Multimodal variational autoencoder for inverse problems in geophysics: application to a 1-D magnetotelluric problem

Estimating subsurface properties from geophysical measurements is a common inverse problem. Several Bayesian methods currently aim to find the solution to a geophysical inverse problem and quantify its uncertainty. However, most geophysical applications exhibit more than one plausible solution. Here, we propose a multimodal variational autoencoder model that employs a mixture of truncated Gaussian densities to provide multiple solutions, along with their probability of occurrence and a quantification of their uncertainty. This autoencoder is assembled with an encoder and a decoder, where the first one provides a mixture of truncated Gaussian densities from a neural network, and the second is the numerical solution of the forward problem given by the geophysical approach. The proposed method is illustrated with a 1-D magnetotelluric inverse problem and recovers multiple plausible solutions with different uncertainty quantification maps and probabilities that are in agreement with known physical observations.PDC2021-121093-I00 IA4TE

Fast 2.5D Finite Element Simulations of Borehole Resistivity Measurements

We develop a rapid 2.5-dimensional (2.5D) finite element method for simulation of borehole resistivity measurements in transversely isotropic (TI) media. The method combines arbitrary high-order $H^1$ - and $H$ (curl)-conforming spatial discretizations. It solves problems where material properties remain constant along one spatial direction, over which we consider a Fourier series expansion and each Fourier mode is solved independently. We propose a novel a priori method to construct quasi-optimal discretizations in physical and Fourier space. This construction is based on examining the analytical (fundamental) solution of the 2.5D formulation over multiple homogeneous spaces and assuming that some of its properties still hold for the 2.5D problem over a spatially heterogeneous formation. In addition, a simple parallelization scheme over multiple measurement positions provides efficient scalability. Our method yields accurate borehole logging simulations for realistic synthetic examples, delivering simulations of borehole resistivity measurements at a rate faster than 0.05 s per measurement location along the well trajectory on a 96-core computer

Finite Element Simulations of Logging-While-Drilling and Extra-Deep Azimuthal Resistivity Measurements using Non-Fitting Grids

We propose a discretization technique using non-fitting grids to simulate magnetic field-based resistivity logging measurements. Non-fitting grids are convenient because they are simpler to generate and handle than fitting grids when the geometry is complex. On the other side, fitting grids have been historically preferred because they offer additional accuracy for a fixed problem size in the general case. In this work, we analyse the use of non-fitting grids to simulate the response of logging instruments that are based on magnetic field resistivity measurements using 2.5D Maxwell’s equations. We provide various examples demonstrating that, for these applications, if the finite element matrix coefficients are properly integrated, the accuracy loss due to the use of non-fitting grids is negligible compared to the case where fitting grids are employed

Modeling and forecasting gender-based violence through machine learning techniques

Gender-Based Violence (GBV) is a serious problem that societies and governments must address using all applicable resources. This requires adequate planning in order to optimize both resources and budget, which demands a thorough understanding of the magnitude of the problem, as well as analysis of its past impact in order to infer future incidence. On the other hand, for years, the rise of Machine Learning techniques and Big Data has led different countries to collect information on both GBV and other general social variables that in one way or another can affect violence levels. In this work, in order to forecast GBV, firstly, a database of features related to more than a decade’s worth of GBV is compiled and prepared from official sources available due to Spain’s open access. Then, secondly, a methodology is proposed that involves testing different methods of features selection so that, with each of the subsets generated, four techniques of predictive algorithms are applied and compared. The tests conducted indicate that it is possible to predict the number of GBV complaints presented to a court at a predictive horizon of six months with an accuracy (Root Median Squared Error) of 0.1686 complaints to the courts per 10,000 inhabitants—throughout the whole Spanish territory—with a Multi-Objective Evolutionary Search Strategy for the selection of variables, and with Random Forest as the predictive algorithm. The proposed methodology has also been successfully applied to three specific Spanish territories of different populations (large, medium, and small), pointing to the presented method’s possible use elsewhere in the world

