643-1564 publication_status: published publisher: American Physical Society quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: yes title: Tuning the Josephson diode response with an ac current tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 6 year: '2024' ... --- _id: '17389' abstract: - lang: eng text: The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning. acknowledged_ssus: - _id: NanoFab acknowledgement: We acknowledge Ang Li, Erik P. A. M. Bakkers (University of Eindhoven) for the fabrication of the Ge/Si nanowire. This work was supported by the Royal Society, the EPSRC National Quantum Technology Hub in Networked Quantum Information Technology (EP/M013243/1), Quantum Technology Capital (EP/N014995/1), EPSRC Platform Grant (EP/R029229/1), the European Research Council (Grant agreement 948932), the Swiss Nanoscience Institute, the NCCR SPIN, the EU H2020 European Microkelvin Platform EMP grant No. 824109, the Scientific Service Units of IST Austria through resources provided by the nanofabrication facility, the FWF-I 05060 and the FWF-P 30207 project. article_number: '17281' article_processing_charge: Yes article_type: original arxiv: 1 author: - first_name: B. full_name: Severin, B. last_name: Severin - first_name: D. T. full_name: Lennon, D. T. last_name: Lennon - first_name: L. C. full_name: Camenzind, L. C. last_name: Camenzind - first_name: F. full_name: Vigneau, F. last_name: Vigneau - first_name: F. full_name: Fedele, F. last_name: Fedele - first_name: Daniel full_name: Jirovec, Daniel id: 4C473F58-F248-11E8-B48F-1D18A9856A87 last_name: Jirovec orcid: 0000-0002-7197-4801 - first_name: A. full_name: Ballabio, A. last_name: Ballabio - first_name: D. full_name: Chrastina, D. last_name: Chrastina - first_name: G. full_name: Isella, G. last_name: Isella - first_name: M. full_name: de Kruijf, M. last_name: de Kruijf - first_name: M. J. full_name: Carballido, M. J. last_name: Carballido - first_name: S. full_name: Svab, S. last_name: Svab - first_name: A. V. full_name: Kuhlmann, A. V. last_name: Kuhlmann - first_name: S. full_name: Geyer, S. last_name: Geyer - first_name: F. N. M. full_name: Froning, F. N. M. last_name: Froning - first_name: H. full_name: Moon, H. last_name: Moon - first_name: M. A. full_name: Osborne, M. A. last_name: Osborne - first_name: D. full_name: Sejdinovic, D. last_name: Sejdinovic - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X - first_name: D. M. full_name: Zumbühl, D. M. last_name: Zumbühl - first_name: G. A. D. full_name: Briggs, G. A. D. last_name: Briggs - first_name: N. full_name: Ares, N. last_name: Ares citation: ama: Severin B, Lennon DT, Camenzind LC, et al. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. 2024;14. doi:10.1038/s41598-024-67787-z apa: Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., … Ares, N. (2024). Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-024-67787-z chicago: Severin, B., D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, Daniel Jirovec, A. Ballabio, et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” Scientific Reports. Springer Nature, 2024. https://doi.org/10.1038/s41598-024-67787-z. ieee: B. Severin et al., “Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning,” Scientific Reports, vol. 14. Springer Nature, 2024. ista: Severin B, Lennon DT, Camenzind LC, Vigneau F, Fedele F, Jirovec D, Ballabio A, Chrastina D, Isella G, de Kruijf M, Carballido MJ, Svab S, Kuhlmann AV, Geyer S, Froning FNM, Moon H, Osborne MA, Sejdinovic D, Katsaros G, Zumbühl DM, Briggs GAD, Ares N. 2024. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. 14, 17281. mla: Severin, B., et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” Scientific Reports, vol. 14, 17281, Springer Nature, 2024, doi:10.1038/s41598-024-67787-z. short: B. Severin, D.T. Lennon, L.C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M.J. Carballido, S. Svab, A.V. Kuhlmann, S. Geyer, F.N.M. Froning, H. Moon, M.A. Osborne, D. Sejdinovic, G. Katsaros, D.M. Zumbühl, G.A.D. Briggs, N. Ares, Scientific Reports 14 (2024). das_tickbox: '1' dataavailabilitystatement: The data acquired by the algorithm during experiments is available from the corresponding author upon reasonable request. date_created: 2024-08-05T08:50:51Z date_published: 2024-07-27T00:00:00Z date_updated: 2026-10-02T11:04:33Z day: '27' ddc: - '530' department: - _id: GeKa doi: 10.1038/s41598-024-67787-z external_id: arxiv: - '2107.12975' isi: - '001281273100062' pmid: - '39068242' file: - access_level: open_access checksum: 0b34b89e5f4f3f7b32ffadf104394594 content_type: application/pdf creator: dernst date_created: 2024-08-05T08:52:14Z date_updated: 2024-08-05T08:52:14Z file_id: '17390' file_name: 2024_ScientificReports_Severin.pdf file_size: 2255741 relation: main_file success: 1 file_date_updated: 2024-08-05T08:52:14Z fulldoi: https://doi.org/10.1038/s41598-024-67787-z has_accepted_license: '1' intvolume: ' 14' isi: 1 language: - iso: eng month: '07' oa: 1 oa_version: Published Version pmid: 1 project: - _id: c0977eea-5a5b-11eb-8a69-a862db0cf4d1 grant_number: I05060 name: High impedance circuit quantum electrodynamics with hole spins - _id: 2641CE5E-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P30207 name: Hole spin orbit qubits in Ge quantum wells publication: Scientific Reports publication_identifier: issn: - 2045-2322 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '10066' relation: earlier_version status: public researchdata_availability: upon request scopus_import: '1' status: public supplementarymaterial: yes title: Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 14 year: '2024' ... --- DOAJ_listed: '1' OA_place: publisher OA_type: gold _id: '18602' abstract: - lang: eng text: Semiconductor quantum dots (QDs) in planar germanium (Ge) heterostructures have emerged as front-runners for future hole-based quantum processors. Here, we present strong coupling between a hole charge qubit, defined in a double quantum dot (DQD) in planar Ge, and microwave photons in a high-impedance (Zr = 1.3 kΩ) resonator based on an array of superconducting quantum interference devices (SQUIDs). Our investigation reveals vacuum-Rabi splittings with coupling strengths up to g0/2π = 260 MHz, and a cooperativity of C ~ 100, dependent on DQD tuning. Furthermore, utilizing the frequency tunability of our resonator, we explore the quenched energy splitting associated with strong Coulomb correlation effects in Ge QDs. The observed enhanced coherence of the strongly correlated excited state signals the presence of distinct symmetries within related spin functions, serving as a precursor to the strong coupling between photons and spin-charge hybrid qubits in planar Ge. This work paves the way towards coherent quantum connections between remote hole qubits in planar Ge, required to scale up hole-based quantum processors. acknowledgement: The authors thank Simone Frasca, Vincent Jouanny, Guillaume Beaulieu, Camille Roy, Dominic Dahinden, Davide Lombardo, Daniel Chrastina, and Siddhart Gautam for contributing to some cleanroom fabrication steps, the measurement setup, device simulations, data analysis, and for the useful discussions. P.S. acknowledges support from the Swiss National Science Foundation (SNSF) through the grants Ref. No. 200021 200418 and Ref. No. 206021_205335, and from the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract number 01042765 SEFRI MB22.00081. W.J. acknowledges support from the EPFL QSE Postdoctoral Fellowship Grant. S.B., D.L., and P.S. acknowledge support from the NCCR Spin Qubit in Silicon (NCCR-SPIN) Grant No. 51NF40-180604. M.J., G.K., G.I., and S.C. acknowledge support from the Horizon Europe Project IGNITE ID 101070193. G.K. acknowledges support from the FWF via the P32235 and I05060 projects. article_number: '10177' article_processing_charge: Yes article_type: original author: - first_name: Franco full_name: De Palma, Franco last_name: De Palma - first_name: Fabian full_name: Oppliger, Fabian last_name: Oppliger - first_name: Wonjin full_name: Jang, Wonjin last_name: Jang - first_name: Stefano full_name: Bosco, Stefano last_name: Bosco - first_name: Marian full_name: Janik, Marian id: 396A1950-F248-11E8-B48F-1D18A9856A87 last_name: Janik orcid: 0009-0003-9037-8831 - first_name: Stefano full_name: Calcaterra, Stefano last_name: Calcaterra - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X - first_name: Giovanni full_name: Isella, Giovanni last_name: Isella - first_name: Daniel full_name: Loss, Daniel last_name: Loss - first_name: Pasquale full_name: Scarlino, Pasquale last_name: Scarlino citation: ama: De Palma F, Oppliger F, Jang W, et al. Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states. Nature Communications. 2024;15. doi:10.1038/s41467-024-54520-7 apa: De Palma, F., Oppliger, F., Jang, W., Bosco, S., Janik, M., Calcaterra, S., … Scarlino, P. (2024). Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-024-54520-7 chicago: De Palma, Franco, Fabian Oppliger, Wonjin Jang, Stefano Bosco, Marian Janik, Stefano Calcaterra, Georgios Katsaros, Giovanni Isella, Daniel Loss, and Pasquale Scarlino. “Strong Hole-Photon Coupling in Planar Ge for Probing Charge Degree and Strongly Correlated States.” Nature Communications. Springer Nature, 2024. https://doi.org/10.1038/s41467-024-54520-7. ieee: F. De Palma et al., “Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states,” Nature Communications, vol. 15. Springer Nature, 2024. ista: De Palma F, Oppliger F, Jang W, Bosco S, Janik M, Calcaterra S, Katsaros G, Isella G, Loss D, Scarlino P. 2024. Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states. Nature Communications. 