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Добірка наукової літератури з теми "Spatio temporal networks"

Автор: Grafiati

Опубліковано: 11 березня 2023

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Статті в журналах з теми "Spatio temporal networks"

Williams, Matthew J., and Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness." Royal Society Open Science 3, no. 6 (June 2016): 160196. http://dx.doi.org/10.1098/rsos.160196.

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Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

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QIN, Chao, and Xiaoguang GAO. "Spatio-Temporal Generative Adversarial Networks." Chinese Journal of Electronics 29, no. 4 (July 1, 2020): 623–31. http://dx.doi.org/10.1049/cje.2020.04.001.

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Chao, Qin, and Gao Xiaoguang. "Distributed spatio-temporal generative adversarial networks." Journal of Systems Engineering and Electronics 31, no. 3 (June 2020): 578–92. http://dx.doi.org/10.23919/jsee.2020.000026.

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Pichardo-Corpus, J. A., H. A. Solano Lamphar, R. Lopez-Farias, and O. Delgadillo Ruiz. "Spatio-temporal networks of light pollution." Journal of Quantitative Spectroscopy and Radiative Transfer 253 (September 2020): 107068. http://dx.doi.org/10.1016/j.jqsrt.2020.107068.

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Gao, Nan, Hao Xue, Wei Shao, Sichen Zhao, Kyle Kai Qin, Arian Prabowo, Mohammad Saiedur Rahaman, and Flora D. Salim. "Generative Adversarial Networks for Spatio-temporal Data: A Survey." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–25. http://dx.doi.org/10.1145/3474838.

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Generative Adversarial Networks (GANs) have shown remarkable success in producing realistic-looking images in the computer vision area. Recently, GAN-based techniques are shown to be promising for spatio-temporal-based applications such as trajectory prediction, events generation, and time-series data imputation. While several reviews for GANs in computer vision have been presented, no one has considered addressing the practical applications and challenges relevant to spatio-temporal data. In this article, we have conducted a comprehensive review of the recent developments of GANs for spatio-temporal data. We summarise the application of popular GAN architectures for spatio-temporal data and the common practices for evaluating the performance of spatio-temporal applications with GANs. Finally, we point out future research directions to benefit researchers in this area.

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Yang, Zhaoqilin, Gaoyun An, and Ruichen Zhang. "STSM: Spatio-Temporal Shift Module for Efficient Action Recognition." Mathematics 10, no. 18 (September 10, 2022): 3290. http://dx.doi.org/10.3390/math10183290.

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The modeling, computational complexity, and accuracy of spatio-temporal models are the three major foci in the field of video action recognition. The traditional 2D convolution has low computational complexity, but it cannot capture the temporal relationships. Although the 3D convolution can obtain good performance, it is with both high computational complexity and a large number of parameters. In this paper, we propose a plug-and-play Spatio-Temporal Shift Module (STSM), which is a both effective and high-performance module. STSM can be easily inserted into other networks to increase or enhance the ability of the network to learn spatio-temporal features, effectively improving performance without increasing the number of parameters and computational complexity. In particular, when 2D CNNs and STSM are integrated, the new network may learn spatio-temporal features and outperform networks based on 3D convolutions. We revisit the shift operation from the perspective of matrix algebra, i.e., the spatio-temporal shift operation is a convolution operation with a sparse convolution kernel. Furthermore, we extensively evaluate the proposed module on Kinetics-400 and Something-Something V2 datasets. The experimental results show the effectiveness of the proposed STSM, and the proposed action recognition networks may also achieve state-of-the-art results on the two action recognition benchmarks.

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Schutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut, and Markus Reischl. "Strategies for supplementing recurrent neural network training for spatio-temporal prediction." at - Automatisierungstechnik 67, no. 7 (July 26, 2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.

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Abstract In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.

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Tempelmeier, Nicolas, Udo Feuerhake, Oskar Wage, and Elena Demidova. "Mining Topological Dependencies of Recurrent Congestion in Road Networks." ISPRS International Journal of Geo-Information 10, no. 4 (April 8, 2021): 248. http://dx.doi.org/10.3390/ijgi10040248.

