Quantum Theory of Probability and Decisions

The probabilistic predictions of quantum theory are conventionally obtained from a special probabilistic axiom. But that is unnecessary because all the practical consequences of such predictions follow from the remaining, non-probabilistic, axioms of quantum theory, together with the non-probabilistic part of classical decision theory

Opportunities for Biomedical Research and the NIH through High Performance Computing and Data Management

The biomedical sciences are advancing at a tremendous rate. Some of the most notable recent accomplishments (such as the assembly of the human genome) have depended upon the use of high performance computing and data management (HPC). There are important areas of opportunity for the biomedical sciences to accelerate advances in knowledge and in practical medical treatments through the use of high performance computing. As we enter into the “century of biology” there are critical challenges in the areas of data organization, management, and analysis; simulation; and translational biomedical research. These challenges can be met only through investment in training, tools, and infrastructure that will enable greater use of high performance computing in biomedical research

GCM solver (ver. 3.0): a {\it Mathematica} notebook for diagonalization of the Geometric Collective Model (Bohr hamiltonian) with generalized Gneuss-Greiner potential

The program diagonalizes the Geometric Collective Model (Bohr Hamiltonian) with generalized Gneuss–Greiner potential with terms up to the sixth power in β . In nuclear physics, the Bohr–Mottelson model with later extensions into the rotovibrational Collective model is an important theoretical tool with predictive power and it represents a fundamental step in the education of a nuclear physicist. Nuclear spectroscopists might find it useful for fitting experimental data, reproducing spectra, EM transitions and moments and trying theoretical predictions, while students might find it useful for learning about connections between the nuclear shape and its quantum origin. Matrix elements for the kinetic energy operator and for scalar invariants as β 2 and β 3 cos ( 3 γ ) have been calculated in a truncated five-dimensional harmonic oscillator basis with a different program, checked with three different methods and stored in a matrix library for the lowest values of angular momentum. These matrices are called by the program that uses them to write generalized Hamiltonians as linear combinations of certain simple operators. Energy levels and eigenfunctions are obtained as outputs of the diagonalization of these Hamiltonian operators

Morphological Computation: Nothing but Physical Computation

The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment