Abstract Based on a modified four-dimensional Hindmarsh-Rose neuron model, two neurons are coupled using chemical synapses to study the effects of different parameters on the bifurcation and synchronization of the coupled system. Matcant software is used to determine the type of equilibrium point and Hopf bifurcation point. It is found that the system generates subcritical Hopf bifurcation with the change of parameters, and the hidden dynamics behavior near the subcritical Hopf bifurcation point is discussed. Based on the bifurcation theory, the effects of single and multiple parameters on the firing pattern, bifurcation, and synchronization of the coupled neuron system are studied, and it is concluded that the coupled neuron system can generate periodic firing, inverse period-doubling discharge, and chaotic cluster discharge under different parameter values, and compare and analyze the synchronization situation of the coupled system before and after adding time lag; moreover, a certain time lag can destroy and delay the synchronization state of the coupled neural system is found. Using the synchronization factor statistic, the synchronous behavior and existence of chimera states of coupled neural network systems are studied, which will provide useful clues to reveal the underlying mechanisms of information encoding and transmission processes in complex neural systems.

Accurate detection of depression through speech analysis offers a promising non-invasive approach for early diagnosis and intervention. However, the high dimensionality and complexity of speech features present significant challenges in identifying the most relevant features for depression detection. This study applies the spFSR (Feature Selection and Ranking via Simultaneous Perturbation Stochastic Approximation) technique to a comprehensive 2,268-dimensional speech feature set, focusing on selecting features specifically relevant to depression. The effectiveness of the spFSR method is evaluated using two well-known datasets: DAIC-WOZ and CMDC. The selected feature set was assessed across various machine learning models, demonstrating substantial improvements in key performance metrics on both datasets. The results indicate that the spFSR method effectively optimizes feature selection for depression detection, leading to more robust and accurate predictive models across different datasets. Our study find that the top 10 features for effective speech-based depression detection include spectral features (e.g., spectral flux, entropy, flatness), fundamental frequency metrics (e.g., lowest percentile), periodic features (e.g., jitter), and MFCC attributes (e.g., segment length, skewness).

Numerous studies have emphasized the essential function of video laryngoscopes in tracheal intubation, where providing clear visualizations of throat anatomy significantly raises success rates. Clinically, less-experienced doctors may encounter difficulties due to limited familiarity with these structures, and in emergency situations, achieving intubation on the first attempt is crucial for patient safety. Although deep learning is widely applied in medical imaging for its strong recognition capabilities, there remains a gap in applying it to throat image recognition. In this study, we gathered and labeled datasets from video laryngoscopes, verified by clinical experts. We designed a deep learning segmentation model, MP-UNet, that incorporates a multi-scale feature extraction block and a Pyramid Fusion Attention block. With these modules, the model is particularly effective at managing the feature extraction and fusion requirements for throat structures across different scales. Our model showed a minimum 10% improvement in IoU, the most critical metric, over the original U-Net. Compared to other models, MP-UNet also demonstrated strong segmentation performance on the throat dataset.

Hyperspectral image (HSI) classification technology has continuously made breakthroughs. Especially with the emergence of convolutional neural networks (CNN), its performance has been rapidly enhanced. However, CNN uses kernels with fixed sizes, which cannot flexibly handle data with irregular patterns, affecting the HSI classification results. Therefore, the paper introduces a graph convolutional network (GCN) to assist CNN in further optimizing the HSI representation and proposes a pixel-superpixel level multiscale graph and spectral-spatial representations fusion network (Ps-MGSRF) that mainly includes a pixel-level feature representation module (PFRM) formed with multiple multiscale feature refiltering blocks and a superpixel-level feature representation module (SFRM) composed of different orders’ residual GCN (ResGCN) blocks. Finally, the loss of the PFRM branch (loss1), the loss of the SFRM branch (loss2), and the loss of the merging of the two branches (loss3) are calculated separately, and the Ps-MGSRF network is updated by adaptive weighting three losses. The test results indicate that the proposed Ps-MGSRF model could achieve better experimental performance than the advanced comparison methods.