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The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and the scheduling of public transportation services. While most existing studies investigate temporal patterns of RC phenomena, the influence of the road network topology on RC is often overlooked. This article proposes the ST-Discovery algorithm, a novel unsupervised spatio-temporal data mining algorithm that facilitates effective data-driven discovery of RC dependencies induced by the road network topology using real-world traffic data. We factor out regularly reoccurring traffic phenomena, such as rush hours, mainly induced by the daytime, by modelling and systematically exploiting temporal traffic load outliers. We present an algorithm that first constructs connected subgraphs of the road network based on the traffic speed outliers. Second, the algorithm identifies pairs of subgraphs that indicate spatio-temporal correlations in their traffic load behaviour to identify topological dependencies within the road network. Finally, we rank the identified subgraph pairs based on the dependency score determined by our algorithm. Our experimental results demonstrate that ST-Discovery can effectively reveal topological dependencies in urban road networks.

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Zhao, Pengpeng, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. "Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5877–84. http://dx.doi.org/10.1609/aaai.v33i01.33015877.

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Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Gated Network (STGN) by enhancing long-short term memory network, where spatio-temporal gates are introduced to capture the spatio-temporal relationships between successive checkins. Specifically, two pairs of time gate and distance gate are designed to control the short-term interest and the longterm interest updates, respectively. Moreover, we introduce coupled input and forget gates to reduce the number of parameters and further improve efficiency. Finally, we evaluate the proposed model using four real-world datasets from various location-based social networks. The experimental results show that our model significantly outperforms the state-ofthe-art approaches for next POI recommendation.

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Li, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, and Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–21. http://dx.doi.org/10.1145/3510829.

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Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.

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Дисертації з теми "Spatio temporal networks"

Moradi, Mohammad Mehdi. "Spatial and spatio-temporal point patterns on linear networks." Doctoral thesis, Universitat Jaume I, 2018. http://hdl.handle.net/10803/664140.

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A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information Systems
The last decade witnessed an extraordinary increase in interest in the analysis of network related data and trajectories. This pervasive interest is partly caused by a strongly expanded availability of such datasets. In the spatial statistics field, there are numerous real examples such as the locations of traffic accidents and geo-coded locations of crimes in the streets of cities that need to restrict the support of the underlying process over such linear networks to set and define a more realistic scenario. Examples of trajectories are the path taken by moving objects such as taxis, human beings, animals, etc. Intensity estimation on a network of lines, such as a road network, seems to be a surprisingly complicated task. Several techniques published in the literature, in geography and computer science, have turned out to be erroneous. We propose several adaptive and non-adaptive intensity estimators, based on kernel smoothing and Voronoi tessellation. Theoretical properties such as bias, variance, asymptotics, bandwidth selection, variance estimation, relative risk estimation, and adaptive smoothing are discussed. Moreover, their statistical performance is studied through simulation studies and is compared with existing methods. Adding the temporal component, we also consider spatio-temporal point patterns with spatial locations restricted to a linear network. We present a nonparametric kernel-based intensity estimator and develop second-order characteristics of spatio-temporal point processes on linear networks such as K-function and pair correlation function to analyse the type of interaction between points. In terms of trajectories, we introduce the R package trajectories that contains different classes and methods to handle, summarise and analyse trajectory data. Simulation and model fitting, intensity estimation, distance analysis, movement smoothing, Chi maps and second-order summary statistics are discussed. Moreover, we analyse different real datasets such as a crime data from Chicago (US), anti-social behaviour in Castell´on (Spain), traffic accidents in Medell´ın (Colombia), traffic accidents in Western Australia, motor vehicle traffic accidents in an area of Houston (US), locations of pine saplings in a Finnish forest, traffic accidents in Eastbourne (UK) and one week taxi movements in Beijing (China).

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O'Donnell, David. "Spatial prediction and spatio-temporal modelling on river networks." Thesis, University of Glasgow, 2012. http://theses.gla.ac.uk/3161/.