Channel Capacities for Different Antenna Arrays with Various Transmitting Angles in Tunnels

[[abstract]]This paper focuses on the research of channel capacity of multiple-input multipleoutput (MIMO) system with different transmitting angles in straight and curvy tunnels.Araytracing technique is developed to calculate channel frequency responses for tunnels, and the channel frequency response is further used to calculate corresponding channel capacity. The channel capacities are calculated based on the realistic environment. The channel capacities of MIMO long term evolution system using spatial and polar antenna arrays by different transmitting angles are computed. Numerical results show that, The channel capacity for transmitting angle at 15◦ is largest compared to the other angles in the tunnels. Moreover, the channel capacity of polar array is better than that of spatial array both in the straight and curvy tunnels. Besides, the channel capacity for the tunnels with traffic is larger than that without traffic. Finally, it isworth noting that in these cases the presentwork provides not only comparative information but also quantitative information on the performance reduction.[[notice]]補正完畢[[incitationindex]]SC

Phase transitions in tumor growth VI: Epithelial–Mesenchymal transition

Herewith we discuss a network model of the epithelial–mesenchymal transition (EMT) based on our previous proposed framework. The EMT appears as a “first order” phase transition process, analogous to the transitions observed in the chemical–physical field. Chiefly, EMT should be considered a transition characterized by a supercritical Andronov–Hopf bifurcation, with the emergence of limit cycle and, consequently, a cascade of saddle-foci Shilnikov's bifurcations. We eventually show that the entropy production rate is an EMT-dependent function and, as such, its formalism reminds the van der Waals equation.Fil: Guerra, A.. Universidad de La Habana; CubaFil: Rodriguez, D. J.. Universidad de La Habana; CubaFil: Montero, S.. Medical Sciences University Of Havana; CubaFil: Betancourt Mar, J. A.. Universidad de La Habana; CubaFil: Martín Pardo, Reinaldo Román. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Investigaciones en Tecnología Química. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Investigaciones en Tecnología Química; Argentina. Mexican Institute Of Complex Systems. Tamaulipas; MéxicoFil: Silva Lamar, Eduardo. Universidad de La Habana; CubaFil: Bizzarri, María Julia. Universidad de La Habana; CubaFil: Cocho, G.. Universidad Nacional Autónoma de México; MéxicoFil: Mansilla, R.. Universidad Nacional Autónoma de México; MéxicoFil: Nieto Villar, José Manuel. Universidad de La Habana; Cub

Imaging neutron capture cross sections: i-TED proof-of-concept and future prospects based on Machine-Learning techniques

Babiano-Suárez, V., et al.i-TED is an innovative detection system which exploits Compton imaging techniques to achieve a superior signal-to-background ratio in (n, γ) cross-section measurements using time-of-flight technique. This work presents the first experimental validation of the i-TED apparatus for high-resolution time-of-flight experiments and demonstrates for the first time the concept proposed for background rejection. To this aim, the Au(n, γ) and Fe(n, γ) reactions were studied at CERN n_TOF using an i-TED demonstrator based on three position-sensitive detectors. Two CD detectors were also used to benchmark the performance of i-TED. The i-TED prototype built for this study shows a factor of ∼ 3 higher detection sensitivity than state-of-the-art CD detectors in the 10 keV neutron-energy region of astrophysical interest. This paper explores also the perspectives of further enhancement in performance attainable with the final i-TED array consisting of twenty position-sensitive detectors and new analysis methodologies based on Machine-Learning techniques.This work has been carried out in the framework of a project funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Consolidator Grant project HYMNS, with grant agreement No. 681740). The authors acknowledge support from the Spanish Ministerio de Ciencia e Innovación under grants PID2019-104714GB-C21, FPA2017-83946-C2-1-P, FIS2015-71688-ERC, CSIC for funding PIE-201750I26, and the funding agencies of the participating institutes. We would like to thank the crew at the Electronics Laboratory of IFIC, in particular Manuel Lopez Redondo and Jorge Nácher Arándiga for their excellent and efficient work

LDL particle size and composition and incident cardiovascular disease in a South-European population: The Hortega-Liposcale Follow-up Study.

