Relevant bibliographies by topics / Temporal Point Processes (TPPs)

Academic literature on the topic 'Temporal Point Processes (TPPs)'

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Published: 7 July 2024
Last updated: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Temporal Point Processes (TPPs)":

1

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.

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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.

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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.

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4

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.

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As an important sequential model, the temporal point process (TPP) plays a central role in real-world sequence modeling and analysis, whose learning is often based on the maximum likelihood estimation (MLE). However, due to imperfect observations, such as incomplete and sparse sequences that are common in practice, the MLE of TPP models often suffers from overfitting and leads to unsatisfactory generalization power. In this work, we develop a novel hierarchical contrastive (HCL) learning method for temporal point processes, which provides a new regularizer of MLE. In principle, our HCL considers the noise contrastive estimation (NCE) problem at the event-level and at the sequence-level jointly. Given a sequence, the event-level NCE maximizes the probability of each observed event given its history while penalizing the conditional probabilities of the unobserved events. At the same time, we generate positive and negative event sequences from the observed sequence and maximize the discrepancy between their likelihoods through the sequence-level NCE. Instead of using time-consuming simulation methods, we generate the positive and negative sequences via a simple but efficient model-guided thinning process. Experimental results show that the MLE method assisted by the HCL regularizer outperforms classic MLE and other contrastive learning methods in learning various TPP models consistently. The code is available at https://github.com/qingmeiwangdaily/HCL_TPP.
5

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.

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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.

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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.

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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.

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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.

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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.

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In this paper we introduce Lévy-driven Cox point processes (LCPs) as Cox point processes with driving intensity function Λ defined by a kernel smoothing of a Lévy basis (an independently scattered, infinitely divisible random measure). We also consider log Lévy-driven Cox point processes (LLCPs) with Λ equal to the exponential of such a kernel smoothing. Special cases are shot noise Cox processes, log Gaussian Cox processes, and log shot noise Cox processes. We study the theoretical properties of Lévy-based Cox processes, including moment properties described by nth-order product densities, mixing properties, specification of inhomogeneity, and spatio-temporal extensions.
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Journal articles Dissertations / Theses

Dissertations / Theses on the topic "Temporal Point Processes (TPPs)":

1

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.

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Dans le domaine de l'imagerie cérébrale non invasive, la magnéto- et l'électroencéphalographie (M/EEG) offrent un précieux aperçu des activités neuronales. Les données enregistrées consistent en des séries temporelles multivariées qui fournissent des informations sur les processus cognitifs et sont souvent complétées par des détails auxiliaires liés au paradigme expérimental, tels que l'horodatage des stimuli externes ou des actions entreprises par les sujets. En outre, l'ensemble des données peut inclure des enregistrements de plusieurs sujets, ce qui facilite les analyses en population.Cette thèse de doctorat présente un nouveau cadre pour l'analyse des signaux M/EEG qui synergise l'Apprentissage Convolutif de Dictionnaire (CDL) et les Processus Ponctuels Temporels (TPP). Ce travail est divisé en deux composantes principales : les avancées en modélisation temporelle et le passage à l'échelle computationnelle. En matière de modélisation temporelle, deux nouveaux modèles de processus ponctuels sont introduits, accompagnés de méthodes d'inférence efficaces pour capturer les activités neuronales liées aux tâches. La méthode proposée d'Inférence Discrétisée Rapide pour les Processus de Hawkes (FaDIn) a également des implications pour des applications plus larges. De plus, ce travail aborde les défis computationnels de l'analyse des données M/EEG à grande échelle basée sur le CDL, en introduisant un nouvel algorithme robuste de CDL avec fenêtrage stochastique. Cet algorithme permet de traiter efficacement les signaux entachés d'artefacts ainsi que les études de population à grande échelle. Le CDL populationnelle a ensuite été utilisée sur le grand ensemble de données en libre accès Cam-CAN, révélant des aspects de l'activité neuronale liée à l'âge
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
2

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.

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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.

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Altieri, Linda <1986&gt. "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.

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Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.
5

Altieri, Linda <1986&gt. "A Bayesian changepoint analysis on spatio-temporal point processes." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2015. http://amsdottorato.unibo.it/6740/.

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Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.
6

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.

