Communications Biophysics

Contains reports on one research project.National Institutes of Health (Grant 5 P01 GM14940-06

A Life of Equations Shifting to a Life of Words

As my technical life diminishes, my writing life increases. Since 2017 I have been writing, first a memoir of my time in the Intelligence Community, then poetry and flash fiction. One of the missions I have assigned to my poetry is to expose to `regular\u27 people the inner life of the mathematical person. The poems in this poetry folder develop three themes. Formulations\u27\u27 pokes a bit of fun at the bloated (and in this case almost musical) titles that can grow from our research; more seriously, it documents the change in self-definition that flows from recognizing the inevitable drift away from high-intensity math as we age. Least Lower Bound\u27\u27 is a bit of an exaggeration of the way we can develop the habit of translating important life events into the sometimes stilted and overelaborate language we use in our technical work. It also illustrates how deeply we can come to use that language to translate the world to ourselves. Academic Inquiry\u27\u27 is the first of several poems I have written about the massive role that teaching has played in my life. If this poem were a wine, a reviewer might say it combines notes of yearning, pride, and challenge with a finish of wry resignation\u27\u27

Communications Biophysics

Contains reports on one research project.National Institutes of Health (Grant 5 PO1 GM14940-07

Planning for the South Bay industrial area Boston, Mass

Thesis (M.C.P.) Massachusetts Institute of Technology. Dept. of Architecture, 1948.Bibliography: leaves 139-143.by Bernard M. Willemain.M.C.P

Cognitive Information Processing

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 GM14940-04)National Institutes of Health (Grant 5 PO1 GM15006-03)Joint Services Electronics Programs (U. S. Army, U. S. Navy, and U. S. Air Force) under Contract DA 28-043-AMC-02536(E

Cognitive Information Processing

Contains reports on six research projects.National Institutes of Health (Grant 5 PO1 GM14940-04)National Institutes of Health (Grant 5 PO1 GM15006-03)Joint Services Electronics Programs (U. S. Army, U.S. Navy, and U.S. Air Force) under Contract DA 28-043-AMC-02536(E

On Double-Boundary Non-Crossing Probability for a Class of Compound Processes with Applications

We develop an efficient method for computing the probability that a non-decreasing, pure jump (compound) stochastic process stays between arbitrary upper and lower boundaries (i.e., deterministic functions, possibly discontinuous) within a finite time period. The compound process is composed of a process modelling the arrivals of certain events (e.g., demands for a product in inventory systems, customers in queuing, or claims/capital gains in insurance/dual risk models), and a sequence of independent and identically distributed random variables modelling the sizes of the events. The events arrival process is assumed to belong to the wide class of point processes with conditional stationary independent increments which includes (non-)homogeneous Poisson, binomial, negative binomial, mixed Poisson and doubly stochastic Poisson (i.e., Cox) processes as special cases. The proposed method is based on expressing the non-exit probability through Chapman-Kolmogorov equations, re-expressing them in terms of a circular convolution of two vectors which is then computed applying fast Fourier transform (FFT). We further demonstrate that our FFT-based method is computationally efficient and can be successfully applied in the context of inventory management (to determine an optimal replenishment policy), ruin theory (to evaluate ruin probabilities and related quantities) and double-barrier option pricing or simply computing non-exit probabilities for Brownian motion with general boundaries

Machine learning of LWR spent nuclear fuel assembly decay heat measurements

Measured decay heat data of light water reactor (LWR) spent nuclear fuel (SNF) assemblies are adopted to train machine learning (ML) models. The measured data is available for fuel assemblies irradiated in commercial reactors operated in the United States and Sweden. The data comes from calorimetric measurements of discharged pressurized water reactor (PWR) and boiling water reactor (BWR) fuel assemblies. 91 and 171 measurements of PWR and BWR assembly decay heat data are used, respectively. Due to the small size of the measurement dataset, we propose: (i) to use the method of multiple runs (ii) to generate and use synthetic data, as large dataset which has similar statistical characteristics as the original dataset. Three ML models are developed based on Gaussian process (GP), support vector ma-chines (SVM) and neural networks (NN), with four inputs including the fuel assembly averaged enrichment, assembly averaged burnup, initial heavy metal mass, and cooling time after discharge. The outcomes of this work are (i) development of ML models which predict LWR fuel assembly decay heat from the four inputs (ii) generation and application of synthetic data which improves the performance of the ML models (iii) uncertainty analysis of the ML models and their predictions. (c) 2021 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)