arXiv subjects

Rico A. R. Picone

Publications and source records attributed to Rico A. R. Picone.

4 recordsLinked to original sources

arXiv2021

New methods for metastimuli: architecture, embeddings, and neural network optimization

Six significant new methodological developments of the previously-presented "metastimuli architecture" for human learning through machine learning of spatially correlated structural position within a user's personal information management system (PIMS), providing the basis for haptic metastimuli, are presented. These include architectural innovation, recurrent (RNN) artificial neural network (ANN) application, a variety of atom embedding techniques (including a novel technique we call "nabla" embedding inspired by linguistics), ANN hyper-parameter (one that affects the network but is not trained, e.g. the learning rate) optimization, and meta-parameter (one that determines the system performance but is not trained and not a hyper-parameter, e.g. the atom embedding technique) optimization for exploring the large design space. A technique for using the system for automatic atom categorization in a user's PIMS is outlined. ANN training and hyper- and meta-parameter optimization results are presented and discussed in service of methodological recommendations.

cs.AI↗

arXiv2016

Application of a thermodynamical framework for transport analysis to the derivation of Dirac's value function

From a non-equilibrium thermodynamical framework for transport analysis based on Onsager's Regression Hypothesis, we derive the value function first described by Dirac for isotope separation. This application of the framework is interpreted as both further validation of the transport framework and as a generalization of Dirac's value function. The framework for the analysis of transport phenomena is introduced, first. From the entropy of mixing, and in the presence of gradients in thermodynamic potentials, this framework generates a dynamical transport model from which Dirac's value function is derived as a measure of separative work performed. Dirac's value function is thus shown to be a measure of separative work for systems that are described by the entropy of mixing. As a further demonstration of its generality, the result is applied to a two-quantity, single spatial-dimension spin magnetization system.

cond-mat.stat-mech↗

arXiv2014

Modeling spin magnetization transport in a spatially varying magnetic field

We present a framework for modeling the transport of any number of globally conserved quantities in any spatial configuration and apply it to obtain a model of magnetization transport for spin-systems that is valid in new regimes (including high-polarization). The framework allows an entropy function to define a model that explicitly respects the laws of thermodynamics. Three facets of the model are explored. First, it is expressed as nonlinear partial differential equations that are valid for the new regime of high dipole-energy and polarization. Second, the nonlinear model is explored in the limit of low dipole-energy (semi-linear), from which is derived a physical parameter characterizing separative magnetization transport (SMT). It is shown that the necessary and sufficient condition for SMT to occur is that the parameter is spatially inhomogeneous. Third, the high spin-temperature (linear) limit is shown to be equivalent to the model of nuclear spin transport of Genack and Redfield. Differences among the three forms of the model are illustrated by numerical solution with parameters corresponding to a magnetic resonance force microscopy (MRFM) experiment. A family of analytic, steady-state solutions to the nonlinear equation is derived and shown to be the spin-temperature analog of the Langevin paramagnetic equation and Curie's law. Finally, we analyze the separative quality of magnetization transport, and a steady-state solution for the magnetization is shown to be compatible with Fenske's separative mass transport equation.

cond-mat.mes-hall↗

arXiv2010

Elements of naturality in dynamical simulation frameworks for Hamiltonian, thermostatic, and Lindbladian flows on classical and quantum state-spaces

The practical focus of this work is the dynamical simulation of polarization transport processes in quantum spin microscopy and spectroscopy. The simulation framework is built-up progressively, beginning with state-spaces (configuration manifolds) that are geometrically natural, introducing coordinates that are algebraically natural; and finally specifying dynamical potentials that are physically natural; in each respect explicit criteria are given for "naturality." The resulting framework encompasses Hamiltonian flow (both classical and quantum), quantum Lindbladian processes, and classical thermostatic processes. Constructive validation and verification criteria are given for metric and symplectic flows on classical, quantum, and hybrid state-spaces, with particular emphasis to tensor network state-spaces. Both classical and quantum examples are presented, including dynamic nuclear polarization (DNP). A broad span of applications and challenges is discussed, ranging from the design and simulation of quantum spin microscopes to the design and simulation of quantum oracles.

quant-ph↗