A Data Structure for real-time Aggregation Queries of Big Brain Networks

2018 ◽  
Author(s):  
Florian Ganglberger ◽  
Joanna Kaczanowska ◽  
Wulf Haubensak ◽  
Katja Bühler
Keyword(s):  
Gene Expression ◽  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
State Of The Art ◽  
Brain Networks ◽  
Spatial Context ◽  
Brain Areas ◽  
Public Data ◽  
Aggregation Queries

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.

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

2019 ◽  
Vol 18 (1) ◽  
pp. 131-149
Author(s):  
Florian Johann Ganglberger ◽  
Joanna Kaczanowska ◽  
Wulf Haubensak ◽  
Katja Bühler
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Brain Networks ◽  
Time Aggregation ◽  
Aggregation Queries

Real Time Simulation for High Fidelity Multibody Dynamics and Mechatronic Systems

2014 ◽  
Author(s):  
William Prescott
Keyword(s):  
Real Time ◽  
Multibody Dynamics ◽  
Large Scale ◽  
State Of The Art ◽  
High Fidelity ◽  
Implicit Integration ◽  
Mechatronic Systems ◽  
Vehicle Simulation ◽  
Current State ◽  
Time Simulation

This paper will investigate the use of large scale multibody dynamics (MBD) models for real-time vehicle simulation. Current state of the art in the real-time solution of vehicle uses 15 degree of freedom models, but there is a need for higher-fidelity systems. To increase the fidelity of models uses this paper will propose the use of the following techniques: implicit integration, parallel processing and co-simulation in a real-time environment.

IMPLEMENTING BACK-PROPAGATION- THROUGH-TIME LEARNING ALGORITHM USING CELLULAR NEURAL NETWORKS

1999 ◽  
Vol 09 (06) ◽  
pp. 1041-1074 ◽  
Author(s):  
TAO YANG ◽  
LEON O. CHUA
Keyword(s):  
Real Time ◽  
Time Complexity ◽  
Large Scale ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Back Propagation ◽  
Cellular Neural Network ◽  
Small Scale ◽  
Von Neumann ◽  
On Line

In a programmable (multistage) cellular neural network (CNN) structure, the CPU is a CNN universal chip which supports massively parallel computations on patterns and images, including videos. In this paper, we decompose the structure of a class of simultaneous recurrent networks (SRN) into a CNN program and run it on a von Neumann-like stored program CNN structure. To train the SRN, we map the back-propagation-through-time (BTT) learning algorithm into a sequence of CNN subroutines to achieve real-time performance via a CNN universal chip. By computing in parallel, the CNN universal chip can be programmed to implement in real time the BTT learning algorithm, which has a very high time complexity. An estimate of the time complexity of the BTT learning algorithm based on the CNN universal chip is presented. For small-scale problems, our simulation results show that a CNN implementation of the BTT learning algorithm for a two-dimensional SRN is at least 10,000 times faster than that based on state-of-the-art sequential workstations. For the few large-scale problems which we have so far simulated, the CNN implemented BTT learning algorithm maintained virtually the same time complexity with a learning time of a few seconds, while those implemented on state-of-the-art sequential workstations dramatically increased their time complexity, often requiring several days of running time. Several examples are presented to demonstrate how efficiently a CNN universal chip can speed up the learning algorithm for both off-line and on-line applications.

scholarly journals scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruoxin Li ◽  
Gerald Quon
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Feature Detection ◽  
State Of The Art ◽  
Cell Type ◽  
Technical Noise ◽  
Single Cell Genomics ◽  
Performance Gains ◽  
New Framework

Abstract Technical variation in feature measurements, such as gene expression and locus accessibility, is a key challenge of large-scale single-cell genomic datasets. We show that this technical variation in both scRNA-seq and scATAC-seq datasets can be mitigated by analyzing feature detection patterns alone and ignoring feature quantification measurements. This result holds when datasets have low detection noise relative to quantification noise. We demonstrate state-of-the-art performance of detection pattern models using our new framework, scBFA, for both cell type identification and trajectory inference. Performance gains can also be realized in one line of R code in existing pipelines.

scholarly journals MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning

2021 ◽  
Author(s):  
Chen Liu ◽  
Bo Li ◽  
Jun Zhao ◽  
Ming Su ◽  
Xu-Dong Liu
Keyword(s):  
Real Time ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Evolutionary Patterns ◽  
Graph Learning ◽  
Fine Grained ◽  
Variant Detection ◽  
Effectiveness And Efficiency ◽  
The Cost

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency.

Microbloggers’ interest inference using a subgraph stream

2021 ◽  
Vol 25 (2) ◽  
pp. 397-417
Author(s):  
Xiaoling Huang ◽  
Hao Wang ◽  
Lei Li ◽  
Yi Zhu ◽  
Chengxiang Hu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
State Of The Art ◽  
Dynamic Change ◽  
Massive Data ◽  
User Interest ◽  
Large Dataset ◽  
Topic Extraction ◽  
Sina Weibo ◽  
Effectiveness And Efficiency

