Feature selection is a crucial step in EEG emotion recognition. However, it was often used as a single objective problem to either reduce the number of features or maximize classification accuracy, while neglecting their balance. To address the issue, we proposed Improved Multi-objective Grey Wolf Optimization Feature Selection (IMGWOFS). First, we designed a population initialization operator via discriminability and independence of features to accelerate search speed. Second, we employed a two-stage update strategy to improve the global search capabilities of the EEG feature subsets. Finally, we incorporated an adaptive mutation operator to escape the local optima. We conducted experiments on SEED and DEAP datasets, and the accuracy were 86.87$\pm$1.62 % and 60.65$\pm$1.51 % in the beta band using a smaller number of EEG features. In addition, the frontal lobe was related to emotion processing. In conclusion, IMGWOFS is an effective and feasible feature selection method for EEG-based emotion recognition.

This article presents a modular decentralized control method for wind farms (WFs) to optimize power dispatch among wind turbines (WTs) without instant communication. By harnessing the nonlinear fitting capability of two data-driven modules, this method facilitates decentralized control of each WT with existing centralized control methods. Specifically, the local-to-global mapping module captures the intricate relationship between the historical local state variables of each WT and the current global state variables of the WF. The open-loop and closed-loop predictive modes enable the demanded power prediction module to locally predict the active power required by the transmission system operator (TSO) under various communication delays. Two classical multiobjective control modes are incorporated into the local controller depending on whether it requires data from the TSO. Testing on a WF with 32 WTs in MATLAB/Simulink validates that the proposed method has robustness and effectiveness closely aligned with centralized control methods.

Automated sleep staging is crucial for assessing sleep quality and diagnosing sleep-related diseases. Single-channel EEG has attracted significant attention due to its portability and accessibility. Most existing automated sleep staging methods often emphasize temporal information and neglect spectral information, the relationship between sleep stage contextual features, and transition rules between sleep stages. To overcome these obstacles, this paper proposes an attention-based two stage temporal-spectral fusion model (BiTS-SleepNet). The BiTS-SleepNet stage 1 network consists of a dual-stream temporal-spectral feature extractor branch and a temporal-spectral feature fusion module based on the cross-attention mechanism. These blocks are designed to autonomously extract and integrate the temporal and spectral features of EEG signals, leveraging temporal-spectral fusion information to discriminate between different sleep stages. The BiTS-SleepNet stage 2 network includes a feature context learning module (FCLM) based on Bi-GRU and a transition rules learning module (TRLM) based on the Conditional Random Field (CRF). The FCLM optimizes preliminary sleep stage results from the stage 1 network by learning dependencies between features of multiple adjacent stages. The TRLM additionally employs transition rules to optimize overall outcomes. We evaluated the BiTS-SleepNet on three public datasets: Sleep-EDF-20, Sleep-EDF-78, and SHHS, achieving accuracies of 88.50%, 85.09%, and 87.01%, respectively. The experimental results demonstrate that BiTS-SleepNet achieves competitive performance in comparison to recently published methods. This highlights its promise for practical applications.

To assist intelligent traffic management, traffic flow prediction, which plays a crucial role in intelligent transportation system, involves forecasting future traffic flow based on road characteristics and historical traffic data. Due to the inherent complexity of traffic systems, achieving high accuracy in long-term traffic flow prediction poses significant challenges. Therefore, we propose a novel neural network model, which is able to capture both temporal and spatial dependencies in the traffic flow data using the combination of GCN layer and LSTM layer. Experiments have demonstrated that our model is effective and accurate when used to predict the short-term traffic flow.

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach$86.88\% \pm 9.25 \%$. More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field. AVAILABILITY AND IMPLEMENTATION: The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI.

Advances in single-cell RNA sequencing (scRNA-seq) technology provide an unbiased and high-throughput analysis of each cell at single-cell resolution, and further facilitate the development of cellular heterogeneity analysis. Despite the promise of scRNA-seq, the data generated by this method are sparse and noisy because of the presence of dropout events, which can greatly impact downstream analyses such as differential gene expression, cell type annotation, and linage trajectory reconstruction. The development of effective and robust computational methods to address both dropout and clustering are thus urgently needed. In this study, we propose a flexible, accurate two-stage algorithm for single cell heterogeneity analysis via hierarchical clustering based on an optimal imputation strategy, called scHOIS. At the first stage, masked non-negative matrix factorization is applied to approximate the original observed scRNA-seq data, with optimal rank determined by variance analysis. At the second stage, hierarchical clustering is applied to group the imputed cells using Pearson correlation to measure similarity, with the optimal number of clusters determined by integrating three classical indexes. We performed extensive experiments on real-world datasets, which showed that scHOIS effectively and robustly distinguished cellular differences and that the clustering performance of this algorithm was superior to that of other state-of-the-art methods.