15, 10177. mla: De Palma, Franco, et al. “Strong Hole-Photon Coupling in Planar Ge for Probing Charge Degree and Strongly Correlated States.” Nature Communications, vol. 15, 10177, Springer Nature, 2024, doi:10.1038/s41467-024-54520-7. short: F. De Palma, F. Oppliger, W. Jang, S. Bosco, M. Janik, S. Calcaterra, G. Katsaros, G. Isella, D. Loss, P. Scarlino, Nature Communications 15 (2024). das_tickbox: '1' dataavailabilitystatement: The datasets generated during the current study are available in Zenodo with the identifier https://doi.org/10.5281/zenodo.13935167. date_created: 2024-12-01T23:01:53Z date_published: 2024-12-01T00:00:00Z date_updated: 2026-10-02T11:02:23Z day: '01' ddc: - '530' department: - _id: GeKa doi: 10.1038/s41467-024-54520-7 external_id: isi: - '001362684200001' pmid: - '39580488' file: - access_level: open_access checksum: ef9f99a84089c388904cc8aa8d89c55a content_type: application/pdf creator: dernst date_created: 2024-12-03T11:00:15Z date_updated: 2024-12-03T11:00:15Z file_id: '18611' file_name: 2024_NatureComm_dePalma.pdf file_size: 5288092 relation: main_file success: 1 file_date_updated: 2024-12-03T11:00:15Z fulldoi: https://doi.org/10.1038/s41467-024-54520-7 has_accepted_license: '1' intvolume: ' 15' isi: 1 language: - iso: eng month: '12' oa: 1 oa_version: Published Version pmid: 1 project: - _id: 237B3DA4-32DE-11EA-91FC-C7463DDC885E call_identifier: FWF grant_number: P32235 name: Towards scalable hut wire quantum devices - _id: c0977eea-5a5b-11eb-8a69-a862db0cf4d1 grant_number: I05060 name: High impedance circuit quantum electrodynamics with hole spins publication: Nature Communications publication_identifier: eissn: - 2041-1723 publication_status: published publisher: Springer Nature quality_controlled: '1' related_material: record: - id: '23037' relation: research_data status: public researchdata_availability: yes scopus_import: '1' status: public supplementarymaterial: yes title: Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 15 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '18653' abstract: - lang: eng text: Charge sensing is a sensitive technique for probing quantum devices, of particular importance for spin-qubit readout. To achieve good readout sensitivities, the proximity of the charge sensor to the device to be measured is a necessity. However, this proximity also means that the operation of the device affects, in turn, the sensor tuning and ultimately the readout sensitivity. We present an approach for compensating for this crosstalk effect allowing for the gate voltages of the measured device to be swept in a 1-V × 1-V window while maintaining a sensor configuration chosen by a Bayesian optimizer. Our algorithm will hopefully be a major contribution to the suite of fully automated solutions required for the operation of large quantum device architectures. acknowledged_ssus: - _id: NanoFab acknowledgement: We thank Nicholas Sim for providing help with the experiment and Sebastian Orbell for helpful discussions. This work was supported by the Royal Society, the Engineering and Physical Sciences Research Council (EPSRC) National Quantum Technology Hub in Networked Quantum Information Technology (Grant No. EP/M013243/1), Quantum Technology Capital (Grant No. EP/N014995/1), the EPSRC Platform Grant (Grant No. EP/R029229/1), the European Research Council (Grant Agreement No. 948932), the Scientific Service Units of the Institute of Science and Technology Austria through resources provided by the nanofabrication facility and, the FWF-I 05060 and HORIZON-RIA 101069515 projects. article_number: '064026' article_processing_charge: No article_type: original author: - first_name: Joseph full_name: Hickie, Joseph last_name: Hickie - first_name: Barnaby full_name: Van Straaten, Barnaby last_name: Van Straaten - first_name: Federico full_name: Fedele, Federico last_name: Fedele - first_name: Daniel full_name: Jirovec, Daniel id: 4C473F58-F248-11E8-B48F-1D18A9856A87 last_name: Jirovec orcid: 0000-0002-7197-4801 - first_name: Andrea full_name: Ballabio, Andrea last_name: Ballabio - first_name: Daniel full_name: Chrastina, Daniel last_name: Chrastina - first_name: Giovanni full_name: Isella, Giovanni last_name: Isella - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X - first_name: Natalia full_name: Ares, Natalia last_name: Ares citation: ama: Hickie J, Van Straaten B, Fedele F, et al. Automated long-range compensation of an rf quantum dot sensor. Physical Review Applied. 2024;22(6). doi:10.1103/PhysRevApplied.22.064026 apa: Hickie, J., Van Straaten, B., Fedele, F., Jirovec, D., Ballabio, A., Chrastina, D., … Ares, N. (2024). Automated long-range compensation of an rf quantum dot sensor. Physical Review Applied. American Physical Society. https://doi.org/10.1103/PhysRevApplied.22.064026 chicago: Hickie, Joseph, Barnaby Van Straaten, Federico Fedele, Daniel Jirovec, Andrea Ballabio, Daniel Chrastina, Giovanni Isella, Georgios Katsaros, and Natalia Ares. “Automated Long-Range Compensation of an Rf Quantum Dot Sensor.” Physical Review Applied. American Physical Society, 2024. https://doi.org/10.1103/PhysRevApplied.22.064026. ieee: J. Hickie et al., “Automated long-range compensation of an rf quantum dot sensor,” Physical Review Applied, vol. 22, no. 6. American Physical Society, 2024. ista: Hickie J, Van Straaten B, Fedele F, Jirovec D, Ballabio A, Chrastina D, Isella G, Katsaros G, Ares N. 2024. Automated long-range compensation of an rf quantum dot sensor. Physical Review Applied. 22(6), 064026. mla: Hickie, Joseph, et al. “Automated Long-Range Compensation of an Rf Quantum Dot Sensor.” Physical Review Applied, vol. 22, no. 6, 064026, American Physical Society, 2024, doi:10.1103/PhysRevApplied.22.064026. short: J. Hickie, B. Van Straaten, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, G. Katsaros, N. Ares, Physical Review Applied 22 (2024). das_tickbox: '0' date_created: 2024-12-15T23:01:50Z date_published: 2024-12-01T00:00:00Z date_updated: 2026-10-02T11:04:04Z day: '01' ddc: - '530' department: - _id: GeKa doi: 10.1103/PhysRevApplied.22.064026 external_id: isi: - '001379155900003' file: - access_level: open_access checksum: bc29a40819abc4969867b6cd6563f7ad content_type: application/pdf creator: dernst date_created: 2024-12-16T11:13:48Z date_updated: 2024-12-16T11:13:48Z file_id: '18662' file_name: 2024_PhysicalReviewApplied_Hickie.pdf file_size: 3560132 relation: main_file success: 1 file_date_updated: 2024-12-16T11:13:48Z fulldoi: https://doi.org/10.1103/PhysRevApplied.22.064026 has_accepted_license: '1' intvolume: ' 22' isi: 1 issue: '6' language: - iso: eng month: '12' oa: 1 oa_version: Published Version project: - _id: c0977eea-5a5b-11eb-8a69-a862db0cf4d1 grant_number: I05060 name: High impedance circuit quantum electrodynamics with hole spins - _id: 34c0acea-11ca-11ed-8bc3-8775e10fd452 grant_number: '101069515' name: Integrated Germanium Quantum Technology publication: Physical Review Applied publication_identifier: eissn: - 2331-7019 publication_status: published publisher: American Physical Society quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: yes title: Automated long-range compensation of an rf quantum dot sensor tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 22 year: '2024' ... --- OA_place: repository OA_type: green _id: '23037' abstract: - lang: eng text: The data for all figures in the main text can be found in csv files in ASCII format in the corresponing folders. For Figures 4-6, the panels are numbered from top to bottom. article_processing_charge: No author: - first_name: Franco full_name: De Palma, Franco last_name: De Palma - first_name: Fabian full_name: Oppliger, Fabian last_name: Oppliger - first_name: Wonjin full_name: Jang, Wonjin last_name: Jang - first_name: Stefano full_name: Bosco, Stefano last_name: Bosco - first_name: Marián full_name: Janík, Marián last_name: Janík - first_name: Stefano full_name: Calcaterra, Stefano last_name: Calcaterra - first_name: Georgios full_name: Katsaros, Georgios last_name: Katsaros - first_name: Giovanni full_name: Isella, Giovanni last_name: Isella - first_name: Daniel full_name: Loss, Daniel last_name: Loss - first_name: Pasquale full_name: Scarlino, Pasquale last_name: Scarlino citation: ama: De Palma F, Oppliger F, Jang W, et al. Online data of “Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states.” 2024. doi:10.5281/zenodo.13935166 apa: De Palma, F., Oppliger, F., Jang, W., Bosco, S., Janík, M., Calcaterra, S., … Scarlino, P. (2024). Online data of “Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states.” Zenodo. https://doi.org/10.5281/zenodo.13935166 chicago: De Palma, Franco, Fabian Oppliger, Wonjin Jang, Stefano Bosco, Marián Janík, Stefano Calcaterra, Georgios Katsaros, Giovanni Isella, Daniel Loss, and Pasquale Scarlino. “Online Data of ‘Strong Hole-Photon Coupling in Planar Ge for Probing Charge Degree and Strongly Correlated States.’” Zenodo, 2024. https://doi.org/10.5281/zenodo.13935166. ieee: F. De Palma et al., “Online data of ‘Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states.’” Zenodo, 2024. ista: De Palma F, Oppliger F, Jang W, Bosco S, Janík M, Calcaterra S, Katsaros G, Isella G, Loss D, Scarlino P. 2024. Online data of ‘Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states’, Zenodo, 10.5281/zenodo.13935166. mla: De Palma, Franco, et al. Online Data of “Strong Hole-Photon Coupling in Planar Ge for Probing Charge Degree and Strongly Correlated States.” Zenodo, 2024, doi:10.5281/zenodo.13935166. short: F. De Palma, F. Oppliger, W. Jang, S. Bosco, M. Janík, S. Calcaterra, G. Katsaros, G. Isella, D. Loss, P. Scarlino, (2024). contributor: - contributor_type: project_member first_name: Fabian last_name: Oppliger date_created: 2026-10-02T11:02:18Z date_published: 2024-10-15T00:00:00Z date_updated: 2026-10-02T11:02:23Z day: '15' ddc: - '530' department: - _id: GeKa doi: 10.5281/zenodo.13935166 fulldoi: https://doi.org/10.5281/zenodo.13935166 main_file_link: - open_access: '1' url: https://doi.org/10.5281/zenodo.13935166 