Human activity is intermittent, and social interaction changes over time. It embodies the time-varying nature of social networks. However, due to the complexity and dynamics of time-varying networks, the analysis opinion dynamics with decision-making over time-varying social networks is still a challenging problem. In this paper, we study the time-varying social network opinion dynamics under one-step ahead optimal decision-making mechanism. An explicit relationship between the supremum/infimum of the ultimate opinions, the maximum desired opinion and the maximum/minimum intensity of the influence of players is given. We provide the criteria for determining that individual achieves the desired opinion. Moreover, an explicit relationship between the ultimate opinions and the influence weight of the decision-making mechanism is presented. Besides, we employ our theory framework to analyze time-varying Friedkin-Johnsen opinion dynamics under one-step ahead optimal decision-making mechanism. Based on the real networks (Dolphin network and western US power grid) and the datasets, simulation experiments applying our theory illustrate that the dolphins achieve the desired performance by the keepers and the substations achieve the desired voltage regulation rates by the technicians.

Research on neural dynamics indicates that cognitive processes are driven by metastable state transitions in the brain, which highlights the temporally discontinuous nature of brain activity. However, many existing methods fail to account for this discontinuity, limiting their effectiveness in modeling the brain’s temporal dynamics. To address this, we propose the Metastability Quantized Variational Autoencoder (MQVAE), a neurologically inspired neural network designed to capture metastable dynamics from electroencephalography (EEG) for brain-computer interface (BCI). In MQVAE, EEG signals are quantized into a time series of discrete latent variables using vector quantization. This quantization facilitates the identification of metastable states and the construction of a latent state space. To model state transition patterns, we incorporate a transformer module to capture global temporal dependencies among the discrete latent variables. Furthermore, a multi-scale spatial convolution layer is embedded within the encoder to extract hierarchical spatial patterns. Experiments on two public datasets demonstrate that MQVAE outperforms state-of-the-art methods in both emotion recognition and motor imagery classification tasks. Interpretability analyses further confirm that the features learned by MQVAE are neurophysiologically meaningful.

The multi-modal neuroimage study has provided insights into understanding the heteromodal relationships between brain network organization and behavioral phenotypes. Integrating data from various modalities facilitates the characterization of the interplay among anatomical, functional, and physiological brain alterations or developments. Graph Neural Networks (GNNs) have recently become popular in analyzing and fusing multi-modal, graph-structured brain networks. However, effectively learning complementary representations from other modalities remains a significant challenge due to the sophisticated and heterogeneous inter-modal dependencies. Furthermore, most existing studies often focus on specific modalities (e.g., only fMRI and DTI), which limits their scalability to other types of brain networks. To overcome these limitations, we propose a HyperComplex Graph Neural Network (HC-GNN) that models multi-modal networks as hypercomplex tensor graphs. In our approach, HC-GNN is conceptualized as a dynamic spatial graph, where the attentively learned inter-modal associations are represented as the adjacency matrix. HC-GNN leverages hypercomplex operations for inter-modal intersections through cross-embedding and cross-aggregation, enriching the deep coupling of multi-modal representations. We conduct a statistical analysis on the saliency maps to associate disease biomarkers. Extensive experiments on three datasets demonstrate the superior classification performance of our method and its strong scalability to various types of modalities. Our work presents a powerful paradigm for the study of multi-modal brain networks.

In practical medical image segmentation tasks, ensuring confidence calibration is crucial. However, medical image segmentation typically relies on hard labels (one-hot vectors), and when minimizing the cross-entropy loss, the model’s softmax predictions are compelled to align with hard labels, resulting in over-confident predictions. To alleviate above problems, this study proposes a novel framework on calibration of medical image segmentation, called CALSeg. The Variational Label Smoothing (VLS) method is innovatively proposed, which learns the latent joint distribution of images and labels through variational inference to capture complex relationships between images and labels. This enables the effective estimation of latent soft labels by learning pixel-level information and semantic probability distribution features. The training of a neural network based on estimated soft labels provides a regularization effect, effectively preventing model overfitting and improving the calibration of the model. Comprehensive experiments on two medical image segmentation datasets demonstrate that CALSeg achieved optimal network calibration while also improving segmentation accuracy. The code is available at https://github.com/Guoxt/CALSeg.

