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Статті в журналах з теми "Temporal Algorithms"
Oettershagen, Lutz, and Petra Mutzel. "Computing top-k temporal closeness in temporal networks." Knowledge and Information Systems 64, no. 2 (January 8, 2022): 507–35. http://dx.doi.org/10.1007/s10115-021-01639-4.
Повний текст джерелаVan Beek, P., and D. W. Manchak. "The Design and Experimental Analysis of Algorithms for Temporal Reasoning." Journal of Artificial Intelligence Research 4 (January 1, 1996): 1–18. http://dx.doi.org/10.1613/jair.232.
Повний текст джерелаLi, Meng He, Chuan Lin, Jing Bei Tian, and Sheng Hui Pan. "An Algorithms for Super-Resolution Reconstruction of Video Based on Spatio-Temporal Adaptive." Advanced Materials Research 532-533 (June 2012): 1680–84. http://dx.doi.org/10.4028/www.scientific.net/amr.532-533.1680.
Повний текст джерелаSun, Xiaoli, Yusong Tan, Qingbo Wu, Jing Wang, and Changxiang Shen. "New Algorithms for Counting Temporal Graph Pattern." Symmetry 11, no. 10 (September 20, 2019): 1188. http://dx.doi.org/10.3390/sym11101188.
Повний текст джерелаLi, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 2022): 1748. http://dx.doi.org/10.3390/s22051748.
Повний текст джерелаAhmed, Nesreen K., Nick Duffield, and Ryan A. Rossi. "Online Sampling of Temporal Networks." ACM Transactions on Knowledge Discovery from Data 15, no. 4 (June 2021): 1–27. http://dx.doi.org/10.1145/3442202.
Повний текст джерелаJain, Anuj, and Sartaj Sahni. "Foremost Walks and Paths in Interval Temporal Graphs." Algorithms 15, no. 10 (September 29, 2022): 361. http://dx.doi.org/10.3390/a15100361.
Повний текст джерелаDeb, Rohan, and Shalabh Bhatnagar. "Gradient Temporal Difference with Momentum: Stability and Convergence." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6488–96. http://dx.doi.org/10.1609/aaai.v36i6.20601.
Повний текст джерелаGuo, Yangnan, Cangjiao Wang, Shaogang Lei, Junzhe Yang, and Yibo Zhao. "A Framework of Spatio-Temporal Fusion Algorithm Selection for Landsat NDVI Time Series Construction." ISPRS International Journal of Geo-Information 9, no. 11 (November 4, 2020): 665. http://dx.doi.org/10.3390/ijgi9110665.
Повний текст джерелаVisca, Jorge, and Javier Baliosian. "rl4dtn: Q-Learning for Opportunistic Networks." Future Internet 14, no. 12 (November 23, 2022): 348. http://dx.doi.org/10.3390/fi14120348.
Повний текст джерелаДисертації з теми "Temporal Algorithms"
Chen, Xiaodong. "Temporal data mining : algorithms, language and system for temporal association rules." Thesis, Manchester Metropolitan University, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297977.
Повний текст джерелаChen, Feng. "Efficient Algorithms for Mining Large Spatio-Temporal Data." Diss., Virginia Tech, 2013. http://hdl.handle.net/10919/19220.
Повний текст джерелаgrowing interests. Recent advances on remote sensing technology mean
that massive amounts of spatio-temporal data are being collected,
and its volume keeps increasing at an ever faster pace. It becomes
critical to design efficient algorithms for identifying novel and
meaningful patterns from massive spatio-temporal datasets. Different
from the other data sources, this data exhibits significant
space-time statistical dependence, and the assumption of i.i.d. is
no longer valid. The exact modeling of space-time dependence will
render the exponential growth of model complexity as the data size
increases. This research focuses on the construction of efficient
and effective approaches using approximate inference techniques for
three main mining tasks, including spatial outlier detection, robust
spatio-temporal prediction, and novel applications to real world
problems.
Spatial novelty patterns, or spatial outliers, are those data points
whose characteristics are markedly different from their spatial
neighbors. There are two major branches of spatial outlier detection
methodologies, which can be either global Kriging based or local
Laplacian smoothing based. The former approach requires the exact
modeling of spatial dependence, which is time extensive; and the
latter approach requires the i.i.d. assumption of the smoothed
observations, which is not statistically solid. These two approaches
are constrained to numerical data, but in real world applications we
are often faced with a variety of non-numerical data types, such as
count, binary, nominal, and ordinal. To summarize, the main research
challenges are: 1) how much spatial dependence can be eliminated via
Laplace smoothing; 2) how to effectively and efficiently detect
outliers for large numerical spatial datasets; 3) how to generalize
numerical detection methods and develop a unified outlier detection
framework suitable for large non-numerical datasets; 4) how to
achieve accurate spatial prediction even when the training data has
been contaminated by outliers; 5) how to deal with spatio-temporal
data for the preceding problems.
