|
|
Dilator, J. M., Jaworski, G., Goasduff, A., Gonzalez, V., Gadea, A., Palacz, M., et al. (2025). Reconstruction of pile-up events using a one-dimensional convolutional autoencoder for the NEDA detector array. Nucl. Sci. Tech., 36(2), 32–9pp.
Abstract: Pulse pile-up is a problem in nuclear spectroscopy and nuclear reaction studies that occurs when two pulses overlap and distort each other, degrading the quality of energy and timing information. Different methods have been used for pile-up rejection, both digital and analogue, but some pile-up events may contain pulses of interest and need to be reconstructed. The paper proposes a new method for reconstructing pile-up events acquired with a neutron detector array (NEDA) using an one-dimensional convolutional autoencoder (1D-CAE). The datasets for training and testing the 1D-CAE are created from data acquired from the NEDA. The new pile-up signal reconstruction method is evaluated from the point of view of how similar the reconstructed signals are to the original ones. Furthermore, it is analysed considering the result of the neutron-gamma discrimination based on charge comparison, comparing the result obtained from original and reconstructed signals.
|
|
|
|
Hirn, J., Garcia, J. E., Montesinos-Navarro, A., Sanchez-Martin, R., Sanz, V., & Verdu, M. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods Ecol. Evol., 13, 1052–1061.
Abstract: Predicting coexistence patterns is a current challenge to understand diversity maintenance, especially in rich communities where these patterns' complexity is magnified through indirect interactions that prevent their approximation with classical experimental approaches. We explore cutting-edge Machine Learning techniques called Generative Artificial Intelligence (GenAI) to predict species coexistence patterns in vegetation patches, training generative adversarial networks (GAN) and variational AutoEncoders (VAE) that are then used to unravel some of the mechanisms behind community assemblage. The GAN accurately reproduces real patches' species composition and plant species' affinity to different soil types, and the VAE also reaches a high level of accuracy, above 99%. Using the artificially generated patches, we found that high-order interactions tend to suppress the positive effects of low-order interactions. Finally, by reconstructing successional trajectories, we could identify the pioneer species with larger potential to generate a high diversity of distinct patches in terms of species composition. Understanding the complexity of species coexistence patterns in diverse ecological communities requires new approaches beyond heuristic rules. Generative Artificial Intelligence can be a powerful tool to this end as it allows to overcome the inherent dimensionality of this challenge.
|
|
|
|
Hirn, J., Sanz, V., Garcia Navarro, J. E., Goberna, M., Montesinos-Navarro, A., Navarro-Cano, J. A., et al. (2024). Transfer learning of species co-occurrence patterns between plant communities. Ecol. Inform., 83, 102826–8pp.
Abstract: Aim: The use of neural networks (NNs) is spreading to all areas of life, and Ecology is no exception. However, the data-hungry nature of NNs can leave out many small, valuable datasets. Here we show how to apply transfer learning to rescue small datasets that can be invaluable in understanding patterns of species co-occurrence. Location: Semiarid plant communities in Spain and Me<acute accent>xico. Time period: 2016-2022. Major taxa studied: Angiosperms. Methods: Based on a large sample of plant species co-occurrence in vegetation patches in a semi-arid area of eastern Spain, we fit a generative artificial intelligence (AI) model that correctly reproduces which species live with which in these patches. Subsequently, we train the same type of model on two communities for which we only have smaller datasets (another semi-arid community in eastern Spain, and a tropical community in Mexico). Results: When we transfer the knowledge learnt from the large dataset directly to the other two, the predictions improve for the community more similar to our reference one. As for the more dissimilar community, improving the accuracy of the transfer requires a further tuning of the model to the local data. In particular, the knowledge transferred relates primarily to species frequency and, to a lesser extent, to their phylogenetic relationships, which are known to be determinants of species interaction patterns. Main conclusions: This AI-based approach can be performed for communities similar or not so similar to the reference community, opening the door to systematic transfer learning for accurate predictions on small datasets. Interestingly, this transfer operates by matching unrelated species between the origin and target datasets, implying that arbitrary datasets can then be transferred to, or even combined in order to augment each other, irrespective of the species involved, potentially allowing such models to be applied to a wide range of plant communities in different climates.
|
|
