References
- Basham, M, Filik, J, Wharmby, MT, Chang, PC, El Kassaby, B, Gerring, M, Aishima, J, Levik, K, Pulford, BC, Sikharulidze, I and Sneddon, D. 2015. Data analysis workbench (DAWN). Journal of synchrotron radiation, 22(3): 853–858. DOI: 10.1107/S1600577515002283
- Bernstein, HJ, Förster, A, Bhowmick, A, Brewster, AS, Brockhauser, S, Gelisio, L, Hall, DR, Leonarski, F, Mariani, V, Santoni, G and Vonrhein, C. 2020. Gold Standard for macromolecular crystallography diffraction data. IUCrJ, 7(5). DOI: 10.1107/S2052252520008672
- Bishop, CM. 2006.
Pattern Recognition and Machine Learning . Springer. - Brockhauser, S, Ravelli, RB and McCarthy, AA. 2013. The use of a mini-κ goniometer head in macromolecular crystallography diffraction experiments. Acta Crystallographica Section D: Biological Crystallography, 69(7): 1241–1251. DOI: 10.1107/S0907444913003880
- Brockhauser, S, Svensson, O, Bowler, MW, Nanao, M, Gordon, E, Leal, RM, Popov, A, Gerring, M, McCarthy, AA and Gotz, A. 2012. The use of workflows in the design and implementation of complex experiments in macromolecular crystallography. Acta Crystallographica Section D: Biological Crystallography, 68(8): 975–984. DOI: 10.1107/S090744491201863X
- Decking, W, Abeghyan, S, Abramian, P, Abramsky, A, Aguirre, A, Albrecht, C, Alou, P, Altarelli, M, Altmann, P, Amyan, K, Anashin, V, Apostolov, E, Appel, K, Auguste, D, Ayvazyan, V, Baark, S, Babies, F, Baboi, N, Bak, P and Zybin, D. 2020. A MHz-repetition-rate hard X-ray free-electron laser driven by a superconducting linear accelerator. Nature Photonics, 14: 1–7. DOI: 10.1038/s41566-020-0607-z
- Deng, L and Yu, D. 2014. “Deep Learning: Methods and Applications” (PDF). Foundations and Trends in Signal Processing. 7(3–4): 1–199. DOI: 10.1561/2000000039
- Donath, T, Rissi, M and Billich, H. 2013. Meeting report: workshop on beamline integration and data formatting. Synchrotron Radiation News, 26(5): 34–35. DOI: 10.1080/08940886.2013.832589
- Duchi, J, Hazan, E and Singer, Y. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of machine learning research, 12(7).
- Edelen, A, Mayes, C, Bowring, D, Ratner, D, Adelmann, A, Ischebeck, R, Snuverink, J, Agapov, I, Kammering, R, Edelen, J and Bazarov, I. 2018. Opportunities in machine learning for particle accelerators. arXiv preprint arXiv:1811.03172.
- Fangohr, H, Beg, M, Bondar, V, Aplin, S, Barty, A, Kuhn, M, Mariani, V and Kluyver, T. 2017. Data analysis support in Karabo at European XFEL. DOI: 10.18429/JACoW-ICALEPCS2017-TUCPA01
- Filik, J, Ashton, AW, Chang, PCY, Chater, PA, Day, SJ, Drakopoulos, M, Gerring, MW, Hart, ML, Magdysyuk, OV, Michalik, S and Smith, A. 2017. Processing two-dimensional X-ray diffraction and small-angle scattering data in DAWN 2. Journal of applied crystallography, 50(3): 959–966. DOI: 10.1107/S1600576717004708
- Galler, A, Gawelda, W, Biednov, M, Bomer, C, Britz, A, Brockhauser, S, Choi, TK, Diez, M, Frankenberger, P, French, M and Görries, D. 2019. Scientific instrument Femtosecond X-ray Experiments (FXE): instrumentation and baseline experimental capabilities. Journal of synchrotron radiation, 26(5). DOI: 10.1107/S1600577519006647
- Ghosh, K, Stuke, A, Todorović, M, Jørgensen, PB, Schmidt, MN, Vehtari, A and Rinke, P. 2019. Deep Learning Spectroscopy: Neural Networks for Molecular Excitation Spectra. Advanced Science, 6: 1801367. DOI: 10.1002/advs.201801367
- Han, J and Moraga, C. June 1995.
The influence of the sigmoid function parameters on the speed of backpropagation learning . In International Workshop on Artificial Neural Networks (pp. 195–201). Berlin, Heidelberg: Springer. ISBN 978-3-540-59497-0. - He, K, Zhang, X, Ren, S and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).
- Hester, J. 2016. A Robust, Format-Agnostic Scientific Data Transfer Framework. Data Science Journal, 15: 12. DOI: 10.5334/dsj-2016-012
- Hinton, G, Srivastava, N and Swersky, K. 2012. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on, 14(8).