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Point processes are mechanisms that beget point patterns. Realisations of point processes are observed in many contexts, for example, locations of stars in the sky, or locations of trees in a forest. Inferring the mechanisms that drive point processes relies on the development of models that appropriately account for the dependencies inherent in the data. Fitting models that adequately capture the complex dependency structures in either space, time, or both is often problematic. This is commonly due to—but not restricted to—the intractability of the likelihood function, or computational burden of the required numerical operations. This thesis primarily focuses on developing point process models with some hierarchical structure, and specifically where this is a latent structure that may be considered as one of the following: (i) some unobserved construct assumed to be generating the observed structure, or (ii) some stochastic process describing the structure of the point pattern. Model fitting procedures utilised in this thesis include either (i) approximate-likelihood techniques to circumvent intractable likelihoods, (ii) stochastic partial differential equations to model continuous spatial latent structures, or (iii) improving computational speed in numerical approximations by exploiting automatic differentiation. Moreover, this thesis extends classic point process models by considering multivariate dependencies. This is achieved through considering a general class of joint point process model, which utilise shared stochastic structures. These structures account for the dependencies inherent in multivariate point process data. These models are applied to data originating from various scientific fields; in particular, applications are considered in ecology, medicine, and geology. In addition, point process models that account for the second order behaviour of these assumed stochastic structures are also considered.
7

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.

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Esta tesis se centra principalmente en el desarrollo de propiedades y características de los estimadores de segundo orden de procesos puntuales y espacio-temporales. En primer lugar, se presenta un marco teórico acerca de procesos puntuales espaciales y espacio-temporales. El resto de la tesis se organiza como sigue. En el capítulo 2, se presenta una nueva familia de kernel positivos y óptimos, además se propone un estimador insensgado alternativo para la función de la densidad del producto. Su rendimiento se compara para varios kernel mediante MISE. En el capítulo 3, se dada un nuevo estimador kernel de la función de la densidad producto espacio-temporal y también se desarrollan expresiones cerradas para la varianza en el caso de Poisson. En el Capítulo 4, nos centramos en los métodos de orientación de segundo orden los cuales proporcionan una herramienta para el análisis natural para los datos espaciales anisótropicos. Finalmente, se proporciona una descripción general de los proyectos de investigación actualmente en curso que han surgido motivadas por la estrecha relación con las propiedades de segundo orden de los procesos puntuales espaciales y espacio-temporales.
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.
8

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.

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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.

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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.

10

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.

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In this thesis, new models and methodologies are introduced for the analysis of dynamic processes characterized by image sequences with spatial temporal overlapping. The spatial temporal overlapping exists in many natural phenomena and should be addressed properly in several Science disciplines such as Microscopy, Material Sciences, Biology, Geostatistics or Communication Networks. This work is related to the Point Process and Random Closed Set theories, within Stochastic Geometry. The proposed models are an extension of Boolean Models in R2 by adding a temporal dimension. The study has been motivated for its application in a multidisciplinary project that combined Statistics, Computer Sciences, Biology and Microscopy, with the aim of analysing the cell exocytosis and endocytosis. Exocytosis is the process by which cells secrete vesicles outside the plasma membrane and endocytosis is the opposite mechanism. Our data were image sequences obtained by Electron Microscopy and Total Internal Reflection Fluorescence Microscopy. Fluorescent tagged-proteins are observed as overlapped clusters with random shape, area and duration. They can be modelled as realizations of a stationary and isotropic stochastic process. The methodology herein proposed could be used to analyze similar phenomena in other Fields of Science. First, the temporal Boolean model is introduced and some estimation methods for the parameters of the model are presented. Second, we proposed a method for the estimation of the event duration distribution function of a univariate temporal Boolean model based on spatial temporal covariance. A simulation study is performed with several duration probability density functions, and an application to the cell endocytosis is realized. Third, we introduce the bivariate temporal Boolean model to study interactions between two overlapped spatial temporal processes and to quantify their overlapping and dependencies. We propose a non-parametric approach based on a generalization of the Ripley K-function, the spatial-temporal covariance and the pair correlation functions for a bivariate temporal random closed set. A Monte Carlo test was performed to test the independence hypothesis. This methodology is not only a test procedure but also allows us to quantify the degree and spatial temporal interval of the interaction. No parametric assumption is needed. A simulation study has been conducted and an application to the study of proteins that mediate in cell endocytosis has been performed. Fourth, from high spatial resolution EM images, we model the distribution of exocytic vesicles (granules) within the cell cytoplasm as a realization of a finite point process (a point pattern), and the point patterns of several cell groups are considered replicates of different point processes. Our aim was to study differences between two treatment groups in terms of granule location. We characterize the spatial distribution of granules with respect to the plasma membrane by means of several functional descriptors, that allowed us to detect significant differences between the two cell groups that would not be observed by a classical approach. To perform image segmentation, we developed an automatic granule detection tool with similar performance to that of the manual one-by-one marking. Finally, we have implemented a software toolbox for the simulation and analysis of temporal Boolean models (available at http : ==www:uv:es=tracs=), so scientists and technicians of any discipline can apply the proposed methods. In summary, the spatial temporal stochastic models proposed allow modelling of dynamic processes from image sequences where several forms of random shape, size and duration overlap. It is the first time these tools are applied to the study of cell exo and endocytosis, and they would contribute to improve their understanding. Our methodologies will help future research in Cell Biology, e.g. in the study of diseases related to secretion dysfunctions, such as diabetes.
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=).
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Books on the topic "Temporal Point Processes (TPPs)":

