scholarly journals Cross-population coupling of neural activity based on Gaussian process current source densities

2021 ◽  
Vol 17 (11) ◽  
pp. e1009601
Author(s):  
Natalie Klein ◽  
Joshua H. Siegle ◽  
Tobias Teichert ◽  
Robert E. Kass
Keyword(s):  
Gaussian Process ◽  
Current Source ◽  
Cortical Layer ◽  
Estimation Methods ◽  
Multiple Time ◽  
Multiple Sources ◽  
Phase Coupling ◽  
Using Data ◽  
Source Signals ◽  
Specific Phase

Because local field potentials (LFPs) arise from multiple sources in different spatial locations, they do not easily reveal coordinated activity across neural populations on a trial-to-trial basis. As we show here, however, once disparate source signals are decoupled, their trial-to-trial fluctuations become more accessible, and cross-population correlations become more apparent. To decouple sources we introduce a general framework for estimation of current source densities (CSDs). In this framework, the set of LFPs result from noise being added to the transform of the CSD by a biophysical forward model, while the CSD is considered to be the sum of a zero-mean, stationary, spatiotemporal Gaussian process, having fast and slow components, and a mean function, which is the sum of multiple time-varying functions distributed across space, each varying across trials. We derived biophysical forward models relevant to the data we analyzed. In simulation studies this approach improved identification of source signals compared to existing CSD estimation methods. Using data recorded from primate auditory cortex, we analyzed trial-to-trial fluctuations in both steady-state and task-evoked signals. We found cortical layer-specific phase coupling between two probes and showed that the same analysis applied directly to LFPs did not recover these patterns. We also found task-evoked CSDs to be correlated across probes, at specific cortical depths. Using data from Neuropixels probes in mouse visual areas, we again found evidence for depth-specific phase coupling of primary visual cortex and lateromedial area based on the CSDs.

scholarly journals Extending Geodemographics Using Data Primitives: A Review and a Methodological Proposal

2021 ◽  
Vol 10 (6) ◽  
pp. 386
Author(s):  
Jennie Gray ◽  
Lisa Buckner ◽  
Alexis Comber
Keyword(s):  
Social Dynamics ◽  
Feature Space ◽  
Social Processes ◽  
Multiple Time ◽  
Current State ◽  
Cluster Label ◽  
Static Data ◽  
The Social ◽  
Using Data

This paper reviews geodemographic classifications and developments in contemporary classifications. It develops a critique of current approaches and identifiea a number of key limitations. These include the problems associated with the geodemographic cluster label (few cluster members are typical or have the same properties as the cluster centre) and the failure of the static label to describe anything about the underlying neighbourhood processes and dynamics. To address these limitations, this paper proposed a data primitives approach. Data primitives are the fundamental dimensions or measurements that capture the processes of interest. They can be used to describe the current state of an area in a multivariate feature space, and states can be compared over multiple time periods for which data are available, through for example a change vector approach. In this way, emergent social processes, which may be too weak to result in a change in a cluster label, but are nonetheless important signals, can be captured. As states are updated (for example, as new data become available), inferences about different social processes can be made, as well as classification updates if required. State changes can also be used to determine neighbourhood trajectories and to predict or infer future states. A list of data primitives was suggested from a review of the mechanisms driving a number of neighbourhood-level social processes, with the aim of improving the wider understanding of the interaction of complex neighbourhood processes and their effects. A small case study was provided to illustrate the approach. In this way, the methods outlined in this paper suggest a more nuanced approach to geodemographic research, away from a focus on classifications and static data, towards approaches that capture the social dynamics experienced by neighbourhoods.

scholarly journals Distance-Based Estimation Methods for Models for Discrete and Mixed-Scale Data

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 107
Author(s):  
Elisavet M. Sofikitou ◽  
Ray Liu ◽  
Huipei Wang ◽  
Marianthi Markatou
Keyword(s):  
Data Model ◽  
Null Hypothesis ◽  
Model Misspecification ◽  
Asymptotic Properties ◽  
Estimation Methods ◽  
Measurement Scale ◽  
Interval Scale ◽  
Pearson Residuals ◽  
Using Data ◽  
Scale Data

Pearson residuals aid the task of identifying model misspecification because they compare the estimated, using data, model with the model assumed under the null hypothesis. We present different formulations of the Pearson residual system that account for the measurement scale of the data and study their properties. We further concentrate on the case of mixed-scale data, that is, data measured in both categorical and interval scale. We study the asymptotic properties and the robustness of minimum disparity estimators obtained in the case of mixed-scale data and exemplify the performance of the methods via simulation.

