scholarly journals Multi-scale account of the network structure of macaque visual cortex

2017 ◽  
Vol 223 (3) ◽  
pp. 1409-1435 ◽  
Author(s):  
Maximilian Schmidt ◽  
Rembrandt Bakker ◽  
Claus C. Hilgetag ◽  
Markus Diesmann ◽  
Sacha J. van Albada
Keyword(s):  
Network Structure ◽  
Multiple Scales ◽  
Volume Density ◽  
Structural Differentiation ◽  
Multi Scale ◽  
Neuron Density ◽  
Connection Probability ◽  
Layer 4 ◽  
Local Circuits ◽  
Anterograde And Retrograde Tracing

Abstract Cortical network structure has been extensively characterized at the level of local circuits and in terms of long-range connectivity, but seldom in a manner that integrates both of these scales. Furthermore, while the connectivity of cortex is known to be related to its architecture, this knowledge has not been used to derive a comprehensive cortical connectivity map. In this study, we integrate data on cortical architecture and axonal tracing data into a consistent multi-scale framework of the structure of one hemisphere of macaque vision-related cortex. The connectivity model predicts the connection probability between any two neurons based on their types and locations within areas and layers. Our analysis reveals regularities of cortical structure. We confirm that cortical thickness decays with cell density. A gradual reduction in neuron density together with the relative constancy of the volume density of synapses across cortical areas yields denser connectivity in visual areas more remote from sensory inputs and of lower structural differentiation. Further, we find a systematic relation between laminar patterns on source and target sides of cortical projections, extending previous findings from combined anterograde and retrograde tracing experiments. Going beyond the classical schemes, we statistically assign synapses to target neurons based on anatomical reconstructions, which suggests that layer 4 neurons receive substantial feedback input. Our derived connectivity exhibits a community structure that corresponds more closely with known functional groupings than previous connectivity maps and identifies layer-specific directional differences in cortico-cortical pathways. The resulting network can form the basis for studies relating structure to neural dynamics in mammalian cortex at multiple scales.

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6780
Author(s):  
Zhitong Lai ◽  
Rui Tian ◽  
Zhiguo Wu ◽  
Nannan Ding ◽  
Linjian Sun ◽  
...  
Keyword(s):  
Multiple Scales ◽  
Feature Fusion ◽  
State Of The Art ◽  
Depth Estimation ◽  
Multi Scale ◽  
Pyramid Structure ◽  
Benchmark Datasets ◽  
The Common ◽  
Monocular Depth ◽  
Multiple Stages

Pyramid architecture is a useful strategy to fuse multi-scale features in deep monocular depth estimation approaches. However, most pyramid networks fuse features only within the adjacent stages in a pyramid structure. To take full advantage of the pyramid structure, inspired by the success of DenseNet, this paper presents DCPNet, a densely connected pyramid network that fuses multi-scale features from multiple stages of the pyramid structure. DCPNet not only performs feature fusion between the adjacent stages, but also non-adjacent stages. To fuse these features, we design a simple and effective dense connection module (DCM). In addition, we offer a new consideration of the common upscale operation in our approach. We believe DCPNet offers a more efficient way to fuse features from multiple scales in a pyramid-like network. We perform extensive experiments using both outdoor and indoor benchmark datasets (i.e., the KITTI and the NYU Depth V2 datasets) and DCPNet achieves the state-of-the-art results.

A Novel Multi-Scale Feature Fusion Method for Region Proposal Network in Fast Object Detection

2020 ◽  
Vol 16 (3) ◽  
pp. 132-145
Author(s):  
Gang Liu ◽  
Chuyi Wang
Keyword(s):  
Object Detection ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Uniform Space ◽  
Fusion Method ◽  
Well Performance ◽  
Feature Maps ◽  
Neural Network Models ◽  
Scale Feature ◽  
Multi Scale

