Convolutional Neural Network (CNN) uses convolutional layers to explore spatial/temporal adjacency to construct new feature representations. So, CNN is commonly used for data with strong temporal/spatial correlations, but cannot be directly applied to generic learning tasks. In this paper, we propose to enable CNN for learning from generic data to improve classification accuracy. To take the full advantage of CNN’s feature learning power, we propose to convert each instance of the original dataset into a synthetic matrix/image format. To maximize the correlation in the constructed matrix/image, we use 0/1 optimization to reorder features and ensure that the ones with strong correlations are adjacent to each other. By using a feature reordering matrix, we are able to create a synthetic image to represent each instance. Because the constructed synthetic image preserves the original feature values and correlation, CNN can be applied to learn effective features for classification. Experiments and comparisons, on 22 benchmark datasets, demonstrate clear performance gain of applying CNN to generic datasets, compared to conventional machine learning methods. Furthermore, our method consistently outperforms approaches which directly apply CNN to generic datasets in naive ways. This research allows deep learning to be broadly applied to generic datasets.
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Ambient-pressure lignin valorization to high-performance polymers by intensified reductive catalytic deconstruction
Process intensification was leveraged to develop a scalable route to upgrade lignin to high-performance materials and chemicals.
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- Award ID(s):
- 1934887
- NSF-PAR ID:
- 10356328
- Date Published:
- Journal Name:
- Science Advances
- Volume:
- 8
- Issue:
- 3
- ISSN:
- 2375-2548
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The dataset includes the measurements of individual subduction zones defined in the convergence-parallel, trench-perpendicular, and spreading-parallel direction.
Table S3. Location of each trench, arc, and back-arc defined in a direction parallel to the convergence, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200).
Table S4. Location of each trench, arc, and back-arc defined in a direction perpendicular to the trench, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200).
Table S5. Location of each trench, arc, and back-arc defined in a direction parallel to the spreading direction, and the corresponding distance from the trench to the arc (D_TA), subarc slab depth (H), and from the trench to the back-arc spreading center (D_TB). The slab dip is measured at 50km (Dip50), 100km (Dip100), and 200km (Dip200) and averaged from 0 to 50 km (Dip050), 0 to 100km (Dip0100), 0 to 200km (Dip0200), and 50 to 200km (Dip50200).
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1126/sciadv.abj7523
