Medicine is not science

ABSTRACT: Abstract Most modern knowledge is not science. The physical sciences have successfully validated theories to infer they can be used universally to predict in previously unexperienced circumstances. According to the conventional conception of science such inferences are falsified by a single irregular outcome. And verification is by the scientific method which requires strict regularity of outcome and establishes cause and effect. Medicine, medical research and many “soft” sciences are concerned with individual people in complex heterogeneous populations. These populations cannot be tested to demonstrate strict regularity of outcome in every individual. Neither randomised controlled trials nor observational studies in medicine are science in the conventional conception. Establishing and using medical and other “soft science” theories cannot be scientific. It requires conceptually different means: requiring expert judgement applying all available evidence in the relevant available factual matrix. The practice of medicine is observational. Prediction of outcomes for the individual requires professional expertise applying available medical knowledge and evidence. Expertise in any profession can only be acquired through experience. Prior cases are the fundament of knowledge and expertise in medicine. Case histories, studies and series can provide knowledge of extremely high reliability applicable to establishing reliable general theories and falsifying others. Their collation, study and analysis should be a priority in medicine. Their devaluation as evidence, the failure to apply their lessons, the devaluation of expert professional judgement and the attempt to emulate the scientific method are all historic errors in the theory and practice of modern medicine

Exact Computation of a Manifold Metric, via Lipschitz Embeddings and Shortest Paths on a Graph

Data-sensitive metrics adapt distances locally based the density of data points with the goal of aligning distances and some notion of similarity. In this paper, we give the first exact algorithm for computing a data-sensitive metric called the nearest neighbor metric. In fact, we prove the surprising result that a previously published $3$-approximation is an exact algorithm. The nearest neighbor metric can be viewed as a special case of a density-based distance used in machine learning, or it can be seen as an example of a manifold metric. Previous computational research on such metrics despaired of computing exact distances on account of the apparent difficulty of minimizing over all continuous paths between a pair of points. We leverage the exact computation of the nearest neighbor metric to compute sparse spanners and persistent homology. We also explore the behavior of the metric built from point sets drawn from an underlying distribution and consider the more general case of inputs that are finite collections of path-connected compact sets. The main results connect several classical theories such as the conformal change of Riemannian metrics, the theory of positive definite functions of Schoenberg, and screw function theory of Schoenberg and Von Neumann. We develop novel proof techniques based on the combination of screw functions and Lipschitz extensions that may be of independent interest.Comment: 15 page

A Fast Algorithm for Well-Spaced Points and Approximate Delaunay Graphs

We present a new algorithm that produces a well-spaced superset of points conforming to a given input set in any dimension with guaranteed optimal output size. We also provide an approximate Delaunay graph on the output points. Our algorithm runs in expected time $O(2^{O(d)}(n\log n + m))$, where $n$ is the input size, $m$ is the output point set size, and $d$ is the ambient dimension. The constants only depend on the desired element quality bounds. To gain this new efficiency, the algorithm approximately maintains the Voronoi diagram of the current set of points by storing a superset of the Delaunay neighbors of each point. By retaining quality of the Voronoi diagram and avoiding the storage of the full Voronoi diagram, a simple exponential dependence on $d$ is obtained in the running time. Thus, if one only wants the approximate neighbors structure of a refined Delaunay mesh conforming to a set of input points, the algorithm will return a size $2^{O(d)}m$ graph in $2^{O(d)}(n\log n + m)$ expected time. If $m$ is superlinear in $n$, then we can produce a hierarchically well-spaced superset of size $2^{O(d)}n$ in $2^{O(d)}n\log n$ expected time.Comment: Full versio

Extracting Neutron Star Properties from X-ray Burst Oscillations

Many thermonuclear X-ray bursts exhibit brightness oscillations. The brightness oscillations are thought to be due to the combined effects of non-uniform nuclear burning and rotation of the neutron star. The waveforms of the oscillations contain information about the size and number of burning regions. They also contain substantial information about the mass and radius of the star, and hence about strong gravity and the equation of state of matter at supranuclear densities. We have written general relativistic ray-tracing codes that compute the waveforms and spectra of rotating hot spots as a function of photon energy. Using these codes, we survey the effect on the oscillation waveform and amplitude of parameters such as the compactness of the star, the spot size, the surface rotation velocity, and whether there are one or two spots. We also fit phase lag versus photon energy curves to data from the millisecond X-ray pulsar, SAX J1808--3658.Comment: To appear in Proc. of the 10th Annual October Astrophysics Conference in Maryland: Cosmic Explosions, 4 page

ECON 302 Principles of Macroeconomics

Course syllabus for ECON 302A Principles of Macroeconomics Course description: Studies and theories about the economy as a whole, dealing with economic data and behavior at the aggregate level of the economy. Examines income, output, employment, prices, etc., in terms of its measurement, determination, and policy implications

ECON 302 Principles of Macroeconomics

Course syllabus for ECON 302 Principles of Macroeconomics Course description: Studies and theories about the economy as a whole, dealing with economic data and behavior at the aggregate level of the economy. Examines income, output, employment, prices, etc., in terms of its measurement, determination, and policy implications

A strategy and roles for urban planning in fostering sustainable happiness

The human dimension of sustainability – the balance of planet, people, and prosperity concerns – importantly includes wellbeing that is the means to the end of happiness. This paper explores what we think that we know about happiness, possible roles of urban planning in fostering happiness, and how environmental features can be enhanced by planning and so contribute to the happiness of a city’s residents and visitors. Recent developments in positive psychology provide a basis for this inquiry, and are reviewed. A focus of this paper is on The Happiness Initiative in Seattle, Washington. This recent non-governmental program, begun by Sustainable Seattle, is inspired by the earlier effort in Bhutan to replace or augment Gross Domestic Product with measurement and attention to the Gross Domestic Happiness. Finally, the paper will explore how findings from research on factors contributing to happiness can be applied in urban planning.Eje 1: Dilemas del desarrollo socio-territorial y la planificación urbano-regional frente a los retos de la sustentabilidad.Facultad de Arquitectura y Urbanism

ECON 301 Principles of Microeconomics

Course syllabus for ECON 301 Principles of Microeconomics Course description: Study of price formation, demand, and production decisions, examines the individual and interrelated behavior of consumers, firms and industries