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Статті в журналах з теми "Temporal Point Processes (TPPs)":
Sun, Sally, Owen Ward, Jing Wu, Lihao Xiao, Xiaoxi Zhao, and Tian Zheng. "ppdiag: Diagnostic Tools for Temporal Point Processes." Journal of Open Source Software 6, no. 61 (May 27, 2021): 3133. http://dx.doi.org/10.21105/joss.03133.
Lysenko, Anton, Egor Shikov, and Klavdiya Bochenina. "Temporal point processes for purchase categories forecasting." Procedia Computer Science 156 (2019): 255–63. http://dx.doi.org/10.1016/j.procs.2019.08.201.
Stoyan, Dietrich, Francisco J. Rodríguez-Cortés, Jorge Mateu, and Wilfried Gille. "Mark variograms for spatio-temporal point processes." Spatial Statistics 20 (May 2017): 125–47. http://dx.doi.org/10.1016/j.spasta.2017.02.006.
Wang, Qingmei, Minjie Cheng, Shen Yuan, and Hongteng Xu. "Hierarchical Contrastive Learning for Temporal Point Processes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 10166–74. http://dx.doi.org/10.1609/aaai.v37i8.26211.
Paik Schoenberg, Frederic. "Testing Separability in Spatial-Temporal Marked Point Processes." Biometrics 60, no. 2 (June 2004): 471–81. http://dx.doi.org/10.1111/j.0006-341x.2004.00192.x.
Cronie, O., and M. N. M. Van Lieshout. "AJ-function for Inhomogeneous Spatio-temporal Point Processes." Scandinavian Journal of Statistics 42, no. 2 (October 7, 2014): 562–79. http://dx.doi.org/10.1111/sjos.12123.
Grillenzoni, Carlo. "Non-parametric smoothing of spatio-temporal point processes." Journal of Statistical Planning and Inference 128, no. 1 (January 2005): 61–78. http://dx.doi.org/10.1016/j.jspi.2003.09.030.
Altieri, Linda, E. Marian Scott, Daniela Cocchi, and Janine B. Illian. "A changepoint analysis of spatio-temporal point processes." Spatial Statistics 14 (November 2015): 197–207. http://dx.doi.org/10.1016/j.spasta.2015.05.005.
Marcon, G., G. Adelfio, and M. Chiodi. "Gamma Kernel Intensity Estimation in Temporal Point Processes." Communications in Statistics - Simulation and Computation 40, no. 8 (April 18, 2011): 1146–62. http://dx.doi.org/10.1080/03610918.2011.563158.
Hellmund, Gunnar, Michaela Prokešová, and Eva B. Vedel Jensen. "Lévy-based Cox point processes." Advances in Applied Probability 40, no. 3 (September 2008): 603–29. http://dx.doi.org/10.1239/aap/1222868178.
Дисертації з теми "Temporal Point Processes (TPPs)":
Allain, Cédric. "Temporal point processes and scalable convolutional dictionary learning : a unified framework for m/eeg signal analysis in neuroscience." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG008.
In the field of non-invasive brain imaging, Magnetoencephalography and Electroencephalography (M/EEG) offer invaluable insights into neural activities. The recorded data consist of multivariate time series that provide information about cognitive processes and are often complemented by auxiliary details related to the experimental paradigm, such as timestamps of external stimuli or actions undertaken by the subjects. Additionally, the dataset may include recordings from multiple subjects, facilitating population- level analyses.This doctoral research presents a novel framework for M/EEG signal analysis that synergizes Convolutional Dictionary Learning (CDL) and Temporal Point Processes (TPPs). The work is segmented into two primary components: temporal modeling advancements and computational scalability. For temporal modeling, two novel point process models are introduced with efficient inference methods to capture task-specific neural activities. The proposed Fast Discretized Inference for Hawkes Processes (FaDIn) method also has implications for broader applications. Additionally, this work addresses the computational challenges of large-scale M/EEG data CDL-based analysis, by introducing a novel Stochastic Robust Windowing CDL algorithm. This algorithm allows to process efficiently artifact-ridden signals as well as large population studies. Population CDL was then used on the large open-access dataset Cam-CAN, shedding light on age-related neural activity
D'ANGELO, Nicoletta. "Local methods for complex spatio-temporal point processes." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/574349.
Kaimi, Irene. "Spatial and spatio-Temporal point processes, modelling and estimation." Thesis, Lancaster University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525335.
Altieri, Linda <1986>. "A Bayesian changepoint analysis on spatio-temporal point processes." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6740/1/altieri_linda_tesi.pdf.
Altieri, Linda <1986>. "A Bayesian changepoint analysis on spatio-temporal point processes." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6740/.
Jones-Todd, Charlotte M. "Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data." Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12009.
Rodríguez, Cortés Francisco Javier. "Modelling, Estimation and Applications of Second-Order Spatio-Temporal Characteristics of Point Processes." Doctoral thesis, Universitat Jaume I, 2014. http://hdl.handle.net/10803/394025.
