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Becker, L. M., Berbon, M., Vallet, A., Grelard, A., Morvan, E., Bardiaux, B.,https://doi.org/10.1002/anie.202219314__Schanda, P. (2023). The rigid core and flexible surface of amyloid fibrils probed by MagicAngle Spinning NMR of aromatic residues. Angewandte Chemie International Edition. Wiley.https://doi.org/10.1002/anie.202219314 
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Becker, L. M., &; Schanda, P. (2023). Research data to: The rigid core and flexible surface of amyloid fibrils probed by magic-angle-spinning NMR spectroscopy of aromatic residues. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:12497 
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Danzl, J. G. (2023). Research data for the publication “Imaging brain tissue architecture across millimeter to nanometer scales.”https://doi.org/10.15479/AT:ISTA:13126__Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:13126 
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Dunajova, Z., Prats Mateu, B., Radler, P., Lim, K., Brandis, D., Velicky, P.,https://doi.org/10.15479/AT:ISTA:13116__Loose, M. (2023). Chiral and nematic phases of flexible active filaments. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:13116 
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Dunajova, Z., Prats Mateu, B., Radler, P., Lim, K., Brandis, D., Velicky, P.,https://doi.org/10.1038/s41567-023-02218-w__Loose, M. (2023). Chiral and nematic phases of flexible active filaments. Nature Physics. Springer Nature.https://doi.org/10.1038/s41567-023-02218-w 
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Michalska, J. M., Lyudchik, J., Velicky, P., Korinkova, H., Watson, J., Cenameri, A.,https://doi.org/10.1038/s41587-023-01911-8__Danzl, J. G. (2023). Imaging brain tissue architecture across millimeter to nanometer scales. Nature Biotechnology. Springer Nature.https://doi.org/10.1038/s41587-023-01911-8 
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Valentini, M. (2023). Mesoscopic phenomena in hybrid semiconductor-superconductor nanodevicesf: From full-shell nanowires to two-dimensional hole gas in germanium. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:13286 
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Curk, S. (2023). aggregation_data. Figshare.
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Majumdar, R., Mallik, K., Rychlicki, M., Schmuck, A.-K., &; Soudjani, S. (2023). A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties. In 35th International Conference on Computer Aided Verification (Vol. 13966, pp. 3–15). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37709-9_1 
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Hwong, Y.-L., Colin, M., Aglas, P., Muller, C. J., &; Sherwood, S. C. (2023). Assessing memory in convection schemes using idealized tests. Journal of Advances in Modeling Earth Systems. Wiley.https://doi.org/10.1029/2023MS003726 
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Meggendorfer, T. (2023). Correct approximation of stationary distributions. In TACAS 2023: Tools and Algorithms for the Construction and Analysis of Systems (Vol. 13993, pp. 489–507). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-30823-9_25 
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Hwong, Y.-L., Colin, M., Aglas, P., Muller, C. J., &; Sherwood, S. C. (2023). Data-assessing memory in convection schemes using idealized tests. Zenodo.https://doi.org/10.5281/ZENODO.7757041 
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Meggendorfer, T. (2023). Artefact for: Correct Approximation of Stationary Distributions. Zenodo.https://doi.org/10.5281/ZENODO.7548214 
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Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. In 35th International Conference on Computer Aided Verification  (Vol. 13964, pp. 156–169). Paris, France: Springer Nature.https://doi.org/10.1007/978-3-031-37706-8_8 
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Koval, N., Fedorov, A., Sokolova, M., Tsitelov, D., &; Alistarh, D.-A. (2023). Lincheck: A practical framework for testing concurrent data structures on JVM. Zenodo.https://doi.org/10.5281/ZENODO.7877757 
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Majumdar, R., Mallik, K., Rychlicki, M., Schmuck, A.-K., &; Soudjani, S. (2023). A flexible toolchain for symbolic rabin games under fair and stochastic uncertainties. Zenodo.https://doi.org/10.5281/ZENODO.7877790 
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Zikelic, D., Lechner, M., Verma, A., Chatterjee, K., &; Henzinger, T. A. (2023). Compositional policy learning in stochastic control systems with formal guarantees. In 37th Conference on Neural Information Processing Systems. New Orleans, LO, United States.