1

Surkova, Galina. Atmospheric chemistry. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1079840.

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The textbook contains material corresponding to the course of lectures on atmospheric chemistry prepared for students studying meteorology and climatology. The basic concepts of atmospheric chemistry are given, its gaseous components, as well as aerosols and chemical processes related to their life cycles, which are important from the point of view of the formation of the radiation, temperature and dynamic regime of the atmosphere, as well as its pollution, are considered. The main regularities of the transport of impurities in the atmosphere and the role of processes of different spatial and temporal scales in this process are presented. The concept of approaches of varying degrees of complexity used to model the transport of matter in the atmosphere, taking into account its chemical transformations, is presented. The processes in the gaseous and liquid phases that affect the chemical composition and acidity of clouds and precipitation are described. Modern methods of using information about the concentration and state of chemical compounds, including their radioactive and stable isotopes, to obtain information about the meteorological regime of the atmosphere in the present and past are considered. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the field of training "Hydrometeorology".
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Wikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.

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The climate system consists of interactions between physical, biological, chemical, and human processes across a wide range of spatial and temporal scales. Characterizing the behavior of components of this system is crucial for scientists and decision makers. There is substantial uncertainty associated with observations of this system as well as our understanding of various system components and their interaction. Thus, inference and prediction in climate science should accommodate uncertainty in order to facilitate the decision-making process. Statistical science is designed to provide the tools to perform inference and prediction in the presence of uncertainty. In particular, the field of spatial statistics considers inference and prediction for uncertain processes that exhibit dependence in space and/or time. Traditionally, this is done descriptively through the characterization of the first two moments of the process, one expressing the mean structure and one accounting for dependence through covariability.Historically, there are three primary areas of methodological development in spatial statistics: geostatistics, which considers processes that vary continuously over space; areal or lattice processes, which considers processes that are defined on a countable discrete domain (e.g., political units); and, spatial point patterns (or point processes), which consider the locations of events in space to be a random process. All of these methods have been used in the climate sciences, but the most prominent has been the geostatistical methodology. This methodology was simultaneously discovered in geology and in meteorology and provides a way to do optimal prediction (interpolation) in space and can facilitate parameter inference for spatial data. These methods rely strongly on Gaussian process theory, which is increasingly of interest in machine learning. These methods are common in the spatial statistics literature, but much development is still being done in the area to accommodate more complex processes and “big data” applications. Newer approaches are based on restricting models to neighbor-based representations or reformulating the random spatial process in terms of a basis expansion. There are many computational and flexibility advantages to these approaches, depending on the specific implementation. Complexity is also increasingly being accommodated through the use of the hierarchical modeling paradigm, which provides a probabilistically consistent way to decompose the data, process, and parameters corresponding to the spatial or spatio-temporal process.Perhaps the biggest challenge in modern applications of spatial and spatio-temporal statistics is to develop methods that are flexible yet can account for the complex dependencies between and across processes, account for uncertainty in all aspects of the problem, and still be computationally tractable. These are daunting challenges, yet it is a very active area of research, and new solutions are constantly being developed. New methods are also being rapidly developed in the machine learning community, and these methods are increasingly more applicable to dependent processes. The interaction and cross-fertilization between the machine learning and spatial statistics community is growing, which will likely lead to a new generation of spatial statistical methods that are applicable to climate science.
3

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.

