The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.

Controlling complex systems is a fundamental challenge of network science. Recent advances indicate that control over the system can be achieved through a minimum driver node set (MDS). The existence of multiple MDS's suggests that nodes do not participate in control equally, prompting us to quantify their participations. Here we introduce control capacity quantifying the likelihood that a node is a driver node. To efficiently measure this quantity, we develop a random sampling algorithm. This algorithm not only provides a statistical estimate of the control capacity, but also bridges the gap between multiple microscopic control configurations and macroscopic properties of the network under control. We demonstrate that the possibility of being a driver node decreases with a node's in-degree and is independent of its out-degree. Given the inherent multiplicity of MDS's, our findings offer tools to explore control in various complex systems.

Regulation of intrinsic noise in gene expression is essential for many cellular functions. Correspondingly, there is considerable interest in understanding how different molecular mechanisms of gene expression impact variations in protein levels across a population of cells. In this work, we analyze a stochastic model of bursty gene expression which considers general waiting-time distributions governing arrival and decay of proteins. By mapping the system to models analyzed in queueing theory, we derive analytical expressions for the noise in steady-state protein distributions. The derived results extend previous work by including the effects of arbitrary probability distributions representing the effects of molecular memory and bursting. The analytical expressions obtained provide insight into the role of transcriptional, post-transcriptional, and post-translational mechanisms in controlling the noise in gene expression.

With the widespread innovation of the Internet of Things, software-defined networking (SDN), and cloud computing, cyber-physical systems (CPSs) have been developed and widely adopted to facilitate our daily life and economy. In particular, modern society heavily relies on all kinds of CPSs, such as smart grids, and transportation systems. So the shutdown of critical services can lead to serious consequences. Meanwhile, distributed denial-of-service (DDoS) attacks are becoming a major threat to the CPSs due to their ease of execution and the devastation they cause. In addition, owing to the constant updating of attack methods, there is an urgent need for a method to defend against both the known and unknown DDoS attacks. In this article, we present an adaptive DDoS attack mitigation (ADAM) scheme to detect and mitigate DDoS attacks in software-defined CPSs. By combining information entropy and unsupervised anomaly detection methods, ADAM can not only automatically determine the current state, but also adaptively identify suspicious features and thereafter precisely mitigate DDoS attacks. We also propose a pipeline filtering mechanism to accurately drop attack traffic, and this method can be implemented in the existing SDN networks without additional devices required. Unlike most of the classification-based DDoS mitigation scenarios, we aim to mitigate a wide spectrum of DDoS attacks without defining attack characteristics in advance. Real data-driven experimental results show that ADAM has an average mitigation accuracy of 99.13% under high-intensity DDoS attacks. Compared to similar work, our method reduces the false-positive rate by 35%-59%.