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Currin, C., Asiedu , M. N., Fourie, C., Rosman, B., Turki, H., Lambebo Tonja, A.,  Machangara, D. (2023). A framework for grassroots research collaboration in machine learning and global health. In 1st Workshop on Machine Learning &; Global Health. Kigali, Rwanda: OpenReview.
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Chalupa, M., &; Henzinger, T. A. (2023). Monitoring hyperproperties with prefix transducers. In 23nd International Conference on Runtime Verification (Vol. 14245, pp. 168–190). Thessaloniki, Greek: Springer Nature.https://doi.org/10.1007/978-3-031-44267-4_9 
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Tennage, P., Basescu, C., Kokoris Kogias, E., Syta, E., Jovanovic, P., Estrada-Galinanes, V., &; Ford, B. (2023). QuePaxa: Escaping the tyranny of timeouts in consensus. In Proceedings of the 29th Symposium on Operating Systems Principles (pp. 281–297). Koblenz, Germany: Association for Computing Machinery.https://doi.org/10.1145/3600006.3613150 
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Chalupa, M., &; Henzinger, T. A. (2023). Monitoring hyperproperties with prefix transducers. Zenodo.https://doi.org/10.5281/ZENODO.8191723 
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Prach, B., &; Lampert, C. (n.d.). 1-Lipschitz neural networks are more expressive with N-activations. arXiv.https://doi.org/10.48550/ARXIV.2311.06103 
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Stankowski, S. (2023). Data and code for: The genetic architecture of a recent transition to live-bearing in marine snails. Zenodo.https://doi.org/10.5281/ZENODO.8318995 
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Abualia, R., Riegler, S., &; Benková, E. (2023). Nitrate, auxin and cytokinin - a trio to tango. Cells. MDPI.https://doi.org/10.3390/cells12121613 
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Shigemoto, R. (2023). Transition from tonic to phasic neurotransmitter release by presynaptic GABAB receptor activation in medial habenula terminals. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:13173 
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Zheng, D. W., &; Henzinger, M. H. (2023). Multiplicative auction algorithm for approximate maximum weight bipartite matching. In International Conference on Integer Programming and Combinatorial Optimization (Vol. 13904, pp. 453–465). Madison, WI, United States: Springer Nature.https://doi.org/10.1007/978-3-031-32726-1_32 
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Biswas, R., Cultrera di Montesano, S., Edelsbrunner, H., &; Saghafian, M. (2023). Geometric characterization of the persistence of 1D maps. Journal of Applied and Computational Topology. Springer Nature.https://doi.org/10.1007/s41468-023-00126-9 
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Mondal, S., Jethwa, R. B., Pant, B., Hauschild, R., &; Freunberger, S. A. (2023). Singlet oxygen in non-aqueous oxygen redox: Direct spectroscopic evidence for formation pathways and reliability of chemical probes. Faraday Discussions. Royal Society of Chemistry.https://doi.org/10.1039/d3fd00088e 
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Marveggio, A. (2023). Weak-strong stability and phase-field approximation of interface evolution problems in fluid mechanics and in material sciences. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:14587 
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Esposito, A. R., &; Mondelli, M. (2023). Concentration without independence via information measures. In Proceedings of 2023 IEEE International Symposium on Information Theory (pp. 400–405). Taipei, Taiwan: IEEE.https://doi.org/10.1109/isit54713.2023.10206899 
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Kwan, M. A., Sah, A., &; Sawhney, M. (2023). Enumerating matroids and linear spaces. Comptes Rendus Mathematique. Academie des Sciences.https://doi.org/10.5802/crmath.423 
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Agresti, A., &; Hussein, A. (2023). Maximal Lp-regularity and He-calculus for block operator matrices and applications. Journal of Functional Analysis. Elsevier.https://doi.org/10.1016/j.jfa.2023.110146 
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Riabov, V. (n.d.). Mesoscopic eigenvalue statistics for Wigner-type matrices. arXiv.https://doi.org/10.48550/arXiv.2301.01712 
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Olusanya, O. O., Khudiakova, K., &; Sachdeva, H. (n.d.). Genetic load, eco-evolutionary feedback and extinction in a metapopulation. bioRxiv.https://doi.org/10.1101/2023.12.02.569702 
