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Статті в журналах з теми "Temporal Point Processes (TPPs)"

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Автор: Grafiati

Опубліковано: 7 липня 2024

Оновлено: 7 липня 2024

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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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2

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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3

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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6

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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7

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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8

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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9

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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10

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.

11

Hellmund, Gunnar, Michaela Prokešová, and Eva B. Vedel Jensen. "Lévy-based Cox point processes." Advances in Applied Probability 40, no. 03 (September 2008): 603–29. http://dx.doi.org/10.1017/s0001867800002718.

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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.

12

Renshaw, Eric, and Aila Särkkä. "Gibbs point processes for studying the development of spatial-temporal stochastic processes." Computational Statistics & Data Analysis 36, no. 1 (March 2001): 85–105. http://dx.doi.org/10.1016/s0167-9473(00)00028-1.

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13

Steinbach, Markus, Anshul Jindal, Mohak Chadha, Michael Gerndt, and Shajulin Benedict. "TppFaaS: Modeling Serverless Functions Invocations via Temporal Point Processes." IEEE Access 10 (2022): 9059–84. http://dx.doi.org/10.1109/access.2022.3144078.

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14

Diggle, Peter J. "Spatio-temporal point processes, partial likelihood, foot and mouth disease." Statistical Methods in Medical Research 15, no. 4 (August 2006): 325–36. http://dx.doi.org/10.1191/0962280206sm454oa.

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15

Flint, Ian, and Giovanni Luca Torrisi. "A Clark–Ocone formula for temporal point processes and applications." Annals of Probability 45, no. 5 (September 2017): 3266–92. http://dx.doi.org/10.1214/16-aop1136.

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16

Gabriel, Edith. "Estimating Second-Order Characteristics of Inhomogeneous Spatio-Temporal Point Processes." Methodology and Computing in Applied Probability 16, no. 2 (July 9, 2013): 411–31. http://dx.doi.org/10.1007/s11009-013-9358-3.

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17

Renshaw, Eric. "Spatial-temporal marked point processes: a spectrum of stochastic models." Environmetrics 21, no. 3-4 (September 1, 2009): 253–69. http://dx.doi.org/10.1002/env.1019.

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18

Calenda, G., and F. Napolitano. "Parameter estimation of Neyman–Scott processes for temporal point rainfall simulation." Journal of Hydrology 225, no. 1-2 (November 1999): 45–66. http://dx.doi.org/10.1016/s0022-1694(99)00133-x.

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19

Assunção, Renato, and Alexandra Maia. "A Note on Testing Separability in Spatial-Temporal Marked Point Processes." Biometrics 63, no. 1 (March 2007): 290–94. http://dx.doi.org/10.1111/j.1541-0420.2007.00737_1.x.

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20

Qu, Chao, Xiaoyu Tan, Siqiao Xue, Xiaoming Shi, James Zhang, and Hongyuan Mei. "Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9543–51. http://dx.doi.org/10.1609/aaai.v37i8.26142.

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We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such that its long-term reward is maximized. This problem exists ubiquitously in social media, finance and health informatics but is rarely investigated by the conventional research in reinforcement learning. To this end, we present a novel framework of the model-based reinforcement learning where the agent's actions and observations are asynchronous stochastic discrete events occurring in continuous-time. We model the dynamics of the environment by Hawkes process with external intervention control term and develop an algorithm to embed such process in the Bellman equation which guides the direction of the value gradient. We demonstrate the superiority of our method in both synthetic simulator and real-data experiments.

21

Borovkov, K. A., and D. A. Odell. "On spatial thinning-replacement processes based on Voronoi cells." Advances in Applied Probability 39, no. 2 (June 2007): 293–306. http://dx.doi.org/10.1239/aap/1183667610.