month: '10' oa: 1 oa_version: None publisher: Zenodo related_material: record: - id: '18602' relation: used_in_publication status: public status: public title: Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states" type: research_data_reference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '15018' abstract: - lang: eng text: The epitaxial growth of a strained Ge layer, which is a promising candidate for the channel material of a hole spin qubit, has been demonstrated on 300 mm Si wafers using commercially available Si0.3Ge0.7 strain relaxed buffer (SRB) layers. The assessment of the layer and the interface qualities for a buried strained Ge layer embedded in Si0.3Ge0.7 layers is reported. The XRD reciprocal space mapping confirmed that the reduction of the growth temperature enables the 2-dimensional growth of the Ge layer fully strained with respect to the Si0.3Ge0.7. Nevertheless, dislocations at the top and/or bottom interface of the Ge layer were observed by means of electron channeling contrast imaging, suggesting the importance of the careful dislocation assessment. The interface abruptness does not depend on the selection of the precursor gases, but it is strongly influenced by the growth temperature which affects the coverage of the surface H-passivation. The mobility of 2.7 × 105 cm2/Vs is promising, while the low percolation density of 3 × 1010 /cm2 measured with a Hall-bar device at 7 K illustrates the high quality of the heterostructure thanks to the high Si0.3Ge0.7 SRB quality. acknowledgement: The Ge project received funding from the European Union's Horizon Europe programme under the Grant Agreement 101069515 – IGNITE. Siltronic AG is acknowledged for providing the SRB wafers. This work was supported by Imec's Industrial Affiliation Program on Quantum Computing. article_number: '108231' article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Yosuke full_name: Shimura, Yosuke last_name: Shimura - first_name: Clement full_name: Godfrin, Clement last_name: Godfrin - first_name: Andriy full_name: Hikavyy, Andriy last_name: Hikavyy - first_name: Roy full_name: Li, Roy last_name: Li - first_name: Juan L full_name: Aguilera Servin, Juan L id: 2A67C376-F248-11E8-B48F-1D18A9856A87 last_name: Aguilera Servin orcid: 0000-0002-2862-8372 - first_name: Georgios full_name: Katsaros, Georgios id: 38DB5788-F248-11E8-B48F-1D18A9856A87 last_name: Katsaros orcid: 0000-0001-8342-202X - first_name: Paola full_name: Favia, Paola last_name: Favia - first_name: Han full_name: Han, Han last_name: Han - first_name: Danny full_name: Wan, Danny last_name: Wan - first_name: Kristiaan full_name: de Greve, Kristiaan last_name: de Greve - first_name: Roger full_name: Loo, Roger last_name: Loo citation: ama: Shimura Y, Godfrin C, Hikavyy A, et al. Compressively strained epitaxial Ge layers for quantum computing applications. Materials Science in Semiconductor Processing. 2024;174(5). doi:10.1016/j.mssp.2024.108231 apa: Shimura, Y., Godfrin, C., Hikavyy, A., Li, R., Aguilera Servin, J. L., Katsaros, G., … Loo, R. (2024). Compressively strained epitaxial Ge layers for quantum computing applications. Materials Science in Semiconductor Processing. Elsevier. https://doi.org/10.1016/j.mssp.2024.108231 chicago: Shimura, Yosuke, Clement Godfrin, Andriy Hikavyy, Roy Li, Juan L Aguilera Servin, Georgios Katsaros, Paola Favia, et al. “Compressively Strained Epitaxial Ge Layers for Quantum Computing Applications.” Materials Science in Semiconductor Processing. Elsevier, 2024. https://doi.org/10.1016/j.mssp.2024.108231. ieee: Y. Shimura et al., “Compressively strained epitaxial Ge layers for quantum computing applications,” Materials Science in Semiconductor Processing, vol. 174, no. 5. Elsevier, 2024. ista: Shimura Y, Godfrin C, Hikavyy A, Li R, Aguilera Servin JL, Katsaros G, Favia P, Han H, Wan D, de Greve K, Loo R. 2024. Compressively strained epitaxial Ge layers for quantum computing applications. Materials Science in Semiconductor Processing. 174(5), 108231. mla: Shimura, Yosuke, et al. “Compressively Strained Epitaxial Ge Layers for Quantum Computing Applications.” Materials Science in Semiconductor Processing, vol. 174, no. 5, 108231, Elsevier, 2024, doi:10.1016/j.mssp.2024.108231. short: Y. Shimura, C. Godfrin, A. Hikavyy, R. Li, J.L. Aguilera Servin, G. Katsaros, P. Favia, H. Han, D. Wan, K. de Greve, R. Loo, Materials Science in Semiconductor Processing 174 (2024). das_tickbox: '1' dataavailabilitystatement: No data was used for the research described in the article. date_created: 2024-02-22T14:10:40Z date_published: 2024-05-20T00:00:00Z date_updated: 2026-10-02T11:06:21Z day: '20' ddc: - '530' department: - _id: GeKa - _id: NanoFab doi: 10.1016/j.mssp.2024.108231 external_id: isi: - '001188520000001' file: - access_level: open_access checksum: 62e8e9ae960387a3dca32ec7f5e413ab content_type: application/pdf creator: dernst date_created: 2024-07-22T11:56:08Z date_updated: 2024-07-22T11:56:08Z file_id: '17312' file_name: 2024_MaterialsScience_Shimura.pdf file_size: 4220165 relation: main_file success: 1 file_date_updated: 2024-07-22T11:56:08Z fulldoi: https://doi.org/10.1016/j.mssp.2024.108231 has_accepted_license: '1' intvolume: ' 174' isi: 1 issue: '5' keyword: - Mechanical Engineering - Mechanics of Materials - Condensed Matter Physics - General Materials Science language: - iso: eng month: '05' oa: 1 oa_version: Published Version project: - _id: 34c0acea-11ca-11ed-8bc3-8775e10fd452 grant_number: '101069515' name: Integrated Germanium Quantum Technology publication: Materials Science in Semiconductor Processing publication_identifier: issn: - 1369-8001 publication_status: published publisher: Elsevier quality_controlled: '1' researchdata_availability: not applicable scopus_import: '1' status: public supplementarymaterial: no title: Compressively strained epitaxial Ge layers for quantum computing applications tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 174 year: '2024' ... --- _id: '14705' abstract: - lang: eng text: Since the commercialization of brine shrimp (genus Artemia) in the 1950s, this lineage, and in particular the model species Artemia franciscana, has been the subject of extensive research. However, our understanding of the genetic mechanisms underlying various aspects of their reproductive biology, including sex determination, are still lacking. This is partly due to the scarcity of genomic resources for Artemia species and crustaceans in general. Here, we present a chromosome-level genome assembly of Artemia franciscana (Kellogg 1906), from the Great Salt Lake, USA. The genome is 1GB, and the majority of the genome (81%) is scaffolded into 21 linkage groups using a previously published high-density linkage map. We performed coverage and FST analyses using male and female genomic and transcriptomic reads to quantify the extent of differentiation between the Z and W chromosomes. Additionally, we quantified the expression levels in male and female heads and gonads and found further evidence for dosage compensation in this species. article_processing_charge: No author: - first_name: Marwan N full_name: Elkrewi, Marwan N id: 0B46FACA-A8E1-11E9-9BD3-79D1E5697425 last_name: Elkrewi orcid: 0000-0002-5328-7231 citation: ama: Elkrewi MN. Data from “Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation.” 2024. doi:10.15479/AT:ISTA:14705 apa: Elkrewi, M. N. (2024). Data from “Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:14705 chicago: Elkrewi, Marwan N. “Data from ‘Chromosome-Level Assembly of Artemia Franciscana Sheds Light on Sex-Chromosome Differentiation.’” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/AT:ISTA:14705. ieee: M. N. Elkrewi, “Data from ‘Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation.’” Institute of Science and Technology Austria, 2024. ista: Elkrewi MN. 2024. Data from ‘Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation’, Institute of Science and Technology Austria, 10.15479/AT:ISTA:14705. mla: Elkrewi, Marwan N. Data from “Chromosome-Level Assembly of Artemia Franciscana Sheds Light on Sex-Chromosome Differentiation.” Institute of Science and Technology Austria, 2024, doi:10.15479/AT:ISTA:14705. short: M.N. Elkrewi, (2024). contributor: - contributor_type: researcher first_name: Vincent K id: 57854184-AAE0-11E9-8D04-98D6E5697425 last_name: Bett - contributor_type: project_member first_name: Ariana id: 2A0848E2-F248-11E8-B48F-1D18A9856A87 last_name: Macon - contributor_type: supervisor first_name: Beatriz id: 49E1C5C6-F248-11E8-B48F-1D18A9856A87 last_name: Vicoso orcid: 0000-0002-4579-8306 - contributor_type: researcher first_name: Marwan N id: 0B46FACA-A8E1-11E9-9BD3-79D1E5697425 last_name: Elkrewi orcid: 0000-0002-5328-7231 corr_author: '1' date_created: 2023-12-22T13:40:48Z date_published: 2024-01-02T00:00:00Z date_updated: 2026-10-02T11:13:43Z day: '02' ddc: - '576' department: - _id: GradSch - _id: BeVi doi: 10.15479/AT:ISTA:14705 doi_confirm: '1' file: - access_level: open_access checksum: bdaf1392867786634ec5466d528c36ca content_type: text/plain creator: melkrewi date_created: 2023-12-22T13:54:21Z date_updated: 2023-12-22T13:54:21Z file_id: '14707' file_name: readme.txt.txt file_size: 847 relation: main_file success: 1 - access_level: open_access checksum: 973e1cbdab923a71709782177980829f content_type: application/x-zip-compressed creator: melkrewi date_created: 2023-12-22T14:14:06Z date_updated: 2023-12-22T14:14:06Z file_id: '14708' file_name: data_artemia_franciscana_genome.zip file_size: 343632753 relation: main_file success: 1 file_date_updated: 2023-12-22T14:14:06Z fulldoi: https://doi.org/10.15479/AT:ISTA:14705 has_accepted_license: '1' keyword: - sex chromosome evolution - genome assembly - dosage compensation month: '01' oa: 1 oa_version: Published Version project: - _id: 