A Data Structure for Real-Time Aggregation Queries of Big Brain Networks

AbstractRecent advances in neuro-imaging allowed big brain-initiatives and consortia to create vast resources of brain data that can be mined by researchers for their individual projects. Exploring the relationship between genes, brain circuitry, and behavior is one of key elements of neuroscience research. This requires fusion of spatial connectivity data at varying scales, such as whole brain correlated gene expression, structural and functional connectivity. With ever-increasing resolution, those exceed the past state-of-the art in several orders of magnitude in size and complexity. Current analytical workflows in neuroscience involve time-consuming manual aggregation of the data and only sparsely incorporate spatial context to operate continuously on multiple scales. Incorporating techniques for handling big connectivity data is therefore a necessity.We propose a data structure to explore heterogeneous neurobiological connectivity data for integrated visual analytics workflows. Aggregation Queries, i.e. the aggregated connectivity from, to or between brain areas allow experts the comparison of multimodal networks residing at different scales, or levels of hierarchically organized anatomical atlases. Executed on-demand on volumetric gene expression and connectivity data, they enable an interactive dissection of networks, with billions of edges, in real-time, and based on their spatial context. The data structure is optimized to be accessed directly from the hard disk, since connectivity of large-scale networks typically exceed the memory size of current consumer level PCs. This allows experts to embed and explore their own experimental data in the framework of public data resources without large-scale infrastructure.Our novel data structure outperforms state-of-the-art graph engines in retrieving connectivity of local brain areas experimentally. We demonstrate the application of our approach for neuroscience by analyzing fear-related functional neuroanatomy in mice. Further, we show its versatility by comparing multimodal brain networks linked to autism. Importantly, we achieve cross-species congruence in retrieving human psychiatric traits networks, which facilitates selection of neural substrates to be further studied in mouse models.

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Graph-Based Construction of 3D Korean Giwa House Models

Buildings ◽

10.3390/buildings9030068 ◽

2019 ◽

Vol 9 (3) ◽

pp. 68

Author(s):

Mankyu Sung

Keyword(s):

Data Structure ◽

Computer Graphics ◽

Real Time ◽

Texture Image ◽

Time Rate ◽

Commercial Software ◽

Spline Curve ◽

High Level ◽

Complicated Part ◽

Different Parts

This paper proposes a graph-based algorithm for constructing 3D Korean traditional houses automatically using a computer graphics technique. In particular, we target designing the most popular traditional house type, a giwa house, whose roof is covered with a set of Korean traditional roof tiles called giwa. In our approach, we divided the whole design processes into two different parts. At a high level, we propose a special data structure called ‘modeling graphs’. A modeling graph consists of a set of nodes and edges. A node represents a particular component of the house and an edge represents the connection between two components with all associated parameters, including an offset vector between components. Users can easily add/ delete nodes and make them connect by an edge through a few mouse clicks. Once a modeling graph is built, then it is interpreted and rendered on a component-by-component basis by traversing nodes in a procedural way. At a low level, we came up with all the required parameters for constructing the components. Among all the components, the most beautiful but complicated part is the gently curved roof structures. In order to represent the sophisticated roof style, we introduce a spline curve-based modeling technique that is able to create curvy silhouettes of three different roof styles. In this process, rather than just applying a simple texture image onto the roof, which is widely used in commercial software, we actually laid out 3D giwa tiles on the roof seamlessly, which generated more realistic looks. Through many experiments, we verified that the proposed algorithm can model and render the giwa house at a real time rate.

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A Method for Real-Time Aggregation of a Product Footprint During Manufacturing

Journal of Sustainable Development of Energy Water and Environment Systems ◽

10.13044/j.sdewes.2016.04.0028 ◽

2016 ◽

Vol 4 (4) ◽

pp. 360-378 ◽

Cited By ~ 1

Author(s):

Peter Smolek ◽

Ines Leobner ◽

Bernhard Heinzl ◽

Georgios Gourlis ◽

Karl Ponweiser

Keyword(s):

Real Time ◽

Time Aggregation

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GeoTree: A Data Structure for Constant Time Geospatial Search Enabling a Real-Time Property Index

Lecture Notes in Networks and Systems - Intelligent Computing ◽

10.1007/978-3-030-80126-7_12 ◽

2021 ◽

pp. 152-165

Author(s):

Robert Miller ◽

Phil Maguire

Keyword(s):

Data Structure ◽

Real Time ◽

Constant Time ◽

Time Property ◽

Property Index

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Advances in Electrical Neuroimaging, Brain Networks and Neurofeedback Protocols

Smart Biofeedback - Perspectives and Applications ◽

10.5772/intechopen.94326 ◽

2020 ◽

Author(s):