Neural network models have been widely used in the field of object detecting. The region proposal methods are widely used in the current object detection networks and have achieved well performance. The common region proposal methods hunt the objects by generating thousands of the candidate boxes. Compared to other region proposal methods, the region proposal network (RPN) method improves the accuracy and detection speed with several hundred candidate boxes. However, since the feature maps contains insufficient information, the ability of RPN to detect and locate small-sized objects is poor. A novel multi-scale feature fusion method for region proposal network to solve the above problems is proposed in this article. The proposed method is called multi-scale region proposal network (MS-RPN) which can generate suitable feature maps for the region proposal network. In MS-RPN, the selected feature maps at multiple scales are fine turned respectively and compressed into a uniform space. The generated fusion feature maps are called refined fusion features (RFFs). RFFs incorporate abundant detail information and context information. And RFFs are sent to RPN to generate better region proposals. The proposed approach is evaluated on PASCAL VOC 2007 and MS COCO benchmark tasks. MS-RPN obtains significant improvements over the comparable state-of-the-art detection models.

scholarly journals Quantifying the multi-scale performance of network inference algorithms

2014 ◽  
Vol 13 (5) ◽  
Author(s):  
Chris J. Oates ◽  
Richard Amos ◽  
Simon E.F. Spencer
Keyword(s):  
Graphical Models ◽  
Network Inference ◽  
Multiple Scales ◽  
Biological Systems ◽  
Higher Order ◽  
Wisdom Of Crowds ◽  
Multi Scale ◽  
Reverse Engineer ◽  
Multiple Length Scales ◽  
Inference Algorithms

AbstractGraphical models are widely used to study complex multivariate biological systems. Network inference algorithms aim to reverse-engineer such models from noisy experimental data. It is common to assess such algorithms using techniques from classifier analysis. These metrics, based on ability to correctly infer individual edges, possess a number of appealing features including invariance to rank-preserving transformation. However, regulation in biological systems occurs on multiple scales and existing metrics do not take into account the correctness of higher-order network structure. In this paper novel performance scores are presented that share the appealing properties of existing scores, whilst capturing ability to uncover regulation on multiple scales. Theoretical results confirm that performance of a network inference algorithm depends crucially on the scale at which inferences are to be made; in particular strong local performance does not guarantee accurate reconstruction of higher-order topology. Applying these scores to a large corpus of data from the DREAM5 challenge, we undertake a data-driven assessment of estimator performance. We find that the “wisdom of crowds” network, that demonstrated superior local performance in the DREAM5 challenge, is also among the best performing methodologies for inference of regulation on multiple length scales.

scholarly journals MSICA: multi-scale signal decomposition based on independent component analysis with application to denoising and reliable multi-channel

2020 ◽  
Vol 1 (1) ◽  
pp. 25-35
Author(s):  
Abolfazl Hajisami ◽  
Dario Pompili
Keyword(s):  
Independent Component Analysis ◽  
Multiple Scales ◽  
Digital Signal ◽  
Component Analysis ◽  
Scale Space ◽  
Independent Component ◽  
Signal Denoising ◽  
Multi Scale ◽  
Information Preservation ◽  
Bank Structure

Multi-scale decomposition is a signal description method in which the signal is decomposed into multiple scales, which has been shown to be a valuable method in information preservation. Much focus on multi-scale decomposition has been based on scale-space theory and wavelet transform. In this article, a new powerful method to perform multi-scale decomposition exploiting Independent Component Analysis (ICA), called MSICA, is proposed to translate an original signal into multiple statistically independent scales. It is proven that extracting the independent components of the even and odd samples of a digital signal results in the decomposition of the same into approximation and detail. It is also proven that the whitening procedure in ICA is equivalent to a filter bank structure. Performance results of MSICA in signal denoising are presented; also, the statistical independency of the approximation and detail is exploited to propose a novel signal-denoising strategy for multi-channel noisy transmissions aimed at improving communication reliability by exploiting channel diversity.

scholarly journals Formulation of a multi-scale watershed ecological model using a statistical approach

2020 ◽  
Author(s):  
Bruce Pruitt ◽  
K. Killgore ◽  
William Slack ◽  
Ramune Matuliauskaite
Keyword(s):  
Aquatic Ecosystems ◽  
Statistical Approach ◽  
Ecological Model ◽  
Multiple Scales ◽  
Cost Benefit ◽  
Condition Index ◽  
Multi Scale ◽  
Stream Condition ◽  
Ecosystem Studies ◽  
General Statistical

