scholarly journals A Global Geographic Grid System for Visualizing Bathymetry

2020 ◽  
Author(s):  
Colin Ware ◽  
Larry Mayer ◽  
Paul Johnson ◽  
Martin Jakobsson ◽  
Vicki Ferrini
Keyword(s):  
Grid Cell ◽  
Grid System ◽  
Proof Of Concept ◽  
Data Grids ◽  
Important Goal ◽  
High Resolution Data ◽  
Bathymetric Data ◽  
Quad Tree ◽  
Software Packages ◽  
Coarse Grids

Abstract. A Global Geographic Grid System (Global GGS) is here introduced to support the display of gridded bathymetric data at whatever resolution is available in a visually seamless manner. The Global GGS combines a quad-tree metagrid hierarchy with a system of compatible data grids. Metagrid nodes define the boundaries of data grids. Data grids are regular grids of depth values, coarse grids are used to represent sparse data and finer grids are used to represent high resolution data. Both metagrids and data grids are defined in geographic coordinates to allow broad compatibility with the widest range of geospatial software packages. An important goal of the Global GGS is to support the meshing of adjacent tiles with different resolutions so as to create a seamless surface. This is accomplished by ensuring that abutting data grids either match exactly with respect to their grid-cell size or only differ by powers of two. The oversampling of geographic data grids, which occurs towards the poles due to the convergence of meridians, is addressed by reducing the number of columns (longitude sampling) by powers of two at appropriate lines of latitude. In addition to the specification of the Global GGS. This paper describes a proof-of-concept implementation and some possible variants.

scholarly journals A global geographic grid system for visualizing bathymetry

2020 ◽  
Vol 9 (2) ◽  
pp. 375-384
Author(s):  
Colin Ware ◽  
Larry Mayer ◽  
Paul Johnson ◽  
Martin Jakobsson ◽  
Vicki Ferrini
Keyword(s):  
Grid Cell ◽  
Grid System ◽  
Proof Of Concept ◽  
Data Grids ◽  
Important Goal ◽  
High Resolution Data ◽  
Bathymetric Data ◽  
Software Packages ◽  
Powers Of 2 ◽  
Coarse Grids

Abstract. A global geographic grid system (Global GGS) is here introduced to support the display of gridded bathymetric data at whatever resolution is available in a visually seamless manner. The Global GGS combines a quadtree metagrid hierarchy with a system of compatible data grids. Metagrid nodes define the boundaries of data grids. Data grids are regular grids of depth values, coarse grids are used to represent sparse data and finer grids are used to represent high-resolution data. Both metagrids and data grids are defined in geographic coordinates to allow broad compatibility with the widest range of geospatial software packages. An important goal of the Global GGS is to support the meshing of adjacent tiles with different resolutions so as to create a seamless surface. This is accomplished by ensuring that abutting data grids either match exactly with respect to their grid-cell size or only differ by powers of 2. The oversampling of geographic data grids, which occurs towards the poles due to the convergence of meridians, is addressed by reducing the number of columns (longitude sampling) by powers of 2 at appropriate lines of latitude. In addition to the specification of the Global GGS, this paper describes a proof-of-concept implementation and some possible variants.

scholarly journals Optimal configurations of spatial scale for grid cell firing under noise and uncertainty

2014 ◽  
Vol 369 (1635) ◽  
pp. 20130290 ◽  
Author(s):  
Benjamin W. Towse ◽  
Caswell Barry ◽  
Daniel Bush ◽  
Neil Burgess
Keyword(s):  
Spatial Scale ◽  
Spatial Scales ◽  
Grid Cell ◽  
Spatial Uncertainty ◽  
Grid System ◽  
Grid Cells ◽  
Spatial Cues ◽  
Firing Patterns ◽  
Wide Range ◽  
Spatial Noise