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We introduce a new class of spatial-temporal point processes based on Voronoi tessellations. At each step of such a process, a point is chosen at random according to a distribution determined by the associated Voronoi cells. The point is then removed, and a new random point is added to the configuration. The dynamics are simple and intuitive and could be applied to modelling natural phenomena. We prove ergodicity of these processes under wide conditions.

22

Borovkov, K. A., and D. A. Odell. "On spatial thinning-replacement processes based on Voronoi cells." Advances in Applied Probability 39, no. 02 (June 2007): 293–306. http://dx.doi.org/10.1017/s0001867800001750.

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We introduce a new class of spatial-temporal point processes based on Voronoi tessellations. At each step of such a process, a point is chosen at random according to a distribution determined by the associated Voronoi cells. The point is then removed, and a new random point is added to the configuration. The dynamics are simple and intuitive and could be applied to modelling natural phenomena. We prove ergodicity of these processes under wide conditions.

23

Gu, Yulong. "Attentive Neural Point Processes for Event Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7592–600. http://dx.doi.org/10.1609/aaai.v35i9.16929.

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Event sequence, where each event is associated with a marker and a timestamp, is increasingly ubiquitous in various applications. Accordingly, event forecasting emerges to be a crucial problem, which aims to predict the next event based on the historical sequence. In this paper, we propose ANPP, an Attentive Neural Point Processes framework to solve this problem. In comparison with state-of-the-art methods like recurrent marked temporal point processes, ANPP leverages the time-aware self-attention mechanism to explicitly model the influence between every pair of historical events, resulting in more accurate predictions of events and better interpretation ability. Extensive experiments on one synthetic and four real-world datasets demonstrate that ANPP can achieve significant performance gains against state-of-the-art methods for predictions of both timings and markers. To facilitate future research, we release the codes and datasets at https://github.com/guyulongcs/AAAI2021\_ANPP.

24

Tal, Idan, and Moshe Abeles. "Temporal accuracy of human cortico-cortical interactions." Journal of Neurophysiology 115, no. 4 (April 1, 2016): 1810–20. http://dx.doi.org/10.1152/jn.00956.2015.

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The precision in space and time of interactions among multiple cortical sites was evaluated by examining repeating precise spatiotemporal patterns of instances in which cortical currents showed brief amplitude undulations. The amplitudes of the cortical current dipoles were estimated by applying a variant of synthetic aperture magnetometry to magnetoencephalographic (MEG) recordings of subjects tapping to metric auditory rhythms of drum beats. Brief amplitude undulations were detected in the currents by template matching at a rate of 2–3 per second. Their timing was treated as point processes, and precise spatiotemporal patterns were searched for. By randomly teetering these point processes within a time window W, we estimated the accuracy of the timing of these brief amplitude undulations and compared the results with those obtained by applying the same analysis to traces composed of random numbers. The results demonstrated that the timing accuracy of patterns was better than 3 ms. Successful classification of two different cognitive processes based on these patterns suggests that at least some of the repeating patterns are specific to a cognitive process.

25

OKAWA, Maya, Yusuke TANAKA, Takeshi KURASHIMA, Hiroyuki TODA, and Tomohiro YAMADA. "Marked Temporal Point Processes for Trip Demand Prediction in Bike Sharing Systems." IEICE Transactions on Information and Systems E102.D, no. 9 (September 1, 2019): 1635–43. http://dx.doi.org/10.1587/transinf.2018ofp0004.

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26

Gupta, Vinayak, Srikanta Bedathur, and Abir De. "Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4005–13. http://dx.doi.org/10.1609/aaai.v36i4.20317.

