Comparison of a Visual and a Textual Notation to Express Data Constraints in Aspect-Oriented Join Point Selections: A Controlled Experiment

2011 ◽  
Author(s):  
Dominik Stein ◽  
Stefan Hanenberg
Keyword(s):  
Controlled Experiment ◽  
Join Point ◽  
Data Constraints

scholarly journals An Ecological Service System Based Study on Suburban Rural Landscape Multifunction

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 232
Author(s):  
Jie Zheng ◽  
Lisha Na ◽  
Binglin Liu ◽  
Tiantian Zhang ◽  
Hao Wang
Keyword(s):  
Rural Communities ◽  
Urban Areas ◽  
Support Function ◽  
Service System ◽  
Rural Landscape ◽  
Dual Function ◽  
The Sustainable Development ◽  
Ecological Service ◽  
Single Function ◽  
Data Constraints

Suburban rural landscape multifunction has received increasing attention from scholars due to its high demand and impact on main urban areas. However, few studies have been focused on suburban rural landscape multifunction because of data constraints. The present study quantified the four landscape services based on ecological service system, i.e., regulating function (RF), provision function (PF), culture function (CF), and support function (SF), determined the interaction through the Spearman correlation coefficient, and ultimately identified the landscape multifunction hotspots and dominant functions through overlay analysis. The result indicated that suburban rural communities have exhibited the characteristics of regional multifunction, and the landscape multifunction hotspots accounted for 64.2%; it should be particularly noted that, among single-function, dual-function, and multifunction hotspots, both support function, and culture function was dominant, while only one case was found in which the regulating function was dominant. Furthermore, all landscape functions other than SF-CF exhibited certain correlations. The study suggests that planning and management should be performed in future in combination with landscape multifunction to ensure the sustainable development of suburban rural communities.

Short‐Termist CEO Compensation in Speculative Markets: A Controlled Experiment†

2021 ◽  
Author(s):  
Yen‐Cheng Chang ◽  
Minjie Huang ◽  
Yu‐Siang Su ◽  
Kevin Tseng
Keyword(s):  
Ceo Compensation ◽  
Controlled Experiment

scholarly journals Learning Subject-Generalized Topographical EEG Embeddings Using Deep Variational Autoencoders and Domain-Adversarial Regularization

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1792
Author(s):  
Juan Hagad ◽  
Tsukasa Kimura ◽  
Ken-ichi Fukui ◽  
Masayuki Numao
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Emotion Classification ◽  
Limited Data ◽  
Neural Network Architecture ◽  
Building Models ◽  
The Subject ◽  
Data Constraints ◽  
Input Level ◽  
Normally Distributed

Two of the biggest challenges in building models for detecting emotions from electroencephalography (EEG) devices are the relatively small amount of labeled samples and the strong variability of signal feature distributions between different subjects. In this study, we propose a context-generalized model that tackles the data constraints and subject variability simultaneously using a deep neural network architecture optimized for normally distributed subject-independent feature embeddings. Variational autoencoders (VAEs) at the input level allow the lower feature layers of the model to be trained on both labeled and unlabeled samples, maximizing the use of the limited data resources. Meanwhile, variational regularization encourages the model to learn Gaussian-distributed feature embeddings, resulting in robustness to small dataset imbalances. Subject-adversarial regularization applied to the bi-lateral features further enforces subject-independence on the final feature embedding used for emotion classification. The results from subject-independent performance experiments on the SEED and DEAP EEG-emotion datasets show that our model generalizes better across subjects than other state-of-the-art feature embeddings when paired with deep learning classifiers. Furthermore, qualitative analysis of the embedding space reveals that our proposed subject-invariant bi-lateral variational domain adversarial neural network (BiVDANN) architecture may improve the subject-independent performance by discovering normally distributed features.

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