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The application of existing geostatistical theory to the context of stream networks provides a number of interesting and challenging problems. The most important of these is how to adapt existing theory to allow for stream, as opposed to Euclidean, distance to be used. Valid stream distance based models for the covariance structure have been denied in the literature, and this thesis explores the use of such models using data from the River Tweed. The data span a period of twenty-one years, beginning in 1986. During this time period, up to eighty-three stations are monitored for a variety of chemical and biological determinands. This thesis will focus on nitrogen, a key nutrient in determining water quality, especially given the Nitrates Directive (adopted in 1991) and the Water Framework Directive(adopted in 2002). These are European Union legislations that have set legally enforcable guidelines for controlling pollution which national bodies must comply with. The focus of analysis is on several choices that must be made in order to carry out spatial prediction on a river network. The role of spatial trend, whether it be based on stream or Euclidean distance, is discussed and the impact of the bandwidth of the estimate of nonparametric trend is explored. The stream distance based "tail-up" covariance model structure of Ver Hoef and Peterson (2010) is assessed and combined with a standard Euclidean distance based structure to form a mixture model. This is then evaluated using crossvalidation studies in order to determine the optimum mixture of the two covariance models for the data. Finally, the covariance models used for each of the elements of the mixture model are explored to determine the impact they have on the lowest root mean squared error, and the mixing proportion at which it is found. Using the predicted values at unobserved locations on the River Tweed, the distribution of yearly averaged nitrate levels around the river network is predicted and evaluated. Changes through the 21 years of data are noted and areas exceeding the limits set by the Nitrates Directive are highlighted. The differences in fitted values caused by using stream or Euclidean distance are evident in these predictions. The data is then modelled through space and time using additive models. A novel smoothing function for the spatial trend is defined. It is adapted from the tail-up model in order to retain its core features of flow connectivity and flow volume based weightings, in addition to being based on stream distance. This is then used to model all of the River Tweed data through space and time and identify temporal trends and seasonal patterns at different locations on the river.

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Sutherland, Connie. "Spatio-temporal feedback in stochastic neural networks." Thesis, University of Ottawa (Canada), 2007. http://hdl.handle.net/10393/27559.

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The mechanisms by which groups of neurons interact is an important facet to understanding how the brain functions. Here we study stochastic neural networks with delayed feedback. The first part of our study looks at how feedback and noise affect the mean firing rate of the network. Secondly we look at how the spatial profile of the feedback affects the behavior of the network. Our numerical and theoretical results show that negative (inhibitory) feedback linearizes the frequency vs input current (f-I) curve via the divisive gain effect it has on the network. The interaction of the inhibitory feedback and the input bias is what produces the divisive decrease in the slope (known as the gain) of the f-I curve. Our work predicts that an increase in noise is required along with increase in inhibitory feedback to attain a divisive and subtractive shift of the gain as seen in experiments [1]. Our results also show that, although the spatial profile of the feedback does not effect the mean activity of the network, it does influence the overall dynamics of the network. Local feedback generates a network oscillation, which is more robust against disruption by noise or uncorrelated input or network heterogeneity, than that for the global feedback (all-to-all coupling) case. For example uncorrelated input completely disrupts the network oscillation generated by global feedback, but only diminishes the network oscillation due to local feedback. This is characterized by 1st and 2nd order spike train statistics. Further, our theory agrees well with numerical simulations of network dynamics.

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Mitchell, Elaine Irwin. "Spatio-temporal modelling of gene regulatory networks." Thesis, University of Dundee, 2018. https://discovery.dundee.ac.uk/en/studentTheses/259d76f6-76cf-474d-a26a-2802808b126e.

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Akbarzadeh, Vahab. "Spatio-temporal coverage optimization of sensor networks." Doctoral thesis, Université Laval, 2016. http://hdl.handle.net/20.500.11794/27065.