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Recent developments in predictive modeling using marked temporal point processes (MTPPs) have enabled an accurate characterization of several real-world applications involving continuous-time event sequences (CTESs). However, the retrieval problem of such sequences remains largely unaddressed in literature. To tackle this, we propose NEUROSEQRET which learns to retrieve and rank a relevant set of continuous-time event sequences for a given query sequence, from a large corpus of sequences. More specifically, NEUROSEQRET first applies a trainable unwarping function on the query sequence, which makes it comparable with corpus sequences, especially when a relevant query-corpus pair has individually different attributes. Next, it feeds the unwarped query sequence and the corpus sequence into MTPP guided neural relevance models. We develop two variants of the relevance model which offer a tradeoff between accuracy and efficiency. We also propose an optimization framework to learn binary sequence embeddings from the relevance scores, suitable for the locality-sensitive hashing leading to a significant speedup in returning top-K results for a given query sequence. Our experiments with several datasets show the significant accuracy boost of NEUROSEQRET beyond several baselines, as well as the efficacy of our hashing mechanism.

27

Beneš, Viktor, and Blažena Frcalová. "MODELLING AND SIMULATION OF A NEUROPHYSIOLOGICAL EXPERIMENT BY SPATIO-TEMPORAL POINT PROCESSES." Image Analysis & Stereology 27, no. 1 (May 3, 2011): 47. http://dx.doi.org/10.5566/ias.v27.p47-52.

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We present a stochastic model of an experimentmonitoring the spiking activity of a place cell of hippocampus of an experimental animal moving in an arena. Doubly stochastic spatio-temporal point process is used to model and quantify overdispersion. Stochastic intensity is modelled by a Lévy based random field while the animal path is simplified to a discrete random walk. In a simulation study first a method suggested previously is used. Then it is shown that a solution of the filtering problem yields the desired inference to the random intensity. Two approaches are suggested and the new one based on finite point process density is applied. Using Markov chain Monte Carlo we obtain numerical results from the simulated model. The methodology is discussed.

28

Møller, Jesper, and Mohammad Ghorbani. "Aspects of second-order analysis of structured inhomogeneous spatio-temporal point processes." Statistica Neerlandica 66, no. 4 (June 18, 2012): 472–91. http://dx.doi.org/10.1111/j.1467-9574.2012.00526.x.

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29

Reinhart, Alex. "A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications." Statistical Science 33, no. 3 (August 2018): 299–318. http://dx.doi.org/10.1214/17-sts629.

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30

Rathbun, Stephen L. "Asymptotic properties of the maximum likelihood estimator for spatio-temporal point processes." Journal of Statistical Planning and Inference 51, no. 1 (April 1996): 55–74. http://dx.doi.org/10.1016/0378-3758(95)00070-4.

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31

Díaz-Avalos, Carlos, P. Juan, and J. Mateu. "Significance tests for covariate-dependent trends in inhomogeneous spatio-temporal point processes." Stochastic Environmental Research and Risk Assessment 28, no. 3 (August 9, 2013): 593–609. http://dx.doi.org/10.1007/s00477-013-0775-1.

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32

van Lieshout, M. N. M., and A. Stein. "Earthquake Modelling at the Country Level Using Aggregated Spatio-Temporal Point Processes." Mathematical Geosciences 44, no. 3 (January 17, 2012): 309–26. http://dx.doi.org/10.1007/s11004-011-9380-3.

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33

Gracious, Tony, and Ambedkar Dukkipati. "Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 7748–56. http://dx.doi.org/10.1609/aaai.v37i6.25939.

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The explosion of digital information and the growing involvement of people in social networks led to enormous research activity to develop methods that can extract meaningful information from interaction data. Commonly, interactions are represented by edges in a network or a graph, which implicitly assumes that the interactions are pairwise and static. However, real-world interactions deviate from these assumptions: (i) interactions can be multi-way, involving more than two nodes or individuals (e.g., family relationships, protein interactions), and (ii) interactions can change over a period of time (e.g., change of opinions and friendship status). While pairwise interactions have been studied in a dynamic network setting and multi-way interactions have been studied using hypergraphs in static networks, there exists no method, at present, that can predict multi-way interactions or hyperedges in dynamic settings. Existing related methods cannot answer temporal queries like what type of interaction will occur next and when it will occur. This paper proposes a temporal point process model for hyperedge prediction to address these problems. Our proposed model uses dynamic representation learning techniques for nodes in a neural point process framework to forecast hyperedges. We present several experimental results and set benchmark results. As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.

