Data Assimilation using a GPU Accelerated Path Integral Monte Carlo Approach

The answers to data assimilation questions can be expressed as path integrals over all possible state and parameter histories. We show how these path integrals can be evaluated numerically using a Markov Chain Monte Carlo method designed to run in parallel on a Graphics Processing Unit (GPU). We demonstrate the application of the method to an example with a transmembrane voltage time series of a simulated neuron as an input, and using a Hodgkin-Huxley neuron model. By taking advantage of GPU computing, we gain a parallel speedup factor of up to about 300, compared to an equivalent serial computation on a CPU, with performance increasing as the length of the observation time used for data assimilation increases.Comment: 5 figures, submitted to Journal of Computational Physic

Processing techniques development, volume 3. Part 2: Data preprocessing and information extraction techniques

There are no author-identified significant results in this report

Planning level decision support for the selection of robust configurations of airport passenger buildings

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 1995.Includes bibliographical references (leaves 252-255).by Tom Svrcek.Ph.D

Non-Extensive Entropy Econometrics for Low Frequency Series

The second edition of Non-extensive Entropy Econometrics for Low Frequency Series provides a new and robust power-law-based, non-extensive entropy econometrics approach to the economic modelling of ill-behaved inverse problems. Particular attention is paid to national account-based general equilibrium models known for their relative complexity. This new proposed approach could extend the frontier of theoretical and applied econometrics

Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks

A methodology for estimating population for transportation risk analysis

In selecting a route for the shipment of hazardous materials, the primary public safety concern is that routing minimizes risk by avoiding populated areas while utilizing the shortest and safest possible routes to reduce time in-state shipments; This research presents a methodology for estimating population index which reflects density ranges for various population groupings. The population index is based on the number of tracts, tract density, and county density. The population index is initially defined for population {dollar}{dollar}8000. in the absence of more data to validate the results in the latter grouping, the index is redefined for population {dollar}\u3c{dollar}2500 and population {dollar}\geq{dollar}2500. The index is used to determine the level of analysis required in evaluating population: county, tract, or block level; Routing analysis based on minimization of risk by minimizing affected population is performed for the state of Nevada. The analysis focuses on two alternative rail routes that are being considered by the Department of Energy (DOE) for the shipment of high level wastes and spent nuclear fuels. (Abstract shortened by UMI.)

Cognitive finance: Behavioural strategies of spending, saving, and investing.

Research in economics is increasingly open to empirical results. The advances in behavioural approaches are expanded here by applying cognitive methods to financial questions. The field of "cognitive finance" is approached by the exploration of decision strategies in the financial settings of spending, saving, and investing. Individual strategies in these different domains are searched for and elaborated to derive explanations for observed irregularities in financial decision making. Strong context-dependency and adaptive learning form the basis for this cognition-based approach to finance. Experiments, ratings, and real world data analysis are carried out in specific financial settings, combining different research methods to improve the understanding of natural financial behaviour. People use various strategies in the domains of spending, saving, and investing. Specific spending profiles can be elaborated for a better understanding of individual spending differences. It was found that people differ along four dimensions of spending, which can be labelled: General Leisure, Regular Maintenance, Risk Orientation, and Future Orientation. Saving behaviour is strongly dependent on how people mentally structure their finance and on their self-control attitude towards decision space restrictions, environmental cues, and contingency structures. Investment strategies depend on how companies, in which investments are placed, are evaluated on factors such as Honesty, Prestige, Innovation, and Power. Further on, different information integration strategies can be learned in decision situations with direct feedback. The mapping of cognitive processes in financial decision making is discussed and adaptive learning mechanisms are proposed for the observed behavioural differences. The construal of a "financial personality" is proposed in accordance with other dimensions of personality measures, to better acknowledge and predict variations in financial behaviour. This perspective enriches economic theories and provides a useful ground for improving individual financial services

Volume of the steady-state space of financial flows in a monetary stock-flow-consistent model

We show that a steady-state stock-flow consistent macro-economic model can be represented as a Constraint Satisfaction Problem (CSP).The set of solutions is a polytope, which volume depends on the constraintsapplied and reveals the potential fragility of the economic circuit,with no need to study the dynamics. Several methods to compute the volume are compared, inspired by operations research methods and theanalysis of metabolic networks, both exact and approximate.We also introduce a random transaction matrix, and study the particularcase of linear flows with respect to money stocks