Convolutional neural networks (CNNs) are widely utilized in hyperspectral image (HSI) classification due to their powerful capability to automatically learn features. However, ordinary CNN mainly captures the spatial characteristics of HSI and ignores the spectral information. To alleviate the issue, this work proposes a CNN-enhanced multi-level Haar wavelet features fusion network (CNN-MHWF2N), which combines the spatial features obtained through 2-D-CNN with the Haar wavelet decomposition features to obtain sufficient spectral–spatial features. Specifically, factor analysis is first used to reduce the HSI dimension. Then, four-level decomposition features are obtained through the Haar wavelet decomposition algorithm, which of them are, respectively, concatenated with four-layer convolution features for combining spatial with spectral information. In this way, spectral–spatial features achieve better information interaction. Besides, a double filtrating feature fusion module is designed, which is operated following each level spectral–spatial features to obtain finer characteristics. Finally, those recognizable features are merged via a fusion operator. The whole designed model is conducive to enhancing the final HSI classification performance. In addition, experiments also reveal that the designed model is superior on three benchmark databases compared with the state-of-the-art approaches.

The use of deep learning methods in hyperspectral image (HSI) classification has been a promising approach due to its powerful ability to automatically extract features in recent years. This article proposes a novel deep framework for HSI classification problems, referred to as feature-grouped network based on spectral–spatial connected attention mechanism (FG-SSCA). Different from the existing deep learning methods, the proposed framework integrates the spectral attention module and spatial attention module continuously from the raw HSI input, which is embedded into convolutional neural networks and could enhance the distinguishing ability of spectral bands and learn the spatial relevance between the neighboring pixels together. Meanwhile, the generating feature maps are sliced into a series of small groups in sequence along the direction of spectral bands and each group sequentially extracts spatial–spectral features through multiple spectral and spatial residual blocks. This feature-grouped strategy could fully utilize the redundancy and difference of bands and obtain more available and valuable information. The proposed FG-SSCA method could greatly improve generalization performance and make tremendous successes in HSI classification. Experimental results on several HSI benchmark data sets verify the effectiveness and superiority of the proposed method in comparison with the state-of-the-art approaches for HSI classification.

Prompted by the remarkable progress in cloud computing, more and more game service providers are starting to deploy their gaming applications on infrastructure as a service cloud. To ensure quality of service required by gamers, game service providers need to maintain a large number of cloud servers running game instances requested by players. Therefore, game service providers need not only pay attention to the costs of operating games, but also consider the quality of game services. This paper proposes a quality-of-service aware cost optimization scheme for online gaming services deployed on IaaS clouds which combines dynamic virtual machine provisioning and Long Short-Term Memory (LSTM) based gamer request dispatching to reduce the operating costs while ensuring quality of service. The effectiveness of our proposed scheme is assessed by simulation experiments based on the real-world data sets. The results show that, compared to the state-of-the-art approaches applied to similar problems, our scheme can achieve up to 35% cost savings while providing the just-good-enough quality of service to gamers under rapidly changing workloads.

Abstract The application of graph convolutional networks (GCN) in hyperspectral image (HSI) classification has become a promising method, thanks to its flexible convolution operation in any irregular image region. For the classification of HSI, GCN can extract more superpixel‐level features with a topological structure, in comparison to the traditional convolutional neural networks (CNNs) using fixed square kernels distilling pixel‐level features. To fully leverage the different levels of features, this study proposes a novel deep network referred to as a CNN‐combined graph residual network (GRN), which integrates the multilevel graph residual module and spectral‐spatial features continuous learning module. During the extraction of topology information using the former module, HSI pixels are divided into superpixels and served as input nodes of the module to reduce the computational complexity and obtain the multilevel spatial relevance between adjacent superpixels. Besides, for the latter module, the spectral‐spatial features are learnt continuously, which could obtain the finer pixel‐level features. Finally, the captured spectral‐spatial features of different levels are concatenated. This strategy could not only adequately utilize the correlation and difference of adjacent spatial but also obtain the finer and more valuable spectral‐spatial information, which makes a significant boost in the HSI classification. Additionally, the experiment results demonstrate the superiority and availability of the GRN on three benchmark datasets of HSI, compared with the state‐of‐the‐art methods for the classification of HSI.