publications

Publications by years by researchers affiliated with TOELT LLC.

Table of Contents

2022

  1. Michelucci, U., & Venturini, F. (2022). New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences. ArXiv Preprint.
    @article{michelucci2022errorsArxiv,
      title = {New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {arXiv preprint},
      year = {2022},
      month = oct,
      topic = {mltheory},
      abbr = {Theory},
      selected = {true}
    }
    
  2. Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 336, 111198.
    @article{venturini2023extraction,
      title = {Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil},
      author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Gucciardi, Arnaud and Martos, Vanessa M. and Deriu, Marco A.},
      journal = {Journal of Food Engineering},
      volume = {336},
      pages = {111198},
      year = {2022},
      month = sep,
      publisher = {Elsevier},
      selected = {true},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food}
    }
    
  3. Venturini, F., Sperti, M., Michelucci, U., Gucciardi, A., Martos, V. M., & Deriu, M. A. (2022). Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil. Journal of Food Engineering, 111198.
    @article{venturini2022extraction,
      title = {Extraction of physicochemical properties from the fluorescence spectrum with 1D convolutional neural networks: Application to olive oil},
      author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Gucciardi, Arnaud and Martos, Vanessa M and Deriu, Marco A},
      journal = {Journal of Food Engineering},
      pages = {111198},
      year = {2022},
      publisher = {Elsevier}
    }
    
  4. Michelucci, U. (2022). Applied Deep Learning with TensorFlow 2 [Book]. Springer Nature/Apress.
    @book{michelucciapplied,
      title = {Applied Deep Learning with TensorFlow 2},
      author = {Michelucci, Umberto},
      publisher = {Springer Nature/Apress},
      year = {2022},
      abbr = {Book},
      topic = {mltheory},
      type = {Book},
      selected = {true},
      bibtex_show = {true}
    }
    
  5. Michelucci, U., & Venturini, F. (2022). New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences. ArXiv Preprint ArXiv:2209.15588.
    @article{michelucci2022new,
      title = {New Metric Formulas that Include Measurement Errors in Machine Learning for Natural Sciences},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {arXiv preprint arXiv:2209.15588},
      year = {2022}
    }
    
  6. Arnaud, G., Michelucci, U., Venturini, F., Sperti, M., & Deriu, M. A. (2022). Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil. Optical Sensing and Detection VII, 12139.
    @inproceedings{arnaud2022compact,
      title = {Compact optical fluorescence sensor for food quality control using artificial neural networks: application to olive oil},
      author = {Arnaud, Gucciardi and Michelucci, Umberto and Venturini, Francesca and Sperti, Michela and Deriu, Marco Agostino},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  7. Venturini, F., Michelucci, U., Sperti, M., Gucciardi, A., & Deriu, M. A. (2022). One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra. Optical Sensing and Detection VII, 12139, 326–333.
    @inproceedings{venturini2022one,
      title = {One-dimensional convolutional neural networks design for fluorescence spectroscopy with prior knowledge: explainability techniques applied to olive oil fluorescence spectra},
      author = {Venturini, Francesca and Michelucci, Umberto and Sperti, Michela and Gucciardi, Arnaud and Deriu, Marco A},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      pages = {326--333},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  8. Michelucci, U. (2022). An Introduction to Autoencoders [Theory]. ArXiv Preprint ArXiv:2201.03898.
    @article{michelucci2022introduction,
      title = {An Introduction to Autoencoders},
      author = {Michelucci, Umberto},
      journal = {arXiv preprint arXiv:2201.03898},
      year = {2022},
      topic = {mltheory},
      type = {Theory},
      abbr = {Theory}
    }
    
  9. Sperti, M., Gucciardi, A., Michelucci, U., Venturini, F., & Deriu, M. A. (2022). Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks. Optical Sensing and Detection VII, 12139.
    @inproceedings{sperti2022chemical,
      title = {Chemical analysis of olive oils from fluorescence spectra thanks to one-dimensional convolutional neural networks},
      author = {Sperti, Michela and Gucciardi, Arnaud and Michelucci, Umberto and Venturini, Francesca and Deriu, Marco A},
      booktitle = {Optical Sensing and Detection VII},
      volume = {12139},
      year = {2022},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      organization = {SPIE}
    }
    