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Fedorov, A., Hashemi, D., Nadiradze, G., &; Alistarh, D.-A. (2023). Provably-efficient and internally-deterministic parallel Union-Find. In Proceedings of the 35th ACM Symposium on Parallelism in Algorithms and Architectures (pp. 261–271). Orlando, FL, United States: Association for Computing Machinery.https://doi.org/10.1145/3558481.3591082 
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Condor, J., Piovarci, M., Bickel, B., &; Didyk, P. (2023). Gloss-aware color correction for 3D printing. In SIGGRAPH ’23 Conference Proceedings. Los Angeles, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3588432.3591546 
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Henzinger, T. A., Karimi, M., Kueffner, K., &; Mallik, K. (2023). Runtime monitoring of dynamic fairness properties. In FAccT ’23: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (pp. 604–614). Chicago, IL, United States: Association for Computing Machinery.https://doi.org/10.1145/3593013.3594028 
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Piovarci, M., Chapiro, A., &; Bickel, B. (2023). Skin-Screen: A computational fabrication framework for color tattoos. Transactions on Graphics. Los Angeles, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3592432 
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Stankowski, S., Zagrodzka, Z. B., Galindo, J., Montaño-Rendón, M., Faria, R., Mikhailova, N.,https://doi.org/10.1093/evolinnean/kzad002__Butlin, R. K. (2023). Whole-genome phylogeography of the intertidal snail Littorina saxatilis. Evolutionary Journal of the Linnean Society. Oxford University Press.https://doi.org/10.1093/evolinnean/kzad002 
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Lasne, C., Elkrewi, M. N., Toups, M. A., Layana Franco, L. A., Macon, A., &; Vicoso, B. (2023). The scorpionfly (Panorpa cognata) genome highlights conserved and derived features of the peculiar dipteran X chromosome. Molecular Biology and Evolution. Oxford University Press.https://doi.org/10.1093/molbev/msad245 
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Lasne, C., &; Elkrewi, M. N. (2023). The scorpionfly (Panorpa cognata) genome highlights conserved and derived features of the peculiar dipteran X chromosome. Institute of Science and Technology Austria.https://doi.org/10.15479/AT:ISTA:14614 
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Gnyliukh, N., Johnson, A. J., Nagel, M.-K., Monzer, A., Hlavata, A., Isono, E.,https://doi.org/10.1101/2023.10.09.561523__Friml, J. (n.d.). Role of dynamin-related proteins 2 and S in clathrin-mediated endocytosis in plants. bioRxiv.https://doi.org/10.1101/2023.10.09.561523 
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Zens, B. (2023). Ultrastructural characterization of natively preserved extracellular matrix by cryo-electron tomography. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12491 
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Schauer, A. (2023). Mesendoderm formation in zebrafish gastrulation: The role of extraembryonic tissues. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12891 
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Kravchuk, V. (2023). Structural and mechanistic study of bacterial complex I and its cyanobacterial ortholog. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12781 
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Alcarva, C. (2023). Plasticity in the cerebellum: What molecular mechanisms are behind physiological learning. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12809 
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Wang, Y., Yuan, Z., Wang, J., Xiao, H., Wan, L., Li, L.,https://doi.org/10.1073/pnas.2221313120__Zhang, J. (2023). The nitrate transporter NR.1 directly antagonizes PIN7-mediated auxin transport for root growth adaptation. Proceedings of the National Academy of Sciences of the United States of America. National Academy of Sciences.https://doi.org/10.1073/pnas.2221313120 
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Stephenson, E. R. (2023). Generalizing medial axes with homology switches. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:14226 
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Kirillova, K. (2023). Panoramic functional gradients across the mouse retina. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12531 