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Les réseaux de capteurs sont formés d’un ensemble de dispositifs capables de prendre individuellement des mesures d’un environnement particulier et d’échanger de l’information afin d’obtenir une représentation de haut niveau sur les activités en cours dans la zone d’intérêt. Une telle détection distribuée, avec de nombreux appareils situés à proximité des phénomènes d’intérêt, est pertinente dans des domaines tels que la surveillance, l’agriculture, l’observation environnementale, la surveillance industrielle, etc. Nous proposons dans cette thèse plusieurs approches pour effectuer l’optimisation des opérations spatio-temporelles de ces dispositifs, en déterminant où les placer dans l’environnement et comment les contrôler au fil du temps afin de détecter les cibles mobiles d’intérêt. La première nouveauté consiste en un modèle de détection réaliste représentant la couverture d’un réseau de capteurs dans son environnement. Nous proposons pour cela un modèle 3D probabiliste de la capacité de détection d’un capteur sur ses abords. Ce modèle inègre également de l’information sur l’environnement grâce à l’évaluation de la visibilité selon le champ de vision. À partir de ce modèle de détection, l’optimisation spatiale est effectuée par la recherche du meilleur emplacement et l’orientation de chaque capteur du réseau. Pour ce faire, nous proposons un nouvel algorithme basé sur la descente du gradient qui a été favorablement comparée avec d’autres méthodes génériques d’optimisation «boites noires» sous l’aspect de la couverture du terrain, tout en étant plus efficace en terme de calculs. Une fois que les capteurs placés dans l’environnement, l’optimisation temporelle consiste à bien couvrir un groupe de cibles mobiles dans l’environnement. D’abord, on effectue la prédiction de la position future des cibles mobiles détectées par les capteurs. La prédiction se fait soit à l’aide de l’historique des autres cibles qui ont traversé le même environnement (prédiction à long terme), ou seulement en utilisant les déplacements précédents de la même cible (prédiction à court terme). Nous proposons de nouveaux algorithmes dans chaque catégorie qui performent mieux ou produits des résultats comparables par rapport aux méthodes existantes. Une fois que les futurs emplacements de cibles sont prédits, les paramètres des capteurs sont optimisés afin que les cibles soient correctement couvertes pendant un certain temps, selon les prédictions. À cet effet, nous proposons une méthode heuristique pour faire un contrôle de capteurs, qui se base sur les prévisions probabilistes de trajectoire des cibles et également sur la couverture probabiliste des capteurs des cibles. Et pour terminer, les méthodes d’optimisation spatiales et temporelles proposées ont été intégrées et appliquées avec succès, ce qui démontre une approche complète et efficace pour l’optimisation spatio-temporelle des réseaux de capteurs.
Sensor networks consist in a set of devices able to individually capture information on a given environment and to exchange information in order to obtain a higher level representation on the activities going on in the area of interest. Such a distributed sensing with many devices close to the phenomena of interest is of great interest in domains such as surveillance, agriculture, environmental monitoring, industrial monitoring, etc. We are proposing in this thesis several approaches to achieve spatiotemporal optimization of the operations of these devices, by determining where to place them in the environment and how to control them over time in order to sense the moving targets of interest. The first novelty consists in a realistic sensing model representing the coverage of a sensor network in its environment. We are proposing for that a probabilistic 3D model of sensing capacity of a sensor over its surrounding area. This model also includes information on the environment through the evaluation of line-of-sight visibility. From this sensing model, spatial optimization is conducted by searching for the best location and direction of each sensor making a network. For that purpose, we are proposing a new algorithm based on gradient descent, which has been favourably compared to other generic black box optimization methods in term of performance, while being more effective when considering processing requirements. Once the sensors are placed in the environment, the temporal optimization consists in covering well a group of moving targets in the environment. That starts by predicting the future location of the mobile targets detected by the sensors. The prediction is done either by using the history of other targets who traversed the same environment (long term prediction), or only by using the previous displacements of the same target (short term prediction). We are proposing new algorithms under each category which outperformed or produced comparable results when compared to existing methods. Once future locations of targets are predicted, the parameters of the sensors are optimized so that targets are properly covered in some future time according to the predictions. For that purpose, we are proposing a heuristics for making such sensor control, which deals with both the probabilistic targets trajectory predictions and probabilistic coverage of sensors over the targets. In the final stage, both spatial and temporal optimization method have been successfully integrated and applied, demonstrating a complete and effective pipeline for spatiotemporal optimization of sensor networks.

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Dondo, C. "Bayesian networks for spatio-temporal integrated catchment assessment." Doctoral thesis, University of Cape Town, 2010. http://hdl.handle.net/11427/10327.