Inferring user interest over large-scale microblogs have attracted much attention in recent years. However, the emergence of the massive data, dynamic change of information and persistence of microblogs pose challenges to interest inference. Most of the existing approaches rarely take into account the combination of these microbloggers’ characteristics within the model, which may incur information loss with nontrivial magnitude in real-time extraction of user interest and massive social data processing. To address these problems, in this paper, we propose a novel User-Networked Interest Topic Extraction in the form of Subgraph Stream (UNITE_SS) for microbloggers’ interest inference. To be specific, we develop several strategies for the construction of subgraph stream to select the better strategy for user interest inference. Moreover, the information of microblogs in each subgraph is utilized to obtain a real-time and effective interest for microbloggers. The experimental evaluation on a large dataset from Sina Weibo, one of the most popular microblogs in China, demonstrates that the proposed approach outperforms the state-of-the-art baselines in terms of precision, mean reciprocal rank (MRR) as well as runtime from the effectiveness and efficiency perspectives.

scholarly journals Synthesis of reversible circuits for large reversible functions

2010 ◽  
Vol 23 (3) ◽  
pp. 273-286 ◽  
Author(s):  
Nouraddin Alhagi ◽  
Maher Hawash ◽  
Marek Perkowski
Keyword(s):  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
Optimal Solution ◽  
Truth Table ◽  
Synthesis Process ◽  
Quantum Cost ◽  
Reversible Circuits ◽  
Gate Count ◽  
Reversible Circuit

This paper presents a new algorithm MP (multiple pass) to synthesize large reversible binary circuits without ancilla bits. The well-known MMD algorithm for synthesis of reversible circuits requires to store a truth table (or a Reed-Muller - RM transform) as a 2n vector to represent a reversible function of n variables. This representation prohibits synthesis of large functions. However, in MP we do not store such an exponentially growing data structure. The values of minterms are calculated in MP dynamically, one-by-one, from a set of logic equations that specify the reversible circuit to be designed. This allows for synthesis of large scale reversible circuits (30-bits), which is not possible with any existing algorithm. In addition, our unique multi-pass approach where the circuit is synthesized with various, yet specific, minterm orders yields quasi-optimal solution. The algorithm returns a description of the quasi-optimal circuit with respect to gate count or to its 'quantum cost'. Although the synthesis process in MP is relatively slower, the solution is found in real-time for smaller circuits of 8 bits or less.

scholarly journals Displacement and visualization of point symbols based on spatial distribution characteristics

2019 ◽  
Vol 1 ◽  
pp. 1-2
Author(s):  
Haipeng Liu ◽  
Yi Long ◽  
Yi Zheng
Keyword(s):  
Spatial Distribution ◽  
Data Structure ◽  
Real Time ◽  
Large Scale ◽  
Density Difference ◽  
Distribution Characteristics ◽  
Displacement Operator ◽  
Specific Location ◽  
Loss Of Information ◽  
Spatial Distribution Characteristics

<p><strong>Abstract.</strong> In WEB2.0 environment, the number of map-based mashups which display user-led POI data keeps increasing. When the cartographic processing of these map mashups is lacking, the display of the POI data showed on the maps are quite unsatisfactory because of the overlapping of symbols.</p><p>At present, some widely used methods commonly use selection and simplification operations based on a quadtree data structure, which can get a good result in the small and medium scales in which users mainly focus on the distribution characteristics and the density difference of POI, but will lose a lot of information in the large scales in which users mainly focus on the specific location and detailed information of the data. For example, two hotels with the same size will retain only one symbol after using selection or simplification operation although in the large scale if they are adjacent to each other, which will bring trouble to users when using maps. Displacement is a suitable operation to deal with this situation, however, current displacement methods face the problems of symbol position drift and nevertheless the loss of information in high-density areas.</p><p>In order to address these problems, this paper proposes a real-time POI visualization algorithm combining the characteristics of traditional quadtree data structure and the advantages of an improved displacement operator.</p>

scholarly journals Brain connectivity dynamics: Multilayer network switching rate predicts brain performance

2018 ◽  
Author(s):  
Mangor Pedersen ◽  
Andrew Zalesky ◽  
Amir Omidvarnia ◽  
Graeme D. Jackson
Keyword(s):  
Large Scale ◽  
Brain Connectivity ◽  
Brain Networks ◽  
Brain Dynamics ◽  
Model Parameters ◽  
Brain Areas ◽  
Multilayer Network ◽  
Time Resolved ◽  
Brain Functions ◽  
Human Connectome Project

AbstractLarge-scale brain dynamics measures repeating spatiotemporal connectivity patterns that reflect a range of putative different brain states that underlie the dynamic repertoire of brain functions. The role of transition between brain networks is poorly understood and whether switching between these states is important for behavior has been little studied. Our aim here is to model switching between functional brain networks using multilayer network methods and test for associations between model parameters and behavioral measures. We calculated time-resolved functional MRI (fMRI) connectivity from one-hour long data recordings in 1003 healthy human adults from the Human Connectome Project. The time-resolved fMRI connectivity data was used to generate a spatiotemporal multilayer modularity model enabling us to quantify network switching which we define as the rate at which each brain region transits between different fMRI networks. We found i) an inverse relationship between network switching and connectivity dynamics –defined as the difference in variance between time-resolved fMRI connectivity signals and phase randomized surrogates–; ii) brain connectivity was lower during intervals of network switching; iii) brain areas with frequent network switching had greater temporal complexity; iv) brain areas with high network switching were located in association cortices; and v) using cross-validated Elastic Net regression, network switching predicted inter-subject variation in working memory performance, planning/reasoning and amount of sleep. Our findings shed new light on the importance of brain dynamics predicting task performance and amount of sleep. The ability to switch between network configurations thus appears to be a fundamental feature of optimal brain function.

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