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Julseth, M. (2023). The effect of local population structure on genetic variation at selected loci in the A. majus hybrid zone. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12800 
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Hafner, C. (2023). Inverse shape design with parametric representations: Kirchhoff Rods and parametric surface models. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12897 
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Hafner, C., &; Bickel, B. (2023). The design space of Kirchhoff rods. ACM Transactions on Graphics. Association for Computing Machinery.https://doi.org/10.1145/3606033 
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Michalska, J. M. (2023). A versatile toolbox for the comprehensive analysis of nervous tissue organization with light microscopy. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12470 
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Hosten, O. (2022). Constraints on probing quantum coherence to infer gravitational entanglement. Physical Review Research. American Physical Society.https://doi.org/10.1103/PhysRevResearch.4.013023 
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Dubini, R. C. A., Korytiaková, E., Schinkel, T., Heinrichs, P., Carell, T., &; Rovo, P. (2022). 1H NMR chemical exchange techniques reveal local and global effects of oxidized cytosine derivatives. ACS Physical Chemistry Au. American Chemical Society.https://doi.org/10.1021/acsphyschemau.1c00050 
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Bartocci, E., Ferrere, T., Henzinger, T. A., Nickovic, D., &; Da Costa, A. O. (2022). Flavors of sequential information flow. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13182, pp. 1–19). Philadelphia, PA, United States: Springer Nature.https://doi.org/10.1007/978-3-030-94583-1_1 
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Daguerre, L., Torroba, G., Medina Ramos, R. A., &; Solís, M. (2022). Non relativistic quantum field theory: Dynamics and irreversibility. Anales de la Asociacion Fisica Argentina. Asociacion Fisica Argentina.https://doi.org/10.31527/analesafa.2021.32.4.93 
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Lombardi, F., Herrmann, H. J., Parrino, L., Plenz, D., Scarpetta, S., Vaudano, A. E.,https://doi.org/10.1101/2022.03.03.482657__Shriki, O. (2022). Alpha rhythm induces attenuation-amplification dynamics in neural activity cascades. bioRxiv. Cold Spring Harbor Laboratory.https://doi.org/10.1101/2022.03.03.482657 
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Maslov, M., Lemeshko, M., &; Volosniev, A. (2022). Impurity with a resonance in the vicinity of the Fermi energy. Physical Review Research. American Physical Society.https://doi.org/10.1103/PhysRevResearch.4.013160 
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Davidovi, A., Chait, R. P., Batt, G., &; Ruess, J. (2022). Parameter inference for stochastic biochemical models from perturbation experiments parallelised at the single cell level. PLoS Computational Biology. Public Library of Science.https://doi.org/10.1371/journal.pcbi.1009950 
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Alistarh, D.-A., Gelashvili, R., &; Rybicki, J. (2022). Fast graphical population protocols. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.14 
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Nikabadi, A., &; Korhonen, J. (2022). Beyond distributed subgraph detection: Induced subgraphs, multicolored problems and graph parameters. In Q. Bramas, V. Gramoli, &; A. Milani (Eds.), 25th International Conference on Principles of Distributed Systems (Vol. 217). Strasbourg, France: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.OPODIS.2021.15 
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Kroll, J., Ruiz-Fernandez, M. J. A., Braun, M. B., Merrin, J., &; Renkawitz, J. (2022). Quantifying the probing and selection of microenvironmental pores by motile immune cells. Current Protocols. Wiley.https://doi.org/10.1002/cpz1.407 
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Chambers, E., Fillmore, C. D., Stephenson, E. R., &; Wintraecken, M. (2022). A cautionary tale: Burning the medial axis is unstable. In X. Goaoc &; M. Kerber (Eds.), 38th International Symposium on Computational Geometry (Vol. 224, p. 66:1-66:9). Berlin, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.SoCG.2022.66 
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Shevchenko, A., Kungurtsev, V., &; Mondelli, M. (2022). Mean-field analysis of piecewise linear solutions for wide ReLU networks. Journal of Machine Learning Research. Journal of Machine Learning Research.