- Hochreiter, S and Schmidhuber, J. 1997. Long short-term memory. Neural computation, 9(8): 1735–1780
- Kingma, DP and Ba, J. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
- Könnecke, M, Akeroyd, FA, Bernstein, HJ, Brewster, AS, Campbell, SI, Clausen, B, Cottrell, S, Hoffmann, JU, Jemian, PR, Männicke, D and Osborn, R. 2015. The NeXus data format. Journal of applied crystallography, 48(1): 301–305. DOI: 10.1107/S1600576714027575
- Kulin, M, Kazaz, T, Moerman, I and De Poorter, E. 2018. End-to-end learning from spectrum data: A deep learning approach for wireless signal identification in spectrum monitoring applications. IEEE Access, 6: 18484–18501. DOI: 10.1109/ACCESS.2018.2818794
- Kuster, M, Boukhelef, D, Donato, M, Dambietz, JS, Hauf, S, Maia, L, Raab, N, Szuba, J, Turcato, M, Wrona, K and Youngman, C. 2014. Detectors and calibration concept for the European XFEL. Synchrotron radiation news, 27(4): 35–38. DOI: 10.1080/08940886.2014.930809
- Le Guyader, L, Döring, F, Rösner, B, Eschenlohr, A, Beye, M, Agarwal, N, Broers, C, Carley, R, Delitz, JT, Gerasimova, N and Gort, R. 2020. Beam-splitting off-axis zone plate for time-resolved X-ray absorption spectroscopy at the SCS instrument. In European XFEL Users’ Meeting 2020 (No. POSTER-2020-010).
- LeCun, Y, Bengio, Y and Hinton, G. 2015. Deep learning. Nature, 521(7553): 436–444. DOI: 10.1038/nature14539
- Matsumoto, M and Nishimura, T. 1998. Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation (TOMACS), 8(1): 3–30.
- Mercurio, G, Broers, C, Carley, R, Delitz, JT, Gerasimova, N, Le Guyarder, L, Mercadier, L, Reich, A, Schlappa, J, Teichmann, M and Yaroslavtsev, A. September 2019.
First commissioning results of the KB mirrors at the SCS instrument of the European XFEL . In Advances in Metrology for X-Ray and EUV Optics VIII (Vol. 11109, p. 111090F). International Society for Optics and Photonics. DOI: 10.1117/12.2530725 - Mohri, M, Rostamizadeh, A and Talwalkar, A. 2018. Foundations of machine learning. MIT press.
- Nair, V and Hinton, GE. January 2010. Rectified linear units improve restricted boltzmann machines. In ICML.
- Nakatsutsumi, M, Tschentscher, T, Cowan, T, Ferrari, A, Schlenvoigt, HP, Appel, K, Strempfer, J and Zimmermann, MV. 2014. Scientific Instrument High Energy Density Physics (HED).
- Namin, AH, Leboeuf, K, Muscedere, R, Wu, H and Ahmadi, M. May 2009. Efficient hardware implementation of the hyperbolic tangent sigmoid function. In 2009 IEEE International Symposium on Circuits and Systems (pp. 2117–2120).
IEEE . - Pennicard, D, Smoljanin, S, Pithan, F, Sarajlic, M, Rothkirch, A, Yu, Y, Liermann, HP, Morgenroth, W, Winkler, B, Jenei, Z and Stawitz, H. 2018. LAMBDA 2M GaAs—A multi-megapixel hard X-ray detector for synchrotrons. Journal of Instrumentation, 13(01): C01026. DOI: 10.1088/1748-0221/13/01/C01026
- Ramsundar, B and Zadeh, RB. 2018. TensorFlow for deep learning: from linear regression to reinforcement learning. “O’Reilly Media, Inc.”.
- RDA COVID-19 Working Group. 2020. Recommendations and guidelines on data sharing. Research Data Alliance, 10. DOI: 10.15497/rda00052
- Ruder, S. 2016. An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
- Singh, S. 2001. Quantifying structural time varying changes in helical data. Neural Computing & Applications, 10(2): 148–154.
- Suganya, S and Charles, EYA. September 2019. Speech Emotion Recognition Using Deep Learning on audio recordings. In 2019 19th International Conference on Advances in ICT for Emerging Regions (ICTer), 250: 1–6.
IEEE . DOI: 10.1109/ICTer48817.2019.9023737 - Tschentscher, T, Bressler, C, Gruenert, J, Madsen, A, Mancuso, A, Meyer, M, Scherz, A, Sinn, H and Zastrau, U. 2017. Photon Beam Transport and Scientific Instruments at the European XFEL. Applied Sciences, 7: 592. DOI: 10.3390/app7060592
- Vaswani, A, Shazeer, N, Parmar, N, Uszkoreit, J, Jones, L, Gomez, AN, Kaiser, L and Polosukhin, I. 2017. Attention is all you need. arXiv preprint arXiv:1706.03762.
- Wilkinson, MD, Dumontier, M, Aalbersberg, IJ, Appleton, G, Axton, M, Baak, A, Blomberg, N, Boiten, JW, da Silva Santos, LB, Bourne, PE and Bouwman, J. 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data, 3(1): 1–9. DOI: 10.1038/sdata.2016.18
- Yang, J, Xu, J, Zhang, X, Wu, C, Lin, T and Ying, Y. 2019. Deep learning for vibrational spectral analysis: Recent progress and a practical guide. Analytica Chimica Acta, 1081: 6–17. DOI: 10.1016/j.aca.2019.06.012
- Zalden, P, Quirin, F, Schumacher, M, Siegel, J, Wei, S, Koc, A, Nicoul, M, Trigo, M, Andreasson, P, Enquist, H, Shu, MJ, Pardini, T, Chollet, M, Zhu, D, Lemke, H, Ronneberger, I, Larsson, J, Lindenberg, AM, Fischer, HE, Hau-Riege, S, Reis, DA, Mazzarello, R, Wuttig, M and Sokolowski-Tinten, K. 2019. ‘Femtosecond x-ray diffraction reveals a liquid–liquid phase transition in phase-change materials’, Science, 364(6445): 1062–1067. DOI: 10.1126/science.aaw1773
- Zeiler, MD. 2012. ADADELTA: An adaptive learning rate method. arXiv 2012. arXiv preprint arXiv:1212.5701, 1212.