Majumdar, R., Sixt, M. K., &; Parent, C. (2014). New paradigms in the establishment and maintenance of gradients during directed cell migration. Current Opinion in Cell Biology. Elsevier.https://doi.org/10.1016/j.ceb.2014.05.010 
LibreCat: https://research-explorer.ista.ac.at/record/2158
DOI: 10.1016/j.ceb.2014.05.010
PMID: 24959970

Chai, X., Münzner, G., Zhao, S., Tinnes, S., Kowalski, J., Häussler, U.,https://doi.org/10.1093/cercor/bht067__Frotscher, M. (2014). Epilepsy-induced motility of differentiated neurons. Cerebral Cortex. Oxford University Press.https://doi.org/10.1093/cercor/bht067 
LibreCat: https://research-explorer.ista.ac.at/record/2164
DOI: 10.1093/cercor/bht067

Kelleher, J., Etheridge, A., &; Barton, N. H. (2014). Coalescent simulation in continuous space: Algorithms for large neighbourhood size. Theoretical Population Biology. Academic Press.https://doi.org/10.1016/j.tpb.2014.05.001 
LibreCat: https://research-explorer.ista.ac.at/record/2168
DOI: 10.1016/j.tpb.2014.05.001

Barton, N. H., Novak, S., &; Paixao, T. (2014). Diverse forms of selection in evolution and computer science. PNAS. National Academy of Sciences.https://doi.org/10.1073/pnas.1410107111 
LibreCat: https://research-explorer.ista.ac.at/record/2169
DOI: 10.1073/pnas.1410107111

Kolesnikov, A., Guillaumin, M., Ferrari, V., &; Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. In D. Fleet, T. Pajdla, B. Schiele, &; T. Tuytelaars (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer.https://doi.org/10.1007/978-3-319-10578-9_36 
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

Khamis, S., &; Lampert, C. (2014). CoConut: Co-classification with output space regularization. In Proceedings of the British Machine Vision Conference 2014. Nottingham, UK: BMVA Press.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Sydorov, V., Sakurada, M., &; Lampert, C. (2014). Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (pp. 1402–1409). Columbus, USA: IEEE.https://doi.org/10.1109/CVPR.2014.182 
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

De Vladar, H., &; Barton, N. H. (2014). Stability and response of polygenic traits to stabilizing selection and mutation. Genetics. Genetics Society of America.https://doi.org/10.1534/genetics.113.159111 
LibreCat: https://research-explorer.ista.ac.at/record/2174
DOI: 10.1534/genetics.113.159111

Ajanki, O. H., Erdös, L., &; Krüger, T. H. (2014). Local semicircle law with imprimitive variance matrix. Electronic Communications in Probability. Institute of Mathematical Statistics.https://doi.org/10.1214/ECP.v19-3121 
LibreCat: https://research-explorer.ista.ac.at/record/2179
DOI: 10.1214/ECP.v19-3121

Studer, D., Zhao, S., Chai, X., Jonas, P. M., Graber, W., Nestel, S., &; Frotscher, M. (2014). Capture of activity-induced ultrastructural changes at synapses by high-pressure freezing of brain tissue. Nature Protocols. Nature Publishing Group.https://doi.org/10.1038/nprot.2014.099 
LibreCat: https://research-explorer.ista.ac.at/record/2176
DOI: 10.1038/nprot.2014.099

Haws, D., Martin del Campo Sanchez, A., Takemura, A., &; Yoshida, R. (2014). Markov degree of the three-state toric homogeneous Markov chain model. Beitrage Zur Algebra Und Geometrie. Springer.https://doi.org/10.1007/s13366-013-0178-y 
LibreCat: https://research-explorer.ista.ac.at/record/2178
DOI: 10.1007/s13366-013-0178-y

Edelsbrunner, H., &; Parsa, S. (2014). On the computational complexity of betti numbers reductions from matrix rank. In Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 152–160). Portland, USA: SIAM.https://doi.org/10.1137/1.9781611973402.11 
LibreCat: https://research-explorer.ista.ac.at/record/2177
DOI: 10.1137/1.9781611973402.11

Dodis, Y., Pietrzak, K. Z., &; Wichs, D. (2014). Key derivation without entropy waste. In P. Nguyen &; E. Oswald (Eds.) (Vol. 8441, pp. 93–110). Presented at the EUROCRYPT: Theory and Applications of Cryptographic Techniques, Copenhagen, Denmark: Springer.https://doi.org/10.1007/978-3-642-55220-5_6 
LibreCat: https://research-explorer.ista.ac.at/record/2185
DOI: 10.1007/978-3-642-55220-5_6

Bellet, A., Habrard, A., Morvant, E., &; Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning. Springer.https://doi.org/10.1007/s10994-014-5462-z 
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 10.1007/s10994-014-5462-z

cadek, M., Krcál, M., Matousek, J., Sergeraert, F., Vokínek, L., &; Wagner, U. (2014). Computing all maps into a sphere. Journal of the ACM. ACM.https://doi.org/10.1145/2597629 
LibreCat: https://research-explorer.ista.ac.at/record/2184
DOI: 10.1145/2597629

Botella Soler, V., &; Glendinning, P. (2014). Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics.https://doi.org/10.1103/PhysRevE.89.062809 
LibreCat: https://research-explorer.ista.ac.at/record/2183
DOI: 10.1103/PhysRevE.89.062809

Chen, T., Hainzl, C., Pavlovi, N., &; Seiringer, R. (2014). On the well-posedness and scattering for the Gross-Pitaevskii hierarchy via quantum de Finetti. Letters in Mathematical Physics. Springer.https://doi.org/10.1007/s11005-014-0693-2 
LibreCat: https://research-explorer.ista.ac.at/record/2186
DOI: 10.1007/s11005-014-0693-2

Bloem, R., Chatterjee, K., Greimel, K., Henzinger, T. A., Hofferek, G., Jobstmann, B.,https://doi.org/10.1007/s00236-013-0191-5__Könighofer, R. (2014). Synthesizing robust systems. Acta Informatica. Springer.https://doi.org/10.1007/s00236-013-0191-5 
LibreCat: https://research-explorer.ista.ac.at/record/2187
DOI: 10.1007/s00236-013-0191-5

Kania, U., Fendrych, M., &; Friml, J. (2014). Polar delivery in plants; commonalities and differences to animal epithelial cells. Open Biology. Royal Society.https://doi.org/10.1098/rsob.140017 
LibreCat: https://research-explorer.ista.ac.at/record/2188
DOI: 10.1098/rsob.140017

Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France: Elsevier.
LibreCat: https://research-explorer.ista.ac.at/record/2189