  10. Milleville, K., Krishna Kumar Thirukokaranam, C., Blyau, T., Iannello, A., Michelucci, U., & Verstockt, S. (2022). Extraction and Classification of Historical Stamp Cards using Computer Vision. DH Benelux 2022: RE-MIX.
    @inproceedings{benelux,
      title = {Extraction and Classification of Historical Stamp Cards using Computer Vision},
      author = {Milleville, Kenzo and Krishna Kumar Thirukokaranam, Chandrasekar and Blyau, Thibault and Iannello, Aurora and Michelucci, Umberto and Verstockt, Steven},
      booktitle = {DH Benelux 2022: RE-MIX},
      year = {2022},
      organization = {DH Benelux 2022},
      selected = {true},
      abbr = {Imaging}
    }
    

2021

  1. Michelucci, U., Sperti, M., Piga, D., Venturini, F., & Deriu, M. A. (2021). A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification. Algorithms, 14(11), 301.
    @article{michelucci2021model,
      title = {A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification},
      author = {Michelucci, Umberto and Sperti, Michela and Piga, Dario and Venturini, Francesca and Deriu, Marco A},
      journal = {Algorithms},
      volume = {14},
      number = {11},
      pages = {301},
      year = {2021},
      bibtex_show = {true},
      abbr = {Theory},
      publisher = {Multidisciplinary Digital Publishing Institute},
      topic = {mltheory}
    }
    
  2. Venturini, F., Michelucci, U., & Baumgartner, M. (2021). Implementation of multi-task learning neural network architectures for robust industrial optical sensing. Optical Measurement Systems for Industrial Inspection XII, 11782, 117822H.
    @inproceedings{venturini2021implementation,
      title = {Implementation of multi-task learning neural network architectures for robust industrial optical sensing},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Optical Measurement Systems for Industrial Inspection XII},
      volume = {11782},
      pages = {117822H},
      year = {2021},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics}
    }
    
  3. Venturini, F., Sperti, M., Michelucci, U., Herzig, I., Baumgartner, M., Caballero, J. P., Jimenez, A., & Deriu, M. A. (2021). Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques. Foods, 10(5). https://doi.org/10.3390/foods10051010

    Extra virgin olive oil (EVOO) is the highest quality of olive oil and is characterized by highly beneficial nutritional properties. The large increase in both consumption and fraud, for example through adulteration, creates new challenges and an increasing demand for developing new quality assessment methodologies that are easier and cheaper to perform. As of today, the determination of olive oil quality is performed by producers through chemical analysis and organoleptic evaluation. The chemical analysis requires advanced equipment and chemical knowledge of certified laboratories, and has therefore limited accessibility. In this work a minimalist, portable, and low-cost sensor is presented, which can perform olive oil quality assessment using fluorescence spectroscopy. The potential of the proposed technology is explored by analyzing several olive oils of different quality levels, EVOO, virgin olive oil (VOO), and lampante olive oil (LOO). The spectral data were analyzed using a large number of machine learning methods, including artificial neural networks. The analysis performed in this work demonstrates the possibility of performing the classification of olive oil in the three mentioned classes with an accuracy of 100%. These results confirm that this minimalist low-cost sensor has the potential to substitute expensive and complex chemical analysis.

    @article{foods10051010,
      author = {Venturini, Francesca and Sperti, Michela and Michelucci, Umberto and Herzig, Ivo and Baumgartner, Michael and Caballero, Josep Palau and Jimenez, Arturo and Deriu, Marco Agostino},
      title = {Exploration of Spanish Olive Oil Quality with a Miniaturized Low-Cost Fluorescence Sensor and Machine Learning Techniques},
      journal = {Foods},
      volume = {10},
      year = {2021},
      number = {5},
      article-number = {1010},
      link = {https://www.mdpi.com/2304-8158/10/5/1010},
      pubmedid = {34066453},
      bibtex_show = {true},
      abbr = {Food},
      topic = {food},
      issn = {2304-8158},
      doi = {10.3390/foods10051010}
    }
    