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Includes abstract.
Includes bibliographical references (leaves 181-203).
In this thesis, a methodology for integrated catchment water resources assessment using Bayesian Networks was developed. A custom made software application that combines Bayesian Networks with GIS was used to facilitate data pre-processing and spatial modelling. Dynamic Bayesian Networks were implemented in the software for time-series modelling.

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YEGHIKYAN, Gevorg. "Urban Structure and Mobility as Spatio-temporal complex Networks." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/94477.

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Contemporary urban life and functioning have become increasingly dependent on mobility. Having become an inherent constituent of urban dynamics, the role of urban moblity in influencing urban processes and morphology has increased dramat- ically. However, the relationship between urban mobility and spatial socio-economic structure has still not been thoroughly understood. This work will attempt to take a complex network theoretical approach to studying this intricate relationship through • the spatio-temporal evolution of ad-hoc developed network centralities based on the Google PageRank, • multilayer network regression with statistical random graphs respecting net- work structures for explaining urban mobility flows from urban socio-economic attributes, • and Graph Neural Networks for predicting mobility flows to or from a specific location in the city. Making both practical and theoretical contributions to urban science by offering methods for describing, monitoring, explaining, and predicting urban dynamics, this work will thus be aimed at providing a network theoretical framework for developing tools to facilitate better decision-making in urban planning and policy making.

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Su, Jionglong. "Online predictions for spatio-temporal systems using time-varying RBF networks." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.578701.

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In this work. we propose a unified framework called Kalman filter based Radial Basis Functions (KF-RBF) for online functional prediction based on the Radial Basis Functions and the Kalman Filter. The data are nonstationary spatio-ternporal observations irregularly sampled in the spatial domain. We shall assume that a Functional Auto-Regressive (FAR) model is generating the system dynamics. Therefore. to account for the spatial variation. a Radial Basis Function (RBF) network is fitted to the spatial data at every time step. To capture the temporal variation, the regression surfaces arc allowed to change with time. This is achieved by proposing a linear state space model for the RBF weight vectors to evolve temporally. With a fixed functional basis in expressing all regressions. the FAR model call then he re-formulated as a Vector Auto-Regressive (VAR) model embedded in a Kalman Filter. Therefore functional predictions. normally taken place in the Hilbert space. can now be easily implemented 011 a computer. The advantages of our approach are as follows. First it is computationally simple: using the KF. we can obtain the posterior and predictive distributions in closed form. This allows for quick implementation of the model. and provides for full probabilistic inference for the forecasts. Second, the model requires no restrictive assumptions such as stationarity. isotropy or separability of the space/time correlation functions. Third. the method applies to non-lattice data. in which the number and location of sensors can change over time. This framework proposed is further extended by generalizing the real-valued. scalar weights in the functional autoregressive model to operators ill the Reproducing Kernel Hilbert Space (RKHS). This essentially implies that a larger. more intricate class of functions can be represented by this functional autoregressive approach. In other words. the unknown function is expressed as a sum of transformed functions mapped from the past functions in the RKHS. This bigger class of functions can potentially yield a better candidate that is "closer". in the norm sense. to the unknown function. In our research. the KF is used despite the system and observational noise covariance are both unknown. These uncertainties may significantly impact the filter performance. resulting in sub- optimality or divergence. A multiple-model strategy is proposed in view of this. This is motivated by the Interactive Multiple Model (IMM) algorithm in which a collection of filters with different noise characteristics is run in parallel. This strategy avoids the problems associated with the estimation of the noise covariance matrices. Furthermore. it also allows future measurements to be predicted without the assumption of time stationarity of the disturbance terms.

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Sturrock, Marc. "Spatio-temporal modelling of gene regulatory networks containing negative feedback loops." Thesis, University of Dundee, 2013. https://discovery.dundee.ac.uk/en/studentTheses/b824506e-d515-442a-b9dc-ff82568f3c09.

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Holm, Noah, and Emil Plynning. "Spatio-temporal prediction of residential burglaries using convolutional LSTM neural networks." Thesis, KTH, Geoinformatik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-229952.

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The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

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Книги з теми "Spatio temporal networks"

George, Betsy, and Sangho Kim. Spatio-temporal Networks. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4918-8.

