Solar feature correlation tracker

1991 ◽  
Author(s):  
Thomas R. Rimmele ◽  
Oskar von der Luehe ◽  
P. H. Wiborg ◽  
A. Lee Widener ◽  
Richard B. Dunn ◽  
...  
Keyword(s):  
Feature Correlation ◽  
Correlation Tracker ◽  
Solar Feature

Instruction Helps and Hinders Young Children’s Feature Learning

2018 ◽  
Author(s):  
Charles Kalish ◽  
Nigel Noll
Keyword(s):  
Young Children ◽  
Feature Learning ◽  
Irrelevant Information ◽  
Diagnostic Feature ◽  
Older Children ◽  
Prediction Task ◽  
Feature Correlation ◽  
The Future

Existing research suggests that adults and older children experience a tradeoff where instruction and feedback help them solve a problem efficiently, but lead them to ignore currently irrelevant information that might be useful in the future. It is unclear whether young children experience the same tradeoff. Eighty-seven children (ages five- to eight-years) and 42 adults participated in supervised feature prediction tasks either with or without an instructional hint. Follow-up tasks assessed learning of feature correlations and feature frequencies. Younger children tended to learn frequencies of both relevant and irrelevant features without instruction, but not the diagnostic feature correlation needed for the prediction task. With instruction, younger children did learn the diagnostic feature correlation, but then failed to learn the frequencies of irrelevant features. Instruction helped older children learn the correlation without limiting attention to frequencies. Adults learned the diagnostic correlation even without instruction, but with instruction no longer learned about irrelevant frequencies. These results indicate that young children do show some costs of learning with instruction characteristic of older children and adults. However, they also receive some of the benefits. The current study illustrates just what those tradeoffs might be, and how they might change over development.

scholarly journals Connectionist Temporal Modeling of Video and Language: a Joint Model for Translation and Sign Labeling

2019 ◽  
Author(s):  
Dan Guo ◽  
Shengeng Tang ◽  
Meng Wang
Keyword(s):  
Dynamic Programming ◽  
Objective Function ◽  
Visual Representations ◽  
Sequential Learning ◽  
Short Term ◽  
Temporal Modeling ◽  
Temporal Correlations ◽  
Feature Correlation ◽  
3D Cnn

Online sign interpretation suffers from challenges presented by hybrid semantics learning among sequential variations of visual representations, sign linguistics, and textual grammars. This paper proposes a Connectionist Temporal Modeling (CTM) network for sentence translation and sign labeling. To acquire short-term temporal correlations, a Temporal Convolution Pyramid (TCP) module is performed on 2D CNN features to realize (2D+1D)=pseudo 3D' CNN features. CTM aligns the pseudo 3D' with the original 3D CNN clip features and fuses them. Next, we implement a connectionist decoding scheme for long-term sequential learning. Here, we embed dynamic programming into the decoding scheme, which learns temporal mapping among features, sign labels, and the generated sentence directly. The solution using dynamic programming to sign labeling is considered as pseudo labels. Finally, we utilize the pseudo supervision cues in an end-to-end framework. A joint objective function is designed to measure feature correlation, entropy regularization on sign labeling, and probability maximization on sentence decoding. The experimental results using the RWTH-PHOENIX-Weather and USTC-CSL datasets demonstrate the effectiveness of the proposed approach.

scholarly journals Solar feature tracking in both spatial and temporal domains

2007 ◽  
Vol 3 (S247) ◽  
pp. 288-295
Author(s):  
D. B. Jess ◽  
M. Mathioudakis ◽  
R. Erdélyi ◽  
G. Verth ◽  
R. T. J. McAteer ◽  
...  
Keyword(s):  
Coronal Loop ◽  
Feature Tracking ◽  
New Method ◽  
Kink Mode ◽  
Spatial Periodicity ◽  
Mode Oscillation ◽  
Oscillatory Power ◽  
Solar Feature

AbstractA new method for automated coronal loop tracking, in both spatial and temporal domains, is presented. The reliability of this technique was tested with TRACE 171 Å observations. The application of this technique to a flare-induced kink-mode oscillation, revealed a 3500 km spatial periodicity which occur along the loop edge. We establish a reduction in oscillatory power, for these spatial periodicities, of 45% over a 322 s interval. We relate the reduction in oscillatory power to the physical damping of these loop-top oscillations.

Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion

2013 ◽  
Vol 4 (2) ◽  
pp. 46-64 ◽  
Author(s):  
Hsin-Yu Ha ◽  
Fausto C. Fleites ◽  
Shu-Ching Chen
Keyword(s):  
Clustering Algorithm ◽  
Multiple Correspondence Analysis ◽  
Majority Vote ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Semantic Retrieval ◽  
Data Set ◽  
Correlation Clustering ◽  
Feature Correlation ◽  
Fusion Framework

Nowadays, only processing visual features is not enough for multimedia semantic retrieval due to the complexity of multimedia data, which usually involve a variety of modalities, e.g. graphics, text, speech, video, etc. It becomes crucial to fully utilize the correlation between each feature and the target concept, the feature correlation within modalities, and the feature correlation across modalities. In this paper, the authors propose a Feature Correlation Clustering-based Multi-Modality Fusion Framework (FCC-MMF) for multimedia semantic retrieval. Features from different modalities are combined into one feature set with the same representation via a normalization and discretization process. Within and across modalities, multiple correspondence analysis is utilized to obtain the correlation between feature-value pairs, which are then projected onto the two principal components. K-medoids algorithm, which is a widely used partitioned clustering algorithm, is selected to minimize the Euclidean distance within the resulted clusters and produce high intra-correlated feature-value pair clusters. Majority vote is applied to subsequently decide which cluster each feature belongs to. Once the feature clusters are formed, one classifier is built and trained for each cluster. The correlation and confidence of each classifier are considered while fusing the classification scores, and mean average precision is used to evaluate the final ranked classification scores. Finally, the proposed framework is applied on NUS-wide Lite data set to demonstrate the effectiveness in multimedia semantic retrieval.

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