  4. Halasz, G., Sperti, M., Villani, M., Michelucci, U., Agostoni, P., Biagi, A., Rossi, L., Botti, A., Mari, C., Maccarini, M., & others. (2021). A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score. Journal of Medical Internet Research, 23(5), e29058.
    @article{halasz2021machine,
      title = {A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score},
      author = {Halasz, Geza and Sperti, Michela and Villani, Matteo and Michelucci, Umberto and Agostoni, Piergiuseppe and Biagi, Andrea and Rossi, Luca and Botti, Andrea and Mari, Chiara and Maccarini, Marco and others},
      journal = {Journal of Medical Internet Research},
      volume = {23},
      number = {5},
      pages = {e29058},
      year = {2021},
      bibtex_show = {true},
      abbr = {Medicine},
      topic = {medicine},
      publisher = {JMIR Publications Inc., Toronto, Canada},
      link = {https://www.jmir.org/2021/5/e29058}
    }
    
  5. Michelucci, U., & Venturini, F. (2021). Estimating Neural Network’s Performance with Bootstrap: A Tutorial. Machine Learning and Knowledge Extraction, 3(2), 357–373. https://doi.org/10.3390/make3020018

    Neural networks present characteristics where the results are strongly dependent on the training data, the weight initialisation, and the hyperparameters chosen. The determination of the distribution of a statistical estimator, as the Mean Squared Error (MSE) or the accuracy, is fundamental to evaluate the performance of a neural network model (NNM). For many machine learning models, as linear regression, it is possible to analytically obtain information as variance or confidence intervals on the results. Neural networks present the difficulty of not being analytically tractable due to their complexity. Therefore, it is impossible to easily estimate distributions of statistical estimators. When estimating the global performance of an NNM by estimating the MSE in a regression problem, for example, it is important to know the variance of the MSE. Bootstrap is one of the most important resampling techniques to estimate averages and variances, between other properties, of statistical estimators. In this tutorial, the application of resampling techniques (including bootstrap) to the evaluation of neural networks’ performance is explained from both a theoretical and practical point of view. The pseudo-code of the algorithms is provided to facilitate their implementation. Computational aspects, as the training time, are discussed, since resampling techniques always require simulations to be run many thousands of times and, therefore, are computationally intensive. A specific version of the bootstrap algorithm is presented that allows the estimation of the distribution of a statistical estimator when dealing with an NNM in a computationally effective way. Finally, algorithms are compared on both synthetically generated and real data to demonstrate their performance.

    @article{make3020018,
      author = {Michelucci, Umberto and Venturini, Francesca},
      title = {Estimating Neural Network’s Performance with Bootstrap: A Tutorial},
      journal = {Machine Learning and Knowledge Extraction},
      volume = {3},
      year = {2021},
      number = {2},
      pages = {357--373},
      link = {https://www.mdpi.com/2504-4990/3/2/18},
      issn = {2504-4990},
      bibtex_show = {true},
      abbr = {Theory},
      doi = {10.3390/make3020018},
      topic = {mltheory}
    }
    
  6. F.D’Ascenzo, Filippo, O. D., Gallone, G., Mittone, G., Deriu, M. A., Iannaccone, M., Ariza-Solé, A., Liebetrau, C., Manzano-Fernández, S., Quadri, G., Kinnaird, T., Campo, G., Henriques, J. S., Hughes, J., Dominguez-Rodriguez, A., Aldinucci, M., Morbiducci, U., Patti, G., Raposeiras-Roubin, S., … Arfat, Y. (2021). Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets. The Lancet, 397(10270), 199–207.
    @article{2021machine,
      title = {Machine learning-based prediction of adverse events following an acute coronary syndrome (PRAISE): a modelling study of pooled datasets},
      author = {F.D'Ascenzo and Filippo, O. De and Gallone, G. and Mittone, G. and Deriu, M.A. and Iannaccone, M. and Ariza-Solé, A. and Liebetrau, C. and Manzano-Fernández, S. and Quadri, G. and Kinnaird, T. and Campo, G. and Henriques, J. S. and Hughes, J. and Dominguez-Rodriguez, A. and Aldinucci, M. and Morbiducci, U. and Patti, G. and Raposeiras-Roubin, S. and Abu-Assi, E. and Ferrari, G.M. De and Piroli, F. and Saglietto, A. and Conrotto, F. and Omedé, P. and Montefusco, A. and Pennone, M. and Bruno, F. and Bocchino, P. Paolo and Boccuzzi, G. and Cerrato, E. and F. and Varbella, O. and Sperti, M. and Wilton, S. B. and Velicki, L. and Xanthopoulou, I. and Cequier, A. and Iniguez-Romo, A. and Pousa, I. Munoz and Fern, M. Cespon and Queija, B. Caneiro and Cobas-Paz, R. and Lopez-Cuenca, A. and Garay, A. and Blanco, P. Flores and Rognoni, A. and Zoccai, G. Biondi and Biscaglia, S. and Nunez-Gil, I. and Fujii, T. and A. and ro Durante and Song, X. and Kawaji, T. and Alexopoulos, D. and Huczek, Z. and Juanatey, J. R. G. and Nie, S.-P. and Kawashiri, M.-aki and Colonnelli, I. and Cantalupo, B. and Esposito, R. and Leonardi, S. and Marra, W. G. and Chieffo, A. and Michelucci, U. and Piga, D. and Malavolta, M. and Gili, S. and Mennuni, M. and Montalto, C. and Visconti, L. O. and Arfat, Y.},
      journal = {The Lancet},
      volume = {397},
      number = {10270},
      pages = {199--207},
      year = {2021},
      bibtex_show = {true},
      abbr = {Medicine},
      topic = {medicine},
      link = {https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)32519-8/fulltext}
    }
    

