Termansencolon4209

Recently, salient object detection (SOD) has witnessed vast progress with the rapid development of convolutional neural networks (CNNs). However, the improvement of SOD accuracy comes with the increase in network depth and width, resulting in large network size and heavy computational overhead. This prevents state-of-the-art SOD methods from being deployed into practical platforms, especially mobile devices. To promote the deployment of real-world SOD applications, we aim at developing a lightweight SOD model in this article. Our observation comes from that the primate visual system processes visual signals hierarchically with different receptive fields and eccentricities in different visual cortex areas. Inspired by this, we propose a hierarchical visual perception (HVP) module to imitate the primate visual cortex for hierarchical perception learning. find more With the HVP module incorporated, we design a lightweight SOD network, namely, HVPNet. Extensive experiments on popular benchmarks demonstrate that HVPNet achieves highly competitive accuracy compared with state-of-the-art SOD methods while running at 4.3 frames/s CPU speed and 333.2 frames/s GPU speed with only 1.23M parameters.There is uncertainty in the system, and we consider that uncertainty is (possibly fast) time varying, but with definite bound. Fuzzy set theory is used to describe the inexact boundary and then the problem of robust control of uncertain dynamical systems is studied. Based on two adjustable design parameters, a robust control method for general mechanical systems is proposed. The control is deterministic, not the conventional IF-THEN rule based. By using the Lyapunov minimax approach, it is proved that the proposed control can guarantee system performance to be uniformly bounded and uniformly ultimately bounded. In order to find the optimal solution in the prescribed range, a two-player cooperative game is used. To reduce costs while ensuring control performance, two performance indices are developed, each of which is controlled by an adjustable parameter (i.e., player). Both necessary and sufficient conditions for Pareto-optimality are established. Using these conditions, the Pareto-optimal solution can be obtained. The effectiveness of the control design is demonstrated by the simulation of the two-body pendulum.Impulsive control is widely applied to achieve the consensus of multiagent networks (MANs). It is noticed that malicious agents may have adverse effects on the global behaviors, which, however, are not taken into account in the literature. In this study, a novel delayed impulsive control strategy based on sampled data is proposed to achieve the resilient consensus of MANs subject to malicious agents. It is worth pointing out that the proposed control strategy does not require any information on the number of malicious agents, which is usually required in the existing works on resilient consensus. Under appropriate control gains and sampling period, a necessary and sufficient graphic condition is derived to achieve the resilient consensus of the considered MAN. Finally, the effectiveness of the resilient delayed impulsive control is well demonstrated via simulation studies.This article investigates a class of finite-time cooperative tracking problems of heterogeneous mixed-order multiagent systems (MASs) with higher-order dynamics. Different from the previous works of heterogeneous MASs, the agents in this study are considered to have different first-, second-, or even higher-order nonlinear dynamics. It means that, according to different tasks and situations, the following agents can have nonidentical orders or different numbers of states to be synchronized, which is more general for the practical cooperative applications. The leader is a higher-order nonautonomous system and contains full state information to be synchronized for all agents with mixed-order dynamics. Accordingly, the spanning tree is defined based on the specific state rather than on the agent to guarantee that each following agent can receive adequate state information. Distributed control protocols are designed for all agents to achieve the ultimate state synchronization to the leader in finite time. The Lyapunov approach is used for the stability analysis and a practical example of mixed-order mechanical MASs verifies the effectiveness and performance of the proposed distributed control protocols.Network embedding aims to learn the low-dimensional node representations for networks, which has attracted an increasing amount of attention in recent years. Most existing efforts in this field attempt to embed the network based on node similarity, which generally relies on edge existence statistics of the network. Instead of relying on the global edge existence statistics for every node pair, in this article, we utilize the information between a pair of nodes in a local way and propose a model, called node pair information preserving network embedding (NINE), based on adversarial networks. The main idea lies in preserving the node pair information (NI) by means of adversarial networks. The architecture of the proposed NINE model consists of three main components, namely 1) NI embedder; 2) NI generator; and 3) NI discriminator. In the NI embedder, to avoid the complicated similarity calculation for a pair of nodes, the original NI vector calculated from the direct neighbor information of the two nodes is adopted as features, and the edge existence information is taken as labels to learn the embedded NI vector in a supervised learning manner. The second component is the NI generator, which takes the original node representation vectors of a node pair as input and outputs the generated NI vector. In order to make the generated NI vector follow the same distribution of the corresponding embedded NI vector, the generative adversarial network (GAN) is adopted, resulting in the third component, called the NI discriminator. Extensive experiments are conducted on seven real-world datasets in three downstream tasks, namely 1) network reconstruction; 2) link prediction; and 3) node classification. Comparison results with seven state-of-the-art models demonstrate the effectiveness, efficiency, and rationality of our model.