The purpose of this special report is to provide a statistical stepwise process for formulation of ecological models for application at multiple scales using a stream condition index (SCI). Given the global variability of aquatic ecosystems, this guidance is for broad application and may require modification to suit specific watersheds or stream reaches. However, the general statistical treatise provided herein applies across physiographies and at multiple scales. The Duck River Watershed Assessment in Tennessee was used, in part, to develop and test this multiscale, statistical approach; thus, it is considered a case example and referenced throughout this report. The findings of this study can be utilized to (1) prioritize water-sheds for restoration, enhancement, and conservation; (2) plan and conduct site-specific, intensive ecosystem studies; and (3) assess ecosystem outcomes (that is, ecological lift) applicable to future with and without restoration actions including alternative, feasibility, and cost-benefit analyses and adaptive management.

scholarly journals Graduate Curriculum for Biological Information Specialists: A Key to Integration of Scale in Biology

2008 ◽  
Vol 2 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Carole L. Palmer ◽  
Bryan P. Heidorn ◽  
Dan Wright ◽  
Melissa H. Cragin
Keyword(s):  
Biological Sciences ◽  
Multiple Scales ◽  
Scientific Information ◽  
Scientific Data ◽  
High Rate ◽  
Biological Information ◽  
Biological Research ◽  
Multi Scale ◽  
Graduate Curriculum ◽  
Professional Capacity

Scientific data problems do not stand in isolation. They are part of a larger set of challenges associated with the escalation of scientific information and changes in scholarly communication in the digital environment. Biologists in particular are generating enormous sets of data at a high rate, and new discoveries in the biological sciences will increasingly depend on the integration of data across multiple scales. This work will require new kinds of information expertise in key areas. To build this professional capacity we have developed two complementary educational programs: a Biological Information Specialist (BIS) masters degree and a concentration in Data Curation (DC). We believe that BISs will be central in the development of cyberinfrastructure and information services needed to facilitate interdisciplinary and multi-scale science. Here we present three sample cases from our current research projects to illustrate areas in which we expect information specialists to make important contributions to biological research practice.

scholarly journals Time-Aware Multi-Scale RNNs for Time Series Modeling

2021 ◽  
Author(s):  
Zipeng Chen ◽  
Qianli Ma ◽  
Zhenxi Lin
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Multiple Scales ◽  
Multivariate Time Series ◽  
Human Motion ◽  
Series Data ◽  
Time Step ◽  
Multi Scale ◽  
Important Time ◽  
Time Aware

Multi-scale information is crucial for modeling time series. Although most existing methods consider multiple scales in the time-series data, they assume all kinds of scales are equally important for each sample, making them unable to capture the dynamic temporal patterns of time series. To this end, we propose Time-Aware Multi-Scale Recurrent Neural Networks (TAMS-RNNs), which disentangle representations of different scales and adaptively select the most important scale for each sample at each time step. First, the hidden state of the RNN is disentangled into multiple independently updated small hidden states, which use different update frequencies to model time-series multi-scale information. Then, at each time step, the temporal context information is used to modulate the features of different scales, selecting the most important time-series scale. Therefore, the proposed model can capture the multi-scale information for each time series at each time step adaptively. Extensive experiments demonstrate that the model outperforms state-of-the-art methods on multivariate time series classification and human motion prediction tasks. Furthermore, visualized analysis on music genre recognition verifies the effectiveness of the model.

scholarly journals Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

2020 ◽  
Vol 12 (5) ◽  
pp. 872 ◽  
Author(s):  
Ronghua Shang ◽  
Jiyu Zhang ◽  
Licheng Jiao ◽  
Yangyang Li ◽  
Naresh Marturi ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Multiple Scales ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Semantic Features ◽  
Remote Sensing Images ◽  
Global Features ◽  
Global Average ◽  
Multi Scale ◽  
Context Extraction

Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.

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