Link communities of three artifical networks.

<p>(A) The network consists of five overlapping communities. Nodes 1, 7, 12, 16 are overlapping nodes; (B) The network consists of two overlapping communities. Nodes 1 and 2 are overlapping nodes that belong to the two communities, and link (1, 2) belongs to the two communities as well; (C) The network consists of two overlapping cliques and the overlapped subgraph is a 3-clique.</p

The network in Ref. [11] can be correctly partitioned into three communities by our model, and the objective function value is 1.

<p>The network in Ref. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone.0083739-Ahn1" target="_blank">[11]</a> can be correctly partitioned into three communities by our model, and the objective function value is 1.</p

Link communities of three networks of heterogeneous cliques.

<p>(A) The ring network of heterogeneous cliques. Each community is a clique, and two adjacent communities are connected by one node. (B) The ring network of overlapping heterogeneous cliques. Each community is a clique, and two adjacent communities are connected by one node or one link. (C) The tree network of heterogeneous cliques. Each community is a clique, and two adjacent communities are overlapped by one node <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone.0083739-Ahn1" target="_blank">[11]</a>.</p

Link communities of some real-world networks.

<p>(A) The Karate club network; (B) The word association network; (C) The co-appearance network.</p

The parameters used in the GA algorithm for solving the link community detection problem on networks in Figure 2.

<p>The parameters used in the GA algorithm for solving the link community detection problem on networks in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0083739#pone-0083739-g002" target="_blank">Figure 2</a>.</p

Three different partition results of a tree network.

<p>(A) Correct partition. (B,C) Two counter-intuitive partitions. The red links and their adjacent nodes constitute a community, the blue links and their adjacent nodes form another community. The black node is overlapped.</p

DataSheet_1_Deep focus-extended darkfield imaging for in situ observation of marine plankton.pdf

Darkfield imaging can achieve in situ observation of marine plankton with unique advantages of high-resolution, high-contrast and colorful imaging for plankton species identification, size measurement and abundance estimation. However, existing underwater darkfield imagers have very shallow depth-of-field, leading to inefficient seawater sampling for plankton observation. We develop a data-driven method that can algorithmically refocus planktonic objects in their defocused darkfield images, equivalently achieving focus-extension for their acquisition imagers. We devise a set of dual-channel imaging apparatus to quickly capture paired images of live plankton with different defocus degrees in seawater samples, simulating the settings as in in situ darkfield plankton imaging. Through a series of registration and preprocessing operations on the raw image pairs, a dataset consisting of 55 000 pairs of defocused-focused plankter images have been constructed with an accurate defocus distance label for each defocused image. We use the dataset to train an end-to-end deep convolution neural network named IsPlanktonFE, and testify its focus-extension performance through extensive experiments. The experimental results show that IsPlanktonFE has extended the depth-of-field of a 0.5× darkfield imaging system to ~7 times of its original value. Moreover, the model has exhibited good content and instrument generalizability, and considerable accuracy improvement for a pre-trained ResNet-18 network to classify defocused plankton images. This focus-extension technology is expected to greatly enhance the sampling throughput and efficiency for the future in situ marine plankton observation systems, and promote the wide applications of darkfield plankton imaging instruments in marine ecology research and aquatic environment monitoring programs.</p