To address the first and second challenges, we mathematically
validated the effectiveness of Laplacian smoothing on the
elimination of spatial autocorrelations. This work provides
fundamental support for existing Laplacian smoothing based methods.
We also discovered a nontrivial side-effect of Laplacian smoothing,
which ingests additional spatial variations to the data due to
convolution effects. To capture this extra variability, we proposed
a generalized local statistical model, and designed two fast forward
and backward outlier detection methods that achieve a better balance
between computational efficiency and accuracy than most existing
methods, and are well suited to large numerical spatial datasets.
We addressed the third challenge by mapping non-numerical variables
to latent numerical variables via a link function, such as logit
function used in logistic regression, and then utilizing
error-buffer artificial variables, which follow a Student-t
distribution, to capture the large valuations caused by outliers. We
proposed a unified statistical framework, which integrates the
advantages of spatial generalized linear mixed model, robust spatial
linear model, reduced-rank dimension reduction, and Bayesian
hierarchical model. A linear-time approximate inference algorithm
was designed to infer the posterior distribution of the error-buffer
artificial variables conditioned on observations. We demonstrated
that traditional numerical outlier detection methods can be directly
applied to the estimated artificial variables for outliers
detection. To the best of our knowledge, this is the first
linear-time outlier detection algorithm that supports a variety of
spatial attribute types, such as binary, count, ordinal, and
nominal.
To address the fourth and fifth challenges, we proposed a robust
version of the Spatio-Temporal Random Effects (STRE) model, namely
the Robust STRE (R-STRE) model. The regular STRE model is a recently
proposed statistical model for large spatio-temporal data that has a
linear order time complexity, but is not best suited for
non-Gaussian and contaminated datasets. This deficiency can be
systemically addressed by increasing the robustness of the model
using heavy-tailed distributions, such as the Huber, Laplace, or
Student-t distribution to model the measurement error, instead of
the traditional Gaussian. However, the resulting R-STRE model
becomes analytical intractable, and direct application of
approximate inferences techniques still has a cubic order time
complexity. To address the computational challenge, we reformulated
the prediction problem as a maximum a posterior (MAP) problem with a
non-smooth objection function, transformed it to a equivalent
quadratic programming problem, and developed an efficient
interior-point numerical algorithm with a near linear order
complexity. This work presents the first near linear time robust
prediction approach for large spatio-temporal datasets in both
offline and online cases.
Ph. D.
Civelek, Ferda N. (Ferda Nur). "Temporal Connectionist Expert Systems Using a Temporal Backpropagation Algorithm." Thesis, University of North Texas, 1993. https://digital.library.unt.edu/ark:/67531/metadc278824/.
Повний текст джерелаZhu, Linhong, Dong Guo, Junming Yin, Steeg Greg Ver, and Aram Galstyan. "Scalable temporal latent space inference for link prediction in dynamic social networks (extended abstract)." IEEE, 2017. http://hdl.handle.net/10150/626028.
Повний текст джерелаBeaumont, Matthew, and n/a. "Handling Over-Constrained Temporal Constraint Networks." Griffith University. School of Information Technology, 2004. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20041213.084512.
Повний текст джерелаBeaumont, Matthew. "Handling Over-Constrained Temporal Constraint Networks." Thesis, Griffith University, 2004. http://hdl.handle.net/10072/366603.
Повний текст джерелаThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Institute for Integrated and Intelligent Systems
Full Text
Schiratti, Jean-Baptiste. "Methods and algorithms to learn spatio-temporal changes from longitudinal manifold-valued observations." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLX009/document.
Повний текст джерелаWe propose a generic Bayesian mixed-effects model to estimate the temporal progression of a biological phenomenon from manifold-valued observations obtained at multiple time points for an individual or group of individuals. The progression is modeled by continuous trajectories in the space of measurements, which is assumed to be a Riemannian manifold. The group-average trajectory is defined by the fixed effects of the model. To define the individual trajectories, we introduced the notion of « parallel variations » of a curve on a Riemannian manifold. For each individual, the individual trajectory is constructed by considering a parallel variation of the average trajectory and reparametrizing this parallel in time. The subject specific spatiotemporal transformations, namely parallel variation and time reparametrization, are defined by the individual random effects and allow to quantify the changes in direction and pace at which the trajectories are followed. The framework of Riemannian geometry allows the model to be used with any kind of measurements with smooth constraints. A stochastic version of the Expectation-Maximization algorithm, the Monte Carlo Markov Chains Stochastic Approximation EM algorithm (MCMC-SAEM), is used to produce produce maximum a posteriori estimates of the parameters. The use of the MCMC-SAEM together with a numerical scheme for the approximation of parallel transport is discussed. In addition to this, the method is validated on synthetic data and in high-dimensional settings. We also provide experimental results obtained on health data
Montana, Felipe. "Sampling-based algorithms for motion planning with temporal logic specifications." Thesis, University of Sheffield, 2019. http://etheses.whiterose.ac.uk/22637/.