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George, Betsy. Spatio-temporal Networks: Modeling and Algorithms. New York, NY: Springer New York, 2013.

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Spatio-temporal narratives: Historical GIS and the study of global trading networks (1500-1800). Newcastle upon Tyne: Cambridge Scholars Publishing, 2014.

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Zhongguo chan ye ji qun shi kong fa zhan yan jiu: Spatio-temporal development study on industrial clusters in China. Beijing Shi: Jing ji guan li chu ban she, 2011.

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Swain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.

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Swain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.

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Swain, Eric D. Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. Tallahassee, Fla: U.S. Dept. of the Interior, U.S. Geological Survey, 1994.

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Benchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.

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Benchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.

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Benchaib, Abdelkrim. Advanced Control of AC / DC Power Networks: System of Systems Approach Based on Spatio-Temporal Scales. Wiley & Sons, Incorporated, John, 2015.

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Частини книг з теми "Spatio temporal networks"

Manganaro, Gabriele, Paolo Arena, and Luigi Fortuna. "Spatio-temporal Phenomena." In Cellular Neural Networks, 105–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/978-3-642-60044-9_5.

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Holan, Scott H., and Christopher K. Wikle. "Semiparametric Dynamic Design of Monitoring Networks for Non-Gaussian Spatio-Temporal Data." In Spatio-Temporal Design, 269–84. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781118441862.ch12.

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George, Betsy, and Sangho Kim. "Spatio-temporal Networks: An Introduction." In SpringerBriefs in Computer Science, 1–6. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4918-8_1.

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Huaien, Luo, and Sadasivan Puthusserypady. "Neural Networks for fMRI Spatio-temporal Analysis." In Neural Information Processing, 1292–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30499-9_201.

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Babloyantz, A., and J. A. Sepulchre. "Information Processing by Spatio-temporal Chaotic Networks." In ICANN ’93, 670–75. London: Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_183.

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Cozza, Vittoria, Antonio Messina, Danilo Montesi, Luca Arietta, and Matteo Magnani. "Spatio-Temporal Keyword Queries in Social Networks." In Advances in Databases and Information Systems, 70–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40683-6_6.

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Gunturi, Viswanath, Shashi Shekhar, and Arnab Bhattacharya. "Minimum Spanning Tree on Spatio-Temporal Networks." In Lecture Notes in Computer Science, 149–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15251-1_11.

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Steer, Kelly, and Joseph G. Vella. "Link Prediction Based on Spatio-Temporal Networks." In Information Systems and Management Science, 228–39. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86223-7_20.

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Cho, Sangwoo, and Hassan Foroosh. "Spatio-Temporal Fusion Networks for Action Recognition." In Computer Vision – ACCV 2018, 347–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20887-5_22.

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Martirosyan, Anahit, and Azzedine Boukerche. "Spatio-temporal Context in Wireless Sensor Networks." In Monographs in Theoretical Computer Science, 293–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14849-1_10.

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Тези доповідей конференцій з теми "Spatio temporal networks"

Khan, Shujaat, Jawwad Ahmad, Alishba Sadiq, Imran Naseem, and Muhammad Moinuddin. "Spatio-Temporal RBF Neural Networks." In 2018 3rd International Conference on Emerging Trends in Engineering, Sciences and Technology (ICEEST). IEEE, 2018. http://dx.doi.org/10.1109/iceest.2018.8643322.

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Rao, Aniruddha Rajendra, Qiyao Wang, Haiyan Wang, Hamed Khorasgani, and Chetan Gupta. "Spatio-Temporal Functional Neural Networks." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00020.

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Herzig, Roei, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, and Trevor Darrell. "Spatio-Temporal Action Graph Networks." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00288.

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Maiti, Rajib, Arobinda Gupta, and Niloy Ganguly. "Delay tolerant networks as spatio-temporal networks." In 2013 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 2013. http://dx.doi.org/10.1109/infcomw.2013.6970737.

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Chanhyun Kang, A. Pugliese, J. Grant, and V. S. Subrahmanian. "STUN: Spatio-Temporal Uncertain (Social) Networks." In 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012). IEEE, 2012. http://dx.doi.org/10.1109/asonam.2012.93.

