Books: 'FLEXIBLE COMPUTATION'

1

Portes, Jacob. Flexible Computation in Neural Circuits. [New York, N.Y.?]: [publisher not identified], 2022.

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2

Pai, P. Frank. Highly flexible structures: Modeling, computation, and experimentation. Reston, Va: American Institute of Aeronautics and Astronautics, 2007.

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3

Xu, M. Robust and flexible multi-scale medial axis computation. Birmingham: University of Birmingham, 2001.

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4

P, Giesy Daniel, Langley Research Center, and United States. National Aeronautics and Space Administration., eds. Algorithms for efficient computation of transfer functions for large order flexible systems. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1998.

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5

Tran, Fleischer Van, and Hugh L. Dryden Flight Research Center, eds. Methods for in-flight wing shape predictions of highly flexible unmanned aerial vehicles: Formulation of Ko displacement theory. Edwards, Calif: National Aeronautics and Space Administration, Dryden Flight Research Center, 2010.

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6

Simeon, Bernd. Computational Flexible Multibody Dynamics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35158-7.

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7

Yao, Shen Ji, and United States. National Aeronautics and Space Administration., eds. Computational control of flexible aerospace systems. Greensboro, N.C: North Carolina A&T State University, 1994.

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8

A, Brubaker Thomas, Shults James R, and United States. National Aeronautics and Space Administration., eds. Computational tools for multi-linked flexible structures. [Washington, DC: National Aeronautics and Space Administration, 1990.

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9

Ide, Hiroshi. Unsteady full potential aeroelastic computations for flexible configurations. New York: AIAA, 1987.

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10

Egg, Markus. Flexible semantics for reinterpretation phenomena. Stanford, Calif: Center for the Study of Language and Information, 2005.

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11

Egg, Markus. Flexible semantics for reinterpretation phenomena. Stanford, Calif: CSLI Publications, 2006.

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12

United States. National Aeronautics and Space Administration., ed. Euler/Navier-Stokes flow computations on flexible configurations for stability analysis. [Washington, D.C: National Aeronautics and Space Administration, American Institute of Aeronautics and Astronautics, 1995.

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13

United States. National Aeronautics and Space Administration., ed. Euler/Navier-Stokes flow computations on flexible configurations for stability analysis. [Washington, D.C: National Aeronautics and Space Administration, American Institute of Aeronautics and Astronautics, 1995.

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14

United States. National Aeronautics and Space Administration., ed. Euler/Navier-Stokes flow computations on flexible configurations for stability analysis. [Washington, D.C: National Aeronautics and Space Administration, American Institute of Aeronautics and Astronautics, 1995.

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15

C, Park K., Chiou J. C, and United States. National Aeronautics and Space Administration., eds. A computational procedure for multibody systems including flexible beam dynamics. Boulder, Colo: Center for Space Structures and Controls, College of Engineering, University of Colorado, 1990.

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16

Taylor, Larry. Workshop on Computational Aspects in the Control of Flexible Systems, held at the Royce Hotel in Williamsburg, Va., July 12-14, 1988: Proceedings. Hampton, Va: Langley Research Center, 1988.

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17

W, Taylor Lawrence, and Langley Research Center, eds. 4th NASA Workshop on Computational Control of Flexible Aerospace Systems: Proceedings of a workshop. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1991.

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18

Zhang, Xiao-Ping. Flexible AC Transmission Systems: Modelling and Control. 2nd ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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19

Taylor, Lawrence W. 4th NASA Workshop on Computational Control of Flexible Aerospace Systems: Proceedings of a workshop sponsored by the National Aeronautics and Space Administration and held at the Kingsmill Resort, Williamsburg, Virginia, July 11, 1990. Hampton, Va: Langley Reseearch Center, 1991.

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20

Yao, Shen Ji, and United States. National Aeronautics and Space Administration., eds. Final report on the project Computational control of flexible aerospace systems (grant no. NAG-1-1436). Greensboro, N.C: North Carolina A&T State University, 1994.

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21

Yao, Shen Ji, and United States. National Aeronautics and Space Administration., eds. Final report on the project Computational control of flexible aerospace systems (grant no. NAG-1-1436). Greensboro, N.C: North Carolina A&T State University, 1994.

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22

Center, Langley Research, ed. Aeroelastic calculations using CFD for a typical business jet model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1996.

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23

Workshop, on Computational Aspects in the Control of Flexible Systems (1988 Williamsburg Va ). Workshop on Computational Aspects in the Control of Flexible Systems held at the Royce Hotel in Williamsburg, Virginia, July 12-14, 1988. [Washington, DC: National Aeronautics and Space Administration, 1990.

