scholarly journals Tree Log Identity Matching using Convolutional Correlation Networks

2019 ◽  
Author(s):  
Mikko Vihlman ◽  
Jakke Kulovesi ◽  
Arto Visala
Keyword(s):  
Correlation Networks ◽  
Identity Matching

Reflexivity in Rats: Location Control in Generalized Identity Matching-to-Sample

2017 ◽  
Author(s):  
Sarah Beurms ◽  
Ana Gloria Plaza Jurado ◽  
Ana Sánchez-Kuhn ◽  
Jan De Houwer ◽  
Tom Beckers
Keyword(s):  
Sample Stimulus ◽  
Matching To Sample ◽  
Middle Position ◽  
Direct Training ◽  
Nonhuman Animals ◽  
Identity Matching

Reflexivity entails that an organism can match a stimulus to itself (“A=A”) without direct training. Reflexivity is typically studied in identity matching-to-sample tasks wherein subjects are first presented with a sample stimulus in the middle position and trained to select the same stimulus from two comparison stimuli that are subsequently presented in the side positions. However, when the position of the comparisons is altered, nonhuman animals often revert to responding at chance levels, suggesting that they encode the location of stimuli together with their identity as part of the functional stimulus. This might hamper generalization of the task to novel stimuli (i.e., generalized identity matching-to-sample), which would be an observation of reflexivity. To test whether the use of multiple locations facilitates generalized identity matching-to-sample in rats, we used an olfactory matching-to-sample task. Two rats received training in which the location of the stimuli varied randomly. The speed with which they learned to match identical odors and the generalization to new stimuli was compared with two rats that received standard matching-to-sample training in which the location of the stimuli was fixed. We observed generalized identity matching-to-sample in two rats that could not be explained by reinforcement recency. However, we found no evidence that the use of multiple locations facilitated generalized identity matching-to-sample.

scholarly journals COVID-19 lockdown has altered the dynamics between affective symptoms and social isolation among older adults: results from a longitudinal network analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Junhong Yu ◽  
Rathi Mahendran
Keyword(s):  
Older Adults ◽  
Social Isolation ◽  
Anxiety Symptoms ◽  
Community Dwelling ◽  
Depression Symptoms ◽  
Network Analyses ◽  
Correlation Networks ◽  
Affective Symptoms ◽  
Tightly Coupled ◽  
Community Dwelling Older Adults

AbstractThe COVID-19 lockdown has drastically limited social interactions and brought about a climate of fear and uncertainty. These circumstances not only increased affective symptoms and social isolation among community dwelling older adults but also alter the dynamics between them. Using network analyses, we study the changes in these dynamics before and during the lockdown. Community-dwelling older adults (N = 419) completed questionnaires assessing depression, anxiety, and social isolation, before the COVID-19 pandemic, as part of a cohort study, and during the lockdown period. The total scores of these questionnaires were compared across time. For the network analyses, partial correlation networks were constructed using items in the questionnaires as nodes, separately at both timepoints. Changes in edges, as well as nodal and bridge centrality were examined across time. Depression and anxiety symptoms, and social isolation had significantly increased during the lockdown. Significant changes were observed across time on several edges. Greater connectivity between the affective and social isolation nodes at lockdown was observed. Depression symptoms have become more tightly coupled across individuals, and so were the anxiety symptoms. Depression symptoms have also become slightly decoupled from those of anxiety. These changing network dynamics reflect the greater influence of social isolation on affective symptoms across individuals and an increased vulnerability to affective disorders. These findings provide novel perspectives and translational implications on the changing mental health context amidst a COVID-19 pandemic situation.

scholarly journals A Spatio-Temporal Local Association Query Algorithm for Multi-Source Remote Sensing Big Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2333
Author(s):  
Lilu Zhu ◽  
Xiaolu Su ◽  
Yanfeng Hu ◽  
Xianqing Tai ◽  
Kun Fu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Temporal Correlation ◽  
Local Association ◽  
Sensing Data ◽  
Multi Scale ◽  
Correlation Networks ◽  
Efficient Integration ◽  
Spatio Temporal ◽  
Query Algorithm

It is extremely important to extract valuable information and achieve efficient integration of remote sensing data. The multi-source and heterogeneous nature of remote sensing data leads to the increasing complexity of these relationships, and means that the processing mode based on data ontology cannot meet requirements any more. On the other hand, the multi-dimensional features of remote sensing data bring more difficulties in data query and analysis, especially for datasets with a lot of noise. Therefore, data quality has become the bottleneck of data value discovery, and a single batch query is not enough to support the optimal combination of global data resources. In this paper, we propose a spatio-temporal local association query algorithm for remote sensing data (STLAQ). Firstly, we design a spatio-temporal data model and a bottom-up spatio-temporal correlation network. Then, we use the method of partition-based clustering and the method of spectral clustering to measure the correlation between spatio-temporal correlation networks. Finally, we construct a spatio-temporal index to provide joint query capabilities. We carry out local association query efficiency experiments to verify the feasibility of STLAQ on multi-scale datasets. The results show that the STLAQ weakens the barriers between remote sensing data, and improves their application value effectively.

Reconstruction and analysis of correlation networks based on GC–MS metabolomics data for hypercholesterolemia

2021 ◽  
Vol 553 ◽  
pp. 1-8
Author(s):  
Ya-nan OuYang ◽  
Lu-xin Zhou ◽  
Yue-xin Jin ◽  
Guo-feng Hou ◽  
Peng-fei Yang ◽  
...  
Keyword(s):  
Metabolomics Data ◽  
Correlation Networks

scholarly journals Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks

2017 ◽  
Author(s):  
Stefano Beretta ◽  
Mauro Castelli ◽  
Ivo Gonçalves ◽  
Ivan Merelli ◽  
Daniele Ramazzotti
Keyword(s):  
Breast Cancer ◽  
Graphical Models ◽  
Protein Interactions ◽  
Large Scale ◽  
Small Sample Size ◽  
Small Sample ◽  
Cancer Data ◽  
Correlation Networks ◽  
Model Complex ◽  
Cancer Networks

AbstractGene and protein networks are very important to model complex large-scale systems in molecular biology. Inferring or reverseengineering such networks can be defined as the process of identifying gene/protein interactions from experimental data through computational analysis. However, this task is typically complicated by the enormously large scale of the unknowns in a rather small sample size. Furthermore, when the goal is to study causal relationships within the network, tools capable of overcoming the limitations of correlation networks are required. In this work, we make use of Bayesian Graphical Models to attach this problem and, specifically, we perform a comparative study of different state-of-the-art heuristics, analyzing their performance in inferring the structure of the Bayesian Network from breast cancer data.

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