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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.
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Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
Keywords: dark matter; dark matter detection; neural networks; xenon1T; WIMPs
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Huang, F., Sanz, V., Shu, J., & Xue, X. (2021). LIGO as a probe of dark sectors. Phys. Rev. D, 104(10), 095001–9pp.
Abstract: We show how current LIGO data is able to probe interesting theories beyond the Standard Model, particularly dark sectors where a dark Higgs boson triggers symmetry breaking via a first-order phase transition. We use publicly available LIGO O2 data to illustrate how these sectors, even if disconnected from the Standard Model, can be probed by gravitational wave detectors. We link the LIGO measurements with the model content and mass scale of the dark sector, finding that current O2 data are testing a broad set of scenarios that can be mapped into many different types of dark-sector models where the breaking of SU(N) theories with Nf fermions is triggered by a dark Higgs boson at scales ? similar or equal to 108-109 GeV with reasonable parameters for the scalar potential.
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Sanchis-Lozano, M. A., & Sanz, V. (2024). Observable imprints of primordial gravitational waves on the temperature anisotropies of the cosmic microwave background. Phys. Rev. D, 109(6), 063529–11pp.
Abstract: We examine the contribution of tensor modes, in addition to the dominant scalar ones, on the temperature anisotropies of the cosmic microwave background (CMB). To this end, we analyze in detail the temperature two -point angular correlation function C(Theta) from the Planck 2018 dataset, focusing on large angles (Theta greater than or similar to 120 degrees) corresponding to small l multipoles. A hierarchical set of infrared cutoffs are naturally introduced to the scalar and tensor power spectra of the CMB by invoking an extra Kaluza-Klein spatial dimension compactifying at about the grand unified theory scale between the Planck epoch and the start of inflation. We associate this set of lower scalar and tensor cutoffs with the parity of the multipole expansion of the C(Theta) function. By fitting the Planck 2018 data we compute the multipole coefficients, thereby reproducing the well-known odd -parity preference in angular correlations seen by all three satellite missions: Cosmic Background Explorer, WMAP, and Planck. Our fits improve significantly once tensor modes are included in the analysis, hence providing a hint of the imprints of primordial gravitational waves on the temperature correlations observed in the CMB today. To conclude, we suggest a relationship between, on the one hand, the lack of (positive) large -angle correlations and the odd -parity dominance in the CMB and, on the other hand, the effect of primordial gravitational waves on the CMB temperature anisotropies.
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Barenboim, G., Del Debbio, L., Hirn, J., & Sanz, V. (2024). Exploring how a generative AI interprets music. Neural Comput. Appl., 36, 17007–17022.
Abstract: We aim to investigate how closely neural networks (NNs) mimic human thinking. As a step in this direction, we study the behavior of artificial neuron(s) that fire most when the input data score high on some specific emergent concepts. In this paper, we focus on music, where the emergent concepts are those of rhythm, pitch and melody as commonly used by humans. As a black box to pry open, we focus on Google’s MusicVAE, a pre-trained NN that handles music tracks by encoding them in terms of 512 latent variables. We show that several hundreds of these latent variables are “irrelevant” in the sense that can be set to zero with minimal impact on the reconstruction accuracy. The remaining few dozens of latent variables can be sorted by order of relevance by comparing their variance. We show that the first few most relevant variables, and only those, correlate highly with dozens of human-defined measures that describe rhythm and pitch in music pieces, thereby efficiently encapsulating many of these human-understandable concepts in a few nonlinear variables.
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