What drives housing prices, rent and new construction in China

2017 ◽  
Vol 10 (5) ◽  
pp. 662-686
Author(s):  
Dimitrios Staikos ◽  
Wenjun Xue
Keyword(s):  
Real Estate ◽  
Empirical Test ◽  
Housing Prices ◽  
Panel Cointegration ◽  
Real Estate Market ◽  
Estimation Methods ◽  
Content Type ◽  
New Construction ◽  
Using Data ◽  
First Time

Purpose With this paper, the authors aim to investigate the drivers behind three of the most important aspects of the Chinese real estate market, housing prices, housing rent and new construction. At the same time, the authors perform a comprehensive empirical test of the popular 4-quadrant model by Wheaton and DiPasquale. Design/methodology/approach In this paper, the authors utilize panel cointegration estimation methods and data from 35 Chinese metropolitan areas. Findings The results indicate that the 4-quadrant model is well suited to explain the determinants of housing prices. However, the same is not true regarding housing rent and new construction suggesting a more complex theoretical framework may be required for a well-rounded explanation of real estate markets. Originality/value It is the first time that panel data are used to estimate rent and new construction for China. Also, it is the first time a comprehensive test of the Wheaton and DiPasquale 4-quadrant model is performed using data from China.

scholarly journals Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

2020 ◽  
Vol 133 (10) ◽  
pp. 2853-2868
Author(s):  
Mahlet T. Anche ◽  
Nicholas S. Kaczmar ◽  
Nicolas Morales ◽  
James W. Clohessy ◽  
Daniel C. Ilut ◽  
...  
Keyword(s):  
Grain Yield ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Crop Improvement ◽  
Genetic Correlations ◽  
Random Regression ◽  
Multiple Time ◽  
Heritable Variation ◽  
Using Data

Abstract Key message Heritable variation in phenotypes extracted from multi-spectral images (MSIs) and strong genetic correlations with end-of-season traits indicates the value of MSIs for crop improvement and modeling of plant growth curve. Abstract Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

scholarly journals TAX STRUCTURE AND ECONOMIC GROWTH: DO DIFFERENCES IN INCOME LEVEL AND GOVERNMENT EFFECTIVENESS MATTER?

2018 ◽  
Vol 65 (01) ◽  
pp. 217-237 ◽  
Author(s):  
HALIT YANIKKAYA ◽  
TANER TURAN
Keyword(s):  
Low Income ◽  
Low Income Countries ◽  
Estimation Methods ◽  
Property Taxes ◽  
Tax Rates ◽  
Tax Structure ◽  
Tax Rate ◽  
Using Data ◽  
Positive Effect ◽  
Effect On Growth

We examine the effects of both overall tax rate and changes in tax structure on growth by using data for more than 100 high, middle, and low income countries by employing the GMM estimation methods. In general, our results do not support the argument that overall tax rates or changes in tax structure have a significant effect on growth. However, we find that a shift from income to consumption and property taxes leads to a positive and significant effect on growth rate while a shift from consumption and property taxes to income taxes has a positive effect for low-income countries.

scholarly journals An Imbalanced Data Handling Framework for Industrial Big Data Using a Gaussian Process Regression-Based Generative Adversarial Network

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 669 ◽  
Author(s):  
Eunseo Oh ◽  
Hyunsoo Lee
Keyword(s):  
Big Data ◽  
Gaussian Process ◽  
Missing Values ◽  
Gaussian Process Regression ◽  
Estimation Methods ◽  
Data Handling ◽  
Generative Adversarial Network ◽  
Data Set ◽  
Adversarial Network ◽  
Industrial Big Data

