Resting brain activity emerges from wave propagating along spatiotemporal varying hyper-structural connectome

How spontaneous brain activities emerge from the structural connectivity (SC) has puzzled researchers for a long time. The underlying mechanism still remains largely unknown. Previous studies on modeling the resting-state human brain functional connectivity (FC) are normally based on the relatively static structural connectome directly and very few of them concern about the dynamic spatiotemporal variability of FC. Here we establish an explicit wave equation to describe the spontaneous cortical neural activities based on the high-order hypergraph representation of SC. Theoretical solution shows that the dynamic couplings between brain regions fluctuates in the form of an exponential wave regulated by the spatiotemporal varying Laplacian of the hyper-structural connectome (hSC), which orchestrates the cortical activities propagating in both space and time. Ultimately, we present a possible mechanism of how negative correlations emerge during the fluctuation of the hypergraph Laplacian of SC, which helps to further understand the fundamental role of SC in shaping the entire pattern of FC with a new perspective. Comprehensive tests on four connectome datasets with different resolutions confirm our theory and findings.

Geometry and symmetry in biochemical reaction systems

Complex systems of intracellular biochemical reactions have a central role in regulating cell identities and functions. Biochemical reaction systems are typically studied using the language and tools of graph theory. However, graph representations only describe pairwise interactions between molecular species and so are not well suited to modelling complex sets of reactions that may involve numerous reactants and/or products. Here, we make use of a recently developed hypergraph theory of chemical reactions that naturally allows for higher-order interactions to explore the geometry and quantify functional redundancy in biochemical reactions systems. Our results constitute a general theory of automorphisms for oriented hypergraphs and describe the effect of automorphism group structure on hypergraph Laplacian spectra.

Detection of Unusual Targets in Traffic Images Based on One-Class Extreme Machine Learning

In traffic image target detection, unusual targets like a running dog has not been paid sufficient attention. The mature detection methods for general targets cannot be directly applied to detect unusual targets, owing to their high complexity, poor feature expression ability, and requirement for numerous manual labels. To effectively detect unusual targets in traffic images, this paper proposes a multi-level semi-supervised one-class extreme learning machine (ML-S2OCELM). Specifically, the extreme learning machine (ELM) was chosen as the basis to develop a classifier, whose variables could be calculated directly at the cost of limited computing resources. The hypergraph Laplacian array was employed to improve the depiction of data smoothness, making semi-supervised classification more accurate. Furthermore, a stack auto-encoder (AE) was introduced to implement a multi-level neural network (NN), which can extract discriminative eigenvectors with suitable dimensions. Experiments show that the proposed method can efficiently screen out traffic images with unusual targets with only a few positive labels. The research results provide a time-efficient, and resource-saving instrument for feature expression and target detection.

Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification

Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.