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Bleile, B., Garin, A., Heiss, T., Maggs, K., &; Robins, V. (2022). The persistent homology of dual digital image constructions. In E. Gasparovic, V. Robins, &; K. Turner (Eds.), Research in Computational Topology 2 (1st ed., Vol. 30, pp. 1–26). Cham: Springer Nature.https://doi.org/10.1007/978-3-030-95519-9_1 
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Chlebak, C. A., &; Reid, P. H. (2022). From the prefect’s desk: Gerard van Swieten’s library correspondence. Library and Information History. Edinburgh University Press.https://doi.org/10.3366/lih.2022.0097 
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Clark, T., Drach, K., Kozlovski, O., &; Strien, S. V. (2022). The dynamics of complex box mappings. Arnold Mathematical Journal. Springer Nature.https://doi.org/10.1007/s40598-022-00200-7 
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Ngampruetikorn, V., Sachdeva, V., Torrence, J., Humplik, J., Schwab, D. J., &; Palmer, S. E. (2022). Inferring couplings in networks across order-disorder phase transitions. Physical Review Research. American Physical Society.https://doi.org/10.1103/PhysRevResearch.4.023240 
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Barton, N. H. (2022). The “New Synthesis.”https://doi.org/10.1073/pnas.2122147119__Proceedings of the National Academy of Sciences of the United States of America. Proceedings of the National Academy of Sciences.https://doi.org/10.1073/pnas.2122147119 
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Nikolic, N., Sauert, M., Albanese, T. G., &; Moll, I. (2022). Quantifying heterologous gene expression during ectopic MazF production in Escherichia coli. BMC Research Notes. Springer Nature.https://doi.org/10.1186/s13104-022-06061-9 
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Wirth, M. (2022). Kac regularity and domination of quadratic forms. Advances in Operator Theory. Springer Nature.https://doi.org/10.1007/s43036-022-00199-w 
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Krokhin, A., &; Oprsal, J. (2022). An invitation to the promise constraint satisfaction problem. ACM SIGLOG News. Association for Computing Machinery.https://doi.org/10.1145/3559736.3559740 
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Brunnbauer, A., Berducci, L., Brandstatter, A., Lechner, M., Hasani, R., Rus, D., &; Grosu, R. (2022). Latent imagination facilitates zero-shot transfer in autonomous racing. In 2022 International Conference on Robotics and Automation (pp. 7513–7520). Philadelphia, PA, United States: IEEE.https://doi.org/10.1109/ICRA46639.2022.9811650 
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Zhang, Y., Jaggi, S., Langberg, M., &; Sarwate, A. D. (2022). The capacity of causal adversarial channels. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 2523–2528). Espoo, Finland: IEEE.https://doi.org/10.1109/ISIT50566.2022.9834709 
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Fathollahi, D., &; Mondelli, M. (2022). Polar coded computing: The role of the scaling exponent. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 2154–2159). Espoo, Finland: IEEE.https://doi.org/10.1109/ISIT50566.2022.9834712 
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Torkamani, S., Ebrahimi, J. B., Sadeghi, P., D’Oliveira, R. G. L., &; Médard, M. (2022). Heterogeneous differential privacy via graphs. In 2022 IEEE International Symposium on Information Theory (Vol. 2022, pp. 1623–1628). Espoo, Finland: IEEE.https://doi.org/10.1109/ISIT50566.2022.9834711 
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Shute, A. L. (2022). Existence and density problems in Diophantine geometry: From norm forms to Campana points. Institute of Science and Technology Austria.https://doi.org/10.15479/at:ista:12072 
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Ahmadi, A., Chatterjee, K., Goharshady, A. K., Meggendorfer, T., Safavi Hemami, R., &; Zikelic, D. (2022). Algorithms and hardness results for computing cores of Markov chains. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (Vol. 250). Madras, India: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.FSTTCS.2022.29 
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Chatterjee, K., Ibsen-Jensen, R., Jecker, I. R., &; Svoboda, J. (2022). Complexity of spatial games. In 42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (Vol. 250). Madras, India: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.FSTTCS.2022.11 
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Stocker, L., Sack, S., Ferguson, M. S., &; Zilberberg, O. (2022). Entanglement-based observables for quantum impurities. Physical Review Research. American Physical Society.https://doi.org/10.1103/PhysRevResearch.4.043177 
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Chen, B., Piovarci, M., Wang, C., Seidel, H.-P., Didyk, P., Myszkowski, K., &; Serrano, A. (2022). Gloss management for consistent reproduction of real and virtual objects. In SIGGRAPH Asia 2022 Conference Papers (Vol. 2022). Daegu, South Korea: Association for Computing Machinery.https://doi.org/10.1145/3550469.3555406 
