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Littérature scientifique sur le sujet « Computational capabilities »

Auteur : Grafiati

Publié le 11 mars 2023

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Articles de revues sur le sujet "Computational capabilities"

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Lin, Hai-Qing. "Boosting computational capabilities." Nature Materials 15, no. 7 (2016): 693–94. http://dx.doi.org/10.1038/nmat4675.

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Weihe, S., and B. Kröplin. "Computational mechanics: capabilities and restrictions." Computational Materials Science 16, no. 1-4 (1999): 10–16. http://dx.doi.org/10.1016/s0927-0256(99)00040-3.

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Neirotti, J. P., and L. Franco. "Computational capabilities of multilayer committee machines." Journal of Physics A: Mathematical and Theoretical 43, no. 44 (2010): 445103. http://dx.doi.org/10.1088/1751-8113/43/44/445103.

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Scarselli, F., M. Gori, Ah Chung Tsoi, M. Hagenbuchner, and G. Monfardini. "Computational Capabilities of Graph Neural Networks." IEEE Transactions on Neural Networks 20, no. 1 (2009): 81–102. http://dx.doi.org/10.1109/tnn.2008.2005141.

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CABESSA, JÉRÉMIE, and HAVA T. SIEGELMANN. "THE SUPER-TURING COMPUTATIONAL POWER OF PLASTIC RECURRENT NEURAL NETWORKS." International Journal of Neural Systems 24, no. 08 (2014): 1450029. http://dx.doi.org/10.1142/s0129065714500294.

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We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power — as the static analog neural networks — irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

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Siegelmann, H. T., B. G. Horne, and C. L. Giles. "Computational capabilities of recurrent NARX neural networks." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 27, no. 2 (1997): 208–15. http://dx.doi.org/10.1109/3477.558801.

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Priel, Avner, Marcelo Blatt, Tal Grossmann, Eytan Domany, and Ido Kanter. "Computational capabilities of restricted two-layered perceptrons." Physical Review E 50, no. 1 (1994): 577–95. http://dx.doi.org/10.1103/physreve.50.577.

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Huang, Xin, Haotian Yin, Xin Zhang, et al. "Efficient and Secure Pairing Protocol for Devices with Unbalanced Computational Capabilities." Mathematics 10, no. 14 (2022): 2447. http://dx.doi.org/10.3390/math10142447.

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Wearable devices that collect data about human beings are widely used in healthcare applications. Once collected, the health data will be securely transmitted to smartphones in most scenarios. Authenticated Key Exchange (AKE) can protect wireless communications between wearables and smartphones, and a typical solution is the Bluetooth Secure Simple Pairing (SSP) protocol with numeric comparison. However, this protocol requires equivalent computation on both devices, even though their computational capabilities are significantly different. This paper proposes a lightweight numeric comparison protocol for communications in which two parties have unbalanced computational capabilities, e.g., a wearable sensor and a smartphone, named UnBalanced secure Pairing using numeric comparison (UB-Pairing for short). The security of UB-Pairing is analyzed using the modified Bellare–Rogaway model (mBR). The analysis results show that UB-Pairing achieves the security goals. We also carry out a number of experiments to evaluate the performance of UB-Pairing. The results show that UB-Pairing is friendly to wearable devices, and more efficient than standard protocols when the computation capabilities of the two communication parties are highly unbalanced.

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Escribano, Jesús, Francisco Botana, and Miguel A. Abánades. "Adding remote computational capabilities to Dynamic Geometry Systems." Mathematics and Computers in Simulation 80, no. 6 (2010): 1177–84. http://dx.doi.org/10.1016/j.matcom.2008.04.019.

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Chourasiya, Neelesh L., and Neeraj Mohan. "Computational Offloading in Android Devices Using Cloud Computing Capabilities." International Journal on Communications Antenna and Propagation (IRECAP) 8, no. 1 (2018): 9. http://dx.doi.org/10.15866/irecap.v7i6.13349.

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Thèses sur le sujet "Computational capabilities"

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Kolen, John F. "Exploring the computational capabilities of recurrent neural networks /." The Ohio State University, 1994. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487853913100192.

