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Introduction: Studying the main factors and related paths of rumor propagation contributes to the precise governance of rumor information in social networks. Most existing network representation learning methods do not fit with real-world information propagation networks well, and the network cannot effectively model the temporal characteristics and dynamic evolution features of rumor information propagation. Methods: Our study proposes a new dynamic network representation model for information propagation. Additionally, the study introduces a feedback mechanism where the latest node representations are fed back to neighboring nodes. Results: The method solves the problem of delayed network representation and improves network representation performance. Discussion: We conducted experimental simulations, and the results indicate that a higher level of trust contributes to stable group relationships and converging opinions, reducing the likelihood of opinion dispersion. Furthermore, novelty of topics, and interactivity between users, and opinion leaders exhibit distinct characteristics in guiding public opinion. The viewpoint evolution of the newly constructed dynamic network representation model aligns more closely with viewpoint evolution in real-world social networks. [ABSTRACT FROM AUTHOR]

Contrastive-based clustering models usually rely on a large number of negative pairs to capture uniform representations, which requires a large batch size and high computational complexity. In contrast, some self-supervised methods perform non-contrastive learning to capture discriminative representations only with positive pairs, but suffer from the collapse of clustering. To solve these issues, a novel end-to-end self-supervised clustering model is proposed in this paper. The basic self-supervised learning network is first modified, followed by the incorporation of a Softmax layer to obtain cluster assignments as data representation. Then, adversarial learning on the cluster assignments is integrated into the methods to further enhance discrimination across different clusters and mitigate the collapse between clusters. To further encourage clustering-oriented guidance, a new cluster-level discrimination is assembled to promote clustering performance by measuring the self-correlation between the learned cluster assignments. Experimental results on real-world datasets exhibit better performance of the proposed model compared with the existing deep clustering methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) visual tracking has been a hot research topic in the field of remote sensing. Many filter-based UAV trackers have achieved excellent performance. However, existing methods do not distinguish the importance of different feature channels with semantic information and background information, which may hinder the tracker’s ability to adapt to changing environments. To deal with this problem, we propose a channel attentional correlation filters (CACFs) learning model. Specifically, we introduce the fuzzy C-means (FCMs) algorithm to preclassify the extracted features and then perform weight penalty to feature channels with different membership degrees. In addition, the filter can adapt more effectively to the background’s rapid changes during the UAV tracking process by learning the second-order difference between adjacent three frame features. Finally, the comparative experiments are conducted on three mainstream UAV datasets, including DTB70, UAV123@10fps, and UAVDT. The experimental results demonstrate the effectiveness of the proposed method. The tracking performance of CACF surpasses that of other state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Early analytical work showed that it is possible to discretize a continuous-time non-minimum phase (NMP) system using special sample and hold inputs (SHIs) and obtain a discrete-time minimum phase (MP) system with a higher sampling rate than that of conventional zero-order sample and hold. As a result, high-gain discrete-time control law can be used to further improve closed-loop system performance since the resulting discrete-time system is MP. In this paper, this technique is adopted for performance improvement on a mini-Segway (MS), a developed robot equipped with an extremely low-cost microcontroller (Arduino). A dual-loop control tuning method was developed to optimize the overall closed-loop system performance. The system performance improvement is demonstrated by the reduction of the mini-Segway oscillation magnitude under idle conditions. The experimental results show that the low-cost microcontroller can be used for the dual-loop SHI control scheme, and the MS cart displacement oscillation magnitude is significantly reduced by more than 65% over the baseline controller. The main contribution of this paper is implementing the proposed sample and hold input (SHI) scheme into an extremely low-cost micro-controller (Arduino) and demonstrating that low-cost microcontrollers can effectively utilize the proposed SHI. [ABSTRACT FROM AUTHOR]