34

Yang, Sikun, and Hongyuan Zha. "A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16343–51. http://dx.doi.org/10.1609/aaai.v38i15.29570.

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Continuously observed event occurrences, often exhibit self and mutually exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynamics may also change over time, with certain periodic trends. We propose a novel variational autoencoder to capture such a mixture of temporal dynamics. More specifically, the whole time interval of the input sequence is partitioned into a set of sub intervals. The event dynamics are assumed to be stationary within each subinterval, but could be changing across those subintervals. In particular, we use a sequential latent variable model to learn a dependency graph between the observed dimensions, for each subinterval. The model predicts the future event times, by using the learned dependency graph to remove the non contributing influences of past events. By doing so, the proposed model demonstrates its higher accuracy in predicting inter event times and event types for several real world event sequences, compared with existing state of the art neural point processes.

35

Allroggen, Niklas, Daniel Beiter, and Jens Tronicke. "Ground-penetrating radar monitoring of fast subsurface processes." GEOPHYSICS 85, no. 3 (April 24, 2020): A19—A23. http://dx.doi.org/10.1190/geo2019-0737.1.

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Earth and environmental sciences rely on detailed information about subsurface processes. Whereas geophysical techniques typically provide highly resolved spatial images, monitoring subsurface processes is often associated with enormous effort and, therefore, is usually limited to point information in time or space. Thus, the development of spatial and temporal continuous field monitoring methods is a major challenge for the understanding of subsurface processes. We have developed a novel method for ground-penetrating-radar (GPR) reflection monitoring of subsurface flow processes under unsaturated conditions and applied it to a hydrological infiltration experiment performed across a periglacial slope deposit in northwest Luxembourg. Our approach relies on a spatial and temporal quasicontinuous data recording and processing, followed by an attribute analysis based on analyzing differences between individual time steps. The results demonstrate the ability of time-lapse GPR monitoring to visualize the spatial and temporal dynamics of preferential flow processes with a spatial resolution in the order of a few decimeters and temporal resolution in the order of a few minutes. We observe excellent agreement with water table information originating from different boreholes. This demonstrates the potential of surface-based GPR reflection monitoring to observe the spatiotemporal dynamics of water movements in the subsurface. It provides valuable, and so far not accessible, information for example in the field of hydrology and pedology that allows studying the actual subsurface processes rather than deducing them from point information.

36

Holubec, Viktor, Artem Ryabov, Sarah A. M. Loos, and Klaus Kroy. "Equilibrium stochastic delay processes." New Journal of Physics 24, no. 2 (February 1, 2022): 023021. http://dx.doi.org/10.1088/1367-2630/ac4b91.

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Abstract Stochastic processes with temporal delay play an important role in science and engineering whenever finite speeds of signal transmission and processing occur. However, an exact mathematical analysis of their dynamics and thermodynamics is available for linear models only. We introduce a class of stochastic delay processes with nonlinear time-local forces and linear time-delayed forces that obey fluctuation theorems and converge to a Boltzmann equilibrium at long times. From the point of view of control theory, such ‘equilibrium stochastic delay processes’ are stable and energetically passive, by construction. Computationally, they provide diverse exact constraints on general nonlinear stochastic delay problems and can, in various situations, serve as a starting point for their perturbative analysis. Physically, they admit an interpretation in terms of an underdamped Brownian particle that is either subjected to a time-local force in a non-Markovian thermal bath or to a delayed feedback force in a Markovian thermal bath. We illustrate these properties numerically for a setup familiar from feedback cooling and point out experimental implications.