Recent advancements in fMRI-based brain disorder diagnosis have shown that graph neural networks (GNNs) have been state-of-the-art methods for brain network analysis. Among them, transductive and inductive learning can be exploited by GNN. Transductive graphs, such as population graphs, take each subject as a node and use the node classification task for diagnosis. This line of work suffers from high computational costs and poor scalability to unseen data. Inductive methods, on the other hand, only consider labeled data and may suffer from overfitting and poor generalization when training with insufficient samples. To address these limitations, we propose a unified transductive-inductive network to study the properties of both transductive and inductive learning frameworks. Our approach is implemented in a self-knowledge distillation architecture, where transductive predictions are distilled from a transductive population graph network into an inductive network as a self-supervised regularization term. To preserve the topological properties within transductive graphs, i.e., inter-node similarity, we propose a topology-regularized self-knowledge distillation (Topo-KD) approach to regularize the student model’s learning. Evaluations on the ADNI dataset demonstrate the superiority of the approach in performance and scalability.

EEG signal classification using Riemannian manifolds has shown great potential. However, the huge computational cost associated with Riemannian metrics poses challenges for applying Riemannian methods, particularly in high-dimensional feature data. To address these, we propose an efficient ensemble method called MLCSP-TSE-MLP, which aims to reduce the computational cost while achieving superior performance. MLCSP of the ensemble utilizes a Riemannian graph embedding strategy to learn intrinsic low-dimensional sub-manifolds, enhancing discrimination. TSE uses the Euclidean mean as the reference point for tangent space mapping and reducing computational cost. Finally, the ensemble incorporates the MLP classifier to offer improved classification performance. Classification results conducted on three datasets demonstrate that MLCSP-TSE-MLP achieves significant superior performance compared to various competing methods. Notably, the MLCSP-TSE module achieves a remarkable increase in training speed and exhibits much lower test time compared to traditional Riemannian methods. Based on these results, we believe that the proposed MLCSP-TSE-MLP is a powerful tool for handling high-dimensional data and holds great potential for practical applications.

The multi-modal neuroimage study has dramatically facilitated disease diagnosis. Tensor-based methods are commonly used to represent multi-modal data as multi-dimensional arrays and usually implement matrix decomposition. These methods can be seen as a linear algebraic way for the lossy compression of an array. However, involved lossy operations might have a negative impact on performance, and overlook underlying important complementary information between modalities. This study proposes a Tensor-based Complex-valued Graph Neural Network (TC-GNN) to model multimodal neuroimages as complex-valued tensor graphs by investigating underlying complementary associations and cross-modality message aggregation. Experiments on two real-world datasets demonstrate our method’s consistent improvements and superiority over other baseline models in multi-modal brain disease analysis.

Phase synchronization phenomena are directly connected with the underlying neural mechanisms of certain cognitive processes. However, only the amplitude information is utilized in most electroencephalogram (EEG)-based brain-computer interfaces (BCIs). Few of the existing methods can simultaneously measure the amplitude and phase information required for classification. In this study, a novel common amplitude-phase measurement (CAPM) method is proposed. This method is capable of jointly measuring the phase and amplitude information of EEG signals on the Riemannian manifold. The proposed CAPM method comprises a two-step approach. First, a novel Riemannian graph embedding is proposed for dimensionality reduction while performing spatial-spectral filtering. The graph embedding is excellent in capturing the intrinsic features contained by the physiological signal. Second, to enhance robustness, a novel classifier is designed to incorporate the regularized linear regression in the computation of Riemannian distance. Experimental results on two BCI competition datasets demonstrate CAPM can yield high classification performance. The proposed CAPM method is a promising tool in analyzing EEG amplitude-phase characteristics and exhibits great potential in BCI applications.

The pervasiveness and significance of software have upset its subordinate role in the original software-hardware relationship. Software has become a main factor for defining and differentiating various “things”. This has led to a new emerging technology trend called Software-defined Anything (SDx), where a system's functionality and diversity are largely characterized by its software. SDx technologies are now pervading and reshaping many industries. This work initiates the investigation on possible approaches for applying the SDx paradigm in automation and control systems. In particular, a framework consists of commodity control hardware, service-oriented control software, and a cloud computing platform has been proposed. An application in building automation and management system has been developed and deployed to demonstrate the proposed approach's feasibility.

Coded caching has demonstrated the superiority in mitigating traffic pressure through jointly considering content delivery and storage schemes. However, existing works mainly focus on the situation where users have uniform demands with multiple layer of caches. In this paper, we propose a multilevel hierarchical coded caching scheme when users have nonuniform demands in a multi-hop content delivery network scenario. Specifically, to maintain the symmetry constraint of coded caching we utilize K-means to separate the file set with arbitrary distribution of popularity into several file subsets. We also formulate the Jensen's inequality and derive the upper bound of the transmission rate in each layer. To evaluate the system efficiency, we leverage the open-source Netflix dataset as our file set and conduct an experiment on a content delivery network with two layers of caches. Experimental results demonstrates that our multilevel hierarchical coded caching scheme performs much better than the baseline LFU caching scheme.