|
|
Albiol, A., Albiol, F., Paredes, R., Plasencia-Martinez, J. M., Blanco Barrio, A., Garcia Santos, J. M., et al. (2022). A comparison of Covid-19 early detection between convolutional neural networks and radiologists. Insights Imaging, 13(1), 122–12pp.
Abstract: Background The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience. Methods The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx. Results Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx. Conclusion The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.
|
|
|
|
Hueso-Gonzalez, F., Casaña Copado, J. V., Fernandez Prieto, A., Gallas Torreira, A., Lemos Cid, E., Ros Garcia, A., et al. (2022). A dead-time-free data acquisition system for prompt gamma-ray measurements during proton therapy treatments. Nucl. Instrum. Methods Phys. Res. A, 1033, 166701–9pp.
Abstract: In cancer patients undergoing proton therapy, a very intense secondary radiation is produced during the treatment, which lasts around one minute. About one billion prompt gamma-rays are emitted per second, and their detection with fast scintillation detectors is useful for monitoring a correct beam delivery. To cope with the expected count rate and pile-up, as well as the scarce statistics due to the short treatment duration, we developed an eidetic data acquisition system capable of continuously digitizing the detector signal with a high sampling rate and without any dead time. By streaming the fully unprocessed waveforms to the computer, complex pile-up decomposition algorithms can be applied and optimized offline. We describe the data acquisition architecture and the multiple experimental tests designed to verify the sustained data throughput speed and the absence of dead time. While the system is tailored for the proton therapy environment, the methodology can be deployed in any other field requiring the recording of raw waveforms at high sampling rates with zero dead time.
|
|
|
|
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.
|
|
|
|
ATLAS Collaboration. (2022). A detailed map of Higgs boson interactions by the ATLAS experiment ten years after the discovery. Nature, 607(7917), 52–59.
Abstract: The standard model of particle physics(1-4) describes the known fundamental particles and forces that make up our Universe, with the exception of gravity. One of the central features of the standard model is a field that permeates all of space and interacts with fundamental particles(5-9). The quantum excitation of this field, known as the Higgs field, manifests itself as the Higgs boson, the only fundamental particle with no spin. In 2012, a particle with properties consistent with the Higgs boson of the standard model was observed by the ATLAS and CMS experiments at the Large Hadron Collider at CERN10,11. Since then, more than 30 times as many Higgs bosons have been recorded by the ATLAS experiment, enabling much more precise measurements and new tests of the theory. Here, on the basis of this larger dataset, we combine an unprecedented number of production and decay processes of the Higgs boson to scrutinize its interactions with elementary particles. Interactions with gluons, photons, and W and Z bosons-the carriers of the strong, electromagnetic and weak forces-are studied in detail. Interactions with three third-generation matter particles (bottom (b) and top (t) quarks, and tau leptons (tau)) are well measured and indications of interactions with a second-generation particle (muons, mu) are emerging. These tests reveal that the Higgs boson discovered ten years ago is remarkably consistent with the predictions of the theory and provide stringent constraints on many models of new phenomena beyond the standard model.
|
|
|
|
Baeza-Ballesteros, J., Hernandez, P., & Romero-Lopez, F. (2022). A lattice study of pi pi scattering at large N-c. J. High Energy Phys., 06(6), 049–39pp.
Abstract: We present the first lattice study of pion-pion scattering with varying number of colors, N-c. We use lattice simulations with four degenerate quark flavors, N-f = 4, and N-c= 3 – 6. We focus on two scattering channels that do not involve vacuum diagrams. These correspond to two irreducible representations of the SU(4) flavor group: the fully symmetric one, SS, and the fully antisymmetric one, AA. The former is a repulsive channel equivalent to the isospin-2 channel of SU(2). By contrast, the latter is attractive and only exists for N-f >= 4. A representative state is (vertical bar D-s(+) pi(+)> – vertical bar D+ K+ >) /root 2. Using Lfischer's formalism, we extract the near-threshold scattering amplitude and we match our results to Chiral Perturbation Theory (ChPT) at large N-c. For this, we compute the analytical U(N-f) ChPT prediction for two-pion scattering, and use the lattice results to constrain the N-c scaling of the relevant low-energy couplings.
|
|
