AMS Bianka Model (Sets 01 11) 49l
Lino Dober
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Balti was cast in Abel Ferrara's movie Go Go Tales (2008) as one of the erotic dancers. After the movie and her pregnancy, she returned to the catwalk, became the new face of the Cesare Paciotti international campaign, and replaced Angelina Jolie as one of the faces of the fall St. John campaign, with models Hilary Rhoda and Caroline Winberg.[10][9]
Using an endogenous portfolio choice model, this paper examines how different monetary policy regimes can lead to different foreign currency positions by changing the cyclical properties of the nominal exchange rate. We find that strict inflation targeting regimes are associated with a short position in foreign currency, while the opposite is true for non-inflation targeting regimes. We also explore how these different external positions affect the international transmission of monetary shocks through the valuation channel. When central banks follow inflation targeting Taylor-type rules, valuation effects of monetary expansions are beggar-thy-self, but they are beggar-thy-neighbour in a money growth targeting regime (or when monetary policy puts weight on output stabilization)
- "Uncertainty and Monetary Policy: the Role of Precautionary Saving", (with Pawel Zabczyk),Bank of England working papers, forthcoming.This paper analyzes the conduct of monetary policy in an environment in which cyclical swings in risk appetite affect households' propensity to save. It uses a New-Keynesian model featuring external habit formation to show that taking note of precautionary saving motives justifies an accommodative policy bias in the face of persistent, adverse disturbances. Equally, policy should be more restrictive -- i.e. `lean against the wind' -- following positive shocks. Since the size of these `risk-adjustments' is increasing in the degree of macroeconomic volatility, ignoring this channel could lead to larger policy errors in turbulent times -- with good luck translating into good policy.
Figure 1. An example image of a participant with 5 ml WMH volume The top section shows the underlying FLAIR image, the middle section the manual segmentation mask, and the bottom section the BIANCA predicted mask (threshold: 0.8), from a model trained with a sample size of n = 40.
Figure 2. Overview of the resampling procedure for training the Brain Intensity AbNormality Classification Algorithm (BIANCA) models with varying sample sizes and evaluation of white matter hyperintensities (WMH) segmentation performance. The initial dataset consisted of the same 80 participants at baseline (BL) and follow-up (FU), as well as a separate external validation set with 41 participants. To prevent data leakage, all observations of a participant used in the training procedure were not allowed into the corresponding validation set. The resampling parameters are shown in the middle (7 sample sizes ranging from 10 to 40 with increments of 5; per sample size 100 random draws without replacement, only one observation per participant allowed). The corresponding validation sets (internal and external) are shown at the bottom.
Figure 4. Boxplots of absolute errors (BIANCA predicted volume - manually delineated volume) per trained BIANCA model ordered by median values. Overall, there are 700 models (100 per sample size) at a threshold of 0.8; each dot represents a single observation. The plots are stratified in a grid, horizontally by sample size (n = 7) and vertically by validation set (n = 3). The higher the sample size, the higher the chance to train a model with a low deviation from the gold standard (smaller range, less outliers, and smaller IQR). This shows a convergence of the accuracy of the models with increased sample size resulting in a more robust performance. The black line indicates the ideal absolute error (BIANCA - manual volume) of 0. Absolute errors greater than 0 show an overestimation of BIANCA, while absolute errors smaller than 0 show an underestimation.
Figure 5. Comparison of the mean BIANCA predicted volume (A) and mean absolute errors (B) of the two validation set types (internal validation set at BL and FU and external validation set) at increasing sample sizes at a threshold of 0.8. Shown are raincloud plots (Allen et al., 2019) of the mean BIANCA predicted volume (A) and the mean absolute error (B) by the model (n = 100), sample size (n = 7), and validation set (n = 3). Both figures: The trend shows, that if more subjects were randomly chosen for the training of a BIANCA model, the performance (less outliers, closer IQR) in all sets becomes better. This shows a convergence of performance resulting in a more robust performance. (A): Mean absolute lesion volumes increase from BL to FU. (B): Mean absolute errors are on average larger (more positive) at BL compared to FU. Mean absolute errors greater than 0 point toward an overestimation of white matter hyperintensity volume by the automated segmentation with BIANCA, while mean absolute errors smaller than 0 hint toward an underestimation by BIANCA in comparison with the manual delineation performance (reference standard).
Figure 6. Linear fits of each model show the association of manually segmented total WMH volume to automatically predicted volume (BIANCA) stratified by validation set and sample size. Shown are the linear fits of the manual volume on the x-axis to BIANCA predicted volume on the y-axis of each model (n = 100) in a grid stratified by sample size (n = 7) horizontally and validation set (n = 3) vertically. Only data at a white matter hyperintensity probability threshold of 0.8 is shown. The mean linear fit of all models is indicated by the blue line. The red line indicates the ideal fit, with an intercept of 0 and a slope of 1. The higher the sample size, i.e., the more subjects are drawn from the population, the closer the model performances draw to the mean linear fit of all models. This shows a convergence of the accuracy of each model resulting in a more robust performance. The mean linear fits of all models show an increasing underestimation of white matter hyperintensity volumes by BIANCA with increasing lesion volumes in all sample sizes and sets. Please refer to Supplementary Figure 5 for the same plot showing each participant in a density plot instead of the fit per model.
Table 4. Descriptive statistics of the mean absolute errors of lesion volume [Brain Intensity AbNormality Classification Algorithm (BIANCA) predicted white matter hyperintensities (WMH)-manual mask lesion, in ml] per model and validation set at a white matter hyperintensity probability threshold of 0.8.
Although absolute dose values obtained in such studies may not be directly transferable to patient cases, relative values such as the RBE and its dependence on treatment parameters may be more applicable [10]. The rat spinal cord is known as a well-established model to investigate CNS late effects [11], and it has been applied to measure RBE values for C-ion therapy e.g., [10,12,13,14] and protons [15]. These are the main in vivo data that have been used to benchmark the clinically employed LEM version (LEM I), but also the more recent version LEM IV [6].
Afterwards, we adjusted the CL yield to reproduce this photon survival curve by our simulations, and we included this (photon) CL yield in Equation (1) to obtain ion CL yields. This allowed us to derive the CL yields to predict cell survival for many different particle types and energies. Finally, fitting of each of these survival curves by the Linear-Quadratic model S(D) = exp(-αD-βD2) was performed to produce a radiobiological database, i.e., a table of alpha and beta coefficients as a function of particle type and energy.
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