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24

United States. National Aeronautics and Space Administration., ed. Workshop on Computational Aspects in the Control of Flexible Systems held at the Royce Hotel in Williamsburg, Virginia, July 12-14, 1988. Washington, DC: National Aeronautics and Space Administration, 1988.

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25

NASA-UCLA Workshop on Computational Techniques in Identification and Control of Flexible Flight Structures (1989 Lake Arrowhead, Calif.). The proceedings of the NASA-UCLA Workshop on Computational Techniques in Identification and Control of Flexible Flight Structures, Lake Arrowhead, California, November 2-4, 1989. New York: Optimization Software, Publications Division, 1990.

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26

Highly Flexible Structures: Modeling, Computation, and Experimentation (Aiaa Education Series). AIAA (American Institute of Aeronautics & Ast, 2007.

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27

National Aeronautics and Space Administration (NASA) Staff. Algorithms for Efficient Computation of Transfer Functions for Large Order Flexible Systems. Independently Published, 2018.

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28

Anderson, James A. After Digital. Oxford University Press, 2018. http://dx.doi.org/10.1093/acprof:oso/9780199357789.001.0001.

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We are surrounded by digital computers. They do many things well that humans do not and have transformed our lives. But all computers are not the same. Although digital computers dominate today’s world, alternative ways to “compute” might be better and more efficient than digital computation when mechanically performing those tasks, important to humans, that we think of as “cognition.” Cognition, after all, was originally developed to work with our own specific biological hardware. Digital computers require elaborate detailed instructions to work; they are flexible but not simple. Analog computers are designed to do specific tasks. They can be simple but not flexible. Hardware matters. The book discusses two classic kinds of computer, digital and analog, and gives examples of their history, functions, and limitations. The author suggest that when brain “hardware,” with its associated brain “software” work together, it could form a computer architecture that would be useful for the efficient performance of cognitive tasks. This book discusses the essentials of brain hardware—in particular, the cerebral cortex, where cognition lives—and how cortical structure can influence the form taken by the computational operations underlying cognition. Topics include association, understanding complex systems through analogy, formation of abstractions, and the biology of number and its use in arithmetic and mathematics. The author introduces novel “brain-like” control mechanisms: active associative search and traveling waves. There is discussion on computing across scales of organization from single neurons to brain regions containing millions of neurons.

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29

Computational Flexible Multibody Dynamics A Differentialalgebraic Approach. Springer-Verlag Berlin and Heidelberg GmbH &, 2013.

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30

Simeon, Bernd. Computational Flexible Multibody Dynamics: A Differential-Algebraic Approach. Springer, 2013.

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31

Simeon, Bernd. Computational Flexible Multibody Dynamics: A Differential-Algebraic Approach. Springer, 2013.

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32

National Aeronautics and Space Administration (NASA) Staff. Computational Procedure for Multibody Systems Including Flexible Beam Dynamics. Independently Published, 2018.

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33

Euler/Navier-Stokes flow computations on flexible configurations for stability analysis. [Washington, D.C: National Aeronautics and Space Administration, American Institute of Aeronautics and Astronautics, 1995.

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34

A computational procedure for the dynamics of flexible beams within multibody systems. Boulder, Colo: Center for Space Structures and Controls, College of Engineering, University of Colorado, 1990.

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35

Schörgenhumer, Markus. Computational Fluid-Structure Interaction: Coupling of flexible multibody dynamics with particle-based fluid mechanics. AV Akademikerverlag, 2014.

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36

Final report on the project Computational control of flexible aerospace systems (grant no. NAG-1-1436). Greensboro, N.C: North Carolina A&T State University, 1994.

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37

Lehmann, Lutz. Wave Propagation in Infinite Domains: With Applications to Structure Interaction (Lecture Notes in Applied and Computational Mechanics). Springer, 2007.

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38

Balakrishnan, A. V. Computational Techniques in Identification and Control of Flexible Flight Structures: Proceedings of the Nasa-UCLA Workshop, Lake Arrowhead (ComCon conferences proceedings). Optimization Software, 1990.

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39

Redbooks, IBM. Patterns: Flexible Self-service Applications Using Process Choreography (IBM Redbooks). IBM.Com/Redbooks, 2004.

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40

Patterns: Flexible Self-service Applications Using Process Choreography on Z/os. Vervante, 2005.

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41

Gottlieb, Jacqueline. Neuronal Mechanisms of Attentional Control. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.033.

