--- _id: '6354' abstract: - lang: eng text: Blood platelets are critical for hemostasis and thrombosis, but also play diverse roles during immune responses. We have recently reported that platelets migrate at sites of infection in vitro and in vivo. Importantly, platelets use their ability to migrate to collect and bundle fibrin (ogen)-bound bacteria accomplishing efficient intravascular bacterial trapping. Here, we describe a method that allows analyzing platelet migration in vitro, focusing on their ability to collect bacteria and trap bacteria under flow. acknowledgement: ' FöFoLe project 947 (F.G.), the Friedrich-Baur-Stiftung project 41/16 (F.G.)' article_number: e3018 author: - first_name: Shuxia full_name: Fan, Shuxia last_name: Fan - first_name: Michael full_name: Lorenz, Michael last_name: Lorenz - first_name: Steffen full_name: Massberg, Steffen last_name: Massberg - first_name: Florian R full_name: Gärtner, Florian R id: 397A88EE-F248-11E8-B48F-1D18A9856A87 last_name: Gärtner orcid: 0000-0001-6120-3723 citation: ama: Fan S, Lorenz M, Massberg S, Gärtner FR. Platelet migration and bacterial trapping assay under flow. Bio-Protocol. 2018;8(18). doi:10.21769/bioprotoc.3018 apa: Fan, S., Lorenz, M., Massberg, S., & Gärtner, F. R. (2018). Platelet migration and bacterial trapping assay under flow. Bio-Protocol. Bio-Protocol. https://doi.org/10.21769/bioprotoc.3018 chicago: Fan, Shuxia, Michael Lorenz, Steffen Massberg, and Florian R Gärtner. “Platelet Migration and Bacterial Trapping Assay under Flow.” Bio-Protocol. Bio-Protocol, 2018. https://doi.org/10.21769/bioprotoc.3018. ieee: S. Fan, M. Lorenz, S. Massberg, and F. R. Gärtner, “Platelet migration and bacterial trapping assay under flow,” Bio-Protocol, vol. 8, no. 18. Bio-Protocol, 2018. ista: Fan S, Lorenz M, Massberg S, Gärtner FR. 2018. Platelet migration and bacterial trapping assay under flow. Bio-Protocol. 8(18), e3018. mla: Fan, Shuxia, et al. “Platelet Migration and Bacterial Trapping Assay under Flow.” Bio-Protocol, vol. 8, no. 18, e3018, Bio-Protocol, 2018, doi:10.21769/bioprotoc.3018. short: S. Fan, M. Lorenz, S. Massberg, F.R. Gärtner, Bio-Protocol 8 (2018). date_created: 2019-04-29T09:40:33Z date_published: 2018-09-20T00:00:00Z date_updated: 2021-01-12T08:07:12Z day: '20' ddc: - '570' department: - _id: MiSi doi: 10.21769/bioprotoc.3018 ec_funded: 1 file: - access_level: open_access checksum: d4588377e789da7f360b553ae02c5119 content_type: application/pdf creator: dernst date_created: 2019-04-30T08:04:33Z date_updated: 2020-07-14T12:47:28Z file_id: '6360' file_name: 2018_BioProtocol_Fan.pdf file_size: 2928337 relation: main_file file_date_updated: 2020-07-14T12:47:28Z has_accepted_license: '1' intvolume: ' 8' issue: '18' keyword: - Platelets - Cell migration - Bacteria - Shear flow - Fibrinogen - E. coli language: - iso: eng month: '09' oa: 1 oa_version: Published Version project: - _id: 260AA4E2-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '747687' name: Mechanical Adaptation of Lamellipodial Actin Networks in Migrating Cells publication: Bio-Protocol publication_identifier: issn: - 2331-8325 publication_status: published publisher: Bio-Protocol quality_controlled: '1' status: public title: Platelet migration and bacterial trapping assay under flow 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: 3E5EF7F0-F248-11E8-B48F-1D18A9856A87 volume: 8 year: '2018' ... --- _id: '6459' author: - first_name: Barbara full_name: Petritsch, Barbara id: 406048EC-F248-11E8-B48F-1D18A9856A87 last_name: Petritsch orcid: 0000-0003-2724-4614 citation: ama: Petritsch B. Open Access at IST Austria 2009-2017. IST Austria; 2018. doi:10.5281/zenodo.1410279 apa: 'Petritsch, B. (2018). Open Access at IST Austria 2009-2017. Presented at the Open-Access-Tage, Graz, Austria: IST Austria. https://doi.org/10.5281/zenodo.1410279' chicago: Petritsch, Barbara. Open Access at IST Austria 2009-2017. IST Austria, 2018. https://doi.org/10.5281/zenodo.1410279. ieee: B. Petritsch, Open Access at IST Austria 2009-2017. IST Austria, 2018. ista: Petritsch B. 2018. Open Access at IST Austria 2009-2017, IST Austria,p. mla: Petritsch, Barbara. Open Access at IST Austria 2009-2017. IST Austria, 2018, doi:10.5281/zenodo.1410279. short: B. Petritsch, Open Access at IST Austria 2009-2017, IST Austria, 2018. conference: end_date: 2018-09-26 location: Graz, Austria name: Open-Access-Tage start_date: 2018-09-24 date_created: 2019-05-16T07:27:14Z date_published: 2018-09-24T00:00:00Z date_updated: 2020-07-14T23:06:21Z day: '24' ddc: - '020' department: - _id: E-Lib doi: 10.5281/zenodo.1410279 file: - access_level: open_access checksum: 9063ab4d10ea93353c3a03bbf53fbcf1 content_type: application/pdf creator: dernst date_created: 2019-05-16T07:26:25Z date_updated: 2020-07-14T12:47:30Z file_id: '6460' file_name: Poster_Beitrag_125_Petritsch.pdf file_size: 1967778 relation: main_file file_date_updated: 2020-07-14T12:47:30Z has_accepted_license: '1' keyword: - Open Access - Publication Analysis language: - iso: eng month: '09' oa: 1 oa_version: Published Version publication_status: published publisher: IST Austria status: public title: Open Access at IST Austria 2009-2017 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: conference_poster user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ... --- _id: '690' abstract: - lang: eng text: We consider spectral properties and the edge universality of sparse random matrices, the class of random matrices that includes the adjacency matrices of the Erdős–Rényi graph model G(N, p). We prove a local law for the eigenvalue density up to the spectral edges. Under a suitable condition on the sparsity, we also prove that the rescaled extremal eigenvalues exhibit GOE Tracy–Widom fluctuations if a deterministic shift of the spectral edge due to the sparsity is included. For the adjacency matrix of the Erdős–Rényi graph this establishes the Tracy–Widom fluctuations of the second largest eigenvalue when p is much larger than N−2/3 with a deterministic shift of order (Np)−1. article_number: 543-616 author: - first_name: Jii full_name: Lee, Jii last_name: Lee - first_name: Kevin full_name: Schnelli, Kevin id: 434AD0AE-F248-11E8-B48F-1D18A9856A87 last_name: Schnelli orcid: 0000-0003-0954-3231 citation: ama: Lee J, Schnelli K. Local law and Tracy–Widom limit for sparse random matrices. Probability Theory and Related Fields. 2018;171(1-2). doi:10.1007/s00440-017-0787-8 apa: Lee, J., & Schnelli, K. (2018). Local law and Tracy–Widom limit for sparse random matrices. Probability Theory and Related Fields. Springer. https://doi.org/10.1007/s00440-017-0787-8 chicago: Lee, Jii, and Kevin Schnelli. “Local Law and Tracy–Widom Limit for Sparse Random Matrices.” Probability Theory and Related Fields. Springer, 2018. https://doi.org/10.1007/s00440-017-0787-8. ieee: J. Lee and K. Schnelli, “Local law and Tracy–Widom limit for sparse random matrices,” Probability Theory and Related Fields, vol. 171, no. 1–2. Springer, 2018. ista: Lee J, Schnelli K. 2018. Local law and Tracy–Widom limit for sparse random matrices. Probability Theory and Related Fields. 171(1–2), 543–616. mla: Lee, Jii, and Kevin Schnelli. “Local Law and Tracy–Widom Limit for Sparse Random Matrices.” Probability Theory and Related Fields, vol. 171, no. 1–2, 543–616, Springer, 2018, doi:10.1007/s00440-017-0787-8. short: J. Lee, K. Schnelli, Probability Theory and Related Fields 171 (2018). date_created: 2018-12-11T11:47:56Z date_published: 2018-06-14T00:00:00Z date_updated: 2021-01-12T08:09:33Z day: '14' department: - _id: LaEr doi: 10.1007/s00440-017-0787-8 ec_funded: 1 external_id: arxiv: - '1605.08767' intvolume: ' 171' issue: 1-2 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1605.08767 month: '06' oa: 1 oa_version: Preprint project: - _id: 258DCDE6-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '338804' name: Random matrices, universality and disordered quantum systems publication: Probability Theory and Related Fields publication_status: published publisher: Springer publist_id: '7017' quality_controlled: '1' scopus_import: 1 status: public title: Local law and Tracy–Widom limit for sparse random matrices type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 171 year: '2018' ... --- _id: '703' abstract: - lang: eng text: We consider the NP-hard problem of MAP-inference for undirected discrete graphical models. We propose a polynomial time and practically efficient algorithm for finding a part of its optimal solution. Specifically, our algorithm marks some labels of the considered graphical model either as (i) optimal, meaning that they belong to all optimal solutions of the inference problem; (ii) non-optimal if they provably do not belong to any solution. With access to an exact solver of a linear programming relaxation to the MAP-inference problem, our algorithm marks the maximal possible (in a specified sense) number of labels. We also present a version of the algorithm, which has access to a suboptimal dual solver only and still can ensure the (non-)optimality for the marked labels, although the overall number of the marked labels may decrease. We propose an efficient implementation, which runs in time comparable to a single run of a suboptimal dual solver. Our method is well-scalable and shows state-of-the-art results on computational benchmarks from machine learning and computer vision. author: - first_name: Alexander full_name: Shekhovtsov, Alexander last_name: Shekhovtsov - first_name: Paul full_name: Swoboda, Paul id: 446560C6-F248-11E8-B48F-1D18A9856A87 last_name: Swoboda - first_name: Bogdan full_name: Savchynskyy, Bogdan last_name: Savchynskyy citation: ama: Shekhovtsov A, Swoboda P, Savchynskyy B. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(7):1668-1682. doi:10.1109/TPAMI.2017.2730884 apa: Shekhovtsov, A., Swoboda, P., & Savchynskyy, B. (2018). Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2017.2730884 chicago: Shekhovtsov, Alexander, Paul Swoboda, and Bogdan Savchynskyy. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2017.2730884. ieee: A. Shekhovtsov, P. Swoboda, and B. Savchynskyy, “Maximum persistency via iterative relaxed inference with graphical models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7. IEEE, pp. 1668–1682, 2018. ista: Shekhovtsov A, Swoboda P, Savchynskyy B. 2018. Maximum persistency via iterative relaxed inference with graphical models. IEEE Transactions on Pattern Analysis and Machine Intelligence. 40(7), 1668–1682. mla: Shekhovtsov, Alexander, et al. “Maximum Persistency via Iterative Relaxed Inference with Graphical Models.” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 7, IEEE, 2018, pp. 1668–82, doi:10.1109/TPAMI.2017.2730884. short: A. Shekhovtsov, P. Swoboda, B. Savchynskyy, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1668–1682. date_created: 2018-12-11T11:48:01Z date_published: 2018-07-01T00:00:00Z date_updated: 2021-01-12T08:11:32Z day: '01' department: - _id: VlKo doi: 10.1109/TPAMI.2017.2730884 external_id: arxiv: - '1508.07902' intvolume: ' 40' issue: '7' language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1508.07902 month: '07' oa: 1 oa_version: Preprint page: 1668-1682 publication: IEEE Transactions on Pattern Analysis and Machine Intelligence publication_identifier: issn: - '01628828' publication_status: published publisher: IEEE publist_id: '6992' quality_controlled: '1' scopus_import: 1 status: public title: Maximum persistency via iterative relaxed inference with graphical models type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 40 year: '2018' ... --- _id: '7116' abstract: - lang: eng text: 'Training deep learning models has received tremendous research interest recently. In particular, there has been intensive research on reducing the communication cost of training when using multiple computational devices, through reducing the precision of the underlying data representation. Naturally, such methods induce system trade-offs—lowering communication precision could de-crease communication overheads and improve scalability; but, on the other hand, it can also reduce the accuracy of training. In this paper, we study this trade-off space, and ask:Can low-precision communication consistently improve the end-to-end performance of training modern neural networks, with no accuracy loss?From the performance point of view, the answer to this question