Objectives. To study the resorption of the herniated lumbar disc (RHLD) and its mechanism in the SD rats of lumbar intervertebral disc herniation treated with Hui medicine moxibustion (HMM). Methods. Forty SD rats were randomly divided into four groups, normal group, lumbar disc herniation (LDH) group, HMM group, and antagonist (HMM+Met12) group, with 10 rats in each group. The rat model of LDH was prepared with the method of lumbar epidural emplacement of the caudal intervertebral disc. In the HMM group and HMM+Met12 groups, 4 weeks after modeling, HMM therapy was performed in the lumbar spine for 3 months with 1 time per day and 20 min each time, the samples were collected 8 weeks after the treatment. The histological degeneration was observed through HE staining, and the neovascularization of intervertebral disc tissues was detected by the expression of CD34 and vascular endothelial growth factor (VEGF). The apoptosis of nucleus pulpous cells was detected by TUNEL assay, and the activity of caspase-3, -8, and -9 and extracellular matrix enzymes was detected by western blotting. Results. HMM treatment significantly improved the behavioral ability of rats with LDH surgery. The morphological structure was obviously destroyed in the LDH group, but disc structure was significantly repaired in the HMM group, and mild structure alterations were observed in the HMM+Met12 group. Higher levels of CD34 and VEGF were detected in the HMM group indicating that neovascularization is formed. The expression level of FasL was significantly increased in the HMM group. The protein expression levels of cleaved-caspase-3, cleaved-caspase-8, and cleaved-caspase-9 in nucleus pulposus (NP) tissues were also elevated when treated with HMM, and the TUNEL staining showed the same results. The protein expression levels of matrix metalloproteinases- (MMP-) 1, MMP-2, MMP-3, MMP-13, and ADAMTS-4 were markedly promoted in the HMM group. Met12, a small peptide antagonist of FasL, significantly reduced the effects of HMM. Conclusion. HMM can promote the formation of neovascularization of lumbar intervertebral disc, support the apoptosis of NP cells through Fas/FasL signaling, and regulate the degradation of extracellular matrix enzyme, which then accelerates the absorption of lumbar intervertebral disc herniation and the recovery of motor function in rats. [ABSTRACT FROM AUTHOR]

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods. [ABSTRACT FROM AUTHOR]

Tensor-based robust principal component analysis (PCA) methods are efficient to discover the low-rank part of a hyperspectral image for reducing redundant information and guarantee good classification results. However, current methods cannot remove noise adequately, and the residual noise remaining in the low-rank image limits the further improvement of classification performance. Thus, enhancing the robustness to noise is important and helpful for tensor-based robust PCA (RPCA) methods to process hyperspectral images. To this end, we propose a tensor-based RPCA method with a locality preserving graph and frontal slice sparsity (LPGTRPCA) for hyperspectral image classification. Specifically, a tensor $l_{2,2,1}$ norm that requires the frontal slice sparsity of a tensor is defined to extract the noise in the hyperspectral image from the frontal direction. What is more, a position-based Laplacian graph that preserves the local structures of a tensor according to the spatial position is designed for relieving the impact of the residual noise remaining in the low-rank image. Based on the tensor nuclear norm, the tensor $l_{2,2,1}$ norm, and the position-based Laplacian graph, LPGTRPCA efficiently separates the low-rank part with little noise from a raw hyperspectral image and achieves more robust classification results than current methods. LPGTRPCA is optimized by the alternative direction multiplier method (ADMM), and the convergence of solutions is experimentally demonstrated. In the experiments conducted on Indian Pines, Pavia University, and Salinas datasets, LPGTRPCA outperformed various state-of-the-art and classical tensor-based RPCA methods in terms of average class classification accuracy (AA), overall classification accuracy (OA), and kappa coefficient (KC). [ABSTRACT FROM AUTHOR]

Symbiotic autonomous systems (SAS) are advanced intelligent and cognitive systems that exhibit autonomous collective intelligence enabled by coherent symbiosis of human–machine interactions in hybrid societies. Basic research in the emerging field of SAS has triggered advanced general-AI technologies that either function without human intervention or synergize humans and intelligent machines in coherent cognitive systems. This work presents a theoretical framework of SAS underpinned by the latest advances in intelligence, cognition, computer, and system sciences. SAS are characterized by the composition of autonomous and symbiotic systems that adopt bio-brain-social-inspired and heterogeneously synergized structures and autonomous behaviours. This paper explores the cognitive and mathematical foundations of SAS. The challenges to seamless human–machine interactions in a hybrid environment are addressed. SAS-based collective intelligence is explored in order to augment human capability by autonomous machine intelligence towards the next generation of general AI, cognitive computers, and trustworthy mission-critical intelligent systems. Emerging paradigms and engineering applications of SAS are elaborated via autonomous knowledge learning systems that symbiotically work between humans and cognitive robots. This article is part of the theme issue 'Towards symbiotic autonomous systems'. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew it in 2020. It has become the most unprecedented challenge to analytic epidemiology (AE) in general and signal processing (SP) theories specifically. In this regard, medical imaging plays an important role for the management of COVID-19. SP and deep learning (DL) models can assist in the development of robust radiomics solutions for the diagnosis/prognosis, severity assessment, treatment response, and monitoring of COVID-19 patients. [ABSTRACT FROM AUTHOR]

