Spatial Hierarchies and Topological Relationships in the Spatial MultiDimER Model

2005 ◽  
pp. 17-28 ◽  
Author(s):  
Elzbieta Malinowski ◽  
Esteban Zimányi
Keyword(s):  
Topological Relationships

scholarly journals Animal, Body, Data: Starling Murmurations and the Dynamic of Becoming In-formation

2021 ◽  
pp. 1357034X2199284
Author(s):  
Mickey Vallee
Keyword(s):  
Data Modelling ◽  
Animal Body ◽  
Topological Relationships ◽  
Animal Populations ◽  
Animal Data ◽  
Natural Scientists ◽  
The Relationship ◽  
Affective Relations

The aim of this article is to demonstrate that data modelling is becoming a crucial, if not dominant, vector for our understanding of animal populations and is consequential for how we study the affective relations between individual bodies and the communities to which they belong. It takes up the relationship between animal, body and data, following the datafication of starling murmurations, to explore the topological relationships between nature, culture and science. The case study thus embodies a data journey, invoking the tactics claimed by social or natural scientists, who generated recent discoveries in starling murmurations, including their topological expansions and contractions. The article concludes with thoughts and suggestions for further research on animal/data entanglement, and threads the concept of databodiment throughout, as a necessary dynamic for the formation and maintenance of communities.

scholarly journals Distortion Effects in Equal Area Unit Maps

2021 ◽  
Author(s):  
Jochen Schiewe
Keyword(s):  
Hot Spots ◽  
Extreme Values ◽  
Generation Process ◽  
Equal Area ◽  
Topological Relationships ◽  
Area Unit ◽  
Task Oriented ◽  
Local Topology ◽  
Selection Of ◽  
Local Extreme

AbstractMaps that correctly represent the geographic size and shape of regions, taking into account scaling and generalization, have the disadvantage that small regions can easily be overlooked or not seen at all. Hence, for some map use tasks where small regions are of importance, alternative map types are needed. One option is the so-called equal area unit maps (EAUMs), where every enumeration unit has the same area size, possibly also the same basic shape such as squares or hexagons. The geometrical distortion of EAUMs, however, leads to a more difficult search for regions as well as a falsification of topological relationships and spatial patterns. To describe these distortions, a set of analytical measures is proposed. But it turns out that the expressiveness of these measures is rather limited. To better understand and to model the influence of distortions, two user studies were conducted. The study on the search in EAUMs (also with the aim of reconstruct the search strategies of the users) revealed how important it is to consider the local topology (e.g. corner or border positions of regions) during the generation process. With regard to pattern identification, it could be shown that EAUMs significantly increase the detection rate of local extreme values. On the other hand, global lateral gradients or geostatistical hot spots often get blurred or even lost. As a consequence, a task-oriented selection of map types and further developments are recommended.

scholarly journals Analysis of topological relationships and network properties in the interactions of human beings

PLoS ONE ◽  
2017 ◽  
Vol 12 (8) ◽  
pp. e0183686 ◽  
Author(s):  
Ye Yuan ◽  
Xuebo Chen ◽  
Qiubai Sun ◽  
Tianyun Huang
Keyword(s):  
Human Beings ◽  
Topological Relationships ◽  
Network Properties

Attention-based Graph ResNet with focal loss for epileptic seizure detection

2021 ◽  
pp. 1-13
Author(s):  
Changxu Dong ◽  
Yanna Zhao ◽  
Gaobo Zhang ◽  
Mingrui Xue ◽  
Dengyu Chu ◽  
...  
Keyword(s):  
Class Imbalance ◽  
Seizure Detection ◽  
Brain Regions ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Topological Relationships ◽  
Average Accuracy ◽  
Proposed Model ◽  
Eeg Data ◽  
The Central Nervous System

Epilepsy is a chronic brain disease resulted from the central nervous system lesion, which leads to repeated seizure occurs for the patients. Automatic seizure detection with Electroencephalogram (EEG) has witnessed great progress. However, existing methods paid little attention to the topological relationships of different EEG electrodes. Latest neuroscience researches have demonstrated the connectivity between different brain regions. Besides, class-imbalance is a common problem in EEG based seizure detection. The duration of epileptic EEG signals is much shorter than that of normal signals. In order to deal with the above mentioned two challenges, we propose to model the multi-channel EEG data using the Attention-based Graph ResNet (AGRN). In particular, each channel of the EEG signal represents a node of the graph and the inter-channel relations are modeled via the adjacency matrix in the graph. The loss function of the ARGN model is re-designed using focal loss to cope with the class-imbalance problem. The proposed ARGN with focal model could learn discriminative features from the raw EEG data. Experiments are carried out on the CHB-MIT dataset. The proposed model achieves an average accuracy of 98.70%, a sensitivity of 97.94%, a specificity of 98.66% and a precision of 98.62%. The Area Under the ROC Curve (AUC) is 98.69%.

Three-Dimensional Information Retrieval (3DIR)

2018 ◽  
pp. 1016-1029
Author(s):  
Peter Demian ◽  
Kirti Ruikar ◽  
Tarun Sahu ◽  
Anne Morris
Keyword(s):  
Information Retrieval ◽  
3D Visualization ◽  
Three Dimensional ◽  
Relevant Information ◽  
Tight Coupling ◽  
Care Needs ◽  
3D Objects ◽  
Topological Relationships ◽  
3D Geometry ◽  
Other Information

An increasing amount of information is packed into BIMs, with the 3D geometry serving as a central index leading to other information. The 3DIR project investigates information retrieval from such environments. Here, the 3D visualization can be exploited when formulating queries, computing the relevance of information items, or visualizing search results. The need for such a system was specified using workshops with end users. A prototype was built on a commercial BIM platform. Following an evaluation, the system was enhanced to exploit model topology. Relationships between 3D objects are used to widen the search, whereby relevant information items linked to a related 3D object (rather than linked directly to objects selected by the user) are still retrieved but ranked lower. An evaluation of the enhanced prototype demonstrates its effectiveness but highlights its added complexity. Care needs to be taken when exploiting topological relationships, but that a tight coupling between text-based retrieval and the 3D model is generally effective in information retrieval from BIMs.

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