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Needham, Perri. "Enhancing the capabilities of computational chemistry using GPU technology." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/enhancing-the-capabilities-of-computational-chemistry-using-gpu-technology(0988c19e-cc1a-443f-b82f-0c5fe0422d0b).html.

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Three key enhancements were made to a semiempirical molecular orbital program to develop a fast, accurate method of calculating chemical properties of large (> 1000 atom) molecular systems, through the use of quantum theory. In this thesis the key enhancements are presented which are: the implementation of a divide-and-conquer approach to a self-consistent field procedure, in an effort to improve capability; the use of the novel technology, GPU technology, to parallelize the divide-and-conquer self-consistent field procedure, in an effort to improve the speed; the implementation of a newly developed semiempirical model, the Polarized Molecular Orbital Model, in an effort to improve the accuracy. The development of a divide-and-conquer approach to the SCF (DC-SCF) procedure (enhancement 1) was carried out using saturated hydrocarbon chains whereby the saturated hydrocarbon chain is partitioned into small overlapping subsystems and the Roothaan equations solved for each subsystem. An investigation was carried out to find the optimal partitioning scheme for saturated hydrocarbon chains in order to minimize the loss of energy experienced from neglecting some of the interactions in the system whilst maintaining near linear scaling with system size. The DC-SCF procedure was shown to be accurate to 10-3 kcal mol-1 per atom whilst calculating the SCF-energy nearly 6 times faster than using the standard SCF procedure, for a 698-atom system. The development of a parallel DC-SCF procedure and Cartesian forces calculation for use on a GPU (enhancement 2), resulted in a hybrid CPU/GPU DC-SCF implementation that calculated the energy of a 1997-atom saturated hydrocarbon chain 21 times faster than the standard serial SCF implementation and a accelerated Cartesian forces calculation that performed 7 times faster for a saturated hydrocarbon chain of 1205-atoms, when accelerated using an NVidia Tesla C2050 GPU. The hybrid CPU/GPU algorithm made use of commercially accelerated linear algebra libraries, CULA and CUBLAS. A comparison was made between CULA’s accelerated eigensolver routine and the accelerated DC-eigensolver (developed in this research) and it was found that for saturated hydrocarbon chains of > 350 atoms, the accelerated DC-eigensolver performed around twice as fast as the accelerated CULA eigensolver. The implementation of the Polarized Molecular Orbital model (enhancement 3) was validated against published isomerization energies and benchmarked against the non-nitrogen containing complexes in the S66 database. The benchmark complexes were categorized according to dominant intermolecular interactions namely, hydrogen bonding, dispersion interactions and mixed interactions. After assessment it was found that the PMO model predicts interaction energies of complexes with a mixture of dispersive and electrostatic interactions to the highest accuracy (0.69 kcal mol-1 with respect to CCSD(T)/CBS). The dispersion correction within the PMO model was found to ‘overcorrect’ the dispersive contribution for most complexes tested. The outcome of this research is a semiempirical molecular orbital program that calculates the energy of a closed-shell saturated hydrocarbon chain of ~2000 atoms in under 4 minutes instead of 1.5 hours when using a PM3-Hamiltonian and can calculate interaction energies of systems exhibiting a mixture of electrostatic and dispersive interactions to an accuracy of within 1 kcal mol-1 (relative to high-level quantum methods). To demonstrate a suitable application for the enhanced SE-MO program, interaction energies of a series of PAHs with water, phenol and methanol have been investigated. The resultant program is suitable for use in calculating the energy and forces of large material and (in future) biological systems by a fast and accurate method that would be impractical or impossible to do without these enhancements.

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Murabito, Francesca. "Deeply Incorporating Human Capabilities into Machine Learning Models for Fine-Grained Visual Categorization." Doctoral thesis, Università di Catania, 2019. http://hdl.handle.net/10761/4144.

