The experiential world

There are four positions one might take in respect of the ontological status of the physical world: physicalism, which says that the physical world is ontologically fundamental, and nothing else is; substance dualism, which says that the physical world is ontologically fundamental, but so is the human mental realm, and that these are in some strong metaphysical sense separate; idealism, which says that the physical world is constitutively sustained, at least in part, by facts about the human mental realm; and a rough collection of views I term 'compatibilism', which holds that both the physical and the mental are fundamental, but that they are not separate as in substance dualism. Of these positions, I argue mainly against the first and last. I begin by demonstrating that all forms of compatibilism are committed to a radically revisionary definition of 'mental' and 'physical', since in ordinary usage, and for good reason, the terms are taken as mutually exclusive. I formulate a definition of 'mental' according to which it means 'subjective, non-spatial, and non-quantifiable', and demonstrate that these properties are necessarily coextensive. Against physicalism, I consider a range of arguments which purport to show that physical space, as a necessary feature of the physical world, cannot be ontologically fundamental, concluding that physical space, or at least the physical space that we are interested in, must be the one which we inhabit, and that our relationship of inhabitancy of this physical space plays a constitutive role in it. Since this assumes that physical space must be in some way constituted rather than fundamental, I finish by refuting a set of strategies which attempt to show that physical space itself must be constituted

Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

© 2017 The Author(s). This study evaluated, for the first time, the efficacy of quantitative ultrasound (QUS) spectral parametric maps in conjunction with texture-analysis techniques to differentiate non-invasively benign versus malignant breast lesions. Ultrasound B-mode images and radiofrequency data were acquired from 78 patients with suspicious breast lesions. QUS spectral-analysis techniques were performed on radiofrequency data to generate parametric maps of mid-band fit, spectral slope, spectral intercept, spacing among scatterers, average scatterer diameter, and average acoustic concentration. Texture-analysis techniques were applied to determine imaging biomarkers consisting of mean, contrast, correlation, energy and homogeneity features of parametric maps. These biomarkers were utilized to classify benign versus malignant lesions with leave-one-patient-out cross-validation. Results were compared to histopathology findings from biopsy specimens and radiology reports on MR images to evaluate the accuracy of technique. Among the biomarkers investigated, one mean-value parameter and 14 textural features demonstrated statistically significant differences (p < 0.05) between the two lesion types. A hybrid biomarker developed using a stepwise feature selection method could classify the legions with a sensitivity of 96%, a specificity of 84%, and an AUC of 0.97. Findings from this study pave the way towards adapting novel QUS-based frameworks for breast cancer screening and rapid diagnosis in clinic

Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images

VoxLogicA : A Spatial Model Checker for Declarative Image Analysis

Spatial and spatio-temporal model checking techniques have a wide range of application domains, among which large scale distributed systems and signal and image analysis.We explore a new domain, namely (semi-)automatic contouring in Medical Imaging, introducing the tool VoxLogicA which merges the state-of-the-art library of computational imaging algorithms ITK with the unique combination of declarative specification and optimised execution provided by spatial logic model checking. The result is a rapid, logic based analysis development methodology. The analysis of an existing benchmark of medical images for segmentation of brain tumours shows that simple VoxLogicA analysis can reach state-of-the-art accuracy, competing with best-in-class algorithms, with the advantage of explainability and easy replicability. Furthermore, due to a two-orders-of-magnitude speedup compared to the existing generalpurpose spatio-temporal model checker topochecker, VoxLogicA enables interactive development of analysis of 3D medical images, which can greatly facilitate the work of professionals in this domain

Solving the Single-Vehicle Self-Driving Car Trolley Problem Using Risk Theory and Vehicle Dynamics

Questions of what a self-driving car ought to do if it encounters a situation analogous to the ‘trolley problem’ have dominated recent discussion of the ethics of self-driving cars. This paper argues that this interest is misplaced. If a trolley-style dilemma situation actually occurs, given the limits on what information will be available to the car, the dynamics of braking and tyre traction determine that, irrespective of outcome, it is always least risky for the car to brake in a straight line rather than swerve