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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.
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Albiol, F., Corbi, A., & Albiol, A. (2019). Densitometric Radiographic Imaging With Contour Sensors. IEEE Access, 7, 18902–18914.
Abstract: We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D-S) or bone-enhanced (D'(B)) figures. In the D-S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D-B' type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination.
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Alcaide, J., Banerjee, S., Chala, M., & Titov, A. (2019). Probes of the Standard Model effective field theory extended with a right-handed neutrino. J. High Energy Phys., 08(8), 031–18pp.
Abstract: If neutrinos are Dirac particles and, as suggested by the so far null LHC results, any new physics lies at energies well above the electroweak scale, the Standard Model effective field theory has to be extended with operators involving the right-handed neutrinos. In this paper, we study this effective field theory and set constraints on the different dimension-six interactions. To that aim, we use LHC searches for associated production of light (and tau) leptons with missing energy, monojet searches, as well as pion and tau decays. Our bounds are generally above the TeV for order one couplings. One particular exception is given by operators involving top quarks. These provide new signals in top decays not yet studied at colliders. Thus, we also design an LHC analysis to explore these signatures in the tt production. Our results are also valid if the right-handed neutrinos are Majorana and long-lived.
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Alcaide, J., & Mileo, N. I. (2020). LHC sensitivity to singly charged scalars decaying into electrons and muons. Phys. Rev. D, 102(7), 075030–11pp.
Abstract: Current LHC searches for nonsupersymmetric singly charged scalars, based on two-Higgs-doublet models, in general, focus the analysis in third-generation fermions in the final state. However, singly charged scalars in alternative extensions of the scalar sector involve Yukawa couplings not proportional to the mass of the fermions. Assuming the scalar decays into electrons and muons, it can manifest cleaner experimental signatures. In this paper, we suggest that a singly charged scalar singlet, with electroweak production, can start to be probed in the near future with dedicated search strategies. Depending on the strength of the Yukawa couplings, two independent scenarios arc considered: direct pair production (small couplings) and single production via a virtual neutrino exchange (large couplings). We show that, up to a mass as large as 500 GeV, most of the parameter space could be excluded at the 95% C.L. in a high-luminosity phase of the LHC. Our results also apply to other frameworks, provided the singly charged scalar exhibits similar production patterns and dominant decay modes.
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Aldana, M., & Lledo, M. A. (2023). The Fuzzy Bit. Symmetry-Basel, 15(12), 2103–25pp.
Abstract: In this paper, the formulation of Quantum Mechanics in terms of fuzzy logic and fuzzy sets is explored. A result by Pykacz, which establishes a correspondence between (quantum) logics (lattices with certain properties) and certain families of fuzzy sets, is applied to the Birkhoff-von Neumann logic, the lattice of projectors of a Hilbert space. Three cases are considered: the qubit, two qubits entangled, and a qutrit 'nested' inside the two entangled qubits. The membership functions of the fuzzy sets are explicitly computed and all the connectives of the fuzzy sets are interpreted as operations with these particular membership functions. In this way, a complete picture of the standard quantum logic in terms of fuzzy sets is obtained for the systems considered.
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