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Artificial intelligence and machine learning have long attempted to emulate human visual system. With the recent advances in deep neural networks, which take inspiration from the architecture of the primate visual hierarchy, human-level visual abilities are now coming within reach of artificial systems. However, the existing computational models are designed with engineering goals, loosely emulating computations and connections of biological neurons, especially in terms of intermediate visual representations. In this thesis we aim at investigating how human skills can be integrated into computational models in order to perform fine-grained image categorization, a task which requires the application of specific perceptive and cognitive abilities to be solved. In particular, our goal is to develop systems which, either implicitly or explicitly, combine human reasoning processes with deep classification models. Our claims is that by the emulation of the process carried out by humans while performing a recognition task it is possible to yield improved classification performance. To this end, we first attempt to replicate human visual attention by modeling a saliency detection system able to emulate the integration of the top-down (task-controlled, classification-driven) and bottom-up (sensory information) processes; thus, the generated saliency maps are able to represent implicitly the way humans perceive and focus their attention while performing recognition, and, therefore, a useful supervision for the automatic classification system. We then investigate if and to what extent the learned saliency maps can support visual classification in nontrivial cases. To achieve this, we propose SalClassNet, a CNN framework consisting of two networks jointly trained: a) the first one computing top-down saliency maps from input images, and b) the second one exploiting the computed saliency maps for visual classification. Gaze shifts change in relation to a task is not the only process when performing classification in specific domains, but humans also leverage a-priori specialized knowledge to perform recognition. For example, distinguishing between different dog breeds or fruit varieties requires skills that not all human possess but only domain experts. Of course, one may argue that the typical learning-by-example approach can be applied by asking domain experts to collect enough annotations from which machine learning methods can derive the features necessary for the classification. Nevertheless, this is a really costly process and often infeasible. Thus, the second part of this thesis aim at explicitly modeling and exploiting domain-specific knowledge to perform recognition. To this end, we introduce and demonstrate that computational ontologies can explicitly encode human knowledge and that it can be used to support multiple tasks from data annotation to classification. In particular, we propose an ontology-based annotation tool, able to reduce significantly the efforts to collect highly-specialized labels and demonstrate its effectiveness building the VegImage dataset, a collection of about 4,000 images belonging to 24 fruit varieties, annotated with over 65,000 bounding boxes and enriched with a large knowledge base consisting of more than 1,000,000 OWL triples. We then exploit this ontology-structured knowledge by combining a semantic-classifier, which performs inference based on the information encoded in the domain ontology, with a visual convolutional neural network, showing that the integration of semantics into automatic classification models can represents the key to solve a complex task such as the fine-grained recognition of fruit varieties, a task which requires the contribution of domain expert to be completely solved. Performance evaluation of the proposed approaches provides a basis to assess the validity of our claim along with the scientific soundness of developed models.

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Lindberg, Aron. "The Origin, Evolution, and Variation of Routine Structures in Open Source Software Development: Three Mixed Computational-Qualitative Studies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1436527665.

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Lundqvist, Viktor. "A smoothed particle hydrodynamic simulation utilizing the parallel processing capabilites of the GPUs." Thesis, Linköping University, Department of Science and Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-21761.

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<p>Simulating fluid behavior has proven to be a demanding challenge which requires complex computational models and highly efficient data structures. Smoothed Particle Hydrodynamics (SPH) is a particle based computational model used to simulate fluid behavior that has been found capable of producing convincing results. However, the SPH algorithm is computational heavy which makes it cumbersome to work with.</p><p>This master thesis describes how the SPH algorithm can be accelerated by utilizing the GPU’s computational resources. It describes a model for how to distribute the work load on the GPU and presents a suitable data structure. In addition, it proposes a method to represent and handle moving objects in the fluids surroundings. Finally, the performance gain due to the GPU is evaluated by comparing processing times with an identical implementation running solely on the CPU.</p>

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Shedimbi, Prudhvi Rao. "Optimizing Request Routing in Heterogeneous Web Computation Environments." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1468512510.

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Goodwin, Jon Willie III. "Arithmetical computation and associated neuropsychological capabilities in children, adolescents, and young adults with nonsyndromic orofacial clefts." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5761.

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Orofacial clefts are a group of congenital craniofacial deformities characterized by structural defects within and around the oral cavity. While some orofacial clefts are associated with an identifiable genetic or teratogenic syndrome, most are isolated or nonsyndromic. It has been well-documented that children born with nonsyndromic cleft lip and/or palate (NCL/P) are at-risk for poorer academic outcomes, especially within reading. Research into the cognitive functioning of patients with NCL/P has demonstrated that auditory-verbal memory and rapid naming are significant neuropsychological predictors of their lower reading achievement. Despite a solid compendium of research into the reading outcomes of those affected by NCL/P, very little research into the mathematical skills of this population exists. The current study examined whether the arithmetical computation skills of children, adolescents, and young adults with NCL/P differ significantly from healthy control participants. Comparisons of potential neuropsychological predictors of arithmetical computation were also conducted to determine whether these variables differ significantly for participants with NCL/P. Given the influence of language on both reading and mathematics and clear evidence of language impairments in individuals with NCL/P, it was hypothesized that arithmetical computation would be significantly lower for the NCL/P group. It was also hypothesized that the neuropsychological variables associated with lowered reading in NCL/P would be the strongest predictors of arithmetical computation. Results confirmed that arithmetical computation was significantly lower for the NCL/P group. Sustained attention, visual-spatial organization, auditory-verbal memory and rapid naming were significant predictors for the NCL/P group; rapid naming was the lone variable that was significantly more predictive of arithmetical computation for the NCL/P group than for control participants. These results suggest that inefficient verbal label retrieval related to short-term memory deficits underlie the computational difficulties of individuals with NCL/P. These findings have implications for approaches to remediation, as well as future research.

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Olsson, Joakim. "A Critique of the Learning Brain." Thesis, Uppsala universitet, Avdelningen för teoretisk filosofi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-432105.

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The guiding question for this essay is: who is the learner? The aim is to examine and criticize one answer to this question, sometimes referred to as the theory of the learning brain, which suggests that the explanation of human learning can be reduced to the transmitting and storing of information in the brain’s formal and representational architecture, i.e., that the brain is the learner. This essay will argue that this answer is misleading, because it cannot account for the way people strive to learn in an attempt to lead a good life as it misrepresents the intentional life of the mind, which results in its counting ourselves out of the picture when it attempts to provide a scientific theory of the learning process. To criticize the theory of the learning brain, this essay will investigate its philosophical foundation, a theory of mind called cognitivism, which is the basis for the cognitive sciences. Cognitivism is itself built on three main tenets: mentalism, the mind-brain identity theory and the computer analogy. Each of these tenets will be criticized in turn, before the essay turns to criticize the theory of the learning brain itself. The focus of this essay is, in other words, mainly negative. The hope is that this criticism will lay the groundwork for an alternative view of mind, one that is better equipped to give meaningful answers to the important questions we have about what it means to learn, i.e., what we learn, how we do it and why. This alternative will emphasize the holistic and intentional character of the human mind, and consider the learning process as an intentional activity performed, not by isolated brains, but by people with minds that are extended, embodied, enacted and embedded in a sociocultural and physical context.

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Marshall, Robert. "Improving the efficiency and capabilities of document structuring." 2007. http://repository.unimelb.edu.au/10187/1700.

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Natural language generation (NLG), the problem of creating human-readable documents by computer, is one of the major fields of research in computational linguistics The task of creating a document is extremely common in many fields of activity. Accordingly, there are many potential applications for NLG - almost any document creation task could potentially be automated by an NLG system. Advanced forms of NLG could also be used to generate a document in multiple languages, or as an output interface for other programs, which might ordinarily produce a less-manageable collection of data. They may also be able to create documents tailored to the needs of individual users. This thesis deals with document structure, a recent theory which describes those aspects of a document’s layout which affect its meaning. As well as its theoretical interest, it is a useful intermediate representation in the process of NLG. There is a well-defined process for generating a document structure using constraint programming. We show how this process can be made considerably more efficient. This in turn allows us to extend the document structuring task to allow for summarisation and finer control of the document layout. This thesis is organised as follows. Firstly, we review the necessary background material in both natural language processing and constraint programming.

