Unstable equilibria and invariant manifolds in quasi-two-dimensional Kolmogorov-like flow
B. Suri, J. Tithof, R. Grigoriev, M. Schatz, Physical Review E 98 (2018).

Monitoring hippocampal glycine with the computationally designed optical sensor GlyFS
W. Zhang, M. Herde, J. Mitchell, J. Whitfield, A. Wulff, V. Vongsouthi, I. Sanchez-Romero, P. Gulakova, D. Minge, B. Breithausen, S. Schoch, H.L. Janovjak, C. Jackson, C. Henneberger, Nature Chemical Biology 14 (2018) 861–869.

The divergence history of European blue mussel species reconstructed from Approximate Bayesian Computation: The effects of sequencing techniques and sampling strategies
C. Fraisse, C. Roux, P. Gagnaire, J. Romiguier, N. Faivre, J. Welch, N. Bierne, PeerJ 2018 (2018).

Relative contribution of PIN-containing secretory vesicles and plasma membrane PINs to the directed auxin transport: Theoretical estimation
S. Hille, M. Akhmanova, M. Glanc, A.J. Johnson, J. Friml, International Journal of Molecular Sciences 19 (2018).

Space-time interpolants
G. Frehse, M. Giacobbe, T.A. Henzinger, in:, Springer, 2018, pp. 468–486.

Chiral discrimination through bielliptical high-harmonic spectroscopy
D.R. Baykusheva, H.J. Wörner, Physical Review X 8 (2018).

Symbolic algorithms for graphs and Markov decision processes with fairness objectives
K. Chatterjee, M. Henzinger, V. Loitzenbauer, S. Oraee, V. Toman, in:, Springer, 2018, pp. 178–197.

SOM-VAE: Interpretable discrete representation learning on time series
V. Fortuin, M. Hüser, F. Locatello, H. Strathmann, G. Rätsch, in:, International Conference on Learning Representations, 2018.

Reachable set over-approximation for nonlinear systems using piecewise barrier tubes
H. Kong, E. Bartocci, T.A. Henzinger, in:, Springer, 2018, pp. 449–467.

Boosting variational inference: An optimization perspective
F. Locatello, R. Khanna, J. Ghosh, G. Rätsch, in:, Proceedings of the 21st International Conference on Artificial Intelligence and Statistics, ML Research Press, 2018, pp. 464–472.

Boosting black box variational inference
F. Locatello, G. Dresdner, R. Khanna, I. Valera, G. Rätsch, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2018.

A conditional gradient framework for composite convex minimization with applications to semidefinite programming
A. Yurtsever, O. Fercoq, F. Locatello, V. Cevher, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 5727–5736.

On matching pursuit and coordinate descent
F. Locatello, A. Raj, S.P. Karimireddy, G. Rätsch, B. Schölkopf, S.U. Stich, M. Jaggi, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 3198–3207.

Clustering meets implicit generative models
F. Locatello, D. Vincent, I. Tolstikhin, G. Ratsch, S. Gelly, B. Scholkopf, in:, 6th International Conference on Learning Representations, 2018.

Structure and mechanism of the two-component ;1-helical pore-forming toxin YaxAB
B. Bräuning, E. Bertosin, F.M. Praetorius, C. Ihling, A. Schatt, A. Adler, K. Richter, A. Sinz, H. Dietz, M. Groll, Nature Communications 9 (2018).

Efficient algorithms for asymptotic bounds on termination time in VASS
T. Brázdil, K. Chatterjee, A. Kudera, P. Novotný, D. Velan, F. Zuleger, in:, IEEE, 2018, pp. 185–194.

Genetically encoding the spatial arrangement of DNA and proteins in self-assembling nanostructures
F.M. Praetorius, Genetically Encoding the Spatial Arrangement of DNA and Proteins in Self-Assembling Nanostructures, Technische Universität München, 2018.

Competitive training of mixtures of independent deep generative models
F. Locatello, D. Vincent, I. Tolstikhin, G. Rätsch, S. Gelly, B. Schölkopf, ArXiv (n.d.).

A theory of register monitors
T. Ferrere, T.A. Henzinger, E. Saraç, in:, IEEE, 2018, pp. 394–403.

Newly produced synaptic vesicle proteins are preferentially used in synaptic transmission
S.M. Truckenbrodt, A. Viplav, S. Jähne, A. Vogts, A. Denker, H. Wildhagen, E. Fornasiero, S. Rizzoli, The EMBO Journal 37 (2018).