34ae1506-11ca-11ed-8bc3-c14f4c474396 grant_number: F8810 name: The highjacking of meiosis for asexual reproduction publisher: Institute of Science and Technology Austria related_material: record: - id: '15009' relation: used_in_publication status: public status: public title: Data from "Chromosome-level assembly of Artemia franciscana sheds light on sex-chromosome differentiation" 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: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '17214' abstract: - lang: eng text: 'Current numerical algorithms for simulating friction fall in one of two camps: smooth solvers sacrifice the stable treatment of static friction in exchange for fast convergence, and non-smooth solvers accurately compute friction at convergence rates that are often prohibitive for large graphics applications. We introduce a novel bridge between these two ideas that computes static and dynamic friction stably and efficiently. Our key idea is to convert the highly constrained non-smooth problem into an unconstrained smooth problem using logarithmic barriers that converges to the exact solution as accuracy increases. We phrase the problem as an interior point primal-dual problem that can be solved efficiently with Newton iteration. We observe quadratic convergence despite the non-smooth nature of the original problem, and our method is well-suited for large systems of tightly packed objects with many contact points. We demonstrate the efficacy of our method with stable piles of grains and stacks of objects, complex granular flows, and robust interlocking assemblies of rigid bodies.' acknowledgement: We thank Vincent Acary for his help with Siconos, as well as the anonymous reviewers and the members of the Visual Computing Group at ISTA for their helpful comments. This research was funded in part by the European Union (ERC-2021-COG 101045083 CoDiNA). article_processing_charge: Yes (via OA deal) author: - first_name: Yi-Lu full_name: Chen, Yi-Lu id: 0b467602-dbcd-11ea-9d1d-ed480aa46b70 last_name: Chen orcid: 0009-0005-0723-0655 - first_name: Mickaël full_name: Ly, Mickaël id: 6340d7f0-b48d-11eb-b10d-b7487e71d9f1 last_name: Ly - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: 'Chen Y-L, Ly M, Wojtan C. Primal-dual non-smooth friction for rigid body animation. In: Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Association for Computing Machinery; 2024. doi:10.1145/3641519.3657485' apa: 'Chen, Y.-L., Ly, M., & Wojtan, C. (2024). Primal-dual non-smooth friction for rigid body animation. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Denver, United States: Association for Computing Machinery. https://doi.org/10.1145/3641519.3657485' chicago: Chen, Yi-Lu, Mickaël Ly, and Chris Wojtan. “Primal-Dual Non-Smooth Friction for Rigid Body Animation.” In Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. Association for Computing Machinery, 2024. https://doi.org/10.1145/3641519.3657485. ieee: Y.-L. Chen, M. Ly, and C. Wojtan, “Primal-dual non-smooth friction for rigid body animation,” in Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Denver, United States, 2024. ista: 'Chen Y-L, Ly M, Wojtan C. 2024. Primal-dual non-smooth friction for rigid body animation. Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24. SIGGRAPH: Computer Graphics and Interactive Techniques Conference.' mla: Chen, Yi-Lu, et al. “Primal-Dual Non-Smooth Friction for Rigid Body Animation.” Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Association for Computing Machinery, 2024, doi:10.1145/3641519.3657485. short: Y.-L. Chen, M. Ly, C. Wojtan, in:, Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers ’24, Association for Computing Machinery, 2024. conference: end_date: 2024-08-01 location: Denver, United States name: 'SIGGRAPH: Computer Graphics and Interactive Techniques Conference' start_date: 2024-07-28 corr_author: '1' das_tickbox: '0' date_created: 2024-07-10T11:06:20Z date_published: 2024-07-01T00:00:00Z date_updated: 2026-10-02T11:16:26Z day: '01' ddc: - '621' - '531' - '006' department: - _id: GradSch - _id: ChWo doi: 10.1145/3641519.3657485 external_id: isi: - '001282218200091' file: - access_level: open_access checksum: b8b203ed09e3995ba0d7e6a76288663a content_type: application/pdf creator: yichen date_created: 2024-07-10T11:03:14Z date_updated: 2024-07-10T11:03:14Z file_id: '17215' file_name: sig24_friction_authors.pdf file_size: 47309472 relation: main_file success: 1 - access_level: open_access checksum: 89d81b397b4b6469d828808a68b70820 content_type: application/pdf creator: yichen date_created: 2024-07-10T11:03:12Z date_updated: 2024-07-10T11:03:12Z file_id: '17216' file_name: sig24_friction_supplementary.pdf file_size: 10518286 relation: main_file success: 1 - access_level: open_access checksum: 7123deed34a5456810e7b5336a31c657 content_type: video/mp4 creator: yichen date_created: 2024-07-10T11:03:51Z date_updated: 2024-07-10T11:03:51Z file_id: '17217' file_name: friction_paper_extra_video_finished.mp4 file_size: 71789192 relation: main_file success: 1 - access_level: open_access checksum: e606fc1ae8f2610ce3b4421566800b45 content_type: video/mp4 creator: yichen date_created: 2024-07-10T11:03:58Z date_updated: 2024-07-10T11:03:58Z file_id: '17218' file_name: friction_paper_video_finished.mp4 file_size: 280610763 relation: main_file success: 1 file_date_updated: 2024-07-10T11:03:58Z fulldoi: https://doi.org/10.1145/3641519.3657485 has_accepted_license: '1' isi: 1 keyword: - physical simulation - frictional contact - rigid body mechanics - non-smooth dynamics language: - iso: eng month: '07' oa: 1 oa_version: Published Version project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: Special Interest Group on Computer Graphics and Interactive Techniques Conference Conference Papers '24 publication_identifier: isbn: - '9798400705250' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: yes title: Primal-dual non-smooth friction for rigid body animation type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: repository OA_type: green _id: '17426' abstract: - lang: eng text: "The robustness of neural networks against input perturbations with bounded\r\nmagnitude represents a serious concern in the deployment of deep learning\r\nmodels in safety-critical systems. Recently, the scientific community has\r\nfocused on enhancing certifiable robustness guarantees by crafting 1-Lipschitz\r\nneural networks that leverage Lipschitz bounded dense and convolutional layers.\r\nAlthough different methods have been proposed in the literature to achieve this\r\ngoal, understanding the performance of such methods is not straightforward,\r\nsince different metrics can be relevant (e.g., training time, memory usage,\r\naccuracy, certifiable robustness) for different applications. For this reason,\r\nthis work provides a thorough theoretical and empirical comparison between\r\nmethods by evaluating them in terms of memory usage, speed, and certifiable\r\nrobust accuracy. The paper also provides some guidelines and recommendations to\r\nsupport the user in selecting the methods that work best depending on the\r\navailable resources. We provide code at\r\nhttps://github.com/berndprach/1LipschitzLayersCompared." acknowledgement: "This work was partially supported by project SERICS (PE00000014) under the MUR National Recovery and Resilience Plan funded by the European Union - NextGenerationEU.\r\n" article_processing_charge: No arxiv: 1 author: - first_name: Bernd full_name: Prach, Bernd id: 2D561D42-C427-11E9-89B4-9C1AE6697425 last_name: Prach - first_name: Fabio full_name: Brau, Fabio last_name: Brau - first_name: Giorgio full_name: Buttazzo, Giorgio last_name: Buttazzo - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: 'Prach B, Brau F, Buttazzo G, Lampert C. 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Computer Vision Foundation; 2024:24574-24583. doi:10.1109/CVPR52733.2024.02320' apa: 'Prach, B., Brau, F., Buttazzo, G., & Lampert, C. (2024). 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 24574–24583). Seattle, WA, United States: Computer Vision Foundation. https://doi.org/10.1109/CVPR52733.2024.02320' chicago: 'Prach, Bernd, Fabio Brau, Giorgio Buttazzo, and Christoph Lampert. “1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 24574–83. Computer Vision Foundation, 2024. https://doi.org/10.1109/CVPR52733.2024.02320.' ieee: 'B. Prach, F. Brau, G. Buttazzo, and C. Lampert, “1-Lipschitz layers compared: Memory, speed, and certifiable robustness,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, United States, 2024, pp. 24574–24583.' ista: 'Prach B, Brau F, Buttazzo G, Lampert C. 2024. 1-Lipschitz layers compared: Memory, speed, and certifiable robustness. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 24574–24583.' mla: 'Prach, Bernd, et al. “1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–83, doi:10.1109/CVPR52733.2024.02320.' short: B. Prach, F. Brau, G. Buttazzo, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Computer Vision Foundation, 2024, pp. 24574–24583. conference: end_date: 2024-06-22 location: Seattle, WA, United States name: 'CVPR: Conference on Computer Vision and Pattern Recognition' start_date: 2024-06-16 corr_author: '1' das_tickbox: '0' date_created: 2024-08-14T08:42:32Z date_published: 2024-06-01T00:00:00Z date_updated: 2026-10-02T11:16:04Z day: '01' department: - _id: GradSch - _id: ChLa doi: 10.1109/CVPR52733.2024.02320 external_id: arxiv: - '2311.16833' isi: - '001344387500055' fulldoi: https://doi.org/10.1109/CVPR52733.2024.02320 has_accepted_license: '1' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2311.16833 month: '06' oa: 1 oa_version: Preprint page: 24574-24583 publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition publication_status: published publisher: Computer Vision Foundation quality_controlled: '1' related_material: link: - relation: software url: https://github.com/berndprach/1LipschitzLayersCompared record: - id: '19759' relation: dissertation_contains status: public researchdata_availability: no status: public supplementarymaterial: no title: '1-Lipschitz layers compared: Memory, speed, and certifiable robustness' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: repository _id: '18874' abstract: - lang: eng text: "Despite extensive research since the community learned about adversarial\r\nexamples 10 years ago, we still do not know how to train high-accuracy\r\nclassifiers that are guaranteed to be robust to small perturbations of their\r\ninputs. Previous works often argued that this might be because no classifier\r\nexists that is robust and accurate at the same time. However, in computer\r\nvision this assumption does not match reality where humans are usually accurate\r\nand robust on most tasks of interest. We offer an alternative explanation and\r\nshow that in certain settings robust generalization is only possible with\r\nunrealistically large amounts of data. More precisely we find a setting where a\r\nrobust classifier exists, it is easy to learn an accurate classifier, yet it\r\nrequires an exponential amount of data to learn a robust classifier. Based on\r\nthis theoretical result, we explore how well robust classifiers generalize on\r\ndatasets such as CIFAR-10. We come to the conclusion that on this datasets, the\r\nlimitation of current robust models also lies in the generalization, and that\r\nthey require a lot of data to do well on the test set. We also show that the\r\nproblem is not in the expressiveness or generalization capabilities of current\r\narchitectures, and that there are low magnitude features in the data which are\r\nuseful for non-robust generalization but are not available for robust\r\nclassifiers." article_number: '2412.04245' article_processing_charge: No arxiv: 1 author: - first_name: Bernd full_name: Prach, Bernd id: 2D561D42-C427-11E9-89B4-9C1AE6697425 last_name: Prach - first_name: Christoph full_name: Lampert, Christoph id: 40C20FD2-F248-11E8-B48F-1D18A9856A87 last_name: Lampert orcid: 0000-0001-8622-7887 citation: ama: Prach B, Lampert C. Intriguing properties of robust classification. arXiv. doi:10.48550/arXiv.2412.04245 apa: Prach, B., & Lampert, C. (n.d.). Intriguing properties of robust classification. arXiv. https://doi.org/10.48550/arXiv.2412.04245 chicago: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2412.04245. ieee: B. Prach and C. Lampert, “Intriguing properties of robust classification,” arXiv. . ista: Prach B, Lampert C. Intriguing properties of robust classification. arXiv, 2412.04245. mla: Prach, Bernd, and Christoph Lampert. “Intriguing Properties of Robust Classification.” ArXiv, 2412.04245, doi:10.48550/arXiv.2412.04245. short: B. Prach, C. Lampert, ArXiv (n.d.). corr_author: '1' das_tickbox: '0' date_created: 2025-01-24T16:57:29Z date_published: 2024-12-05T00:00:00Z date_updated: 2026-10-02T11:15:43Z day: '05' department: - _id: GradSch - _id: ChLa doi: 10.48550/arXiv.2412.04245 external_id: arxiv: - '2412.04245' fulldoi: https://doi.org/10.48550/arXiv.2412.04245 language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2412.04245 month: '12' oa: 1 oa_version: Preprint publication: arXiv publication_status: draft related_material: record: - id: '20455' relation: later_version status: public - id: '19759' relation: dissertation_contains status: public researchdata_availability: no status: public supplementarymaterial: yes title: Intriguing properties of robust classification type: preprint user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: repository _id: '17361' abstract: - lang: eng text: We present symplectic structures on the shape space of unparameterized space curves that generalize the classical Marsden-Weinstein structure. Our method integrates the Liouville 1-form of the Marsden-Weinstein structure with Riemannian structures that have been introduced in mathematical shape analysis. We also derive Hamiltonian vector fields for several classical Hamiltonian functions with respect to these new symplectic structures. acknowledgement: The authors are grateful to Boris Khesin for valuable comments on the MW symplectic structure and S. Ishida thanks Albert Chern for insightful discussions on space curves and Chris Wojtan for his continuous support. M. Bauer was partially supported by NSF grant DMS-1953244 and by the Binational Science Foundation (BSF). S. Ishida was partially supported by ERC Consolidator Grant 101045083 “CoDiNA” funded by the European Research Council. Some figures were generated by the software Houdini and its education license was provided by SideFX. article_processing_charge: No arxiv: 1 author: - first_name: Martin full_name: Bauer, Martin last_name: Bauer - first_name: Sadashige full_name: Ishida, Sadashige id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425 last_name: Ishida orcid: 0000-0002-3121-3100 - first_name: Peter W. full_name: Michor, Peter W. last_name: Michor citation: ama: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves. arXiv. doi:10.48550/arXiv.2407.19908 apa: Bauer, M., Ishida, S., & Michor, P. W. (n.d.). Symplectic structures on the space of space curves. arXiv. https://doi.org/10.48550/arXiv.2407.19908 chicago: Bauer, Martin, Sadashige Ishida, and Peter W. Michor. “Symplectic Structures on the Space of Space Curves.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2407.19908. ieee: M. Bauer, S. Ishida, and P. W. Michor, “Symplectic structures on the space of space curves,” arXiv. . ista: Bauer M, Ishida S, Michor PW. Symplectic structures on the space of space curves. arXiv, 10.48550/arXiv.2407.19908. mla: Bauer, Martin, et al. “Symplectic Structures on the Space of Space Curves.” ArXiv, doi:10.48550/arXiv.2407.19908. short: M. Bauer, S. Ishida, P.W. Michor, ArXiv (n.d.). das_tickbox: '0' date_created: 2024-08-01T06:34:08Z date_published: 2024-07-29T00:00:00Z date_updated: 2026-10-02T11:16:46Z day: '29' department: - _id: GradSch - _id: ChWo doi: 10.48550/arXiv.2407.19908 external_id: arxiv: - '2407.19908' fulldoi: https://doi.org/10.48550/arXiv.2407.19908 keyword: - space of space curves - symplectic stuctures language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2407.19908 month: '07' oa: 1 oa_version: Preprint project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: arXiv publication_status: draft related_material: record: - id: '20551' relation: dissertation_contains status: public - id: '21743' relation: later_version status: public researchdata_availability: no status: public supplementarymaterial: no title: Symplectic structures on the space of space curves type: preprint user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: publisher OA_type: hybrid _id: '17219' abstract: - lang: eng text: 'We introduce a multi-material non-manifold mesh-based surface tracking algorithm that converts self-intersections into topological changes. Our algorithm generalizes prior work on manifold surface tracking with topological changes: it preserves surface features like mesh-based methods, and it robustly handles topological changes like level set methods. Our method also offers improved efficiency and robustness over the state of the art. We demonstrate the effectiveness of the approach on a range of examples, including complex soap film simulations with thousands of interacting bubbles, and boolean unions of non-manifold meshes consisting of millions of triangles.' acknowledgement: Peter Heiss-Synak helped conceive the project, helped formulate the algorithm structure, contributed ideas and code to Sections 6 & 8, the mesh data structure, algorithm robustness and benchmarks, helped write the paper, and provided supervision and conceptual solutions throughout the project. Aleksei Kalinov contributed ideas and code to Sections 7, 8.5, and 5, the sparse grid data structure, algorithm robustness and benchmarks, optimized the performance, produced all results, most figures, and the supplementary video, helped write the text, and provided conceptual solutions throughout the project. Malina Strugaru helped implement the mesh data structure and designed re-meshing operations for non-manifold triangle meshes. Arian Etemadi developed early prototypes for ideas in Sections 8.1 and 8.3 and helped write the paper. Huidong Yang developed early prototypes for isosurface extraction and visualization. Chris Wojtan helped conceive the project, helped write the paper, and provided supervision, prototype grid data structure code, and conceptual solutions throughout the project. We thank the anonymous reviewers for their helpful comments, the members of the Visual Computing Group at ISTA for their feedback, Christopher Batty for discussions about LosTopos, and SideFX for the Houdini Education software licenses. This research was funded in part by the European Union (ERC-2021-COG 101045083 CoDiNA). article_number: '54' article_processing_charge: Yes (via OA deal) article_type: original author: - first_name: Peter full_name: Synak, Peter id: 331776E2-F248-11E8-B48F-1D18A9856A87 last_name: Synak - first_name: Aleksei full_name: Kalinov, Aleksei id: 44b7120e-eb97-11eb-a6c2-e1557aa81d02 last_name: Kalinov orcid: 0000-0003-2189-3904 - first_name: Irina-Malina full_name: Strugaru, Irina-Malina id: 2afc607f-f128-11eb-9611-8f2a0dfcf074 last_name: Strugaru - first_name: Arian full_name: Etemadihaghighi, Arian id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f last_name: Etemadihaghighi - first_name: Huidong full_name: Yang, Huidong last_name: Yang - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. 2024;43(4). doi:10.1145/3658223 apa: Synak, P., Kalinov, A., Strugaru, I.-M., Etemadi, A., Yang, H., & Wojtan, C. (2024). Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3658223 chicago: Synak, Peter, Aleksei Kalinov, Irina-Malina Strugaru, Arian Etemadi, Huidong Yang, and Chris Wojtan. “Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes.” ACM Transactions on Graphics. Association for Computing Machinery, 2024. https://doi.org/10.1145/3658223. ieee: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, and C. Wojtan, “Multi-material mesh-based surface tracking with implicit topology changes,” ACM Transactions on Graphics, vol. 43, no. 4. Association for Computing Machinery, 2024. ista: Synak P, Kalinov A, Strugaru I-M, Etemadi A, Yang H, Wojtan C. 2024. Multi-material mesh-based surface tracking with implicit topology changes. ACM Transactions on Graphics. 43(4), 54. mla: Synak, Peter, et al. “Multi-Material Mesh-Based Surface Tracking with Implicit Topology Changes.” ACM Transactions on Graphics, vol. 43, no. 4, 54, Association for Computing Machinery, 2024, doi:10.1145/3658223. short: P. Synak, A. Kalinov, I.-M. Strugaru, A. Etemadi, H. Yang, C. Wojtan, ACM Transactions on Graphics 43 (2024). corr_author: '1' das_tickbox: '0' date_created: 2024-07-10T12:24:00Z date_published: 2024-07-01T00:00:00Z date_updated: 2026-10-02T11:19:25Z day: '01' ddc: - '004' department: - _id: GradSch - _id: ChWo doi: 10.1145/3658223 external_id: isi: - '001289270900021' file: - access_level: open_access checksum: 1917067d4b52d7729019b03560004e43 content_type: application/pdf creator: dernst date_created: 2024-07-23T06:35:15Z date_updated: 2024-07-23T06:35:15Z file_id: '17317' file_name: 2024_ACMToG_HeissSynak.pdf file_size: 48763368 relation: main_file success: 1 - access_level: open_access checksum: a4f0e293184bfa034c0c585848806b17 content_type: video/mp4 creator: akalinov date_created: 2024-07-10T12:23:44Z date_updated: 2024-07-10T12:23:44Z file_id: '17221' file_name: sdtopofixer_final.mp4 file_size: 48021463 relation: main_file success: 1 - access_level: open_access checksum: 18fc310a78ec91651148c45a8b89fa44 content_type: application/pdf creator: akalinov date_created: 2025-11-11T09:50:52Z date_updated: 2025-11-11T09:50:52Z file_id: '20633' file_name: SuperDuperTopoFixer.pdf file_size: 48639581 relation: preprint title: Authors' version of the text file_date_updated: 2025-11-11T09:50:52Z fulldoi: https://doi.org/10.1145/3658223 has_accepted_license: '1' intvolume: ' 43' isi: 1 issue: '4' keyword: - surface tracking - topology change - non- manifold meshes - multi-material flows - solid modeling language: - iso: eng license: https://creativecommons.org/licenses/by-nc-sa/4.0/ month: '07' oa: 1 oa_version: Published Version project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: ACM Transactions on Graphics publication_identifier: eissn: - 1557-7368 issn: - 0730-0301 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: record: - id: '19630' relation: dissertation_contains status: public - id: '18301' relation: dissertation_contains status: public researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Multi-material mesh-based surface tracking with implicit topology changes 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: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 43 year: '2024' ... --- OA_place: publisher _id: '18301' abstract: - lang: eng text: Physics simulation in computer graphics can bring triangle meshes into topologically invalid states. The method in this thesis contributed to Heiss-Synak* and Kalinov* et al. [2024] who devised a non-manifold hybrid surface tracker—a surface tracker that repairs explicit non-manifold triangle meshes with the help of the implicit domain. Specifically, this thesis provides an algorithm for filling the holes that are left after removing problematic parts of the mesh. alternative_title: - ISTA Master's Thesis article_processing_charge: No author: - first_name: Arian full_name: Etemadihaghighi, Arian id: 36cea3aa-f38e-11ec-8ae0-c65ae6f6098f last_name: Etemadihaghighi citation: ama: Etemadi A. Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking. 2024. doi:10.15479/at:ista:18301 apa: Etemadi, A. (2024). Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:18301 chicago: Etemadi, Arian. “Filling the Holes of Non-Manifold Self-Intersecting Meshes for Implicit Topology Changes in Surface Tracking.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:18301. ieee: A. Etemadi, “Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking,” Institute of Science and Technology Austria, 2024. ista: Etemadi A. 2024. Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking. Institute of Science and Technology Austria. mla: Etemadi, Arian. Filling the Holes of Non-Manifold Self-Intersecting Meshes for Implicit Topology Changes in Surface Tracking. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:18301. short: A. Etemadi, Filling the Holes of Non-Manifold Self-Intersecting Meshes for Implicit Topology Changes in Surface Tracking, Institute of Science and Technology Austria, 2024. corr_author: '1' das_tickbox: '0' date_created: 2024-10-11T19:52:20Z date_published: 2024-10-15T00:00:00Z date_updated: 2026-10-02T11:19:26Z day: '15' ddc: - '000' degree_awarded: MS department: - _id: GradSch - _id: ChWo doi: 10.15479/at:ista:18301 doi_confirm: '1' file: - access_level: open_access checksum: 80fb7923e229ad9d39253d7c8a8083d0 content_type: application/pdf creator: aetemadi date_created: 2024-10-24T14:34:42Z date_updated: 2024-10-24T14:34:42Z file_id: '18469' file_name: thesis-arian-etemadi.pdf file_size: 8914218 relation: main_file success: 1 - access_level: closed checksum: 1c02586ed7d441d5ec441867650568d1 content_type: application/x-zip-compressed creator: aetemadi date_created: 2024-10-24T14:34:54Z date_updated: 2024-10-24T14:34:54Z file_id: '18470' file_name: thesis-arian-etemadi-latex-source.zip file_size: 9802650 relation: source_file file_date_updated: 2024-10-24T14:34:54Z fulldoi: https://doi.org/10.15479/at:ista:18301 has_accepted_license: '1' keyword: - surface tracking - non-manifold - hole-filling - topology change - multi-material - solid-modeling language: - iso: eng license: https://creativecommons.org/licenses/by-sa/4.0/ month: '10' oa: 1 oa_version: Published Version page: '39' publication_identifier: issn: - 2791-4585 publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '17219' relation: part_of_dissertation status: public researchdata_availability: no status: public supervisor: - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 supplementarymaterial: no title: Filling the holes of non-manifold self-intersecting meshes for implicit topology changes in surface tracking tmp: image: /images/cc_by_sa.png legal_code_url: https://creativecommons.org/licenses/by-sa/4.0/legalcode name: Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) short: CC BY-SA (4.0) type: dissertation user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- OA_place: repository OA_type: green _id: '12846' abstract: - lang: eng text: We present a formula for the signed area of a spherical polygon via prequantization. In contrast to the traditional formula based on the Gauss-Bonnet theorem that requires measuring angles, the new formula mimics Green's theorem and is applicable to a wider range of degenerate spherical curves and polygons. acknowledgement: This work was funded by European Research Council Consolidator grant 101045083 CoDiNA and National Science Foundation CAREER award 2239062. Some figures in the article were generated by the software Houdini and its education license was provided by SideFX. The authors acknowledge anonymous referees for their reviews and insightful suggestions, and Chris Wojtan for his continuous support through discussions. The second author thanks Anna Sisak for a fruitful discussion on prequantum bundles. article_processing_charge: No article_type: original arxiv: 1 author: - first_name: Albert full_name: Chern, Albert last_name: Chern - first_name: Sadashige full_name: Ishida, Sadashige id: 6F7C4B96-A8E9-11E9-A7CA-09ECE5697425 last_name: Ishida orcid: 0000-0002-3121-3100 citation: ama: Chern A, Ishida S. Area formula for spherical polygons via prequantization. SIAM Journal on Applied Algebra and Geometry. 2024;8(3):782-796. doi:10.1137/23M1565255 apa: Chern, A., & Ishida, S. (2024). Area formula for spherical polygons via prequantization. SIAM Journal on Applied Algebra and Geometry. Society for Industrial and Applied Mathematics. https://doi.org/10.1137/23M1565255 chicago: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons via Prequantization.” SIAM Journal on Applied Algebra and Geometry. Society for Industrial and Applied Mathematics, 2024. https://doi.org/10.1137/23M1565255. ieee: A. Chern and S. Ishida, “Area formula for spherical polygons via prequantization,” SIAM Journal on Applied Algebra and Geometry, vol. 8, no. 3. Society for Industrial and Applied Mathematics, pp. 782–796, 2024. ista: Chern A, Ishida S. 2024. Area formula for spherical polygons via prequantization. SIAM Journal on Applied Algebra and Geometry. 