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"Universal computation and other capabilities of hybrid and continuous dynamical systems." Massachusetts Institute of Technology, Laboratory for Information and Decision Systems], 1993. http://hdl.handle.net/1721.1/3347.

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Michael S. Branicky.<br>Caption title.<br>Includes bibliographical references (p. 25-27).<br>Supported by the Army Research Office and the Center for Intelligent Control Systems. DAAL03-92-G-0164 DAAL03-92-G-0115

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Livres sur le sujet "Computational capabilities"

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Lothar, Wolf, Krawiec John, and National Institute of Standards and Technology (U.S.), eds. Evaluation of the HDR fire test data and accompanying computational activities with conclusion from present code capabilities. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 1997.

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Center, Langley Research, and United States. National Aeronautics and Space Administration., eds. Numerical stability and control analysis towards falling-leaf prediction capabilities of splitflow for two generic high-performance aircraft models. National Aeronautics and Space Administration, Langley Research Center, 1998.

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Current Capabilities and Future Directions in Computational Fluid Dynamics. National Academies Press, 1986. http://dx.doi.org/10.17226/18928.

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Quartara, Andrea, and Djordje Stanojevic. Computational and Manufacturing Strategies: Experimental Expressions of Wood Capabilities. Springer, 2018.

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National Aeronautics and Space Administration (NASA) Staff. On the Computational Capabilities of Physical Systems. Part 2; Relationship with Conventional Computer Science. Independently Published, 2018.

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Bandyopadhyay, Avimanyu. Hands-On GPU Computing with Python: Explore the Capabilities of GPUs for Solving High Performance Computational Problems. Packt Publishing, Limited, 2019.

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Butz, Martin V., and Esther F. Kutter. Cognitive Development and Evolution. Oxford University Press, 2017. http://dx.doi.org/10.1093/acprof:oso/9780198739692.003.0004.

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When acknowledging that the mind is embodied, cognitive development and evolution must determine how the body and environment shape the mind. Evolution has evolved structures and computational mechanisms in the body, and the brain that predispose ontogenetic development. Starting with conception, brain, body, and mind co-develop, and shape each other. An infant first develops rudimentary bodily representation and control capabilities, and concurrently uses them to abstract from and generalize over the gathered sensorimotor experiences to develop conceptual understandings and language. Evolution, on the other hand, works on a different time scale. Evolutionary pressures towards survival-suitable cell and bodily structures have dominated much of evolutionary progression. Benefits due to social interactions and coordinated cooperation have led to the evolution of the human brain, enabling the development of human minds. Some details on genetics and on evolutionary computation shed further light on how evolution must have brought about human minds. Thereby, the evolution of suitable bodily structures, of brain modularizations, developmental pathways, adaptive behavioral capabilities, and predispositions for social interactions constitute critical components. Subsequent chapters focus on the computational mechanisms behind embodied cognitive development.

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Service Oriented Infrastructures and Cloud Service Platforms for the Enterprise: A selection of common capabilities validated in real-life business trials by the BEinGRID consortium. Springer, 2009.

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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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Succi, Sauro. Flows at Moderate Reynolds Numbers. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199592357.003.0018.

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This chapter presents the application of LBE to flows at moderate Reynolds numbers, typically hundreds to thousands. This is an important area of theoretical and applied fluid mechanics, one that relates, for instance, to the onset of nonlinear instabilities and their effects on the transport properties of the unsteady flow configuration. The regime of Reynolds numbers at which these instabilities take place is usually not very high, of the order of thousands, hence basically within reach of present day computer capabilities. Nonetheless, following the full evolution of these transitional flows requires very long-time integrations with short time-steps, which command substantial computational power. Therefore, efficient numerical methods are in great demand. Also of major interest are steady-state or pulsatile flows at moderate Reynolds numbers in complex geometries, such as they occur, for instance, in hemodynamic applications. The application of LBE to such flows will also briefly be mentioned

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Chapitres de livres sur le sujet "Computational capabilities"

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Mandal, Sujoy, Koushik Samanta, Debipriya Dutta, and Anindya Bose. "IRNSS capabilities." In Computational Science and Engineering. CRC Press, 2016. http://dx.doi.org/10.1201/9781315375021-29.