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Le Van Quyen, M., J. Martinerie, and F. J. Varela. "SPATIO-TEMPORAL DYNAMICS OF EPILEPTOGENIC NETWORKS." In Proceedings of the Workshop. WORLD SCIENTIFIC, 2000. http://dx.doi.org/10.1142/9789812793782_0008.

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Cheng, Zida, Siheng Chen, and Ya Zhang. "Spatio-Temporal Graph Complementary Scattering Networks." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747790.

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Kim, Joongheon, Seunghoon Park, Soyi Jung, and Seehwan Yoo. "Spatio-Temporal Split Learning." In 2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume (DSN-S). IEEE, 2021. http://dx.doi.org/10.1109/dsn-s52858.2021.00016.

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Machireddy, Amrutha, Prayag Gowgi, and Shayan Srinivasa Garani. "Extracting Temporal Correlations Using Hierarchical Spatio-Temporal Feature Maps." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9534337.

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Wu, Haoze, Jiawei Liu, Zheng-Jun Zha, Zhenzhong Chen, and Xiaoyan Sun. "Mutually Reinforced Spatio-Temporal Convolutional Tube for Human Action Recognition." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/136.

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Recent works use 3D convolutional neural networks to explore spatio-temporal information for human action recognition. However, they either ignore the correlation between spatial and temporal features or suffer from high computational cost by spatio-temporal features extraction. In this work, we propose a novel and efficient Mutually Reinforced Spatio-Temporal Convolutional Tube (MRST) for human action recognition. It decomposes 3D inputs into spatial and temporal representations, mutually enhances both of them by exploiting the interaction of spatial and temporal information and selectively emphasizes informative spatial appearance and temporal motion, meanwhile reducing the complexity of structure. Moreover, we design three types of MRSTs according to the different order of spatial and temporal information enhancement, each of which contains a spatio-temporal decomposition unit, a mutually reinforced unit and a spatio-temporal fusion unit. An end-to-end deep network, MRST-Net, is also proposed based on the MRSTs to better explore spatio-temporal information in human actions. Extensive experiments show MRST-Net yields the best performance, compared to state-of-the-art approaches.

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Звіти організацій з теми "Spatio temporal networks"

Shastri, Lokendra. Spatio-Temporal Neural Networks for Vision, Reasoning and Rapid Decision Making. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada299746.

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Burns, Robert T., Nir Keren, Ross Muhlbauer, Hongwei Xin, Steven J. Hoff, and Randy Swestka. Development of a Wireless Sensor Network to Quantify Spatial and Temporal H2S Concentrations in Swine Houses (A Progress Report). Ames (Iowa): Iowa State University, January 2009. http://dx.doi.org/10.31274/ans_air-180814-279.

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Leis, Sherry. Network scale fire atlas supports land management in national parks. Edited by Tani Hubbard. National Park Service, November 2022. http://dx.doi.org/10.36967/2295133.

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Long-term vegetation monitoring allows land managers to make treatment decisions based on data. Fire management is a commonly used approach to managing grasslands, but fire history in grasslands is challenging to record because of spatial and temporal scales and rapid ecosystem recovery. We built a seven-park fire occurrence record (fire atlas) using a geodatabase tool. Multiple sources for fire perimeters were vetted using a verification and editing process. The fire occurrence geodatabase was then used as the basis for an analysis that used buffering around monitoring site locations to determine burned status through time. The resulting products were beneficial for communicating with managers, administrators, and fire staff. Planning and education projects were also important uses of the information. Future efforts will focus on improving attribute consistency and relating vegetation trends to fire occurrence.

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Leis, Sherry, Mike DeBacker, Lloyd Morrison, Gareth Rowell, and Jennifer Haack. Vegetation community monitoring protocol for the Heartland Inventory and Monitoring Network: Narrative, Version 4.0. Edited by Tani Hubbard. National Park Service, November 2022. http://dx.doi.org/10.36967/2294948.