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Damage to the human inferior parietal lobe produces an attentional disturbance known as contralateral neglect, and neurophysiological studies in monkeys have begun to unravel the cellular basis of this function. Converging evidence suggests that LIP encodes a sparse topographic map of the visual world that highlights attention-worthy objects or locations. LIP cells may facilitate sensory attentional modulations, and ultimately the transient improvement in perceptual thresholds that is the behavioural signature of visual attention. In addition, LIP projects to oculomotor centres where it can prime the production of a rapid eye movement (saccade). Importantly, LIP cells can select visual targets without triggering saccades, showing that they implement an internal (covert) form of selection that can be flexibly linked with action by virtue of additional, independent mechanisms. The target selection response in LIP is modulated by bottom-up factors and by multiple task-related factors. These modulations are likely to arise through learning and may reflect a multitude of computations through which the brain decides when and to what to attend.

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42

Butz, Martin V., and Esther F. Kutter. Decision Making, Control, and Concept Formation. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0012.

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While attention controls the internal, mental focus of attention, motor control directs the bodily control focus. Our nervous system is structured in a cascade of interactive control loops, where the primary self-stabilizing control loops can be found directly in the body’s morphology and the muscles themselves. The hierarchical structure enables flexible and selective motor control and the invocation of motor primitives and motor complexes. The learning of motor primitives and complexes again adheres to certain computational systematicities. Redundant behavioral alternatives are encoded in an abstract manner, enabling fast habitual decision making and slower, more elaborated planning processes for realizing context-dependent behavior adaptations. On a higher level, behavior can be segmented into events, during which a particular behavior unfolds, and event boundaries, which characterize the beginning or the end of a behavior. Combinations of events and event boundaries yield event schemata. Hierarchical combinations of event schemata on shorter and longer time scales yield event taxonomies. When developing event boundary detectors, our mind begins to develop environmental conceptualizations. Evidence is available that suggests that such event-oriented conceptualizations are inherently semantic and closely related to linguistic, generative models. Thus, by optimizing behavioral versatility and developing progressively more abstract codes of environmental interactions and manipulations, cognitive encodings develop, which are supporting symbol grounding and grammatical language development.

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43

Butz, Martin V., and Esther F. Kutter. How the Mind Comes into Being. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.001.0001.

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For more than 2000 years Greek philosophers have thought about the puzzling introspectively assessed dichotomy between our physical bodies and our seemingly non-physical minds. How is it that we can think highly abstract thoughts, seemingly fully detached from actual, physical reality? Despite the obvious interactions between mind and body (we get tired, we are hungry, we stay up late despite being tired, etc.), until today it remains puzzling how our mind controls our body, and vice versa, how our body shapes our mind. Despite a big movement towards embodied cognitive science over the last 20 years or so, introductory books with a functional and computational perspective on how human thought and language capabilities may actually have come about – and are coming about over and over again – are missing. This book fills that gap. Starting with a historical background on traditional cognitive science and resulting fundamental challenges that have not been resolved, embodied cognitive science is introduced and its implications for how human minds have come and continue to come into being are detailed. In particular, the book shows that evolution has produced biological bodies that provide “morphologically intelligent” structures, which foster the development of suitable behavioral and cognitive capabilities. While these capabilities can be modified and optimized given positive and negative reward as feedback, to reach abstract cognitive capabilities, evolution has furthermore produced particular anticipatory control-oriented mechanisms, which cause the development of particular types of predictive encodings, modularizations, and abstractions. Coupled with an embodied motivational system, versatile, goal-directed, self-motivated behavior, learning becomes possible. These lines of thought are introduced and detailed from interdisciplinary, evolutionary, ontogenetic, reinforcement learning, and anticipatory predictive encoding perspectives in the first part of the book. A short excursus then provides an introduction to neuroscience, including general knowledge about brain anatomy, and basic neural and brain functionality, as well as the main research methodologies. With reference to this knowledge, the subsequent chapters then focus on how the human brain manages to develop abstract thought and language. Sensory systems, motor systems, and their predictive, control-oriented interactions are detailed from a functional and computational perspective. Bayesian information processing is introduced along these lines as are generative models. Moreover, it is shown how particular modularizations can develop. When control and attention come into play, these structures develop also dependent on the available motor capabilities. Vice versa, the development of more versatile motor capabilities depends on structural development. Event-oriented abstractions enable conceptualizations and behavioral compositions, paving the path towards abstract thought and language. Also evolutionary drives towards social interactions play a crucial role. Based on the developing sensorimotor- and socially-grounded structures, the human mind becomes language ready. The development of language in each human child then further facilitates the self-motivated generation of abstract, compositional, highly flexible thought about the present, past, and future, as well as about others. In conclusion, the book gives an overview over how the human mind comes into being – sketching out a developmental pathway towards the mastery of abstract and reflective thought, while detailing the critical body and neural functionalities, and computational mechanisms, which enable this development.

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44

Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.

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Books on the topic 'FLEXIBLE COMPUTATION'

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