8(3), 782–796. mla: Chern, Albert, and Sadashige Ishida. “Area Formula for Spherical Polygons via Prequantization.” SIAM Journal on Applied Algebra and Geometry, vol. 8, no. 3, Society for Industrial and Applied Mathematics, 2024, pp. 782–96, doi:10.1137/23M1565255. short: A. Chern, S. Ishida, SIAM Journal on Applied Algebra and Geometry 8 (2024) 782–796. corr_author: '1' das_tickbox: '0' date_created: 2023-04-18T19:16:06Z date_published: 2024-09-23T00:00:00Z date_updated: 2026-10-02T11:17:04Z day: '23' ddc: - '516' department: - _id: GradSch - _id: ChWo doi: 10.1137/23M1565255 external_id: arxiv: - '2303.14555' isi: - '001342265800009' fulldoi: https://doi.org/10.1137/23M1565255 has_accepted_license: '1' intvolume: ' 8' isi: 1 issue: '3' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.48550/arXiv.2303.14555 month: '09' oa: 1 oa_version: Preprint page: 782-796 project: - _id: 34bc2376-11ca-11ed-8bc3-9a3b3961a088 grant_number: '101045083' name: Computational Discovery of Numerical Algorithms for Animation and Simulation of Natural Phenomena publication: SIAM Journal on Applied Algebra and Geometry publication_identifier: eissn: - 2470-6566 publication_status: published publisher: Society for Industrial and Applied Mathematics quality_controlled: '1' related_material: record: - id: '20551' relation: dissertation_contains status: public researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Area formula for spherical polygons via prequantization tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 8 year: '2024' ... --- OA_place: publisher _id: '17485' abstract: - lang: eng text: "Large language models (LLMs) have made tremendous progress in the past few years, from being able to generate coherent text to matching or surpassing humans in a wide variety of creative, knowledge or reasoning tasks. Much of this can be attributed to massively increased scale, both in the size of the model as well as the amount of training data, from 100s of millions to 100s of billions, or even trillions. This trend is expected to continue, which, although exciting, also raises major practical concerns. Already today's 100+ billion parameter LLMs require top-of-the-line hardware just to run. Hence, it is clear that sustaining these developments will require significant efficiency advances.\r\n\r\nHistorically, one of the most practical ways of improving model efficiency has been compression, especially in the form of sparsity or quantization. While this has been studied extensively in the past, existing accurate methods are all designed for models around 100 million parameters; scaling them up to ones literally 1000x larger is highly challenging. In this thesis, we introduce a new unified sparsification and quantization approach OBC, which through additional algorithmic enhancements leads to GPTQ and SparseGPT, the first techniques fast and accurate enough to compress 100+ billion parameter models to 4- or even 3-bit precision and 50% weight-sparsity, respectively. Additionally, we show how weight-only quantizion does not just bring space savings but also up to 4.5x faster generation speed, via custom GPU kernels.\r\n\r\nIn fact, we show for the first time that it is possible to develop an FP16 times INT4 mixed-precision matrix multiplication kernel, called Marlin, which comes close to simultaneously maximizing both memory and compute utilization, making weight-only quantization highly practical even for multi-user serving. Further, we demonstrate that GPTQ can be scaled to widely overparametrized trillion-parameter models, where extreme sub-1-bit compression rates can be achieved without any inference slow-down, by co-designing a bespoke entropy coding scheme together with an efficient kernel.\r\n\r\nFinally, we also study compression from the perspective of someone with access to massive amounts of compute resources for training large models completely from scratch. Here the key questions evolve around the joint scaling behavior between compression, model size, and amount of training data used. Based on extensive experimental results for both vision and text models, we introduce the first scaling law which accurately captures the relationship between weight-sparsity, number of non-zero weights and data. This further allows us to characterize the optimal sparsity, which we find to increase the longer a fixed cost model is being trained.\r\n\r\nOverall, this thesis presents contributions to three different angles of large model efficiency: affordable but accurate algorithms, highly efficient systems implementations, and fundamental scaling laws for compressed training." acknowledged_ssus: - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar citation: ama: 'Frantar E. Compressing large neural networks: Algorithms, systems and scaling laws. 2024. doi:10.15479/at:ista:17485' apa: 'Frantar, E. (2024). Compressing large neural networks: Algorithms, systems and scaling laws. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17485' chicago: 'Frantar, Elias. “Compressing Large Neural Networks: Algorithms, Systems and Scaling Laws.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17485.' ieee: 'E. Frantar, “Compressing large neural networks: Algorithms, systems and scaling laws,” Institute of Science and Technology Austria, 2024.' ista: 'Frantar E. 2024. Compressing large neural networks: Algorithms, systems and scaling laws. Institute of Science and Technology Austria.' mla: 'Frantar, Elias. Compressing Large Neural Networks: Algorithms, Systems and Scaling Laws. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17485.' short: 'E. Frantar, Compressing Large Neural Networks: Algorithms, Systems and Scaling Laws, Institute of Science and Technology Austria, 2024.' corr_author: '1' das_tickbox: '0' date_created: 2024-09-02T11:01:48Z date_published: 2024-09-05T00:00:00Z date_updated: 2026-10-02T11:19:46Z day: '05' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: DaAl doi: 10.15479/at:ista:17485 doi_confirm: '1' ec_funded: 1 file: - access_level: closed checksum: 5d785645805a78c5b4ce7cc3df557b09 content_type: application/zip creator: efrantar date_created: 2024-09-05T12:04:11Z date_updated: 2024-09-05T12:04:11Z file_id: '17570' file_name: thesis-final.zip file_size: 1615167 relation: source_file - access_level: open_access checksum: a9dd1c2d23734986924eb44ebb55fd8f content_type: application/pdf creator: efrantar date_created: 2024-09-06T16:24:59Z date_updated: 2024-09-06T16:24:59Z file_id: '17880' file_name: frantar_thesis_final.pdf file_size: 2376611 relation: main_file success: 1 file_date_updated: 2024-09-06T16:24:59Z fulldoi: https://doi.org/10.15479/at:ista:17485 has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '129' project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '17378' relation: part_of_dissertation status: public - id: '17087' relation: part_of_dissertation status: public - id: '14458' relation: part_of_dissertation status: public - id: '18061' relation: part_of_dissertation status: public - id: '18062' relation: part_of_dissertation status: public researchdata_availability: no status: public supervisor: - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X supplementarymaterial: no title: 'Compressing large neural networks: Algorithms, systems and scaling laws' type: dissertation user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '18062' abstract: - lang: eng text: 'We explore the impact of parameter sparsity on the scaling behavior of Transformers trained on massive datasets (i.e., "foundation models"), in both vision and language domains. In this setting, we identify the first scaling law describing the relationship between weight sparsity, number of non-zero parameters, and amount of training data, which we validate empirically across model and data scales; on ViT/JFT-4B and T5/C4. These results allow us to characterize the "optimal sparsity", the sparsity level which yields the best performance for a given effective model size and training budget. For a fixed number of non-zero parameters, we identify that the optimal sparsity increases with the amount of data used for training. We also extend our study to different sparsity structures (such as the hardware-friendly n:m pattern) and strategies (such as starting from a pretrained dense model). Our findings shed light on the power and limitations of weight sparsity across various parameter and computational settings, offering both theoretical understanding and practical implications for leveraging sparsity towards computational efficiency improvements. We provide pruning and scaling law fitting code at: github.com/google-research/jaxpruner/tree/main/jaxpruner/projects/bigsparse.' article_processing_charge: No arxiv: 1 author: - first_name: Elias full_name: Frantar, Elias id: 09a8f98d-ec99-11ea-ae11-c063a7b7fe5f last_name: Frantar - first_name: Carlos Riquelme full_name: Ruiz, Carlos Riquelme last_name: Ruiz - first_name: Neil full_name: Houlsby, Neil last_name: Houlsby - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X - first_name: Utku full_name: Evci, Utku last_name: Evci citation: ama: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. Scaling laws for sparsely-connected foundation models. In: The Twelfth International Conference on Learning Representations. ; 2024.' apa: Frantar, E., Ruiz, C. R., Houlsby, N., Alistarh, D.-A., & Evci, U. (2024). Scaling laws for sparsely-connected foundation models. In The Twelfth International Conference on Learning Representations. Vienna, Austria. chicago: Frantar, Elias, Carlos Riquelme Ruiz, Neil Houlsby, Dan-Adrian Alistarh, and Utku Evci. “Scaling Laws for Sparsely-Connected Foundation Models.” In The Twelfth International Conference on Learning Representations, 2024. ieee: E. Frantar, C. R. Ruiz, N. Houlsby, D.-A. Alistarh, and U. Evci, “Scaling laws for sparsely-connected foundation models,” in The Twelfth International Conference on Learning Representations, Vienna, Austria, 2024. ista: 'Frantar E, Ruiz CR, Houlsby N, Alistarh D-A, Evci U. 2024. Scaling laws for sparsely-connected foundation models. The Twelfth International Conference on Learning Representations. ICLR: International Conference on Learning Representations.' mla: Frantar, Elias, et al. “Scaling Laws for Sparsely-Connected Foundation Models.” The Twelfth International Conference on Learning Representations, 2024. short: E. Frantar, C.R. Ruiz, N. Houlsby, D.