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Börner, G., Schröder, M., Scarselli, D., Budanur, N. B., Hof, B., &; Timme, M. (2022). Explosive transitions in epidemic dynamics. Journal of Physics: Complexity. IOP Publishing.https://doi.org/10.1088/2632-072x/ac99cd 
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Zoller, B., Gregor, T., &; Tkadik, G. (2022). Eukaryotic gene regulation at equilibrium, or non? Current Opinion in Systems Biology. Elsevier.https://doi.org/10.1016/j.coisb.2022.100435 
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Garcia Soto, M., Henzinger, T. A., &; Schilling, C. (2022). Synthesis of parametric hybrid automata from time series. In 20th International Symposium on Automated Technology for Verification and Analysis (Vol. 13505, pp. 337–353). Virtual: Springer Nature.https://doi.org/10.1007/978-3-031-19992-9_22 
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Pacut, M., Parham, M., Rybicki, J., Schmid, S., Suomela, J., &; Tereshchenko, A. (2022). Brief announcement: Temporal locality in online algorithms. In 36th International Symposium on Distributed Computing (Vol. 246). Augusta, GA, United States: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.DISC.2022.52 
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Cremaschi, T., &; Dello Schiavo, L. (2022). Effective contraction of Skinning maps. Proceedings of the American Mathematical Society, Series B. American Mathematical Society.https://doi.org/10.1090/bproc/134 
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Cipolloni, G., Erdös, L., &; Schröder, D. J. (2022). On the condition number of the shifted real Ginibre ensemble. SIAM Journal on Matrix Analysis and Applications. Society for Industrial and Applied Mathematics.https://doi.org/10.1137/21m1424408 
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Spiegelman, A., Giridharan, N., Sonnino, A., &; Kokoris Kogias, E. (2022). Bullshark: DAG BFT protocols made practical. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 2705–2718). Los Angeles, CA, United States: Association for Computing Machinery.https://doi.org/10.1145/3548606.3559361 
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Ljubotina, M., Roos, B., Abanin, D. A., &; Serbyn, M. (2022). Optimal steering of matrix product states and quantum many-body scars. PRX Quantum. American Physical Society.https://doi.org/10.1103/prxquantum.3.030343 
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Das, S., Yurek, T., Xiang, Z., Miller, A., Kokoris Kogias, E., &; Ren, L. (2022). Practical asynchronous distributed key generation. In 2022 IEEE Symposium on Security and Privacy (pp. 2518–2534). San Francisco, CA, United States: Institute of Electrical and Electronics Engineers.https://doi.org/10.1109/sp46214.2022.9833584 
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Mirkovi, I., Yang, Y., &; Zhao, G. (2022). Loop Grassmannians of Quivers and Affine Quantum Groups. In V. Baranovskky, N. Guay, &; T. Schedler (Eds.), Representation Theory and Algebraic Geometry (1st ed., pp. 347–392). Cham: Springer Nature; Birkhäuser.https://doi.org/10.1007/978-3-030-82007-7_8 
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Henzinger, T. A., Lehtinen, K., &; Totzke, P. (2022). History-deterministic timed automata. In 33rd International Conference on Concurrency Theory (Vol. 243, p. 14:1-14:21). Warsaw, Poland: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.CONCUR.2022.14 
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Avni, G., &; Henzinger, T. A. (2022). An updated survey of bidding games on graphs. In 47th International Symposium on Mathematical Foundations of Computer Science (Vol. 241, p. 3:1-3:6). Dagstuhl, Germany: Schloss Dagstuhl - Leibniz-Zentrum für Informatik.https://doi.org/10.4230/LIPIcs.MFCS.2022.3 
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Venkataramanan, R., Kögler, K., &; Mondelli, M. (2022). Estimation in rotationally invariant generalized linear models via approximate message passing. In Proceedings of the 39th International Conference on Machine Learning (Vol. 162). Baltimore, MD, United States: ML Research Press.
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Barbier, J., Hou, T., Mondelli, M., &; Saenz, M. (n.d.). The price of ignorance: How much does it cost to forget noise structure in low-rank matrix estimation? arXiv.https://doi.org/10.48550/arXiv.2205.10009 
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Meggendorfer, T. (2022). Risk-aware stochastic shortest path. In Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022 (Vol. 36, pp. 9858–9867). Virtual: Association for the Advancement of Artificial Intelligence.https://doi.org/10.1609/aaai.v36i9.21222 
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Scott, J. A., Yeo, M. X., &; Lampert, C. (n.d.). Cross-client Label Propagation for transductive federated learning. arXiv.https://doi.org/10.48550/arXiv.2210.06434 
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Súkeník, P., &; Lampert, C. (n.d.). Generalization in Multi-objective machine learning. arXiv.https://doi.org/10.48550/arXiv.2208.13499 
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