Повний текст джерелаKobakian, Stephanie Rose. "New algorithms for effectively visualising Australian spatio-temporal disease data." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/203908/1/Stephanie_Kobakian_Thesis.pdf.
Повний текст джерелаEriksson, Leif. "Solving Temporal CSPs via Enumeration and SAT Compilation." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162482.
Повний текст джерелаКниги з теми "Temporal Algorithms"
George, Betsy. Spatio-temporal Networks: Modeling and Algorithms. New York, NY: Springer New York, 2013.
Знайти повний текст джерелаStergiou, K. Backtracking algorithms for checking the consistency of temporal constraints. Manchester: UMIST, 1997.
Знайти повний текст джерелаW, Campbell Janet, and Goddard Space Flight Center, eds. Level-3 SeaWiFS data products: Spatial and temporal binning algorithms. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1995.
Знайти повний текст джерелаW, Campbell Janet, and Goddard Space Flight Center, eds. Level-3 SeaWiFS data products: Spatial and temporal binning algorithms. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1995.
Знайти повний текст джерелаMcGuire, Hugh W. Two methods for checking formulas of temporal logic. Stanford, Calif: Dept. of Computer Science, Stanford University, 1995.
Знайти повний текст джерелаKoukoudakis, Alexandros. Visualisation decision algorithm for temporal database management system. Manchester: UMIST, 1996.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for January-June, 1997 : contract number: NAS5-31370. [Washington, DC: National Aeronautics and Space Administration, 1997.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data. [Washington, D.C.]: National Aeronautics and Space Administration, 1994.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for January-June, 1995. [Washington, D.C: National Aeronautics and Space Administration, 1995.
Знайти повний текст джерелаUnited States. National Aeronautics and Space Administration., ed. Land surface temperature measurements from EOS MODIS data: Semi-annual report ... for July-December, 1997 : contract number NAS5-31370. [Washington, DC: National Aeronautics and Space Administration, 1998.
Знайти повний текст джерелаЧастини книг з теми "Temporal Algorithms"
Gudmundsson, Joachim, Jyrki Katajainen, Damian Merrick, Cahya Ong, and Thomas Wolle. "Compressing Spatio-temporal Trajectories." In Algorithms and Computation, 763–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-77120-3_66.
Повний текст джерелаEstivill-Castro, Vladimir, and Michael E. Houle. "Fast Randomized Algorithms for Robust Estimation of Location." In Temporal, Spatial, and Spatio-Temporal Data Mining, 77–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3_7.
Повний текст джерелаDanda, Umesh Sandeep, G. Ramakrishna, Jens M. Schmidt, and M. Srikanth. "On Short Fastest Paths in Temporal Graphs." In WALCOM: Algorithms and Computation, 40–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68211-8_4.
Повний текст джерелаFriedler, Sorelle A., and David M. Mount. "Spatio-temporal Range Searching over Compressed Kinetic Sensor Data." In Algorithms – ESA 2010, 386–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15775-2_33.
Повний текст джерелаMcGeer, Patrick C., and Robert K. Brayton. "False Path Detection Algorithms." In Integrating Functional and Temporal Domains in Logic Design, 55–95. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4615-3960-5_3.
Повний текст джерелаAchtert, Elke, Ahmed Hettab, Hans-Peter Kriegel, Erich Schubert, and Arthur Zimek. "Spatial Outlier Detection: Data, Algorithms, Visualizations." In Advances in Spatial and Temporal Databases, 512–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22922-0_41.
Повний текст джерелаGago, M. Carmen Fernández, Michael Fisher, and Clare Dixon. "Algorithms for Guiding Clausal Temporal Resolution." In KI 2002: Advances in Artificial Intelligence, 235–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45751-8_16.
Повний текст джерелаAllard, Denis, Xavier Emery, Céline Lacaux, and Christian Lantuéjoul. "Simulation of Stationary Gaussian Random Fields with a Gneiting Spatio-Temporal Covariance." In Springer Proceedings in Earth and Environmental Sciences, 43–49. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_4.
Повний текст джерелаZhang, Zhongnan, and Weili Wu. "Composite Spatio-Temporal Co-occurrence Pattern Mining." In Wireless Algorithms, Systems, and Applications, 454–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88582-5_43.
Повний текст джерелаAkrida, Eleni C., and Paul G. Spirakis. "On Verifying and Maintaining Connectivity of Interval Temporal Networks." In Algorithms for Sensor Systems, 142–54. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28472-9_11.
Повний текст джерелаТези доповідей конференцій з теми "Temporal Algorithms"
Deb, Rohan, Meet Gandhi, and Shalabh Bhatnagar. "Schedule Based Temporal Difference Algorithms." In 2022 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton). IEEE, 2022. http://dx.doi.org/10.1109/allerton49937.2022.9929388.
Повний текст джерелаWang, Yuxin, Xiuzhi Li, Zhenyu Jiao, and Lei Zhang. "Pedestrian trajectory prediction based on temporal attention." In International Conference on Algorithms, Microchips, and Network Applications, edited by Fengjie Cen and Ning Sun. SPIE, 2022. http://dx.doi.org/10.1117/12.2636485.
Повний текст джерелаJun Gao. "Adaptive Interpolation Algorithms for Temporal-Oriented Datasets." In Thirteenth International Symposium on Temporal Representation and Reasoning (TIME'06). IEEE, 2006. http://dx.doi.org/10.1109/time.2006.4.
Повний текст джерелаIm, Sungjin, Janardhan Kulkarni, and Benjamin Moseley. "Temporal Fairness of Round Robin." In SPAA '15: 27th ACM Symposium on Parallelism in Algorithms and Architectures. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2755573.2755581.
Повний текст джерелаSilva, Arlei, Ambuj Singh, and Ananthram Swami. "Spectral Algorithms for Temporal Graph Cuts." In the 2018 World Wide Web Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186118.
Повний текст джерелаGendrano, J. A. G., B. C. Huang, J. M. Rodrigue, Bongki Moon, and R. T. Snodgrass. "Parallel algorithms for computing temporal aggregates." In Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337). IEEE, 1999. http://dx.doi.org/10.1109/icde.1999.754958.
Повний текст джерелаHao, Yudong, Yang Zhao, and Dacheng Li. "Design of temporal phase unwrapping algorithms." In International Symposium on Photonics and Applications, edited by Yee Loy Lam, Koji Ikuta, and Metin S. Mangir. SPIE, 1999. http://dx.doi.org/10.1117/12.368502.
Повний текст джерелаMeyer, Dominik, Remy Degenne, Ahmed Omrane, and Hao Shen. "Accelerated gradient temporal difference learning algorithms." In 2014 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL). IEEE, 2014. http://dx.doi.org/10.1109/adprl.2014.7010611.
Повний текст джерелаAllen, Michael, Justyna W. Kosianka, and Mark Perillo. "Algorithms for efficient multi-temporal change detection in SAR imagery." In Algorithms for Synthetic Aperture Radar Imagery XXX, edited by Edmund Zelnio and Frederick D. Garber. SPIE, 2023. http://dx.doi.org/10.1117/12.2663997.
Повний текст джерелаBollig, Benedikt. "Towards Formal Verification of Distributed Algorithms." In 2015 22nd International Symposium on Temporal Representation and Reasoning (TIME). IEEE, 2015. http://dx.doi.org/10.1109/time.2015.23.
Повний текст джерелаЗвіти організацій з теми "Temporal Algorithms"
Bornholdt, S., and D. Graudenz. General asymmetric neutral networks and structure design by genetic algorithms: A learning rule for temporal patterns. Office of Scientific and Technical Information (OSTI), July 1993. http://dx.doi.org/10.2172/10186812.
Повний текст джерелаMiller, William L. Exploring the Temporal and Spatial Dynamics of UV Attenuation and CDOM in the Surface Ocean Using New Algorithms. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada573066.
Повний текст джерелаKularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаMcDermott, Drew. An Algorithm for Probabilistic, Totally-Ordered Temporal Projection. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada277341.
Повний текст джерелаThost, Veronika, Jan Holste, and Özgür Özçep. On Implementing Temporal Query Answering in DL-Lite. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.218.
Повний текст джерелаHorrocks, Ian, and Stephan Tobies. Optimisation of Terminological Reasoning. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.99.
Повний текст джерелаHirsch, Colin, and Stephan Tobies. A Tableau Algorithm for the Clique Guarded Fragment. Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.106.
Повний текст джерелаPrice, Ryan. Hierarchical Temporal Memory Cortical Learning Algorithm for Pattern Recognition on Multi-core Architectures. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.202.
Повний текст джерелаLutz, Carsten, and Maja Miličić. A Tableau Algorithm for DLs with Concrete Domains and GCIs. Technische Universität Dresden, 2005. http://dx.doi.org/10.25368/2022.150.
Повний текст джерела