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Preciado, Víctor M. "Improving Cellular Nonlinear Network Computational Capabilities." In Advances in Artificial Intelligence — IBERAMIA 2002. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36131-6_48.

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Šíma, Jiří. "The Computational Capabilities of Neural Networks." In Artificial Neural Nets and Genetic Algorithms. Springer Vienna, 2001. http://dx.doi.org/10.1007/978-3-7091-6230-9_4.

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Maknickienė, Nijolė, and Algirdas Maknickas. "Prediction Capabilities of Evolino RNN Ensembles." In Studies in Computational Intelligence. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23392-5_26.

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Alfaro-García, Víctor G., Anna M. Gil-Lafuente, and Gerardo G. Alfaro Calderón. "Innovation Capabilities Using Fuzzy Logic Systems." In Applied Mathematics and Computational Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75792-6_20.

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Fernandez, Rolando, Erin Zaroukian, James D. Humann, et al. "Emergent Heterogeneous Strategies from Homogeneous Capabilities in Multi-Agent Systems." In Transactions on Computational Science and Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70296-0_37.

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Iglesias, A., and R. Ipanaqué. "Extending Maple Capabilities for Solving and Displaying Inequalities." In Computational Science – ICCS 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758525_52.

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Fields, Chris, Mark DeYong, and Randall Findley. "Computational Capabilities of Biologically-Realistic Analog Processing Elements." In VLSI for Artificial Intelligence and Neural Networks. Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3752-6_17.

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Rodríguez, Alejandro, Enrique Jimenez, Mateusz Radzimski, et al. "Applying Caching Capabilities to Inference Applications Based on Semantic Web." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03958-4_3.

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Khekare, Ganesh, Lokesh Kumar Bramhane, Chetan Dhule, Rahul Agrawal, and Anil V. Turukmane. "Testing and Analysis of Predictive Capabilities of Machine Learning Algorithms." In Studies in Computational Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99079-4_16.

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Actes de conférences sur le sujet "Computational capabilities"

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Frison, Gianluca, and John Bagterp Jorgensen. "MPC related computational capabilities of ARMv7A processors." In 2015 European Control Conference (ECC). IEEE, 2015. http://dx.doi.org/10.1109/ecc.2015.7331062.

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Zou, Mengsong, Ali Reza Zamani, Javier Diaz-Montes, Ioan Petri, Omer Rana, and Manish Parashar. "Leveraging In-Transit Computational Capabilities in Federated Ecosystems." In 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE). IEEE, 2016. http://dx.doi.org/10.1109/sose.2016.15.

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Pinkevich, Vasiliy. "HARDWARE COMPUTATIONAL UNITS DESIGN WITH COMBINED DEBUG CAPABILITIES." In 17th International Multidisciplinary Scientific GeoConference SGEM2017. Stef92 Technology, 2017. http://dx.doi.org/10.5593/sgem2017/21/s07.011.

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Ritter and Schulten. "Kohonen's self-organizing maps: exploring their computational capabilities." In Proceedings of 1993 IEEE International Conference on Neural Networks (ICNN '93). IEEE, 1988. http://dx.doi.org/10.1109/icnn.1988.23838.

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Chaudhari, Narendra S., Yew Soon Ong, and Veena Trivedi. "Computational Capabilities of Soft-Computing Frameworks: An Overview." In 2006 9th International Conference on Control, Automation, Robotics and Vision. IEEE, 2006. http://dx.doi.org/10.1109/icarcv.2006.345433.

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Jackson, George W., and Shawon S. M. Rahman. "Security Governance, Management and Strategic Alignment via Capabilities." In 2017 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2017. http://dx.doi.org/10.1109/csci.2017.8.

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Gladkikh, Anatoliy A., Dmitriy V. Mishin, Nikolay Y. Chilikhin, and Roman Z. Ibragimov. "Methods of coherent networks matching with codecs computational capabilities." In optical-technologies-in-telecommunications-2018, edited by Anton V. Bourdine, Vladimir A. Burdin, Oleg G. Morozov, Albert H. Sultanov, and Vladimir A. Andreev. SPIE, 2019. http://dx.doi.org/10.1117/12.2526538.

