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Facilitating insight into a simulation model using visualization and dynamic model previews
This paper shows how model simplification, by replacing iterative steps with unitary predictive equations, can enable dynamic interaction with a complex simulation process. Model previews extend the techniques of dynamic querying and query previews into the context of ad hoc simulation model exploration. A case study is presented within the domain of counter-current chromatography. The relatively novel method of insight evaluation was applied, given the exploratory nature of the task. The evaluation data show that the trade-off in accuracy is far outweighed by benefits of dynamic interaction. The number of insights gained using the enhanced interactive version of the computer model was more than six times higher than the number of insights gained using the basic version of the model. There was also a trend for dynamic interaction to facilitate insights of greater domain importance
An Induced Natural Selection Heuristic for Finding Optimal Bayesian Experimental Designs
Bayesian optimal experimental design has immense potential to inform the
collection of data so as to subsequently enhance our understanding of a variety
of processes. However, a major impediment is the difficulty in evaluating
optimal designs for problems with large, or high-dimensional, design spaces. We
propose an efficient search heuristic suitable for general optimisation
problems, with a particular focus on optimal Bayesian experimental design
problems. The heuristic evaluates the objective (utility) function at an
initial, randomly generated set of input values. At each generation of the
algorithm, input values are "accepted" if their corresponding objective
(utility) function satisfies some acceptance criteria, and new inputs are
sampled about these accepted points. We demonstrate the new algorithm by
evaluating the optimal Bayesian experimental designs for the previously
considered death, pharmacokinetic and logistic regression models. Comparisons
to the current "gold-standard" method are given to demonstrate the proposed
algorithm as a computationally-efficient alternative for moderately-large
design problems (i.e., up to approximately 40-dimensions)
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)
This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance
Mechanistic modeling of architectural vulnerability factor
Reliability to soft errors is a significant design challenge in modern microprocessors owing to an exponential increase in the number of transistors on chip and the reduction in operating voltages with each process generation. Architectural Vulnerability Factor (AVF) modeling using microarchitectural simulators enables architects to make informed performance, power, and reliability tradeoffs. However, such simulators are time-consuming and do not reveal the microarchitectural mechanisms that influence AVF. In this article, we present an accurate first-order mechanistic analytical model to compute AVF, developed using the first principles of an out-of-order superscalar execution. This model provides insight into the fundamental interactions between the workload and microarchitecture that together influence AVF. We use the model to perform design space exploration, parametric sweeps, and workload characterization for AVF
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
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