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The Oxford Handbook of Historical Institutionalismoffers an authoritative and accessible state-of-the-art analysis of the historical institutionalism research tradition in Political Science. Devoted to the study of how temporal processes and events influence the origin and transformation of institutions that govern political and economic relations, historical institutionalism has grown considerably since the mid-1990s. With its attention to past, present, and potential future contributions to the research tradition, the volume represents an essential reference point for those interested in historical institutionalism. Written in accessible style by leading scholars, 38 chapters detail the contributions of historical institutionalism to an expanding array of topics in the study of comparative, American, European, and international politics .
4

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.

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Modern Russian Aspectual Senses are presented here as a system with a zero starting point. The monograph has resulted from lifelong teaching Russian as a Foreign Language (RFL) for multilingual students and it figures out the procedure of choosing the Aspect, Aspect-Temporal form in discourse. Essays provide short guidelines starting from the position of a viewer, observing a certain situation, up to Discourse Activities of a speaking person, reflecting the situation in an adequate way. The monograph takes into account the necessary mental processes: awareness of the Aspectual Senses involved in the situation (from the native speaker's point of view), ranking them, choosing the dominant, distinguishing the features, succumbing to expression and not, and putting the last aside. The monograph outlines some Aspectual Forms' Pragmatic Spheres, displays the stylistic peculiarities of using them in modern discourse. The edition is addressed to foreign and home Russian philologists, RFL teachers and students.
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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.

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The research problem undertaken in the study is the occurrence of dispersed and concentrated built-up (in particular residential) area patterns caused by suburbanisation processes in a large urban agglomeration, on the example of the Warsaw metropolitan area. The research concerned the period after 1989, when the political and economic transformation in Poland began. The historical and contemporary socio-economic conditions of suburbanization and urban sprawl are described, which have the features of a spontaneous, chaotic dispersion, quite different than in Western countries. It is partly to blame for faulty spatial planning. The succession of urban development into rural areas is subordinated to the factors of the construction market. In the empirical part of the analysis, topographic data on all buildings in the urban agglomeration and databases on land use derived from satellite images were used to investigate settlement changes. A multidimensional study was carried out relating to various spatial scales, types of spatial relations and territorial units. Measures of spatial concentration of point patterns as well as landscape metrics were used for this purpose. The indicators used were subject to critical methodological evaluation afterwards. The study was performed in several temporal cross-sections. The locations of new development in agricultural, forest and wasteland areas have been identified. Finally, recommendations for the implementation of appropriate spatial policy and improvement of the spatial order in the Warsaw agglomeration were formulated
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Biewener, Andrew, and Sheila Patek. Animal Locomotion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198743156.001.0001.

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This book provides a synthesis of the physical, physiological, evolutionary, and biomechanical principles that underlie animal locomotion. An understanding and full appreciation of animal locomotion requires the integration of these principles. Toward this end, we provide the necessary introductory foundation that will allow a more in-depth understanding of the physical biology and physiology of animal movement. In so doing, we hope that this book will illuminate the fundamentals and breadth of these systems, while inspiring our readers to look more deeply into the scientific literature and investigate new features of animal movement. Several themes run through this book. The first is that by comparing the modes and mechanisms by which animals have evolved the capacity for movement, we can understand the common principles that underlie each mode of locomotion. A second is that size matters. One of the most amazing aspects of biology is the enormous spatial and temporal scale over which organisms and biological processes operate. Within each mode of locomotion, animals have evolved designs and mechanisms that effectively contend with the physical properties and forces imposed on them by their environment. Understanding the constraints of scale that underlie locomotor mechanisms is essential to appreciating how these mechanisms have evolved and how they operate. A third theme is the importance of taking an integrative and comparative evolutionary approach in the study of biology. Organisms share much in common. Much of their molecular and cellular machinery is the same. They also must navigate similar physical properties of their environment. Consequently, an integrative approach to organismal function that spans multiple levels of biological organization provides a strong understanding of animal locomotion. By comparing across species, common principles of design emerge. Such comparisons also highlight how certain organisms may differ and point to strategies that have evolved for movement in diverse environments. Finally, because convergence upon common designs and the generation of new designs result from historical processes governed by natural selection, it is also important that we ask how and why these systems have evolved.

Book chapters on the topic "Temporal Point Processes (TPPs)":

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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.

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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.

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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.

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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.

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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.

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"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.

