B. Kavcic and G. Tkadik, “Token-driven totally asymmetric simple exclusion process,” arXiv. .

M. Calcabrini et al., “Ligand conversion in nanocrystal synthesis: The oxidation of alkylamines to fatty acids by nitrate,” JACS Au, vol. 1, no. 11. American Chemical Society, pp. 1898–1903, 2021.

J. A. Keith et al., “Combining machine learning and computational chemistry for predictive insights into chemical systems,” Chemical Reviews, vol. 121, no. 16. American Chemical Society, pp. 9816–9872, 2021.

M. Verzobio, “Primitive divisors of elliptic divisibility sequences for elliptic curves with j=1728,” Acta Arithmetica, vol. 198, no. 2. Institute of Mathematics, Polish Academy of Sciences, pp. 129–168, 2021.

M. Verzobio, “Primitive divisors of sequences associated to elliptic curves with complex multiplication,” Research in Number Theory, vol. 7, no. 2. Springer Nature, 2021.

A. Schlögl, S. Elefante, A. Hornoiu, and S. Stadlbauer, “Managing software on a heterogenous HPC cluster,” in ASHP – Austrian-Slovenian HPC Meeting 2021, Virtual, 2021, p. 5.

E.-A. Peste, E. B. Iofinova, A. Vladu, and D.-A. Alistarh, “AC/DC: Alternating Compressed/DeCompressed training of deep neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, Online, 2021, vol. 34, pp. 8557–8570.

M. Brooks, M. Lemeshko, D. Lundholm, and E. Yakaboylu, “Emergence of anyons on the two-sphere in molecular impurities,” Atoms, vol. 9, no. 4. MDPI, 2021.

F. Alimisis, P. Davies, and D.-A. Alistarh, “Communication-efficient distributed optimization with quantized preconditioners,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 196–206.

Q. Nguyen, M. Mondelli, and G. Montufar, “Tight bounds on the smallest Eigenvalue of the neural tangent kernel for deep ReLU networks,” in Proceedings of the 38th International Conference on Machine Learning, Virtual, 2021, vol. 139, pp. 8119–8129.

T. A. Henzinger, M. Lechner, and D. Zikelic, “Scalable verification of quantized neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 2021, vol. 35, no. 5A, pp. 3787–3795.

M. Lechner, Ð. dikeli, K. Chatterjee, and T. A. Henzinger, “Infinite time horizon safety of Bayesian neural networks,” in 35th Conference on Neural Information Processing Systems, Virtual, 2021.

J. Ryssy et al., “Lightresponsive dynamic DNAorigamibased plasmonic assemblies,” Angewandte Chemie International Edition, vol. 60, no. 11. Wiley, pp. 5859–5863, 2021.

T. Bian et al., “Electrostatic co-assembly of nanoparticles with oppositely charged small molecules into static and dynamic superstructures,” Nature Chemistry, vol. 13, no. 10. Springer Nature, pp. 940–949, 2021.

D. L. McCartney et al., “Blood-based epigenome-wide analyses of cognitive abilities.” Zenodo, 2021.

S. Randriamanantsoa et al., “Spatiotemporal dynamics of self-organized branching in pancreas-derived organoids.” Zenodo, 2021.

F. A. Fredes Tolorza, M. A. Silva Sifuentes, P. Koppensteiner, K. Kobayashi, M. A. Jösch, and R. Shigemoto, “Ventro-dorsal hippocampal pathway gates novelty-induced contextual memory formation,” Current Biology, vol. 31, no. 1. Elsevier, p. –38., 2021.

F. Lombardi, O. Shriki, H. J. Herrmann, and L. de Arcangelis, “Long-range temporal correlations in the broadband resting state activity of the human brain revealed by neuronal avalanches,” Neurocomputing, vol. 461. Elsevier, pp. 657–666, 2021.

R. A. Samarasinghe et al., Identification of neural oscillations and epileptiform changes in human brain organoids, vol. 24. Springer Nature, 2021.

K. P. Rychlewicz, “The positivity of local equivariant Hirzebruch class for toric varieties,” Bulletin of the London Mathematical Society, vol. 53, no. 2. Wiley, pp. 560–574, 2021.