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Munipalli, Ramakanth, and Vijaya Shankar. "Development of computational capabilities in real gas MHD simulations." In 39th Aerospace Sciences Meeting and Exhibit. American Institute of Aeronautics and Astronautics, 2001. http://dx.doi.org/10.2514/6.2001-198.

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Grange, Camille, and Izak Benbasat. "Information Technology Capabilities for Digital Social Networks." In 2009 International Conference on Computational Science and Engineering. IEEE, 2009. http://dx.doi.org/10.1109/cse.2009.113.

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Hebbar, Akshay. "Augmented intelligence: Enhancing human capabilities." In 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2017. http://dx.doi.org/10.1109/icrcicn.2017.8234515.

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Rapports d'organisations sur le sujet "Computational capabilities"

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Sengupta, Debasis, Shaun Kwak, Alex Vasenkov, Yun Kyung Shin, and Adri van Duin. Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloys. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1170170.

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Sengupta, Debasis, Shaun Kwak, Alex Vasenkov, Yun Kyung Shin, and Adri van Duin. Computational Capabilities for Predictions of Interactions at the Grain Boundary of Refractory Alloys. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1170210.

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Barney, B., and J. Shuler. Purple Computational Environment With Mappings to ACE Requirements for the General Availability User Environment Capabilities. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/900098.

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Clark, Andrew, David Luxat, Michael Higgins, and Mariah Smith. Survey and Assessment of Computational Capabilities for Advanced (Non-LWR) Reactor Mechanistic Source Term Analysis. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1772327.

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Kung, Steven, and Robert Rapp. Development of Computational Capabilities to Predict the Corrosion Wastage of Boiler Tubes in Advanced Combustion Systems. Office of Scientific and Technical Information (OSTI), 2014. http://dx.doi.org/10.2172/1165184.

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Kothe, Douglas B., Kenneth J. Roche, and Ricky A. Kendall. FY 2009 Annual Report of Joule Software Metric SC GG 3.1/2.5.2, Improve Computational Science Capabilities. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/977116.

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Robert E. Spall, Barton Smith, and Thomas Hauser. validation and Enhancement of Computational Fluid Dynamics and Heat Transfer Predictive Capabilities for Generation IV Reactor Systems. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/944056.

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Michael A. Pope, Samuel E. Bays, S. Piet, R. Ferrer, and Mehdi Asgari. Transmutation Performance Analysis for Inert Matrix Fuels in Light Water Reactors and Computational Neutronics Methods Capabilities at INL. Office of Scientific and Technical Information (OSTI), 2009. http://dx.doi.org/10.2172/961935.

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Wissink, Andrew, Jude Dylan, Buvana Jayaraman, et al. New capabilities in CREATE™-AV Helios Version 11. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40883.

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CREATE™-AV Helios is a high-fidelity coupled CFD/CSD infrastructure developed by the U.S. Dept. of Defense for aeromechanics predictions of rotorcraft. This paper discusses new capabilities added to Helios version 11.0. A new fast-running reduced order aerodynamics option called ROAM has been added to enable faster-turnaround analysis. ROAM is Cartesian-based, employing an actuator line model for the rotor and an immersed boundary model for the fuselage. No near-body grid generation is required and simulations are significantly faster through a combination of larger timesteps and reduced cost per step. ROAM calculations of the JVX tiltrotor configuration give a comparably accurate download prediction to traditional body-fitted calculations with Helios, at 50X less computational cost. The unsteady wake in ROAM is not as well resolved, but wake interactions may be a less critical issue for many design considerations. The second capability discussed is the addition of six-degree-of-freedom capability to model store separation. Helios calculations of a generic wing/store/pylon case with the new 6-DOF capability are found to match identically to calculations with CREATE™-AV Kestrel, a code which has been extensively validated for store separation calculations over the past decade.

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Vigil, Benny Manuel, Robert Ballance, and Karen Haskell. Cielo Computational Environment Usage Model With Mappings to ACE Requirements for the General Availability User Environment Capabilities Release Version 1.1. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1048827.

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