In this article, to maximize the dimension of controllable subspace, we consider target controllability problem with maximum covered nodes set in multiplex networks. We call such an issue as maximum-cost target controllability problem. Likewise, minimum-cost target controllability problem is also introduced which is to find minimum covered node set and driver node set. To address these two issues, we first transform them into a minimum-cost maximum-flow problem based on graph theory. Then an algorithm named target minimum-cost maximum-flow (TMM) is proposed. It is shown that the proposed TMM ensures the target nodes in multiplex networks to be controlled with the minimum number of inputs as well as the maximum (minimum) number of covered nodes. Simulation results on Erdos-Rényi (ER-ER) networks, scale-free (SF-SF) networks, and real-life networks illustrate satisfactory performance of the TMM.

Controllability of complex networks refers to the ability of a networked system to steer from any initial state to any desired state in a finite time under feasible control inputs. Its robustness reflects the ability of a network to maintain controllability under various attacks, which records a series of controllability values under continuous attacks and can be directly determined by attack simulations. However, such simulation experiments are very time-consuming. It is necessary to design a model to replace simulation experiments to learn network controllability robustness with high precision and high speed, which is the objective of Network Controllability Robustness Learning . In this paper, given the superiority of spatial graph neural networks in generating network embedding, a novel controllability robustness learning approach via spatial graph neural networks (CRL-SGNN) is proposed by taking into account both generalization and high precision. In this scheme, controllability robustness related complex network representations that contain network topology information and degree-based node attribute information are first generated through the introduced graph convolutional layer and then sent to the prediction module consisting of multiple branches. A large number of experiments show that: 1) CRL-SGNN can overcome the issue of different distributions between training and test sets with satisfactory performance, implying strong generalization ability of CRL-SGNN; 2) Compared to the cutting-edge methods, CRL-SGNN can obtain superior results in less time; and 3) A highlight which cannot be ignored is that the embedding generation module of CRL-SGNN can serve as a transferable feature learning module to deal with complex networks of any size with low prediction error and high efficiency.

Iceberg and ship identification in satellite synthetic aperture radar (SAR) data plays an important role in offering an operational iceberg surveillance program. Here, the identification aims to detect ocean SAR targets and then categorize these targets into iceberg, ship, or unknown. Although the adaptive threshold techniques have achieved promising results on the ship and iceberg detection in SAR images, the discrimination between these two target classes is still very challenging for operational scenarios. This study presents a computational framework for iceberg and ship discrimination based on an ensemble of various deep learning and machine learning algorithms. On one hand, latest deep neural networks - namely, DenseNet and ResNet - are deployed in this study for end-to-end feature exaction and image classification directly on original SAR images. On the other hand, handcrafted features are extracted on de-speckled SAR images, followed by classification using advanced machine learning algorithms - namely, XGBoost and LightGBM. The outcomes from both sides are then combined through min-max median stacking approach to classify the given SAR images into iceberg and ship categories. The proposed framework has recently been deployed as the key kernel for the “Statoil/C-CORE Iceberg Classifier Challenge” organized by Kaggle. The performance is promising as our final scores were ranked 26 and 39 out of 3343 teams on public and private leaderboards, respectively. We hope that by sharing the solutions, we can further promote research interests in the field of iceberg and ship identification.

Sleep-wake detection is of great importance for the measurement of sleep quality. In this article, a novel ensemble deep learning framework is proposed to detect sleep-wake states based on heart rate variability (HRV) and acceleration. We firstly design a local feature based long short-term memory (LF-LSTM) network to encode temporal dependency and learn features from acceleration data with high sampling frequency. In the meantime, some handcrafted features are extracted from HRV which has a special data format. After that, we develop a unified framework to integrate these two types of features, i.e., the features extracted from HRV and the features learned by LF-LSTM from acceleration, to form a complete feature set. Finally, an efficient ensemble learning scheme is proposed to further boost the performance of sleep-wake classification. A real dataset has been collected to verify the effectiveness of the proposed approach. We also compare with some well-known benchmark approaches for sleep-wake detection. The results demonstrate that the proposed ensemble deep learning method outperforms all the benchmark approaches.

So far there are still limited results on the role of network topology matrix A in controlling complex networks with minimum control cost. In this paper, we study this problem and propose an optimization model by considering the topology matrix as a variable to minimize the control cost. As sparsity nature commonly exists in many real-world networks, sparse and non-negative constraints are included in the optimization model. Sparse projected gradient method (SPGM) is proposed to solve the problem numerically. The convergence property of SPGM is theoretically established. As the optimization model is non-convex, a sufficient and necessary condition is further derived to determine whether the converged solution is global optimal or not. Through numerous extensive simulations, we investigate the characteristics of the optimal topology with minimum control cost. It is found that several identical/similar stems are naturally evolved and the number of nodes in each stem is evenly distributed, which is also verified with examples from the cooperation problem in social networks. These findings provide a comprehensive understanding and explanation on the self-similarity phenomenon in real-life networks from the control cost point of view.