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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.

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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.

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"Spatio-Temporal Point Processes." In Handbook of Spatial Statistics, 461–74. CRC Press, 2010. http://dx.doi.org/10.1201/9781420072884-32.

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"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.

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Conference papers on the topic "Temporal Point Processes (TPPs)":

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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.

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Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.
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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.

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Relation discovery for multi-dimensional temporal point processes (MTPP) has received increasing interest for its importance in prediction and interpretability of the underlying dynamics. Traditional statistical MTPP models like Hawkes Process have difficulty in capturing complex relation due to their limited parametric form of the intensity function. While recent neural-network-based models suffer poor interpretability. In this paper, we propose a neural relation inference model namely TPP-NRI. Given MTPP data, it adopts a variational inference framework to model the posterior relation of MTPP data for probabilistic estimation. Specifically, assuming the prior of the relation is known, the conditional probability of the MTPP conditional on a sampled relation is captured by a message passing graph neural network (GNN) based MTPP model. A variational distribution is introduced to approximate the true posterior. Experiments on synthetic and real-world data show that our model outperforms baseline methods on both inference capability and scalability for high-dimensional data.
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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Reports on the topic "Temporal Point Processes (TPPs)":

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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.

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Tillage modifies soil structure, altering conditions for plant growth and transport processes through the soil. However, the resulting loose structure is unstable and susceptible to collapse due to aggregate fragmentation during wetting and drying cycles, and coalescense of moist aggregates by internal capillary forces and external compactive stresses. Presently, limited understanding of these complex processes often leads to consideration of the soil plow layer as a static porous medium. With the purpose of filling some of this knowledge gap, the objectives of this Project were to: 1) Identify and quantify the major factors causing breakdown of primary soil fragments produced by tillage into smaller secondary fragments; 2) Identify and quantify the. physical processes involved in the coalescence of primary and secondary fragments and surfaces of weakness; 3) Measure temporal changes in pore-size distributions and hydraulic properties of reconstructed aggregate beds as a function of specified initial conditions and wetting/drying events; and 4) Construct a process-based model of post-tillage changes in soil structural and hydraulic properties of the plow layer and validate it against field experiments. A dynamic theory of capillary-driven plastic deformation of adjoining aggregates was developed, where instantaneous rate of change in geometry of aggregates and inter-aggregate pores was related to current geometry of the solid-gas-liquid system and measured soil rheological functions. The theory and supporting data showed that consolidation of aggregate beds is largely an event-driven process, restricted to a fairly narrow range of soil water contents where capillary suction is great enough to generate coalescence but where soil mechanical strength is still low enough to allow plastic deforn1ation of aggregates. The theory was also used to explain effects of transient external loading on compaction of aggregate beds. A stochastic forInalism was developed for modeling soil pore space evolution, based on the Fokker Planck equation (FPE). Analytical solutions for the FPE were developed, with parameters which can be measured empirically or related to the mechanistic aggregate deformation model. Pre-existing results from field experiments were used to illustrate how the FPE formalism can be applied to field data. Fragmentation of soil clods after tillage was observed to be an event-driven (as opposed to continuous) process that occurred only during wetting, and only as clods approached the saturation point. The major mechanism of fragmentation of large aggregates seemed to be differential soil swelling behind the wetting front. Aggregate "explosion" due to air entrapment seemed limited to small aggregates wetted simultaneously over their entire surface. Breakdown of large aggregates from 11 clay soils during successive wetting and drying cycles produced fragment size distributions which differed primarily by a scale factor l (essentially equivalent to the Van Bavel mean weight diameter), so that evolution of fragment size distributions could be modeled in terms of changes in l. For a given number of wetting and drying cycles, l decreased systematically with increasing plasticity index. When air-dry soil clods were slightly weakened by a single wetting event, and then allowed to "age" for six weeks at constant high water content, drop-shatter resistance in aged relative to non-aged clods was found to increase in proportion to plasticity index. This seemed consistent with the rheological model, which predicts faster plastic coalescence around small voids and sharp cracks (with resulting soil strengthening) in soils with low resistance to plastic yield and flow. A new theory of crack growth in "idealized" elastoplastic materials was formulated, with potential application to soil fracture phenomena. The theory was preliminarily (and successfully) tested using carbon steel, a ductile material which closely approximates ideal elastoplastic behavior, and for which the necessary fracture data existed in the literature.

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