Deflation for inversion with multiple right-hand sides in QCD

Most calculations in lattice Quantum Chromodynamics (QCD) involve the solution of a series of linear systems of equations with exceedingly large matrices and a large number of right hand sides. Iterative methods for these problems can be sped up significantly if we deflate approximations of appropriate invariant spaces from the initial guesses. Recently we have developed eigCG, a modification of the Conjugate Gradient (CG) method, which while solving a linear system can reuse a window of the CG vectors to compute eigenvectors almost as accurately as the Lanczos method. The number of approximate eigenvectors can increase as more systems are solved. In this paper we review some of the characteristics of eigCG and show how it helps remove the critical slowdown in QCD calculations. Moreover, we study scaling with lattice volume and an extension of the technique to nonsymmetric problems

Application of neural networks to synchro-Compton blazar emission models

Jets from supermassive black holes in the centers of active galaxies are the most powerful persistent sources of electromagnetic radiation in the Universe. To infer the physical conditions in the otherwise out-of-reach regions of extragalactic jets we usually rely on fitting of their spectral energy distribution (SED). The calculation of radiative models for the jet non-thermal emission usually relies on numerical solvers of coupled partial differential equations. In this work machine learning is used to tackle the problem of high computational complexity in order to significantly reduce the SED model evaluation time, which is needed for SED fitting with Bayesian inference methods. We compute SEDs based on the synchrotron self-Compton model for blazar emission using the radiation code ATHE${\nu}$A, and use them to train Neural Networks exploring whether these can replace the original computational expensive code. We find that a Neural Network with Gated Recurrent Unit neurons can effectively replace the ATHE${\nu}$A leptonic code for this application, while it can be efficiently coupled with MCMC and nested sampling algorithms for fitting purposes. We demonstrate this through an application to simulated data sets and with an application to observational data. We offer this tool in the community through a public repository. We present a proof-of-concept application of neural networks to blazar science. This is the first step in a list of future applications involving hadronic processes and even larger parameter spaces.Comment: 12 pages, submitted, comments are welcome, code will be soon available at https://github.com/tzavellas/blazar_m

Mitigating the impact of errors in travel time reporting on mode choice modelling

Travel time is a major component in understanding travel demand. However, the quantification of demand and forecasting hinges on understanding how travel time is perceived and reported. Travel time reporting is typically subject to errors and this paper focuses on the mitigation of their impact on choice models. The aim is to explain the origin of these errors by including elements of travel behaviour (e.g., activities during the trip), which have been shown to significantly affect mode choices and commuting satisfaction. Based on responses from a revealed preferences survey, we estimate a mode choice model that treats travel time as a latent variable and incorporates different sources of data along with information on travel activities. Employing these multiple \u2013 sometimes incongruent \u2013 sources of information in the choice model appears to be beneficial. Results from comparing a logit model assuming error-free inputs and the integrated hybrid model revealed significant impacts on the generated policy scenarios. The model results also contributed to identifying the main travel activity features that affect travel time reporting, providing indications that can assist in understanding and mitigating the impact of imprecise measurements

Assessment of proliferating cell nuclear antigen and its relationship with proinflammatory cytokines and parameters of disease activity in multiple myeloma patients

Multiple myeloma (MM) is a malignant plasma cell disease. Several proinflammatory cytokines produced by malignant plasma cells and bone marrow (BM) stromal cells are involved in the pathogenesis of the disease. We evaluated serum levels of the proinflammatory cytokines Interleukin-1β (IL-1β), Interleukin-6 (IL-6), Interleukin-8 (IL-8), macrophage inflammatory protein-1α (MIP-1α), in MM patients before treatment, and determined its significance in tumor progression. We also analyzed the correlation between measured parameters with proliferating cell nuclear antigen (PCNA). Forty-four MM patients and 20 healthy controls were studied. Serum levels of the proinflammatory cytokines were measured using enzyme-linked immunosorbent assay (ELISA), whereas PCNA value in the BM was determined by immunohistochemistry staining. The mean concentrations of the measured cytokines were significantly different among the three stages of disease, with higher values in advanced disease stage. Furthermore, patients with MM had significantly higher serum levels of the measured cytokines than in controls. A positive correlation was found between IL-6 with IL-1β, IL-8 and MIP-1α. Similarly, IL-8 and MIP-1α were positively correlated with markers of disease activity such as β2 microglobulin and LDH. The proliferation index, determined by PCNA immunostaining, was higher in advanced disease stage. Furthermore PCNA value correlated significantly with β2 microglobulin, LDH and the levels of the measured cytokines. Our results showed that the proliferative activity, as measured with PCNA, increases in parallel with disease stage. The positive correlation between PCNA and other measured mediators supports the involvement of these factors in the biology of myeloma cell growth and can be used as markers of disease activity and as possible therapeutic targets