2020

  1. Michelucci, U., & Venturini, F. (2020). New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference. Engineering Proceedings, 2(1). https://doi.org/10.3390/engproc2020002096

    The determination of multiple parameters via luminescence sensing is of great interest for many applications in different fields, like biosensing and biological imaging, medicine, and diagnostics. The typical approach consists in measuring multiple quantities and in applying complex and frequently just approximated mathematical models to characterize the sensor response. The use of machine learning to extract information from measurements in sensors have been tried in several forms before. But one of the problems with the approaches so far, is the difficulty in getting a training dataset that is representative of the measurements done by the sensor. Additionally, extracting multiple parameters from a single measurement has been so far an impossible problem to solve efficiently in luminescence. In this work a new approach is described for building an autonomous intelligent sensor, which is able to produce the training dataset self-sufficiently, use it for training a neural network, and then use the trained model to do inference on measurements done on the same hardware. For the first time the use of machine learning additionally allows to extract two parameters from one single measurement using multitask learning neural network architectures. This is demonstrated here by a dual oxygen concentration and temperature sensor.

    @article{engproc2020002096,
      author = {Michelucci, Umberto and Venturini, Francesca},
      title = {New Autonomous Intelligent Sensor Design Approach for Multiple Parameter Inference},
      journal = {Engineering Proceedings},
      volume = {2},
      year = {2020},
      number = {1},
      bibtex_show = {true},
      abbr = {Sensing},
      article-number = {96},
      link = {https://www.mdpi.com/2673-4591/2/1/96},
      issn = {2673-4591},
      topic = {oxygen},
      doi = {10.3390/engproc2020002096}
    }
    
  2. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Multi-task learning approach for optical luminescence sensing. Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020.
    @inproceedings{venturini2020multi,
      title = {Multi-task learning approach for optical luminescence sensing},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Applied Machine Learning Days (AMLD), Lausanne, 25-29 January 2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      year = {2020}
    }
    
  3. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks. Optical Sensing and Detection VI, 11354, 113541C.
    @inproceedings{venturini2020dual,
      title = {Dual oxygen and temperature sensing with single indicator using multi-task-learning neural networks},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Optical Sensing and Detection VI},
      volume = {11354},
      pages = {113541C},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics},
      link = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11354/113541C/Dual-oxygen-and-temperature-sensing-with-single-indicator-using-multi/10.1117/12.2554941.short}
    }
    
  4. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Dual oxygen and temperature luminescence learning sensor with parallel inference. Sensors, 20(17), 4886.
    @article{venturini2020duam,
      title = {Dual oxygen and temperature luminescence learning sensor with parallel inference},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      journal = {Sensors},
      volume = {20},
      number = {17},
      pages = {4886},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/1424-8220/20/17/4886/pdf}
    }
    