The developments in the fields of industrial Internet of Things (IIoT) and big data technologies have made it possible to collect a lot of meaningful industrial process and quality-based data. The gathered data are analyzed using contemporary statistical methods and machine learning techniques. Then, the extracted knowledge can be used for predictive maintenance or prognostic health management. However, it is difficult to gather complete data due to several issues in IIoT, such as devices breaking down, running out of battery, or undergoing scheduled maintenance. Data with missing values are often ignored, as they may contain insufficient information from which to draw conclusions. In order to overcome these issues, we propose a novel, effective missing data handling mechanism for the concepts of symmetry principles. While other existing methods only attempt to estimate missing parts, the proposed method generates a whole set of data set using Gaussian process regression and a generative adversarial network. In order to prove the effectiveness of the proposed framework, we examine a real-world, industrial case involving an air pressure system (APS), where we use the proposed method to make quality predictions and compare the results with existing state-of-the-art estimation methods.

Evaluating an interpolation approach for modelling spatial variability in pest development

2002 ◽  
Vol 92 (3) ◽  
pp. 219-231 ◽  
Author(s):  
C.H. Jarvis ◽  
R.H. Collier
Keyword(s):  
Spatial Variability ◽  
Input Data ◽  
Spline Interpolation ◽  
Local Variation ◽  
Estimation Methods ◽  
Horticultural Crops ◽  
Air Temperatures ◽  
Using Data ◽  
The One ◽  
The Uk

AbstractAir temperatures estimated by partial thin plate spline interpolation, or from the ‘nearest station’ (Voronoi polygon method), were used to model the phenology of three pests of horticultural crops throughout England and Wales. Temperatures for a particularly hot (1976) and a particularly cold (1986) year were interpolated to a grid resolution of 1 km. Estimates were made of the timing of spring emergence (Cecidophyopsis ribis (Westwood)), the maximum number of generations completed during the summer (Plutella xylostella (Linnaeus)) and the numbers of days when mating was possible (Merodon equestris (Fabricius)). The relative accuracy of the two temperature estimation methods was compared using jack-knife cross-validation. For C. ribis and P. xylostella, modelling with interpolated temperature input data was more accurate than using data from the ‘nearest station’. Of the three phenology models used, the one that relied on an activity threshold (M. equestris) was the most sensitive to both types of input data. Spatial variability in the activity of M. equestris adults was investigated in the two main areas (south-west peninsula and Lincolnshire) where its host crop (Narcissus) is grown. Modelling at cruder scales (up to 25*25 km) masked local variation, but the degree to which this was important varied from region to region and over time, as did the geography of the variability itself. The results indicate that interpolated data, computed to a resolution of 1 km using the UK synoptic network, have the potential for wider use within agricultural decision support systems for horticultural crops.

The opportunity model of organizational commitment

2017 ◽  
Vol 38 (2) ◽  
pp. 259-273 ◽  
Author(s):  
Elif Cicekli ◽  
Hayat Kabasakal
Keyword(s):  
Organizational Commitment ◽  
Social Exchange ◽  
Social Exchange Theory ◽  
Exchange Theory ◽  
Multiple Sources ◽  
Content Type ◽  
Job Opportunities ◽  
Using Data ◽  
The Relationship ◽  
Practical Implications

Purpose The purpose of this paper is to determine the relationships between promotion, development, and recognition opportunities at work and organizational commitment, and whether these relationships are moderated by the job opportunities employees have in other organizations. Design/methodology/approach An opportunity model of organizational commitment is developed based on social exchange theory and several streams of opportunity research. Factor analyses and hierarchical multiple regression analyses are carried out to test the hypotheses using data from 550 white-collar employees. Findings The results of the analyses show that opportunities for development and recognition are predictors of organizational commitment, that job opportunities employees have in other organizations negatively moderate the relationship between recognition opportunity at work and organizational commitment, and that promotion opportunity does not predict organizational commitment. Research limitations/implications Future researchers could study the issue in the context of other cultures using data from multiple sources. Practical implications Employers who seek to increase their employees’ organizational commitment are advised to divert their energies from struggling to create promotion opportunities for their employees to creating opportunities for development and recognition. Originality/value The study explores the under-researched concept of opportunity at work and connects several streams of opportunity research by drawing on social exchange theory as a theoretical framework. The model is the first to address the effects of opportunity and alternative opportunities on organizational commitment.

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