-A. Alistarh, U. Evci, in:, The Twelfth International Conference on Learning Representations, 2024. conference: end_date: 2024-05-07 location: Vienna, Austria name: 'ICLR: International Conference on Learning Representations' start_date: 2024-05-07 corr_author: '1' das_tickbox: '0' date_created: 2024-09-13T10:31:08Z date_published: 2024-01-16T00:00:00Z date_updated: 2026-10-02T11:19:46Z day: '16' ddc: - '000' department: - _id: DaAl external_id: arxiv: - '2309.08520' language: - iso: eng main_file_link: - open_access: '1' url: https://openreview.net/forum?id=i9K2ZWkYIP month: '01' oa: 1 oa_version: Published Version publication: The Twelfth International Conference on Learning Representations publication_status: published quality_controlled: '1' related_material: record: - id: '17485' relation: dissertation_contains status: public researchdata_availability: no scopus_import: '1' status: public supplementarymaterial: no title: Scaling laws for sparsely-connected foundation models type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ... --- _id: '18061' abstract: - lang: eng text: 'Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently, which makes practical deployment challenging and expensive. In this paper, we present a solution to this memory problem, in form of a new compression and execution framework called QMoE. Specifically, QMoE consists of a scalable algorithm which accurately compresses trillion-parameter MoEs to less than 1 bit per parameter, in a custom format co-designed with bespoke GPU decoding kernels to facilitate efficient end-to-end compressed inference, with minor runtime overheads relative to uncompressed execution. Concretely, QMoE can compress the 1.6 trillion parameter SwitchTransformer-c2048 model to less than 160GB (20x compression, 0.8 bits per parameter) at only minor accuracy loss, in less than a day on a single GPU. This enables, for the first time, the execution of a trillion-parameter model on affordable commodity hardware, like a single server with 4x NVIDIA A6000 or 8x NVIDIA 3090 GPUs, at less than 5% runtime overhead relative to ideal uncompressed inference. The anonymized code is available at: github.com/mlsys24-qmoe/qmoe.' 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. QMoE: Sub-1-bit compression of trillion parameter models. In: Proceedings of Machine Learning and Systems. Vol 6. ; 2024.' apa: 'Frantar, E., & Alistarh, D.-A. (2024). QMoE: Sub-1-bit compression of trillion parameter models. In Proceedings of Machine Learning and Systems (Vol. 6). Santa Clara, CA, United States.' chicago: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” In Proceedings of Machine Learning and Systems, Vol. 6, 2024.' ieee: 'E. Frantar and D.-A. Alistarh, “QMoE: Sub-1-bit compression of trillion parameter models,” in Proceedings of Machine Learning and Systems, Santa Clara, CA, United States, 2024, vol. 6.' ista: 'Frantar E, Alistarh D-A. 2024. QMoE: Sub-1-bit compression of trillion parameter models. Proceedings of Machine Learning and Systems. MLSys: Machine Learning and Systems vol. 6.' mla: 'Frantar, Elias, and Dan-Adrian Alistarh. “QMoE: Sub-1-Bit Compression of Trillion Parameter Models.” Proceedings of Machine Learning and Systems, vol. 6, 2024.' short: E. Frantar, D.-A. Alistarh, in:, Proceedings of Machine Learning and Systems, 2024. conference: end_date: 2024-05-16 location: Santa Clara, CA, United States name: 'MLSys: Machine Learning and Systems' start_date: 2024-05-13 corr_author: '1' das_tickbox: '0' date_created: 2024-09-13T10:01:38Z date_published: 2024-05-01T00:00:00Z date_updated: 2026-10-02T11:19:46Z day: '01' ddc: - '000' department: - _id: DaAl intvolume: ' 6' language: - iso: eng main_file_link: - open_access: '1' url: https://proceedings.mlsys.org/paper_files/paper/2024/hash/c74b624843218d9b6713fcf299d6d5e4-Abstract-Conference.html month: '05' oa: 1 oa_version: Published Version publication: Proceedings of Machine Learning and Systems publication_status: published quality_controlled: '1' related_material: record: - id: '17485' relation: dissertation_contains status: public researchdata_availability: yes status: public supplementarymaterial: no title: 'QMoE: Sub-1-bit compression of trillion parameter models' type: conference user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345 volume: 6 year: '2024' ... --- OA_place: publisher _id: '17490' abstract: - lang: eng text: "Deep learning is essential in numerous applications nowadays, with many recent advancements made possible by training very large models. Despite their broad applicability, training neural networks is often time-intensive, and it is usually impractical to manage large models and datasets on a single machine. To address these issues, distributed deep learning training has become increasingly important. However, distributed training requires synchronization among nodes, and the mini-batch stochastic gradient descent algorithm places a significant load on network connections. A possible solution to tackle the synchronization bottleneck is to reduce a message size by lossy compression.\r\n\r\nIn this thesis, we investigate systems and algorithmic approaches to communication compression during training. From the systems perspective, we demonstrate that a common approach of expensive hardware overprovisioning can be replaced through a thorough system design. We introduce a framework that introduces efficient software support for compressed communication in machine learning applications, applicable to both multi-GPU single-node training and larger-scale multi-node training. Our framework integrates with popular ML frameworks, providing up to 3x speedups for multi-GPU nodes based on commodity hardware and order-of-magnitude improvements in the multi-node setting, with negligible impact on accuracy.\r\n\r\nAlso, we consider an application of our framework to different communication schemes, such as Fully Sharded Data Parallel. We provide strong convergence guarantees for the compression in such a setup. Empirical validation shows that our method preserves model accuracy for GPT-family models with up to 1.3 billion parameters, while completely removing the communication bottlenecks of non-compressed alternatives, providing up to 2.2x speedups end-to-end.\r\n\r\nFrom the algorithmic side, we propose a general framework that dynamically adjusts the degree of compression across a model's layers during training. This approach enhances overall compression and results in significant speedups without compromising accuracy. Our algorithm utilizes an adaptive algorithm that automatically selects the optimal compression parameters for model layers, ensuring the best compression ratio while adhering to an error constraint. Our method is effective across all existing families of compression methods. It achieves up to 2.5x faster training and up to a 5x improvement in compression compared to efficient implementations of current approaches. Additionally, LGreCo can complement existing adaptive algorithms.\r\n" acknowledged_ssus: - _id: ScienComp alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Ilia full_name: Markov, Ilia id: D0CF4148-C985-11E9-8066-0BDEE5697425 last_name: Markov citation: ama: 'Markov I. Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective. 2024. doi:10.15479/at:ista:17490' apa: 'Markov, I. (2024). Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:17490' chicago: 'Markov, Ilia. “Communication-Efficient Distributed Training of Deep Neural Networks : An Algorithms and Systems Perspective.” Institute of Science and Technology Austria, 2024. https://doi.org/10.15479/at:ista:17490.' ieee: 'I. Markov, “Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective,” Institute of Science and Technology Austria, 2024.' ista: 'Markov I. 2024. Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective. Institute of Science and Technology Austria.' mla: 'Markov, Ilia. Communication-Efficient Distributed Training of Deep Neural Networks : An Algorithms and Systems Perspective. Institute of Science and Technology Austria, 2024, doi:10.15479/at:ista:17490.' short: 'I. Markov, Communication-Efficient Distributed Training of Deep Neural Networks : An Algorithms and Systems Perspective, Institute of Science and Technology Austria, 2024.' corr_author: '1' das_tickbox: '0' date_created: 2024-09-04T08:51:11Z date_published: 2024-09-04T00:00:00Z date_updated: 2026-10-02T11:20:06Z day: '04' ddc: - '000' degree_awarded: PhD department: - _id: GradSch - _id: DaAl doi: 10.15479/at:ista:17490 doi_confirm: '1' ec_funded: 1 file: - access_level: closed checksum: 77609f4835d2730e46fa0d42d9134ed9 content_type: application/x-zip-compressed creator: imarkov date_created: 2024-09-04T08:35:35Z date_updated: 2024-09-04T08:35:35Z file_id: '17491' file_name: Thesis.zip file_size: 43327753 relation: source_file - access_level: open_access checksum: 9e68f7217570f756ceb8f70b980938cd content_type: application/pdf creator: imarkov date_created: 2024-09-04T08:36:06Z date_updated: 2024-09-04T08:36:06Z file_id: '17492' file_name: Thesis_final_version_pdfa2.pdf file_size: 2756082 relation: main_file success: 1 file_date_updated: 2024-09-04T08:36:06Z fulldoi: https://doi.org/10.15479/at:ista:17490 has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '102' project: - _id: 268A44D6-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '805223' name: Elastic Coordination for Scalable Machine Learning publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '14461' relation: part_of_dissertation status: public - id: '12780' relation: part_of_dissertation status: public - id: '17456' relation: part_of_dissertation status: public researchdata_availability: no status: public supervisor: - first_name: Dan-Adrian full_name: Alistarh, Dan-Adrian id: 4A899BFC-F248-11E8-B48F-1D18A9856A87 last_name: Alistarh orcid: 0000-0003-3650-940X supplementarymaterial: no title: 'Communication-efficient distributed training of deep neural networks : An algorithms and systems perspective' 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: 317138e5-6ab7-11ef-aa6d-ffef3953e345 year: '2024' ...