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Publications

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Fomsgaard, I. S., Añon, M. C., Barba de la Rosa, A. P., Christophersen, C., Dusek, K., Délano-Frier, J., Espinoza Pérez, J., Fonseca, A., Janovská, D., Kudsk, P. N., Labouriau, R. S., Lacayo Romero, M. L., Martínez, N., Matusová, K., Mathiassen, S. K., Noellemeyer, E. J., Pedersen, H., Stavelikova, H., Steffensen, S. K. ... Taberner, A. (2010). Adding Value to Holy Grain: Providing the Key Tools for the Exploitation of Amaranth - The Protein-rich Grain of the Aztecs: Results from a Joint European - Latin American Research Project. Abstract from Tropentag 2010. World Food System - A contribution from Europe, Zürich, Switzerland.
Fomsgaard, I. S., Christophersen, C., Barba de la Rosa, A. P., Delano-Frier, J., Janovska, D., Matusova, K., Lacayo Romero, M. L., Perez, J. E., Taberner, A., Añon, C., de Troiani, R. M., Dusek, K., Fonseca, A., Kudsk, P. N., Labouriau, R., Martinez, N., Matus, F., Mathiassen, S. K., Noellemeyer, E. ... Steffensen, S. K. (2011). Adding value to holy grain - providing the key tools for the exploitation of amaranth, the protein-rich grain of the Aztecs: Results from a joint European-Latin American research project. Abstract from 241st American Chemical Society National Meeting & Exposition, Anaheim, United States. http://agfd.sites.acs.org/cornucopia11S.pdf
Fissler, T. & Podolskij, M. (2014). Testing the maximal rank of the volatility process for continuous diffusions observed with noise. Institut for Økonomi, Aarhus Universitet. CREATES Research Paper No. 2014-52
Feliu, E., Knudsen, M., Wiuf, C. H. & Andersen, L. N. (2010). An Algebraic Approach to Signaling Cascades with n Layers. Poster session presented at The 11th International Conference on Systems Biology, Edinburgh, United Kingdom.
Exarchakos, G., van der Hofstad, R., Nagy, O. & Pandey, M. (2026). Bringing order to network centrality measures. (pp. 1-24). ArXiv. https://arxiv.org/abs/2601.16236
Engholm, R., Karstoft, H. & Jensen, E. B. V. (2009). Adaptive kernel filtering used in video processing. In Visual Communications and Image Processing 2009 (pp. 72571E). SPIE - International Society for Optical Engineering. https://doi.org/10.1117/12.808155
Embrechts, P., Jensen, J. L., Maejima, M. & Teugels, J. L. (1985). Approximations for compound Poisson and Pólya processes. Advances in Applied Probability, 17(3), 623-637. https://doi.org/10.2307/1427123
Elmholt, S. & Labouriau, R. (2005). Fungi in Danish soils under organic and conventional farming. Agriculture, Ecosystems and Environment, 107, 65-73.
Egendal, I., Brøndum, R. F., Pelizzola, M., Hobolth, A. & Bøgsted, M. (2025). On the Relation Between Linear Autoencoders and Non-Negative Matrix Factorization for Mutational Signature Extraction. Journal of computational biology : a journal of computational molecular cell biology, 32(5), 461-472. https://doi.org/10.1089/cmb.2024.0784
Dorph-Petersen, K.-A., Gundersen, H. J. G., Jensen, E. B. V. & Kiêu, K. (2001). Non-uniform systematic sampling for area estimation of planar objects. In C. Babu Rao, P. Kalyanasundaram, KK. Ray & B. Raj (Eds.), Image Analysis in Materials and Life Sciences (pp. 4-8)
Dorph-Petersen, K.-A., Ziegel, J., Baddeley, A. & Jensen, E. B. V. (2012). Systematic sampling with errors - the effect of a variable intersectional distance on stereological volume estimates. Abstract from 42nd annual meeting of the society for neuroscience, New Orleans, United States.
Dorph-Petersen, K.-A., Ziegel, J., Baddeley, A. & Jensen, E. B. V. (2016). Prediction of the variance of stereological volume estimates in systematic sampling with errors in sampling locations. Abstract from AU Workshop on Stochastic Geometry, Stereology and their Applications, Sønderborg, Denmark.
Dörnemann, N. & Dette, H. (2024). Linear spectral statistics of sequential sample covariance matrices. Annales de l'institut Henri Poincare (B) Probability and Statistics, 60(2), 946-970. https://doi.org/10.1214/22-AIHP1339
Dormann, F., Frisk, O., Andersen, L. N. & Pedersen, C. F. (2021). Not All Noise Is Accounted Equally: How Differentially Private Learning Benefits From Large Sampling Rates. In 2021 IEEE 31st International Workshop on Machine Learning for Signal Processing, MLSP 2021 IEEE. https://doi.org/10.1109/MLSP52302.2021.9596307