The association of low-density lipoprotein (LDL) particle composition with cardiovascular risk has not been explored before. The aim was to evaluate the relationship between baseline LDL particle size and composition (proportions of large, medium and small LDL particles over their sum expressed as small-LDL %, medium-LDL % and large-LDL %) and incident cardiovascular disease in a population-based study. Methods: Direct measurement of LDL particles was performed using a two-dimensional NMR-technique (Liposcale®). LDL cholesterol was assessed using both standard photometrical methods and the Liposcale® technique in a representative sample of 1162 adult men and women from Spain. Results: The geometric mean of total LDL particle concentration in the study sample was 827.2 mg/dL (95% CI 814.7, 839.8). During a mean follow-up of 12.4 ± 3.3 years, a total of 159 events occurred. Medium LDL particles were positively associated with all cardiovascular disease, coronary heart disease (CHD) and stroke after adjustment for traditional risk factors and treatment. Regarding LDL particle composition, the multivariable adjusted hazard ratios for CHD for a 5% increase in medium and small LDL % by a corresponding decrease of large LDL % were 1.93 (1.55, 2.39) and 1.41 (1.14, 1.74), respectively. Conclusions: Medium LDL particles were associated with incident cardiovascular disease. LDL particles showed the strongest association with cardiovascular events when the particle composition, rather than the total concentration, was investigated. A change in baseline composition of LDL particles from large to medium and small LDL particles was associated with an increased cardiovascular risk, especially for CHD

Characteristics of prosthetic joint infections due to Enterococcus sp. and predictors of failure: a multi-national study

n/

Expression of insulin-like growth factor I by activated hepatic stellate cells reduces fibrogenesis and enhances regeneration after liver injury

BACKGROUND/AIM: Hepatic stellate cells (HSCs) express alpha-smooth muscle actin (alphaSMA) and acquire a profibrogenic phenotype upon activation by noxious stimuli. Insulin-like growth I (IGF-I) has been shown to stimulate HSCs proliferation in vitro, but it has been reported to reduce liver damage and fibrogenesis when given to cirrhotic rats. METHODS: The authors used transgenic mice (SMP8-IGF-I) expressing IGF-I under control of alphaSMA promoter to study the influence of IGF-I synthesised by activated HSCs on the recovery from liver injury. RESULTS: The transgene was expressed by HSCs from SMP8-IGF-I mice upon activation in culture and in the livers of these animals after CCl4 challenge. Twenty four hours after administration of CCl4 both transgenic and wild type mice showed similar extensive necrosis and increased levels of serum transaminases. However at 72 hours SMP8-IGF-I mice exhibited lower serum transaminases, reduced hepatic expression of alphaSMA, and improved liver morphology compared with wild type littermates. Remarkably, at this time all eight CCl4 treated wild type mice manifested histological signs of liver necrosis that was severe in six of them, while six out of eight transgenic animals had virtually no necrosis. In SMP8-IGF-I mice robust DNA synthesis occurred earlier than in wild type animals and this was associated with enhanced production of HGF and lower TGFbeta1 mRNA expression in the SMP8-IGF-I group. Moreover, Colalpha1(I) mRNA abundance at 72 hours was reduced in SMP8-IGF-I mice compared with wild type controls. CONCLUSIONS: Targeted overexpression of IGF-I by activated HSCs restricts their activation, attenuates fibrogenesis, and accelerates liver regeneration. These effects appear to be mediated in part by upregulation of HGF and downregulation of TGFbeta1. The data indicate that IGF-I can modulate the cytokine response to liver injury facilitating regeneration and reducing fibrosis