scholarly journals Spatio‐temporal relationships between rainfall and convective clouds during Indian monsoon through a discrete lens

2020 ◽  
Author(s):  
Arjun Sharma ◽  
Adway Mitra ◽  
Vishal Vasan ◽  
Rama Govindarajan
Keyword(s):  
Indian Monsoon ◽  
Convective Clouds ◽  
Temporal Relationships ◽  
Spatio Temporal

scholarly journals Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets

2021 ◽  
Author(s):  
Maria Lucia Parrella ◽  
Giuseppina Albano ◽  
Cira Perna ◽  
Michele La Rocca
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Sample Selection ◽  
Sample Path ◽  
Temporal Relationships ◽  
Empirical Performance ◽  
Spatio Temporal ◽  
Joint Prediction ◽  
Point Forecast ◽  
Prediction Regions

AbstractMissing data reconstruction is a critical step in the analysis and mining of spatio-temporal data. However, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. To take into account the uncertainty in the point forecast, some prediction intervals may be of interest. In particular, for (possibly long) missing sequences of consecutive time points, joint prediction regions are desirable. In this paper we propose a bootstrap resampling scheme to construct joint prediction regions that approximately contain missing paths of a time components in a spatio-temporal framework, with global probability $$1-\alpha $$ 1 - α . In many applications, considering the coverage of the whole missing sample-path might appear too restrictive. To perceive more informative inference, we also derive smaller joint prediction regions that only contain all elements of missing paths up to a small number k of them with probability $$1-\alpha $$ 1 - α . A simulation experiment is performed to validate the empirical performance of the proposed joint bootstrap prediction and to compare it with some alternative procedures based on a simple nominal coverage correction, loosely inspired by the Bonferroni approach, which are expected to work well standard scenarios.

Patterns of Cognitive Test Performance As Functions of the Lateral Localization of Cerebral Abnormalities in the Temporal Lobe

1957 ◽  
Vol 103 (433) ◽  
pp. 758-772 ◽  
Author(s):  
Victor Meyer ◽  
H. Gwynne Jones
Keyword(s):  
Test Performance ◽  
Right Hemisphere ◽  
Cognitive Test ◽  
Temporal Relationships ◽  
Verbal Tasks ◽  
Specific Locus ◽  
Left And Right ◽  
Spatio Temporal ◽  
Cognitive Test Performance ◽  
Lateral Localization

Various investigations into the effects of brain injury on psychological test performance (Weisenburg and McBride, 1935; Patterson and Zangwill, 1944; Anderson, 1951; McFie and Piercy, 1952; Bauer and Becka, 1954; Milner, 1954) suggest the overall conclusion that patients with left hemisphere lesions are relatively poor at verbal tasks, while those with right-sided lesions do worst at practical tasks, particularly the manipulation of spatial or spatio-temporal relationships. Heilbfun's (1956) study confirmed that verbal deficits result from left-sided lesions but his left and right hemisphere groups produced almost identical scores on spatial tests. In so far as these workers paid attention to the specific sites of the lesions, their findings indicate that the pattern of test performance is a function of the hemisphere in which the lesion occurs rather than of its specific locus.

Multimedia Databases and Data Management

2012 ◽  
pp. 1-11
Author(s):  
Shu-Ching Chen
Keyword(s):  
Data Management ◽  
Relevant Information ◽  
Multimedia Databases ◽  
Multimedia Data ◽  
Formal Structure ◽  
Multimedia Database ◽  
Storage Devices ◽  
Temporal Relationships ◽  
Spatio Temporal ◽  
Application Requirements

The exponential growth of the technological advancements has resulted in high-resolution devices, such as digital cameras, scanners, monitors, and printers, which enable the capturing and displaying of multimedia data in high-density storage devices. Furthermore, more and more applications need to live with multimedia data. However, the gap between the characteristics of various media types and the application requirements has created the need to develop advanced techniques for multimedia data management and the extraction of relevant information from multimedia databases. Though many research efforts have been devoted to the areas of multimedia databases and data management, it is still far from maturity. The purpose of this article is to discuss how the existing techniques, methodologies, and tools addressed relevant issues and challenges to enable a better understanding in multimedia databases and data management. The focuses include: (1) how to develop a formal structure that can be used to capture the distinguishing content of the media data in a multimedia database (MMDB) and to form an abstract space for the data to be queried; (2) how to develop advanced content analysis and retrieval techniques that can be used to bridge the gaps between the semantic meaning and low-level media characteristics to improve multimedia information retrieval; and (3) how to develop query mechanisms that can handle complex spatial, temporal, and/or spatio-temporal relationships of multimedia data to answer the imprecise and incomplete queries issued to an MMDB.

scholarly journals A Novel Approach for the Detection of Developing Thunderstorm Cells

2019 ◽  
Vol 11 (4) ◽  
pp. 443 ◽  
Author(s):  
Richard Müller ◽  
Stéphane Haussler ◽  
Matthias Jerg ◽  
Dirk Heizenreder
Keyword(s):  
Water Vapor ◽  
Main Idea ◽  
Probability Of Detection ◽  
Base Case ◽  
Convective Clouds ◽  
Critical Success ◽  
Novel Approach ◽  
Forecast Lead Time ◽  
Spatio Temporal ◽  
Atmospheric Motion Vectors

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

RST: A Connectionist Architecture to Deal with Spatiotemporal Relationships

1998 ◽  
Vol 10 (4) ◽  
pp. 883-902 ◽  
Author(s):  
J.-C. Chappelier ◽  
A. Grumbach
Keyword(s):  
Three Dimensional ◽  
Video Images ◽  
Temporal Relationships ◽  
Leaky Integrator ◽  
Spatial Aspect ◽  
Actual Space ◽  
Temporal Aspect ◽  
Static Pattern ◽  
Spatio Temporal ◽  
Detection And Localization

In the past decade, connectionism has proved its efficiency in the field of static pattern recognition. The next challenge is to deal with spatiotemporal problems. This article presents a new connectionist architecture, RST (ŕeseau spatio temporel [spatio temporal network]), with such spatiotemporal capacities. It aims at taking into account at the architecture level both spatial relationships (e.g., as between neighboring pixels in an image) and temporal relationships (e.g., as between consecutive images in a video sequence). Concerning the spatial aspect, the network is embedded in actual space (two-or three-dimensional), the metrics of which directly influence its structure through a connection distribution function. For the temporal aspect, we looked toward biology and used a leaky-integrator neuron model with a refractory period and postsynaptic potentials. The propagation of activity by spatiotemporal synchronized waves enables RST to perform motion detection and localization in sequences of video images.

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