  5. Venturini, F., Michelucci, U., & Baumgartner, M. (2020). Deep-learning for multi-parameter luminescence sensing: demonstration of dual sensor. Frontiers in Optics, FTu2B–5.
    @inproceedings{venturini2020deep,
      title = {Deep-learning for multi-parameter luminescence sensing: demonstration of dual sensor},
      author = {Venturini, Francesca and Michelucci, Umberto and Baumgartner, Michael},
      booktitle = {Frontiers in Optics},
      pages = {FTu2B--5},
      year = {2020},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {Optical Society of America},
      link = {https://www.osapublishing.org/viewmedia.cfm?uri=FiO-2020-FTu2B.5&seq=0}
    }
    

2019

  1. Michelucci, U., Baumgartner, M., & Venturini, F. (2019). Optical oxygen sensing with artificial intelligence. Sensors, 19(4), 777.
    @article{michelucci2019optical,
      title = {Optical oxygen sensing with artificial intelligence},
      author = {Michelucci, Umberto and Baumgartner, Michael and Venturini, Francesca},
      journal = {Sensors},
      volume = {19},
      number = {4},
      pages = {777},
      year = {2019},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/1424-8220/19/4/777/pdf},
      bibtex_show = {true},
      topic = {oxygen},
      abbr = {Sensing}
    }
    
  2. Venturini, F., Baumgartner, M., & Michelucci, U. (2019). New approach for luminescence sensing based on machine learning. Optical Data Science II, 10937, 109370H.
    @inproceedings{venturini2019new,
      title = {New approach for luminescence sensing based on machine learning},
      author = {Venturini, Francesca and Baumgartner, Michael and Michelucci, Umberto},
      booktitle = {Optical Data Science II},
      volume = {10937},
      pages = {109370H},
      year = {2019},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      organization = {International Society for Optics and Photonics},
      link = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10937/109370H/New-approach-for-luminescence-sensing-based-on-machine-learning/10.1117/12.2508969.short}
    }
    
  3. Michelucci, U. (2019). Advanced applied deep learning: convolutional neural networks and object detection [Book]. Springer Nature.
    @article{michelucci2019advanced,
      title = {Advanced applied deep learning: convolutional neural networks and object detection},
      author = {Michelucci, Umberto},
      year = {2019},
      publisher = {Springer Nature/Apress},
      journal = {Springer Nature},
      link = {https://www.apress.com/gp/book/9781484249758},
      bibtex_show = {true},
      abbr = {Book},
      type = {Book},
      topic = {mltheory}
    }
    
  4. Michelucci, U., & Venturini, F. (2019). Multi-task learning for multi-dimensional regression: application to luminescence sensing. Applied Sciences, 9(22), 4748.
    @article{michelucci2019multi,
      title = {Multi-task learning for multi-dimensional regression: application to luminescence sensing},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {Applied Sciences},
      volume = {9},
      number = {22},
      pages = {4748},
      year = {2019},
      bibtex_show = {true},
      abbr = {Sensing},
      topic = {oxygen},
      publisher = {Multidisciplinary Digital Publishing Institute},
      link = {https://www.mdpi.com/2076-3417/9/22/4748/pdf}
    }
    

2017

  1. Michelucci, U., & Venturini, F. (2017). Novel semi-parametric algorithm for interference-immune tunable absorption spectroscopy gas Sensing. Sensors, 17(10), 2281.
    @article{michelucci2017novel,
      title = {Novel semi-parametric algorithm for interference-immune tunable absorption spectroscopy gas Sensing},
      author = {Michelucci, Umberto and Venturini, Francesca},
      journal = {Sensors},
      volume = {17},
      number = {10},
      pages = {2281},
      year = {2017},
      publisher = {Multidisciplinary Digital Publishing Institute},
      bibtex_show = {true},
      topic = {mltheory},
      abbr = {Algorithms}
    }
    
  2. Venturini, F., & Michelucci, U. (2017). Novel Algorithm for Calibration-Free Absorption Spectroscopy Sensor. Multidisciplinary Digital Publishing Institute Proceedings, 1(8), 833.
    @article{venturini2017novel,
      title = {Novel Algorithm for Calibration-Free Absorption Spectroscopy Sensor},
      author = {Venturini, Francesca and Michelucci, Umberto},
      journal = {Multidisciplinary Digital Publishing Institute Proceedings},
      volume = {1},
      number = {8},
      pages = {833},
      year = {2017},
      bibtex_show = {true},
      topic = {photonics},
      abbr = {Algorithms}
    }