115 research outputs found
Nonparametric estimation of first passage time distributions in flowgraph models
Statistical flowgraphs represent multistate semi-Markov processes using integral transforms of transition time distributions between adjacent states; these are combined algebraically and inverted to derive parametric estimates for first passage time distributions between nonadjacent states. This dissertation extends previous work in the field by developing estimation methods for flowgraphs using empirical transforms based on sample data, with no assumption of specific parametric probability models for transition times. We prove strong convergence of empirical flowgraph results to the exact parametric results; develop alternatives for numerical inversion of empirical transforms and compare them in terms of computational complexity, accuracy, and ability to determine error bounds; discuss (with examples) the difficulties of determining confidence bands for distribution estimates obtained in this way; develop confidence intervals for moment-based quantities such as the mean; and show how methods based on empirical transforms can be modified to accommodate censored data. Several applications of the nonparametric method, based on reliability and survival data, are presented in detail
The design and implementation of the programming language Natural
This paper reports progress on the development of the programming language Natural, currently under design by Dr. Thomas J. Sager at the University of Missouri--Rolla. Natural is a very high-level language with a mathematical flavor, and includes several concepts relatively uncommon in programming language design.
The text also discusses an implementation on the IBM Personal Computer of Mini-Natural, a subset of Natural, and presents examples of programs written in Mini-Natural --Abstract, page ii
Toward an idiomatic framework for cognitive robotics
Inspired by the "Cognitive Hour-glass" model presented in
https://doi.org/10.1515/jagi-2016-0001, we propose a new framework for
developing cognitive architectures aimed at cognitive robotics. The purpose of
the proposed framework is foremost to ease the development of cognitive
architectures by encouraging and mitigating cooperation and re-use of existing
results. This is done by proposing a framework dividing the development of
cognitive architectures into a series of layers that can be considered partly
in isolation, and some of which directly relate to other research fields.
Finally, we give introductions to and review some topics essential to the
proposed framework.Comment: 16 pages, 24 figure
Toward an idiomatic framework for cognitive robotics
Inspired by the “cognitive hourglass” model presented by the researchers behind the cognitive architecture called Sigma, we propose a framework for developing cognitive architectures for cognitive robotics. The main purpose of the proposed framework is to ease development of cognitive architectures by encouraging cooperation and re-use of existing results. This is done by proposing a framework dividing development of cognitive architectures into a series of layers that can be considered partly in isolation, some of which directly relate to other research fields. Finally, we introduce and review some topics essential for the proposed framework. We also outline a set of applications
ACC Saturator: Automatic Kernel Optimization for Directive-Based GPU Code
Automatic code optimization is a complex process that typically involves the
application of multiple discrete algorithms that modify the program structure
irreversibly. However, the design of these algorithms is often monolithic, and
they require repetitive implementation to perform similar analyses due to the
lack of cooperation. To address this issue, modern optimization techniques,
such as equality saturation, allow for exhaustive term rewriting at various
levels of inputs, thereby simplifying compiler design.
In this paper, we propose equality saturation to optimize sequential codes
utilized in directive-based programming for GPUs. Our approach simultaneously
realizes less computation, less memory access, and high memory throughput. Our
fully-automated framework constructs single-assignment forms from inputs to be
entirely rewritten while keeping dependencies and extracts optimal cases.
Through practical benchmarks, we demonstrate a significant performance
improvement on several compilers. Furthermore, we highlight the advantages of
computational reordering and emphasize the significance of memory-access order
for modern GPUs
Programmiersprachen und Rechenkonzepte
Seit 1984 veranstaltet die GI-Fachgruppe "Programmiersprachen und Rechenkonzepte", die aus den ehemaligen Fachgruppen 2.1.3 "Implementierung von Programmiersprachen" und 2.1.4 "Alternative Konzepte für Sprachen und Rechner" hervorgegangen ist, regelmäßig im Frühjahr einen Workshop im Physikzentrum Bad Honnef. Das Treffen dient in erster Linie dem gegenseitigen Kennenlernen, dem Erfahrungsaustausch, der Diskussion und der Vertiefung gegenseitiger Kontakte
- …