may appear deceptively easy: compressing communication through low precision should help when the ratio between communication and computation is high. However, this answer is less straightforward when we try to generalize this principle across various neural network architectures (e.g., AlexNet vs. ResNet),number of GPUs (e.g., 2 vs. 8 GPUs), machine configurations(e.g., EC2 instances vs. NVIDIA DGX-1), communication primitives (e.g., MPI vs. NCCL), and even different GPU architectures(e.g., Kepler vs. Pascal). Currently, it is not clear how a realistic realization of all these factors maps to the speed up provided by low-precision communication. In this paper, we conduct an empirical study to answer this question and report the insights.' article_processing_charge: No author: - first_name: Demjan full_name: Grubic, Demjan last_name: Grubic - first_name: Leo full_name: Tam, Leo last_name: Tam - 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: Ce full_name: Zhang, Ce last_name: Zhang citation: ama: 'Grubic D, Tam L, Alistarh D-A, Zhang C. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In: Proceedings of the 21st International Conference on Extending Database Technology. OpenProceedings; 2018:145-156. doi:10.5441/002/EDBT.2018.14' apa: 'Grubic, D., Tam, L., Alistarh, D.-A., & Zhang, C. (2018). Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. In Proceedings of the 21st International Conference on Extending Database Technology (pp. 145–156). Vienna, Austria: OpenProceedings. https://doi.org/10.5441/002/EDBT.2018.14' chicago: 'Grubic, Demjan, Leo Tam, Dan-Adrian Alistarh, and Ce Zhang. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.” In Proceedings of the 21st International Conference on Extending Database Technology, 145–56. OpenProceedings, 2018. https://doi.org/10.5441/002/EDBT.2018.14.' ieee: 'D. Grubic, L. Tam, D.-A. Alistarh, and C. Zhang, “Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study,” in Proceedings of the 21st International Conference on Extending Database Technology, Vienna, Austria, 2018, pp. 145–156.' ista: 'Grubic D, Tam L, Alistarh D-A, Zhang C. 2018. Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study. Proceedings of the 21st International Conference on Extending Database Technology. EDBT: Conference on Extending Database Technology, 145–156.' mla: 'Grubic, Demjan, et al. “Synchronous Multi-GPU Training for Deep Learning with Low-Precision Communications: An Empirical Study.” Proceedings of the 21st International Conference on Extending Database Technology, OpenProceedings, 2018, pp. 145–56, doi:10.5441/002/EDBT.2018.14.' short: D. Grubic, L. Tam, D.-A. Alistarh, C. Zhang, in:, Proceedings of the 21st International Conference on Extending Database Technology, OpenProceedings, 2018, pp. 145–156. conference: end_date: 2018-03-29 location: Vienna, Austria name: 'EDBT: Conference on Extending Database Technology' start_date: 2018-03-26 date_created: 2019-11-26T14:19:11Z date_published: 2018-03-26T00:00:00Z date_updated: 2023-02-23T12:59:17Z day: '26' ddc: - '000' department: - _id: DaAl doi: 10.5441/002/EDBT.2018.14 file: - access_level: open_access checksum: ec979b56abc71016d6e6adfdadbb4afe content_type: application/pdf creator: dernst date_created: 2019-11-26T14:23:04Z date_updated: 2020-07-14T12:47:49Z file_id: '7118' file_name: 2018_OpenProceedings_Grubic.pdf file_size: 1603204 relation: main_file file_date_updated: 2020-07-14T12:47:49Z has_accepted_license: '1' language: - iso: eng month: '03' oa: 1 oa_version: Published Version page: 145-156 publication: Proceedings of the 21st International Conference on Extending Database Technology publication_identifier: isbn: - '9783893180783' issn: - 2367-2005 publication_status: published publisher: OpenProceedings quality_controlled: '1' scopus_import: 1 status: public title: 'Synchronous multi-GPU training for deep learning with low-precision communications: An empirical study' tmp: image: /images/cc_by_nc_nd.png legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) short: CC BY-NC-ND (4.0) type: conference user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ...