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L. Moschella, V. Maiorca, M. Fumero, A. Norelli, F. Locatello, and E. Rodolà, “Relative representations enable zero-shot latent space communication,” in The 11th International Conference on Learning Representations, Kigali, Rwanda, 2023.
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M. Nagano, K. Aoshima, H. Shimamura, D. E. Siekhaus, J. Y. Toshima, and J. Toshima, “Distinct role of TGN-resident clathrin adaptors for Vpp activation in the TGN-endosome trafficking pathway,” Journal of Cell Science, vol. 136, no. 17. The Company of Biologists, 2023.
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P. Brighi, “Ergodicity breaking in disordered and kinetically constrained quantum many-body systems,” Institute of Science and Technology Austria, 2023.
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L. Bocanegra, “Epithelial dynamics during mouse neural tube development,” Institute of Science and Technology Austria, 2023.
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S. Köse, “Exterior algebra and combinatorics,” Institute of Science and Technology Austria, 2023.
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S. Stockmaier, Y. Ulrich, G. F. Albery, S. Cremer, and P. C. Lopes, “Behavioural defences against parasites across host social structures,” Functional Ecology, vol. 37, no. 4. British Ecological Society, pp. 809–820, 2023.
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J. D. Boissonnat and M. Wintraecken, “The reach of subsets of manifolds,” Journal of Applied and Computational Topology, vol. 7. Springer Nature, pp. 619–641, 2023.
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P. Tomaszewska and C. Lampert, “On the implementation of baselines and lightweight conditional model extrapolation (LIMES) under class-prior shift,” in International Workshop on Reproducible Research in Pattern Recognition, Montreal, Canada, 2023, vol. 14068, pp. 67–73.
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J. D. Boissonnat, S. Kachanovich, and M. Wintraecken, “Tracing isomanifolds in Rd in time polynomial in d using Coxeter–Freudenthal–Kuhn triangulations,” SIAM Journal on Computing, vol. 52, no. 2. Society for Industrial and Applied Mathematics, pp. 452–486, 2023.
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L. comi, G. Largeteau-Skapin, R. Zrour, R. Biswas, and E. Andres, “Discrete analytical objects in the body-centered cubic grid,” Pattern Recognition, vol. 142, no. 10. Elsevier, 2023.
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R. Bunting, F. Wodaczek, T. Torabi, and B. Cheng, “Reactivity of single-atom alloy nanoparticles: Modeling the dehydrogenation of propane,” Journal of the American Chemical Society, vol. 145, no. 27. American Chemical Society, pp. 14894–14902, 2023.
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I. A. Unterweger, J. Klepstad, E. B. Hannezo, P. R. Lundegaard, A. Trusina, and E. A. Ober, “Lineage tracing identifies heterogeneous hepatoblast contribution to cell lineages and postembryonic organ growth dynamics,” PLoS Biology, vol. 21, no. 10. Public Library of Science, 2023.
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Y. Dodis, N. Ferguson, E. Goldin, P. Hall, and K. Z. Pietrzak, “Random oracle combiners: Breaking the concatenation barrier for collision-resistance,” in 43rd Annual International Cryptology Conference, Santa Barbara, CA, United States, 2023, vol. 14082, pp. 514–546.
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A. F. Leithner, J. Merrin, and M. K. Sixt, “En-Face Imaging of T Cell-Dendritic Cell Immunological Synapses,” in The Immune Synapse, vol. 2654, C. Baldari and M. Dustin, Eds. New York, NY: Springer Nature, 2023, pp. 137–147.
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L. Qiu, R. Sahu, W. J. Hease, G. M. Arnold, and J. M. Fink, “Coherent optical control of a superconducting microwave cavity via electro-optical dynamical back-action,” Nature Communications, vol. 14. Nature Research, 2023.
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J. Barbier, F. Camilli, M. Mondelli, and M. Sáenz, “Fundamental limits in structured principal component analysis and how to reach them,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 30. National Academy of Sciences, 2023.
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O. Vardi et al., “Nuclear spin effects in biological processes,” Proceedings of the National Academy of Sciences of the United States of America, vol. 120, no. 32. National Academy of Sciences, 2023.
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G. Cipolloni, L. Erdös, and D. J. Schröder, “Functional central limit theorems for Wigner matrices,” Annals of Applied Probability, vol. 33, no. 1. Institute of Mathematical Statistics, pp. 447–489, 2023.
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T. D. Browning, P. L. Boudec, and W. Sawin, “The Hasse principle for random Fano hypersurfaces,” Annals of Mathematics, vol. 197, no. 3. Princeton University, pp. 1115–1203, 2023.
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J. C. Mckerral, M. Kleshnina, V. Ejov, L. Bartle, J. G. Mitchell, and J. A. Filar, “Empirical parameterisation and dynamical analysis of the allometric Rosenzweig-MacArthur equations,” PLoS One, vol. 18, no. 2. Public Library of Science, p. e0279838, 2023.
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K. Eguchi, E. Le Monnier, and R. Shigemoto, “Nanoscale phosphoinositide distribution on cell membranes of mouse cerebellar neurons,” The Journal of Neuroscience, vol. 43, no. 23. Society for Neuroscience, pp. 4197–4216, 2023.
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B. J. Confavreux, “Synapseek: Meta-learning synaptic plasticity rules,” Institute of Science and Technology Austria, 2023.
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B. Roos, “Boundary superconductivity in BCS theory,” Institute of Science and Technology Austria, 2023.
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C. Hainzl, B. Roos, and R. Seiringer, “Boundary superconductivity in the BCS model,” Journal of Spectral Theory, vol. 12, no. 4. EMS Press, pp. 1507–1540, 2023.
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N. H. Barton, A. M. Etheridge, and A. Véber, “The infinitesimal model with dominance,” Genetics, vol. 225, no. 2. Oxford Academic, 2023.
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N. H. Barton, “The infinitesimal model with dominance.” Institute of Science and Technology Austria, 2023.
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I. Markov, A. Vladu, Q. Guo, and D.-A. Alistarh, “Quantized distributed training of large models with convergence guarantees,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 24020–24044.
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H. Fichtenberger, M. H. Henzinger, and J. Upadhyay, “Constant matters: Fine-grained error bound on differentially private continual observation,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 10072–10092.
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A. Shevchenko, K. Kögler, H. Hassani, and M. Mondelli, “Fundamental limits of two-layer autoencoders, and achieving them with gradient methods,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 31151–31209.
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M. Nikdan, T. Pegolotti, E. B. Iofinova, E. Kurtic, and D.-A. Alistarh, “SparseProp: Efficient sparse backpropagation for faster training of neural networks at the edge,” in Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, HI, United States, 2023, vol. 202, pp. 26215–26227.
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arXiv: 2302.04852