We examined the accuracy with which the location of an agent moving within an environment could be decoded from the simulated firing of systems of grid cells. Grid cells were modelled with Poisson spiking dynamics and organized into multiple ‘modules’ of cells, with firing patterns of similar spatial scale within modules and a wide range of spatial scales across modules. The number of grid cells per module, the spatial scaling factor between modules and the size of the environment were varied. Errors in decoded location can take two forms: small errors of precision and larger errors resulting from ambiguity in decoding periodic firing patterns. With enough cells per module (e.g. eight modules of 100 cells each) grid systems are highly robust to ambiguity errors, even over ranges much larger than the largest grid scale (e.g. over a 500 m range when the maximum grid scale is 264 cm). Results did not depend strongly on the precise organization of scales across modules (geometric, co-prime or random). However, independent spatial noise across modules, which would occur if modules receive independent spatial inputs and might increase with spatial uncertainty, dramatically degrades the performance of the grid system. This effect of spatial uncertainty can be mitigated by uniform expansion of grid scales. Thus, in the realistic regimes simulated here, the optimal overall scale for a grid system represents a trade-off between minimizing spatial uncertainty (requiring large scales) and maximizing precision (requiring small scales). Within this view, the temporary expansion of grid scales observed in novel environments may be an optimal response to increased spatial uncertainty induced by the unfamiliarity of the available spatial cues.

scholarly journals Environmental anchoring of grid-like representations minimizes spatial uncertainty during navigation

2017 ◽  
Author(s):  
Tobias Navarro Schröder ◽  
Benjamin W. Towse ◽  
Matthias Nau ◽  
Neil Burgess ◽  
Caswell Barry ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Motion Parallax ◽  
Grid Cell ◽  
Cell System ◽  
Neural Mechanisms ◽  
Spatial Uncertainty ◽  
Grid System ◽  
Virtual Navigation ◽  
Human Participants

SummaryMinimizing spatial uncertainty is essential for navigation, but the neural mechanisms remain elusive. Here we combine predictions of a simulated grid cell system with behavioural and fMRI measures in humans during virtual navigation. First, we showed that polarising cues produce anisotropy in motion parallax. Secondly, we simulated entorhinal grid cells in an environment with anisotropic information and found that self-location is decoded best when grid-patterns are aligned with the axis of greatest information. Thirdly, when exposing human participants to polarised virtual reality environments, we found that navigation performance is anisotropic, in line with the use of parallax. Eye movements showed that participants preferentially viewed polarising cues, which correlated with navigation performance. Finally, using fMRI we found that the orientation of grid-cell-like representations in entorhinal cortex anchored to the environmental axis of greatest parallax information, orthogonal to the polarisation axis. In sum, we demonstrate a crucial role of the entorhinal grid system in reducing uncertainty in representations of self-location and find evidence for adaptive spatial computations underlying entorhinal representations in service of optimal navigation.

scholarly journals Analyzing genomic data using tensor-based orthogonal polynomials with application to synthetic RNAs

2020 ◽  
Vol 2 (4) ◽  
Author(s):  
Saba Nafees ◽  
Sean H Rice ◽  
Catherine A Wakeman
Keyword(s):  
Sequence Space ◽  
Functional Relationship ◽  
Genomic Sequence ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Strong Correlations ◽  
Important Goal ◽  
Order Analysis ◽  
Command Line Tool ◽  
The Relationship

Abstract An important goal in molecular biology is to quantify both the patterns across a genomic sequence and the relationship between phenotype and underlying sequence. We propose a multivariate tensor-based orthogonal polynomial approach to characterize nucleotides or amino acids in a given sequence and map corresponding phenotypes onto the sequence space. We have applied this method to a previously published case of small transcription activating RNAs. Covariance patterns along the sequence showcased strong correlations between nucleotides at the ends of the sequence. However, when the phenotype is projected onto the sequence space, this pattern does not emerge. When doing second order analysis and quantifying the functional relationship between the phenotype and pairs of sites along the sequence, we identified sites with high regressions spread across the sequence, indicating potential intramolecular binding. In addition to quantifying interactions between different parts of a sequence, the method quantifies sequence–phenotype interactions at first and higher order levels. We discuss the strengths and constraints of the method and compare it to computational methods such as machine learning approaches. An accompanying command line tool to compute these polynomials is provided. We show proof of concept of this approach and demonstrate its potential application to other biological systems.