37

Bootsma, Reinoud J. "Accuracy of Perceptual Processes Subserving Different Perception–Action Systems." Quarterly Journal of Experimental Psychology Section A 41, no. 3 (August 1989): 489–500. http://dx.doi.org/10.1080/14640748908402378.

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The accuracy of perceptual processes subserving different perception–action systems was evaluated by presenting subjects (N = 17) with similar optic flow patterns, while requiring different actions from them. Squash balls were dropped along a fixed trajectory, and subjects were asked to (a) hit the ball using their own arm, (b) release an artificial arm to hit the ball, and (c) indicate when the ball was at the point of contact of conditions (a) and (b). The variability of the temporal initiation point of the actions, which served as an operationalization of perceptual accuracy, was compared under all three conditions. The results indicated that the variability of the temporal initiation point was smallest under the natural arm condition, even though movement time was more variable here than under the artificial arm condition. It is argued that, because perception and action are intimately interwoven components of a perceiving/acting system, it is not an extrinsically (experimenter-) determined simplicity of perceptual and/or motor aspects of the task, but the intrinsic make-up of this overarching system that determines which couplings lead to a more accurate performance.

38

Møller, Jesper, Mohammad Ghorbani, and Ege Rubak. "Mechanistic spatio-temporal point process models for marked point processes, with a view to forest stand data." Biometrics 72, no. 3 (December 21, 2015): 687–96. http://dx.doi.org/10.1111/biom.12466.

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39

Strel’tsov, V. "Japan: Course Towards Joining to Trans-Pacific Community." World Economy and International Relations, no. 12 (2012): 70–77. http://dx.doi.org/10.20542/0131-2227-2012-12-70-77.

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Currently, ten countries are negotiating the membership in the Trans-Pacific Partnership (TTP). After joining of Japan (Japan is the third-largest economy of Asia-Pacific region after USA and China) the TTP will acquire a qualitatively new status while becoming a full-fledged prototype of a Pacific free trade area. As a result, such a zone could cover a wider range of countries on both sides of the Pacific Ocean. From this point of view, Japan’s participation certainly will mark a qualitative leap in the TPPs evolution, namely a transfer from the status of a peripheral economic block into a structure of global importance. Participation of Japan (which is an economic heavyweight) in the TTP will give a new impetus to the development of the world economy. Also, it will be an important step in coping with the aftermaths of the global financial and economic crisis. A happy end story, i.e. the creation of an effective framework for economic integration in the Asia-Pacific region, would have a considerable positive impact on the integration processes in Europe, where the acute debt crisis has put under question the viability of the European integration model.

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Fortino, Giancarlo, Antonella Guzzo, Michele Ianni, Francesco Leotta, and Massimo Mecella. "Predicting activities of daily living via temporal point processes: Approaches and experimental results." Computers & Electrical Engineering 96 (December 2021): 107567. http://dx.doi.org/10.1016/j.compeleceng.2021.107567.

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Moradi, M. Mehdi, and Jorge Mateu. "First- and Second-Order Characteristics of Spatio-Temporal Point Processes on Linear Networks." Journal of Computational and Graphical Statistics 29, no. 3 (December 23, 2019): 432–43. http://dx.doi.org/10.1080/10618600.2019.1694524.

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Adelfio, Giada, Marianna Siino, Jorge Mateu, and Francisco J. Rodríguez-Cortés. "Some properties of local weighted second-order statistics for spatio-temporal point processes." Stochastic Environmental Research and Risk Assessment 34, no. 1 (November 21, 2019): 149–68. http://dx.doi.org/10.1007/s00477-019-01748-1.

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Reinhart, Alex. "Rejoinder: A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications." Statistical Science 33, no. 3 (August 2018): 330–33. http://dx.doi.org/10.1214/18-sts654.

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Yan, Junchi, Hongteng Xu, Liangda Li, Mehrdad Farajtab, and Xiaokang Yang. "Guest Editorial Robust Learning of Spatio-Temporal Point Processes: Modeling, Algorithm, and Applications." IEEE Transactions on Neural Networks and Learning Systems 34, no. 4 (April 2023): 1634–38. http://dx.doi.org/10.1109/tnnls.2023.3258199.

