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Borja, E. F., Garay, I., & Vidotto, F. (2012). Learning about Quantum Gravity with a Couple of Nodes. Symmetry Integr. Geom., 8, 015–44pp.
Abstract: Loop Quantum Gravity provides a natural truncation of the infinite degrees of freedom of gravity, obtained by studying the theory on a given finite graph. We review this procedure and we present the construction of the canonical theory on a simple graph, formed by only two nodes. We review the U(N) framework, which provides a powerful tool for the canonical study of this model, and a formulation of the system based on spinors. We consider also the covariant theory, which permits to derive the model from a more complex formulation, paying special attention to the cosmological interpretation of the theory.
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Aliaga, R. J., & Guirao, A. J. (2019). On the preserved extremal structure of Lipschitz-free spaces. Studia Math., 245(1), 1–14.
Abstract: We characterize preserved extreme points of the unit ball of Lipschitz-free spaces F (X) in terms of simple geometric conditions on the underlying metric space (X, d). Namely, the preserved extreme points are the elementary molecules corresponding to pairs of points p, q in X such that the triangle inequality d (p, q) <= d (p, r) + d (q, r) is uniformly strict for r away from p, q. For compact X, this condition reduces to the triangle inequality being strict. As a consequence, we give an affirmative answer to a conjecture of N. Weaver that compact spaces are concave if and only if they have no triple of metrically aligned points, and we show that all extreme points are preserved for several classes of compact metric spaces X, including Holder and countable compacta.
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Gomez-Cadenas, J. J., Benlloch-Rodriguez, J. M., & Ferrario, P. (2016). Application of scintillating properties of liquid xenon and silicon photomultiplier technology to medical imaging. Spectroc. Acta Pt. B, 118, 6–13.
Abstract: We describe a new positron emission time-of-flight apparatus using liquid xenon. The detector is based in a liquid xenon scintillating cell. The cell shape and dimensions can be optimized depending on the intended application. In its simplest form, the liquid xenon scintillating cell is a box in which two faces are covered by silicon photomultipliers and the others by a reflecting material such as Teflon. It is a compact, homogenous and highly efficient detector which shares many of the desirable properties of monolithic crystals, with the added advantage of high yield and fast scintillation offered by liquid xenon. Our initial studies suggest that good energy and spatial resolution comparable with that achieved by lutetium oxyorthosilicate crystals can be obtained with a detector based in liquid xenon scintillating cells. In addition, the system can potentially achieve an excellent coincidence resolving time of better than 100 ps.
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Conde, D., Castillo, F. L., Escobar, C., García, C., Garcia Navarro, J. E., Sanz, V., et al. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather, 21(11), e2023SW003474–27pp.
Abstract: Severe space weather produced by disturbed conditions on the Sun results in harmful effects both for humans in space and in high-latitude flights, and for technological systems such as spacecraft or communications. Also, geomagnetically induced currents (GICs) flowing on long ground-based conductors, such as power networks, potentially threaten critical infrastructures on Earth. The first step in developing an alarm system against GICs is to forecast them. This is a challenging task given the highly non-linear dependencies of the response of the magnetosphere to these perturbations. In the last few years, modern machine-learning models have shown to be very good at predicting magnetic activity indices. However, such complex models are on the one hand difficult to tune, and on the other hand they are known to bring along potentially large prediction uncertainties which are generally difficult to estimate. In this work we aim at predicting the SYM-H index characterizing geomagnetic storms multiple-hour ahead, using public interplanetary magnetic field (IMF) data from the Sun-Earth L1 Lagrange point and SYM-H data. We implement a type of machine-learning model called long short-term memory (LSTM) network. Our scope is to estimate the prediction uncertainties coming from a deep-learning model in the context of forecasting the SYM-H index. These uncertainties will be essential to set reliable alarm thresholds. The resulting uncertainties turn out to be sizable at the critical stages of the geomagnetic storms. Our methodology includes as well an efficient optimization of important hyper-parameters of the LSTM network and robustness tests.
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SCiMMA and SNEWS Collaborations(Baxter, A. L. et al), & Colomer, M. (2022). Collaborative experience between scientific software projects using Agile Scrum development. Softw.-Pract. Exp., 52, 2077–2096.
Abstract: Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.
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