This thesis is mainly focused on developing properties and estimators for second-order characteristics of spatio-temporal point processes. First, we present a theoretical framework of spatial and spatio-temporal point processes. The rest of the thesis is organized as follows. In Chapter 2 we present a new family of optimal and positive kernels an alternative unbiased estimator for the product density function. Its performance is compare under several kernel through MISE. In Chapter 3 a new kernel estimator of spatio-temporal product density function are given and also are developed close expressions for the variance under the Poisson case. En el capítulo 4 nos centramos en los métodos de orientación de segundo orden que proporcionan una herramienta natural para el análisis de los datos de proceso Punto espaciales anisótropas. Finally, we provide a general description of the currently ongoing research projects which have emerged motivated by the close relationship with the second-order properties.
Comas, Rodriguez Carlos. "Modelling forest dynamics through the development of spatial and temporal marked point processes." Thesis, University of Strathclyde, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415363.
Afzal, Muhammad. "Modelling temporal aspects of healthcare processes with Ontologies." Thesis, Jönköping University, JTH, Computer and Electrical Engineering, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12781.
This thesis represents the ontological model for the Time Aspects for a Healthcare Organization. It provides information about activities which take place at different interval of time at Ryhov Hospital. These activities are series of actions which may be happen in predefined sequence and at predefined times or may be happen at any time in a General ward or in Emergency ward of a Ryhov Hospital.
For achieving above mentioned objective, our supervisor conducts a workshop at the start of thesis. In this workshop, the domain experts explain the main idea of ward activities. From this workshop; the author got a lot of knowledge about activities and time aspects. After this, the author start literature review for achieving valuable knowledge about ward activities, time aspects and also methodology steps which are essentials for ontological model. After developing ontological model for Time Aspects, our supervisor also conducts a second workshop. In this workshop, the author presents the model for evaluation purpose.
Díaz, Fernández Ester. "Modelling estimation and analysis of dynamic processes from image sequences using temporal random closed sets and point processes with application to the cell exocytosis and endocytosis." Doctoral thesis, Universitat de València, 2010. http://hdl.handle.net/10803/62137.
En esta tesis presentamos nuevos modelos y metodolog as para el an alisis de pro- cesos din amicos a partir de secuencias de im agenes, con solapamiento espacial y tem- poral de los objetos de an alisis, un fen omeno habitual en la naturaleza. El trabajo realizado se enmarca en la teor a de Procesos Puntuales y Conjuntos Aleatorios Ce- rrados (RACS), dentro de la Geometr a Estoc astica. Los modelos propuestos son una extensi on de la teor a de modelos booleanos en R2 incorporando una componente temporal. La motivaci on del trabajo fue su aplicaci on a un proyecto multidisciplinar donde analizamos la exocitosis y la endocitosis celular, procesos en que la c elula segrega o absorbe sustancias a trav es de la membrana citoplasm atica, respectivamente. El es- tudio se realiz o utilizando secuencias de im agenes obtenidas con microscop a TIRFM, donde se observan las prote nas como agrupaciones uorescentes superpuestas. Mo- delizamos las im agenes como realizaciones de un proceso estoc astico estacionario e isotr opico. Esta metodolog a permite analizar fen omenos reales en otros campos de la Ciencia con superposici on espacio-temporal de objetos con formas y duraciones aleatorias, como Geolog a, Qu mica, Comunicaciones, etc. Primero, introducimos el modelo booleano temporal. Presentamos un m etodo de estimaci on de la funci on de distribuci on de la duraci on basado en la covarianza espacio-temporal, y el estudio de simulaci on realizado. Segundo, estudiamos la in- terrelaci on entre dos procesos espacio-temporales mediante la K-funci on de Ripley, la covarianza espacio-temporal y la funci on de correlaci on para conjuntos aleatorios bivariados. Realizamos un estudio de simulaci on y una aplicaci on a la endocitosis celular. Tercero, modelizamos la distribuci on de ves culas exoc ticas (gr anulos) en el cito- plasma celular como un proceso puntual nito. Caracterizamos su distribuci on espa- cial respecto a la membrana mediante varios descriptores funcionales. Para segmentar las im agenes, desarrollamos una herramienta autom atica de detecci on de gr anulos. Hemos desarrollado una herramienta de software completa para la simulaci on y es- timaci on de modelos booleanos temporales (disponible en http : ==www:uv:es=tracs=).
Книги з теми "Temporal Point Processes (TPPs)":
Surkova, Galina. Atmospheric chemistry. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1079840.
Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Fioretos, Orfeo, Tulia G. Falleti, and Adam Sheingate, eds. The Oxford Handbook of Historical Institutionalism. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199662814.001.0001.
Grekova, Olga. Essays on Modern Russian Functional Aspectology. Book one. LCC MAKS Press, 2022. http://dx.doi.org/10.29003/m3020.978-5-317-06861-5.
Sudra, Paweł. Rozpraszanie i koncentracja zabudowy na przykładzie aglomeracji warszawskiej po 1989 roku = Dispersion and concentration of built-up areas on the example of the Warsaw agglomeration after 1989. Instytut Geografii i Przestrzennego Zagospodarowania im. Stanisława Leszczyckiego, Polska Akademia Nauk, 2020. http://dx.doi.org/10.7163/9788361590057.