scholarly journals Charting the Course for Future Developments in Marine Geomorphometry: An Introduction to the Special Issue

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 477 ◽  
Author(s):  
Vanessa Lucieer ◽  
Vincent Lecours ◽  
Margaret Dolan
Keyword(s):  
Multiple Scales ◽  
Analytical Techniques ◽  
Resource Assessment ◽  
Special Issue ◽  
Ecological Processes ◽  
High Resolution Data ◽  
Community Needs ◽  
Bathymetric Data ◽  
Novel Technology ◽  
Seabed Mapping

The use of spatial analytical techniques for describing and classifying seafloor terrain has become increasingly widespread in recent years, facilitated by a combination of improved mapping technologies and computer power and the common use of Geographic Information Systems. Considering that the seafloor represents 71% of the surface of our planet, this is an important step towards understanding the Earth in its entirety. Bathymetric mapping systems, spanning a variety of sensors, have now developed to a point where the data they provide are able to capture seabed morphology at multiple scales, opening up the possibility of linking these data to oceanic, geological, and ecological processes. Applications of marine geomorphometry have now moved beyond the simple adoption of techniques developed for terrestrial studies. Whilst some former challenges have been largely resolved, we find new challenges constantly emerging from novel technology and applications. As increasing volumes of bathymetric data are acquired across the entire ocean floor at scales relevant to marine geosciences, resource assessment, and biodiversity evaluation, the scientific community needs to balance the influx of high-resolution data with robust quantitative processing and analysis techniques. This will allow marine geomorphometry to become more widely recognized as a sub-discipline of geomorphometry as well as to begin to tread its own path to meet the specific challenges that are associated with seabed mapping. This special issue brings together a collection of research articles that reflect the types of studies that are helping to chart the course for the future of marine geomorphometry.

scholarly journals Analyzing Genomic Data Using Tensor-Based Orthogonal Polynomials with Application to Synthetic RNAs

2020 ◽  
Author(s):  
Saba Nafees ◽  
Sean H. Rice ◽  
Catherine A. Wakeman
Keyword(s):  
Sequence Space ◽  
Functional Relationship ◽  
Genomic Sequence ◽  
Learning Approaches ◽  
Proof Of Concept ◽  
Strong Correlations ◽  
Important Goal ◽  
Order Analysis ◽  
Command Line Tool ◽  
The Relationship

ABSTRACTAn important goal in molecular biology is to quantify both the patterns across a genomic sequence and the relationship between phenotype and underlying sequence. We propose a multivariate tensor-based orthogonal polynomial approach to characterize nucleotides or amino acids in a given sequence and map corresponding phenotypes onto the sequence space. We have applied this method to a previously published case of small transcription activating RNAs (STARs). Covariance patterns along the sequence showcased strong correlations between nucleotides at the ends of the sequence. However, when the phenotype is projected onto the sequence space, this pattern doesn’t emerge. When doing second order analysis and quantifying the functional relationship between the phenotype and pairs of sites along the sequence, we identified sites with high regressions spread across the sequence, indicating potential intramolecular binding. In addition to quantifying interactions between different parts of a sequence, the method quantifies sequence-phenotype interactions at first and higher order levels. We discuss the strengths and constraints of the method and compare it to computational methods such as machine learning approaches. An accompanying command line tool to compute these polynomials is provided. We show proof of concept of this approach and demonstrate its potential application to other biological systems.

scholarly journals Complete coverage of space favors modularity of the grid system in the brain

