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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 r

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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 pr

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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 dev

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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 compu

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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 G

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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 pre

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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, whi

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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

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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. Evolutio

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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

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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 flow

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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

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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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