This paper presents a control-oriented LPV (Linear Parameter-Varying) model for commercial vehicle air brake systems, where a pneumatic valve actuator is used to control the brake chamber pressure. To improve the brake system response time and reduce the vehicle stopping distance, the traditional treadle valves used in the air brake system are replaced by electro-pneumatic valves. Also, to develop the model-based brake control strategy, a nonlinear mathematical model is developed based on Newton's second law, fluid dynamics of the orifice, force balance of spool, and solenoid dynamic characteristics. The brake chamber dynamics is also considered during the charging and discharging processes. The developed nonlinear model is calibrated based on both valve actuator geometry and test bench experimental results. It is proposed to model the nonlinear system in the LPV form so that gain-scheduling controllers can be developed. To obtain the LPV model, system identification is conducted using the calibrated nonlinear model to obtain a set of linearized models under different brake chamber pressure levels, and the resulting identified linear models are assembled to form the LPV model with brake chamber pressure as the varying parameters. A linear infinite-horizon continuous-time LQR (Linear Quadratic Regulator) controller was designed for the braking system based on the developed LPV model with the fixed parameter to demonstrate the effectiveness of the developed LPV model. [ABSTRACT FROM AUTHOR]

Big data play an indispensable role not only in the cognitive mechanisms of human sensation, quantification, qualification, estimation, memory, and reasoning, but also in a wide range of engineering applications. A basic study on the theoretical foundations of big data science is presented with a coherent set of general principles and analytic methodologies for big data systems. Cognitive foundations of big data are explored in order to formally explain the origination and nature of big data. A set of mathematical models of big data are created that rigorously elicit the general essences and patterns of big data across pervasive domains in sciences, engineering, and societies. A significant finding towards big data science is that big data systems in nature are a recursive -dimensional-typed hyperstructure (RNTHS) rather than pure numbers. The fundamental topological property of big data reveals a set of denotational mathematical solutions for dealing with inherited complexities and unprecedented challenges in big data engineering. [ABSTRACT FROM AUTHOR]

The advantages of minimum-phase (MP) systems for control applications are well known. Prior research has shown that a non-MP (NMP) system can be discretized to an MP system using either a zero-order hold (ZOH) or a square-pulse sample-and-hold input (SHI). This paper investigates the MP characteristics of the discrete-time (DT) system obtained by discretizing a continuous-time single-input single-output NMP system using different SHIs. Two new SHIs (forward and backward triangular) are studied in addition to the square pulse. Numerical simulations were adopted for studying the MP property of the resulting DT system as a function of sample-and-hold parameters. The simulation results show that it is possible to find a smaller sampling period that results in an MP DT system using the proposed SHIs compared with the ZOH. The $q$ -Markov cover system identification with pseudorandom binary signal was then used for a hardware-in-the-loop (HIL) simulation study. A resistor–capacitor filter was used to represent the implementation error of the SHI due to unmodeled actuator dynamics. The HIL simulation results show that the proposed SHI scheme is robust to actuator modeling error. The MP properties of the DT systems with three SHIs are compared. The results show that the forward triangle SHI has the best performance due to its robustness to unmodeled actuator dynamics and the capability of retaining the MP property of the discretized system at small sampling periods. [ABSTRACT FROM AUTHOR]

Basic studies in system science explore the theories, principles, and properties of abstract and concrete systems as well as their applications in system engineering. Systems are the most complicated entities and phenomena in abstract, physical, information, cognitive, brain, and social worlds across a wide range of science and engineering disciplines. The mathematical model of a general system is embodied as a hyperstructure of the abstract system. The theoretical framework of system science is formally described by a set of algebraic operations on abstract systems known as system algebra. A set of abstract structures, properties, behaviors, and principles is rigorously formalized in contemporary system theories. Applications of the formal theories of system science in system engineering, intelligent engineering, cognitive informatics, cognitive robotics, software engineering, cognitive linguistics, and cognitive computing are demonstrated, which reveals how system structural and behavioral complexities may be efficiently reduced in system representation, modeling, analysis, synthesis, inference, and implementation. [ABSTRACT FROM AUTHOR]