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Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor.

Cities represent a large and concentrated portion of global greenhouse gas emissions, including methane. Quantifying methane emissions from urban areas is difficult, and inventories made using bottom-up accounting methods often differ greatly from top-down estimates generated from atmospheric observations. Emissions from leaks in natural gas infrastructure are difficult to predict and are therefore poorly constrained in bottom-up inventories. Natural gas infrastructure leaks and emissions from end uses can be spread throughout the city, and this diffuse source can represent a significant fraction of a city's total emissions. We investigated diffuse methane emissions of the city of Indianapolis, USA, during a field campaign in May 2016. A network of five portable solar-tracking Fourier transform infrared (FTIR) spectrometers was deployed throughout the city. These instruments measure the mole fraction of methane in a total column of air, giving them sensitivity to larger areas of the city than in situ sensors at the surface. We present an innovative inversion method to link these total column concentrations to surface fluxes. This method combines a Lagrangian transport model with a Bayesian inversion framework to estimate surface emissions and their uncertainties, together with determining the concentrations of methane in the air flowing into the city. Variations exceeding 10 ppb were observed in the inflowing air on a typical day, which is somewhat larger than the enhancements due to urban emissions (

Heutige CAM-Systeme bieten viele hilfreiche Features wie die Bahngenerierung und Kollisionskontrolle. Die wichtige Prognose des für die Fertigung eines bestimmten Bauteils erforderlichen Zeit- und Energiebedarfs ist dagegen nicht hinreichend genau möglich. Dieser Fachbeitrag zeigt für die spanende Fertigung (Fräsen), wie auf der Grundlage von Standard-NC-Programmcodes - angereichert um ausgewählte Prozess- und Maschinenparameter - eine realitätsnahe zeitliche sowie energetische Prognose in ein CAM- beziehungsweise PLM-System integriert werden kann.

Proxy records from stony reef corals are established tools for reconstructing climate features. However, validation procedures for proving the calibrations are often insufficient or even lacking. We address this problem by the establishment and validation of statistical regression models for the reconstruction of large-scale climate patterns from local coral records (upscaling). We apply the upscaling technique to the annual growth rates of two coral species from Bermuda, representing a period of about 140 years. The coral records are related to reanalyzed sea surface temperature (SST) and sea level pressure (SLP) fields. In the western North Atlantic, the correlation maps between the SSTs and the single coral records and their leading principal component show similar structures. On the basis of regression patterns for limited areas, upscaling models are established. Validation of such models for the SST yields an explained variance of 30% of the low-pass-filtered data using the first principal component of both corals. For the detrended SLP time series, representing an almost hemispheric SLP pattern, 21% of the interannual variability is explained by means of only one coral. Thus different corals from neighboring sites represent similar climate signals, however, to slightly varying degrees, leading to different reconstruction skills. Problems remain in reconstructing long-term trends. We conclude that reconstructions from multiple corals are not a priori predominant to those from single corals. A calibration to what extent a single coral represents a climate parameter should be the first step before combining multiple corals as the basis for a reconstruction.