2016 ◽  
Author(s):  
A. Sanzeni ◽  
V. Balasubramanian ◽  
G. Tiana ◽  
M. Vergassola
Keyword(s):  
Statistical Physics ◽  
Physical Space ◽  
Grid Cell ◽  
Triangular Grid ◽  
Grid System ◽  
Grid Cells ◽  
Scaling Relation ◽  
Complete Coverage ◽  
Space Experiments ◽  
The Brain

Grid cells in the entorhinal cortex fire when animals that are exploring a certain region of space occupy the vertices of a triangular grid that spans the environment. Different neurons feature triangular grids that differ in their properties of periodicity, orientation and ellipticity. Taken together, these grids allow the animal to maintain an internal, mental representation of physical space. Experiments show that grid cells are modular, i.e. there are groups of neurons which have grids with similar periodicity, orientation and ellipticity. We use statistical physics methods to derive a relation between variability of the properties of the grids within a module and the range of space that can be covered completely (i.e. without gaps) by the grid system with high probability. Larger variability shrinks the range of representation, providing a functional rationale for the experimentally observed co-modularity of grid cell periodicity, orientation and ellipticity. We obtain a scaling relation between the number of neurons and the period of a module, given the variability and coverage range. Specifically, we predict how many more neurons are required at smaller grid scales than at larger ones.

A CLIENT-SERVER PROTOTYPE FOR GRID-ENABLING APPLICATION TEMPLATE DESIGN

2004 ◽  
Vol 14 (02) ◽  
pp. 241-253 ◽  
Author(s):  
MARK L. GREEN ◽  
RUSS MILLER
Keyword(s):  
New York ◽  
Structural Biology ◽  
Storage Systems ◽  
Grid System ◽  
Proof Of Concept ◽  
Scientific Application ◽  
Client Server ◽  
Computational Research ◽  
Template Design ◽  
And Storage

A computational and data grid was developed at the Center for Computational Research in Buffalo, New York, in order to provide a platform to support scientific and engineering applications across a variety of computer and storage systems. This proof-of-concept grid has been deployed using a critical scientific application in the field of structural biology. The design and functionality of the prototype grid is described, along with plans for a production level grid system based on Globus.

scholarly journals A Universal Generating Algorithm of the Polyhedral Discrete Grid Based on Unit Duplication

2019 ◽  
Vol 8 (3) ◽  
pp. 146 ◽  
Author(s):  
Li Meng ◽  
Xiaochong Tong ◽  
Shuaibo Fan ◽  
Chengqi Cheng ◽  
Bo Chen ◽  
...  
Keyword(s):  
Coordinate System ◽  
Grid Cell ◽  
Triangular Grid ◽  
Grid System ◽  
Rectangular Coordinate System ◽  
Factors Affecting ◽  
Grid Systems ◽  
Starting Point ◽  
Rectangular Coordinates ◽  
Discrete Grid

Based on the analysis of the problems in the generation algorithm of discrete grid systems domestically and abroad, a new universal algorithm for the unit duplication of a polyhedral discrete grid is proposed, and its core is “simple unit replication + effective region restriction”. First, the grid coordinate system and the corresponding spatial rectangular coordinate system are established to determine the rectangular coordinates of any grid cell node. Then, the type of the subdivision grid system to be calculated is determined to identify the three key factors affecting the grid types, which are the position of the starting point, the length of the starting edge, and the direction of the starting edge. On this basis, the effective boundary of a multiscale grid can be determined and the grid coordinates of a multiscale grid can be obtained. A one-to-one correspondence between the multiscale grids and subdivision types can be established. Through the appropriate rotation, translation and scaling of the multiscale grid, the node coordinates of a single triangular grid system are calculated, and the relationships between the nodes of different levels are established. Finally, this paper takes a hexagonal grid as an example to carry out the experiment verifications by converting a single triangular grid system (plane) directly to a single triangular grid with a positive icosahedral surface to generate a positive icosahedral surface grid. The experimental results show that the algorithm has good universality and can generate the multiscale grid of an arbitrary grid configuration by adjusting the corresponding starting transformation parameters.

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