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Class imbalance poses difficulties in training a classifier that perform well on minority classes, especially when there is a high imbalance ratio and significant class overlap. Existing data-level methods often suffer from problems like information loss and overfitting. To address these problems, we introduce a novel majority affiliation based under-sampling method (MAUS). The MAUS method employs a support vector data description model to capture the distribution of the minority class, thereby forming a hyper-sphere to establish a majority affiliation for each sample. The high-dimensional hyper-sphere constructed through all minority class samples avoids the problem of overfitting. Leveraging the majority affiliation in conjunction with the k-nearest neighbor algorithm, MAUS is capable of identifying region of class overlap and subsequently removing majority samples within these regions that negatively impact classification performance. This selective removal process minimizes excessive information loss at classification boundaries while alleviating the issue of class overlap. Furthermore, by removing those majority samples that are situated far from the classification boundary, MAUS reduces the imbalance ratio to the expected value, resulting in the attainment of a balanced dataset. To validate the effectiveness of our method, we conducted extensive experiments comparing it with state-of-the-art methods on 30 publicly available datasets. The results indicate that our approach outperforms existing methods on most of datasets and classifiers. • Based on hyper-sphere built by all minority samples, majority affiliation quantifies the probability of a sample belonging to the majority class. • Each k-nearest neighbor majority sample within the overlapping region that adversely impacts the classification performance were removed. • Constrained by the expected imbalance ratio, majority sample with the largest weight is removed in turn, ultimately yielding a balanced dataset. • Two sets of experiments were conducted on 30 real-world datasets to evaluate its performance against 13 state-of-the-art resampling methods. [ABSTRACT FROM AUTHOR]

In recent years, the coexistence of different states in the neural system has attracted widespread interest. Researchers have found a coexisting state of spiking and resting in homogeneous networks, which is known as the chimera-like state. The real cortical network is a much more complex and heterogeneous network. Therefore, the excitatory-inhibitory cortical neuronal network is constructed based on Hodgkin-Huxley neuronal model in this paper, and the chimera-like state is further investigated in the heterogeneous network. It is found that the chimera-like state is related to the balance between excitatory and inhibitory synaptic currents. The excitatory coupling current can counteract the initial condition effect and promote synchronized firing of neurons in the network. The inhibitory coupling current desynchronizes the network and thus induces synaptic noise, resulting in an inverse bell-shaped dependence of the change in the number of spiking neurons. We analyzed the underlying mechanisms of synaptic noise in the phase plane diagram and found it has asymmetry for the neuronal state transition. In addition, neurons with low degrees have a higher probability of undergoing state transitions. Finally, we verified that the chimera-like state is robust to network topology and initial conditions. The results provide a new insight into neuronal interactions in heterogeneous networks and might help to reveal the mechanisms of coexistence of different states in the cortical network. • A excitatory-inhibitory cortical neuronal network model is proposed to study the chimera-like state. • The existence of the chimera-like state is related to the balance between excitatory and inhibitory synaptic currents. • The inhibitory coupling strength can lead to network desynchronization and thus induces synaptic noise. • Neurons in the network have a higher probability of state transitions at low in-degree. [ABSTRACT FROM AUTHOR]

The simplicial contagion model is employed to study the spreads of two epidemics with mutation in high-order networks. The original epidemic can give birth to a mutated epidemic, but not vice versa. Numerical simulations and mean-field theory results reveal that the spread of the mutated epidemic is entirely dependent on the original epidemic if it cannot spread independently. Conversely, the spread of the original epidemic is entirely inhibited when mutated epidemic spreads by itself. The stability analysis of mean-field theory explains the extinction of the original epidemic and the emergence of a bistable region. Two stable equilibrium points remain unchanged despite variations in parameters like the original epidemic's infection probabilities and mutation rates. While the neighborhood of the stable equilibrium points is regulated by the above parameters. Our conclusions have also been validated in real-world networks. • Two epidemics with mutation spreads in high-order network is investigated. • Mutated epidemic is entirely on the original epidemic if it cannot spread independently. • The spread of the original epidemic is inhibited if the mutated epidemic spreads by itself. • There is a bistable region when mutated epidemics spread independently. [ABSTRACT FROM AUTHOR]