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Hössjer, Ola. "A spatio-temporal point process model for particle growth." Journal of Applied Probability 56, no. 01 (March 2019): 23–38. http://dx.doi.org/10.1017/jpr.2019.3.

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AbstractA spatio-temporal model of particle or star growth is defined, whereby new unit masses arrive sequentially in discrete time. These unit masses are referred to as candidate stars, which tend to arrive in mass-dense regions and then either form a new star or are absorbed by some neighbouring star of high mass. We analyse the system as time increases, and derive the asymptotic growth rate of the number of stars as well as the size of a randomly chosen star. We also prove that the size-biased mass distribution converges to a Poisson–Dirichlet distribution. This is achieved by embedding our model into a continuous-time Markov process, so that new stars arrive according to a marked Poisson process, with locations as marks, whereas existing stars grow as independent Yule processes. Our approach can be interpreted as a Hoppe-type urn scheme with a spatial structure. We discuss its relevance for and connection to models of population genetics, particle aggregation, image segmentation, epidemic spread, and random graphs with preferential attachment.

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Ogden, Ruth S., Rhiannon MacKenzie-Phelan, Catharine Mongtomery, John E. Fisk, and John H. Wearden. "Executive processes and timing: Comparing timing with and without reference memory." Quarterly Journal of Experimental Psychology 72, no. 3 (January 22, 2018): 377–88. http://dx.doi.org/10.1177/1747021817751869.

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Temporal perception is influenced by executive function. However, performance on different temporal tasks is often associated with different executive functions. This study examined whether using reference memory during a task influenced how performance was associated with executive resources. Participants completed temporal generalisation and bisection tasks, in their normal versions involving reference memory and in episodic versions without reference memory. Each timing task had two difficulty levels: easy and hard. Correlations between performance on these tasks and measures of executive function (updating, inhibition, task switching, and access to semantic memory) were assessed. Accuracy on the temporal generalisation task was correlated with memory access for all versions of the task. Updating correlated with accuracy only for the reference memory-based version of the task. Temporal bisection performance presented a different pattern of correlations. The bisection point was negatively correlated with inhibition scores, except for the easy episodic condition. The Weber ratio, considered a measure of temporal sensitivity, was negatively correlated with memory access only in the hard episodic condition. Together, the findings suggest that previous models of generalisation and bisection may not accurately reflect the underlying cognitive processes involved in the tasks.

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Platkiewicz, Jonathan, Eran Stark, and Asohan Amarasingham. "Spike-Centered Jitter Can Mistake Temporal Structure." Neural Computation 29, no. 3 (March 2017): 783–803. http://dx.doi.org/10.1162/neco_a_00927.

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Jitter-type spike resampling methods are routinely applied in neurophysiology for detecting temporal structure in spike trains (point processes). Several variations have been proposed. The concern has been raised, based on numerical experiments involving Poisson spike processes, that such procedures can be conservative. We study the issue and find it can be resolved by reemphasizing the distinction between spike-centered (basic) jitter and interval jitter. Focusing on spiking processes with no temporal structure, interval jitter generates an exact hypothesis test, guaranteeing valid conclusions. In contrast, such a guarantee is not available for spike-centered jitter. We construct explicit examples in which spike-centered jitter hallucinates temporal structure, in the sense of exaggerated false-positive rates. Finally, we illustrate numerically that Poisson approximations to jitter computations, while computationally efficient, can also result in inaccurate hypothesis tests. We highlight the value of classical statistical frameworks for guiding the design and interpretation of spike resampling methods.