Depression is a heterogeneous syndrome with certain individual differences among subjects. Exploring a feature selection method that can effectively mine the commonness intra-groups and the differences inter-groups in depression recognition is therefore of great significance. This study proposed a new clustering-fusion feature selection method. Hierarchical clustering (HC) algorithm was used to capture the heterogeneity distribution of subjects. Average and similarity network fusion (SNF) algorithms were adopted to characterize the brain network atlas of different populations. Differences analysis was also utilized to obtain the features with discriminant performance. Experiments showed that compared with traditional feature selection methods, HCSNF method yielded the optimal classification results of depression recognition in both sensor and source layers of electroencephalography (EEG) data. Especially in the beta band of EEG data at sensor layer, the classification performance was improved by more than 6%. Moreover, the long-distance connections between parietal-occipital lobe and other brain regions not only have high discriminative power, but also significantly correlate with depressive symptoms, indicating the important role of these features in depression recognition. Therefore, this study may provide methodological guidance for the discovery of reproducible electrophysiological biomarkers and new insights into common neuropathological mechanisms of heterogeneous depression diseases.

Neural circuit dysfunction underlies the biological mechanisms of major depressive disorder (MDD). However, little is known about how the brain’s dynamic connectomes differentiate between depressed patients and normal controls. As a result, we collected resting-state Electroencephalography from 16 MDD patients and 16 controls using 128-electrode geodesic sensor net. Static and dynamic network metrics were later applied to explore the abnormal topological structure of MDD patients and identify them from normal controls using traditional machine learning algorithms with feature selection methods. Results showed that the MDD tend to have a more randomized formation both in static and dynamic network. We also found that the combined static-dynamic feature set usually outperformed others with a highest accuracy of 79.25% under delta band. Lower frequency band (delta, theta) showed relatively better outcomes compared to higher frequency band (alpha, beta). It also indicate the role of functional segregation features as a potential biomarker for depression. In conclusion, neuropathological mechanism of depression may be more objectively quantified and evaluated from the perspective of combining static and dynamic network.

Perceptual video coding has the potential to provide the same visual quality at a lower bit-rate, compared with the traditional objective quality based scheme. Visual saliency represents the probability of human attention over frames, and it is used for allocating coding bits or controlling visual quality. In this paper, a HEVC compliant perceptual video coding scheme is proposed based on visual saliency. At first visual saliency map is attained to indicate the distribution of saliency. Then refined distortion allocating method is performed in CU level with adaptive QP which is adjusted by the average visual saliency. Besides, a fast CU mode decision algorithm suitable for perceptual video coding in HEVC is proposed to accelerate the encoder. In the fast algorithm, the average saliency is used to estimate texture complexity and movements in videos. Experimental results show that up to 22.52% bit-rate and 43.48% encoding time can be saved by our methods with negligible perceptual quality loss.

Data deduplication has become necessary to improve the space-efficiency of large-scale distributed storage systems, as the global data have accumulated at an exponential rate and they have significant redundancy. However, the negative impact on restore performance is a main challenge for deduplication systems. One of the key reasons is that when restoring data, the low average useful data ratio (UDR) of containers wastes a considerable part of disk bandwidth to read useless data. This is mainly attributed to the uncontrollable compositions of containers. To solve this problem, we propose a new approach called Delayed Container Organization (DCO) to delay the construction of containers after accumulating some redundant data chunks in fast Non-Volatile Memory (NVM) devices to organize high-UDR containers. For example, data chunks in the intersection of some data segments can be organized together in one container to achieve both high deduplication ratio and high UDRs when restoring these related data segments. DCO is implemented in a prototype deduplication system. The experimental results indicate that compared with Capping, DCO promotes the average UDR of containers by 38.30 percent, improves the restore performance by a factor of 2.2, and achieves better space-efficiency and higher cost performance.

We propose a general framework of risk-averse reinforcement learning for algorithmic trading. Our approach is tested in an experiment based on 1.5 years of millisecond time-scale limit order data from NASDAQ, which contain the data around the 2010 flash crash. The results show that our algorithm outperforms the risk-neutral reinforcement learning algorithm by 1) keeping the trading cost at a substantially low level at the spot when the flash crash happened, and 2) significantly reducing the risk over the whole test period.