Biewener, Andrew, and Sheila Patek. Animal Locomotion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198743156.001.0001.
Частини книг з теми "Temporal Point Processes (TPPs)":
Ojeda, César Ali Marin, Kostadin Cvejoski, Rafet Sifa, Jannis Schuecker, and Christian Bauckhage. "Patterns and Outliers in Temporal Point Processes." In Advances in Intelligent Systems and Computing, 507–26. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_40.
Li, Zhuoqun, Zihan Zhou, Mingxuan Sun, and Hongteng Xu. "Debiased Imitation Learning for Modulated Temporal Point Processes." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 460–68. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch52.
Illian, Janine B. "Spatial and spatio-temporal point processes in ecological applications." In Handbook of Environmental and Ecological Statistics, 97–132. Boca Raton : Taylor & Francis, 2018.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315152509-6.
Borrajo, M. I., I. Fuentes-Santos, and W. González-Manteiga. "Nonparametric First-Order Analysis of Spatial and Spatio-Temporal Point Processes." In Springer Proceedings in Mathematics & Statistics, 101–11. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57306-5_10.
Likhyani, Ankita, Vinayak Gupta, P. K. Srijith, P. Deepak, and Srikanta Bedathur. "Modeling Implicit Communities from Geo-Tagged Event Traces Using Spatio-Temporal Point Processes." In Web Information Systems Engineering – WISE 2020, 153–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62005-9_12.
"Spatio-temporal point processes." In Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, 227–40. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b15326-15.
Diggle, Peter, and Edith Gabriel. "Spatio-Temporal Point Processes." In Chapman & Hall/CRC Handbooks of Modern Statistical Methods, 449–61. CRC Press, 2010. http://dx.doi.org/10.1201/9781420072884-c25.
Diggle, Peter. "Spatio-Temporal Point Processes." In C&H/CRC Monographs on Statistics & Applied Probability, 1–45. Chapman and Hall/CRC, 2006. http://dx.doi.org/10.1201/9781420011050.ch1.
"Spatio-Temporal Point Processes." In Handbook of Spatial Statistics, 461–74. CRC Press, 2010. http://dx.doi.org/10.1201/9781420072884-32.
"Spatial point processes." In Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, 87–114. Chapman and Hall/CRC, 2013. http://dx.doi.org/10.1201/b15326-9.
Тези доповідей конференцій з теми "Temporal Point Processes (TPPs)":
Shchur, Oleksandr, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann. "Neural Temporal Point Processes: A Review." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/623.
Zhang, Yunhao, and Junchi Yan. "Neural Relation Inference for Multi-dimensional Temporal Point Processes via Message Passing Graph." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/469.
Du, Nan, Hanjun Dai, Rakshit Trivedi, Utkarsh Upadhyay, Manuel Gomez-Rodriguez, and Le Song. "Recurrent Marked Temporal Point Processes." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939875.
Yuan, Yuan, Jingtao Ding, Chenyang Shao, Depeng Jin, and Yong Li. "Spatio-temporal Diffusion Point Processes." In KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3580305.3599511.
Yan, Junchi, Hongteng Xu, and Liangda Li. "Modeling and Applications for Temporal Point Processes." In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3292500.3332298.
Deshpande, Prathamesh, Kamlesh Marathe, Abir De, and Sunita Sarawagi. "Long Horizon Forecasting with Temporal Point Processes." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441740.
Wu, Weichang, Junchi Yan, Xiaokang Yang, and Hongyuan Zha. "Decoupled Learning for Factorial Marked Temporal Point Processes." In KDD '18: The 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3219819.3220035.
Yuan, Shuhan, Panpan Zheng, Xintao Wu, and Qinghua Li. "Insider Threat Detection via Hierarchical Neural Temporal Point Processes." In 2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019. http://dx.doi.org/10.1109/bigdata47090.2019.9005589.
Kamath, Vinayaka, Eva Sinclair, Damon Gilkerson, Venkat Padmanabhan, and Sreangsu Acharyya. "Modeling Email Server I/O Events As Multi-temporal Point Processes." In AIMLSystems 2022: The Second International Conference on AI-ML Systems. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3564121.3564129.
Fortino, Giancarlo, Antonella Guzzo, Michele Ianni, Francesco Leotta, and Massimo Mecella. "Exploiting Marked Temporal Point Processes for Predicting Activities of Daily Living." In 2020 IEEE International Conference on Human-Machine Systems (ICHMS). IEEE, 2020. http://dx.doi.org/10.1109/ichms49158.2020.9209398.
Звіти організацій з теми "Temporal Point Processes (TPPs)":
Snyder, Victor A., Dani Or, Amos Hadas, and S. Assouline. Characterization of Post-Tillage Soil Fragmentation and Rejoining Affecting Soil Pore Space Evolution and Transport Properties. United States Department of Agriculture, April 2002. http://dx.doi.org/10.32747/2002.7580670.bard.