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Redko, Ihor, Yurii Burda, Yurii Pivnenko, and Rafael Levanovich Dzhyoiev. "RESEARCH OF BROWN COAL BURNING PROCESSES IN THE VORTEX FIREPLACE BY COMPUTER MODELING." Collected scientific works of Ukrainian State University of Railway Transport, no. 200 (June 17, 2022): 25–35. http://dx.doi.org/10.18664/1994-7852.200.2022.262683.

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As part of the course for European integration in Ukraine, a new Energy Strategywas approved to reduce the share of coal among primary sources to 12.5 % by 2035, the share ofelectricity generation at TPPs and CHPs to 3 2%. In 2021, Ukraine joined the EU's Green Dealstrategy with a commitment to completely stop coal production and close coal energy and close coalenergy by 2050.At the same time, coal energy has advantages - significant reserves of domestic coal (first inEurope and eighth in the world), which make it a guarantor of energy independence.Given the growing share of "green" generation in Ukraine, the share of pulverized coalproduction should be kept at least 30% of total energy productionThe use of expensive imported natural gas in Ukraine's fuel balance stimulates the developmentof industrial and small energy based on cheap local fuels and combustible waste. The use of cheapcoal, peat, wood waste can reduce the cost of heat production in housing and communal services,industrial enterprises.Combustion of low-grade fuels is constrained due to the complexity of organizing a sustainablecombustion process.Local fuels are characterized by high humidity and ash content, low heat of combustion. Thezone of autogenous (self-sustaining) combustion is defined by the limits: combustible - more than25 %, moisture - less than 50 %, ash - less than 60% by working weight.Vegetation and wood are the starting point for the stages of metamorphism (carbonization):plant residues, peat, brown coal, coal, anthracite. Characteristics of humolites, the organic part of the fuel, by stages of metamorphism. Fuels ofdifferent types and different degrees of carbonization - peat, lignite is peroxidized and contain a lotof oxygen in the combustible mass, have the lowest thermal stability and the highest yield of volatilesubstances.In the process of metamorphism, oxygen is removed and the thermal stability of the organicmatter of the fuel is increased.

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Kosarevych, Rostyslav, Izabela Jonek-Kowalska, Bohdan Rusyn, Anatoliy Sachenko, and Oleksiy Lutsyk. "Analysing Pine Disease Spread Using Random Point Process by Remote Sensing of a Forest Stand." Remote Sensing 15, no. 16 (August 9, 2023): 3941. http://dx.doi.org/10.3390/rs15163941.

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The application of a process model to investigate pine tree infestation caused by bark beetles is discussed. The analysis of this disease was carried out using spatial and spatio−temporal models of random point patterns. Spatial point patterns were constructed for remote sensing images of pine trees damaged by the apical bark beetle. The method of random point processes was used for their analysis. A number of known models of point pattern processes with pairwise interaction were fitted to actual data. The best model to describe the real data was chosen using the Akaike information index. The residual K−function was used to check the fit of the model to the real data. According to values of the Akaike information criterion and the residual K−function, two models were found to correspond best to the investigated data. These are the generalized Geyer model of the point process of saturation and the pair interaction process with the piecewise constant potential of a pair of points. For the first time, a spatio−temporal model of the contagious process was used for analysis of tree damage.

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Telesca, L., C. C. Chen, and Y. T. Lee. "Scaling behaviour in temporal fluctuations of crustal seismicity in Taiwan." Natural Hazards and Earth System Sciences 9, no. 6 (December 11, 2009): 2067–71. http://dx.doi.org/10.5194/nhess-9-2067-2009.

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Abstract. The crustal seismicity of Taiwan was investigated by means of the Allan Factor analysis and Count-based Periodogram, which allow to identify scaling behaviour in point processes and to quantify their temporal fluctuations by means of the estimate of the scaling exponent. Our findings point out to the presence of two time-scaling regions in the crustal Taiwanese seismicity. The first region, involving the intermediate timescales can be probably linked with aftershock activity, while the second region, involving the large timescales could be related with the background seismicity.