Robert W. Thatcher ◽

Carl J. Biver ◽

Ernesto Palermero Soler ◽

Joel Lubar ◽

J. Lucas Koberda

Keyword(s):

Human Brain ◽

Real Time ◽

Brain Mapping ◽

Brain Networks ◽

Three Dimensional ◽

Brain Network ◽

Eeg Biofeedback ◽

Reference Database ◽

Z Score ◽

Electrical Neuroimaging

Human EEG biofeedback (neurofeedback) started in the 1940s using 1 EEG recording channel, then to 4 channels in the 1990s. New advancements in electrical neuroimaging expanded EEG biofeedback to 19 channels using Low Resolution Electromagnetic Tomography (LORETA) three-dimensional current sources of the EEG. In 2004–2006 the concept of a “real-time” comparison of the EEG to a healthy reference database was developed and tested using surface EEG z-score neurofeedback based on a statistical bell curve called “real-time” z-scores. The “real-time” or “live” normative reference database comparison was developed to help reduce the uncertainty of what threshold to select to activate a feedback signal and to unify all EEG measures to a single value, i.e., the distance from the mean of an age matched reference sample. In 2009 LORETA z-score neurofeedback further increased the specificity by targeting brain network hubs referred to as Brodmann areas. A symptom check list program to help link symptoms to dysregulation of brain networks based on fMRI and PET and neurology was created in 2009. The symptom checklist and NIH based networks linking symptoms to brain networks grew out of the human brain mapping program starting in 1990 which is continuing today. A goal is to increase specificity of EEG biofeedback by targeting brain network hubs and connections between hubs likely linked to the patient’s symptoms. New advancements in electrical neuroimaging introduced in 2017 provide increased resolution of three-dimensional source localization with 12,700 voxels using swLORETA with the capacity to conduct cerebellar neurofeedback and neurofeedback of subcortical brain hubs such as the thalamus, amygdala and habenula. Future applications of swLORETA z-score neurofeedback represents another example of the transfer of knowledge gained by the human brain mapping initiatives to further aid in helping people with cognition problems as well as balance problems and parkinsonism. A brief review of the past, present and future predictions of z-score neurofeedback are discussed with special emphasis on new developments that point toward a bright and enlightened future in the field of EEG biofeedback.

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Design and Implementation of Digital Television Closed Caption

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.667.201 ◽

2014 ◽

Vol 667 ◽

pp. 201-207

Author(s):

Qi Deng ◽

Pin Xu ◽

Shao Bin Li ◽

Xin Yao Zhang

Keyword(s):

Data Structure ◽

Real Time ◽

Xml Schema ◽

Digital Television ◽

Television System ◽

The Real ◽

Design And Implementation ◽

Technology Standard ◽

Real Time Transmission ◽

Representation Method

In China, closed caption is the latest technology standard separating the caption from the image. In current digital television system, it is an urgent issue to be resolved that how to implement the real-time transmission of closed caption with its host video. This paper proposed a kind of closed caption and its representation method. It also designed a closed caption data structure based on XML and provided a solution about how to package XML file and XML Schema file into MXF files.

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A Load Balancing Mechanism Using Bloom Filter in Storm System

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.1972 ◽

2014 ◽

Vol 543-547 ◽

pp. 1972-1976

Author(s):

Huai Lin Dong ◽

Ming Yuan He ◽

Qing Feng Wu ◽

Sheng Hang Wu

Keyword(s):

Data Structure ◽

Load Balancing ◽

Real Time ◽

Data Transmission ◽

Streaming Media ◽

Bloom Filter ◽

Time System ◽

Real Time System ◽

Membership Queries ◽

Probabilistic Data Structure

When membership queries are evaluated in a set, the performance can be improved by a Bloom filter which is a space-efficient probabilistic data structure. According to its space-efficient character, Bloom Filter presented to address the load balancing problem for streaming media information in Storm system which is free and open source distributed real time computation system. This method increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves real time system Storm efficiently in saving the data transmission time and reducing the calculation complexity.

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Design for Virtual Laboratory of Mechanical Drawing on Web

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1037.457 ◽

2014 ◽

Vol 1037 ◽

pp. 457-460

Author(s):

Long Hui Qiu ◽

Gang Wang

Keyword(s):

Data Structure ◽

Real Time ◽

Virtual Laboratory ◽

Time Deformation

This paper discusses the design for virtual laboratory of mechanical drawing, and analyzes the characteristic of mechanical drawing virtual laboratory and the requirements for virtual laboratory, select design platform for virtual laboratory. The method of solid design is discussed in detail. The data structure and the algorithm of modifying data with real-time deformation is designed. And the operating interface with strong interactivity is designed. And at last, a virtual laboratory of mechanical drawing base on Internet is designed.

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