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Native and restored plant communities are part of the foundation of park ecosystems and provide a natural context to cultural and historical events in parks throughout the Heartland Inventory and Monitoring Network (HTLN). Vegetation communities across the HTLN are primarily of three types: prairie, woodland, and forest. Park resource managers need an eﬀective plant community monitoring protocol to guide the development and adaptation of management strategies for maintaining and/or restoring composition and structure of prairies, woodland, and forest communities. Our monitoring design attempts to balance the needs of managers for current information and the need for insight into the changes occurring in vegetation communities over time. This monitoring protocol consists of a protocol narrative (this document) and 18 standard operating procedures (SOPs) for monitoring plant communities in HTLN parks. The scientiﬁc objectives of HTLN plant community monitoring are to (1) describe the species composition, structure, and diversity of prairie, woodland, and forested communities; (2) determine temporal changes in the species composition, structure and diversity of prairie, woodland, and forested communities; and (3) determine the relationship between temporal and spatial changes and environmental variables, including speciﬁc management practices where possible. This protocol narrative describes the sampling design for plant communities, including the response design (data collection methods), spatial design (distribution of sampling sites within a park), and revisit design (timing and frequency of monitoring visits). Details can be found in the SOPs, which are listed in the Revision History section and available at the Integrated Resource Management Applications (IRMA) website (irma.nps.gov). Other aspects of the protocol summarized in the narrative include procedures for data management and reporting, personnel and operating requirements, and instructions for how to revise the protocol.

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Pstuty, Norbert, Mark Duffy, Dennis Skidds, Tanya Silveira, Andrea Habeck, Katherine Ames, and Glenn Liu. Northeast Coastal and Barrier Network Geomorphological Monitoring Protocol: Part I—Ocean Shoreline Position, Version 2. National Park Service, June 2022. http://dx.doi.org/10.36967/2293713.

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Following a review of Vital Signs – indicators of ecosystem health – in the coastal parks of the Northeast Coastal and Barrier Network (NCBN), knowledge of shoreline change was ranked as the top variable for monitoring. Shoreline change is a basic element in the management of any coastal system because it contributes to the understanding of the functioning of the natural resources and to the administration of the cultural resources within the parks. Collection of information on the vectors of change relies on the establishment of a rigorous system of protocols to monitor elements of the coastal geomorphology that are guided by three basic principles: 1) all of the elements in the protocols are to be based on scientific principles; 2) the products of the monitoring must relate to issues of importance to park management; and 3) the application of the protocols must be capable of implementation at the local level within the NCBN. Changes in ocean shoreline position are recognized as interacting with many other elements of the Ocean Beach-Dune Ecosystem and are thus both driving and responding to the variety of natural and cultural factors active at the coast at a variety of temporal and spatial scales. The direction and magnitude of shoreline change can be monitored through the application of a protocol that tracks the spatial position of the neap-tide, high tide swash line under well-defined conditions of temporal sampling. Spring and fall surveys conducted in accordance with standard operating procedures will generate consistent and comparable shoreline position data sets that can be incorporated within a data matrix and subsequently analyzed for temporal and spatial variations. The Ocean Shoreline Position Monitoring Protocol will be applied to six parks in the NCBN: Assateague Island National Seashore, Cape Cod National Seashore, Fire Island National Seashore, Gateway National Recreation Area, George Washington Birthplace National Monument, and Sagamore Hill National Historic Site. Monitoring will be accomplished with a Global Positioning System (GPS )/ Global Navigation Satellite System (GNSS) unit capable of sub-meter horizontal accuracy that is usually mounted on an off-road vehicle and driven along the swash line. Under the guidance of a set of Standard Operating Procedures (SOPs) (Psuty et al., 2022), the monitoring will generate comparable data sets. The protocol will produce shoreline change metrics following the methodology of the Digital Shoreline Analysis System developed by the United States Geological Survey. Annual Data Summaries and Trend Reports will present and analyze the collected data sets. All collected data will undergo rigorous quality-assurance and quality-control procedures and will be archived at the offices of the NCBN. All monitoring products will be made available via the National Park Service’s Integrated Resource Management Applications Portal.

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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.

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Spatial and temporal statistical analysis of a ground-water level network, Broward County, Florida. US Geological Survey, 1994. http://dx.doi.org/10.3133/wri944076.

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