Sleep stage classification typically requires the uncomfortable and expensive polysomnography (PSG) test, which limits its widespread use in long-term monitoring and home-based environments. In this paper, we propose SleepTFANet, a novel deep learning model designed for automatic sleep stage classification using respiratory signals. SleepTFANet introduces two key modules: the Time Attention Module (TAM) and the Frequency Attention Module (FAM), to concurrently capture local and global dependencies in time-series data. TAM utilizes Transformer-based patch segmentation to capture temporal dependencies, while FAM leverages spectral analysis to extract frequency-based features. To further enhance performance, we propose the Enhanced Harmonic Energy Allocation (EHEA) method that dynamically adjusts the weighting between these two modules based on the periodicity of the input time series, allowing the model to better adapt to varying signal dynamics. Experimental results demonstrate that SleepTFANet consistently outperforms benchmark models and current state-of-the-art methods, showcasing superior robustness and generalization across multiple real-world datasets. Furthermore, ablation studies confirm the indispensability of each component in our proposed model, offering a promising approach for automatic sleep stage classification using respiratory signals.

The synthesis of high-quality multivariate time series (MTS) is critical for enhancing the performance of predictive models and data-driven decision-making. This paper introduces a novel approach to MTS generation, integrating multi-scale data fusion with self-attention mechanisms to capture complex interdependencies across temporal scales, which to some extent aids the generator in producing coherent MTS, improving the representational capacity of the data. The methodology extends beyond conventional spatial feature extraction by incorporating Fourier Transformation analysis in the frequency domain and Contrastive Learning in the time domain. This multi-domain approach uncovers deeper insights into the underlying patterns and cyclic behaviors of time series data. Extensive experimentation across different datasets and configurations demonstrate superiority over existing techniques, achieving greater accuracy and realism in synthesized MTS.

Sleep Apnea Syndrome (SAS) is a prevalent sleep-related disorder that poses multiple health risks. Traditional detection methods, relying on polysomnography (PSG), are cumbersome and time-consuming, creating a pressing need for more convenient automated detection approaches. This paper presents CoMTAnet, a deep learning framework based on supervised contrastive learning, designed for automatic detection of SAS using multi-channel respiratory signals. The CoMTAnet framework employs an encoder-classifier architecture, where the encoder leverages a TimesBlock to extract deep temporal features from single-channel signals and Residual Blocks to derive high-level information across multi-channel features, trained using Supervised Contrastive Learning (SCL). The classifier categorizes the signal features into normal, insufficient breathing, and apnea classes. Tested on two extensive public datasets, MESA and SHHS, CoMTAnet demonstrates superior performance in terms of accuracy, precision, recall, and F1 score compared to other state-of-the-art models. This highlights the exceptional effectiveness of the supervised contrastive learning approach in the detection of SAS.

Sleep apnea is one of the most common sleep breathing disorders and can lead to other serious diseases. The automated detection of sleep apnea and hypopnea events is crucial for preventing the of potential health complications associated with these disorders. This paper presents a novel deep learning model, the GRU-TSMixers, designed for the efficient and accurate identification of these events using a single channel of raw respiratory signals. Our approach leverages Gated Recurrent Units (GRU) to capture the temporal dynamics of the respiratory signal, while employing TSMixers to intricately extract features without any feature engineering. The model’s effectiveness is demonstrated through rigorous evaluation on two large-scale datasets, the Sleep-Heart-Health-Study (SHHS) and the Multi-Ethnic Study of Atherosclerosis (MESA), where it consistently outperforms traditional CNN and LSTM models as well as other state-of-the-art approaches. Our findings suggest that GRU-TSMixers not only sets a new benchmark for sleep event detection but also paves the way for advancements in non-intrusive diagnostic tools in sleep medicine.

Clustering is a fundamental task in data mining , aiming to group similar objects together based on their features or attributes. With the rapid increase in data analysis volume and the growing complexity of high-dimensional data distribution , clustering has become increasingly important in numerous applications, including image analysis, text mining, and anomaly detection . DBSCAN is a powerful tool for clustering analysis and is widely used in density-based clustering algorithms . However, DBSCAN and its variants encounter challenges when confronted with datasets exhibiting clusters of varying densities in intricate high-dimensional spaces affected by significant disturbance factors . A typical example is multi-density clustering connected by a few data points with strong internal correlations, a scenario commonly encountered in the analysis of crowd mobility. To address these challenges, we propose a Self-adaptive Density-Based Clustering Algorithm for Varying Densities Datasets with Strong Disturbance Factor (SADBSCAN). This algorithm comprises a data block splitter, a local clustering module, a global clustering module, and a data block merger to obtain adaptive clustering results . We conduct extensive experiments on both artificial and real-world datasets to evaluate the effectiveness of SADBSCAN. The experimental results indicate that SADBSCAN significantly outperforms several strong baselines across different metrics, demonstrating the high adaptability and scalability of our algorithm.

The large language models based on transformers have shown strong text generation ability. However, due to the need for significant computing resources, little work has been done to generate emotional text using language models such as GPT-2. To address this issue, the authors proposed an affective prompt-tuning-based language model (APT-LM) equipped with an affective decoding (AD) method, aiming to enhance emotional text generation with limited computing resources. In detail, the proposed model incorporates the emotional attributes into the soft prompt by using the NRC emotion intensity lexicon and updates the additional parameters while freezing the language model. Then, it steers the generation toward a given emotion by calculating the cosine distance between the affective soft prompt and the candidate tokens generated by the language model. Experimental results show that the proposed APT-LM model significantly improves emotional text generation and achieves competitive performance on sentence fluency compared to baseline models across automatic evaluation and human evaluation.

Sleep apnea is a prevalent sleep disorder that poses a significant public health concern. Polysomnography, the gold standard for detecting sleep apnea, is costly and time-consuming, making widespread adoption challenging. To address this issue, in this paper, we propose a novel Comprehensive Feature Aggregation Network(CFAN) model comprising an embedding layer, an attention module, and a temporal module. The proposed CFAN leverages one of the respiratory signals as its input. Grouped dilated convolutional neural networks with various dilation rates, multi-head self-attention mechanism, and bidirectional Gated Recurrent Unit effectively integrate global and local spatial-temporal information from the input signal, enabling the characterization of intricate internal details and facilitate the detection of sleep apnea syndrome. Experimental results unequivocally demonstrate the superior performance of our proposed model, surpassing both benchmark models and the current state-of-the-art in terms of its comprehensive ability and robustness. Furthermore, ablation experiments confirm the indispensability of each component within the model. Our proposed method offers a convenient and innovative approach to automatically detect sleep apnea syndrome using respiratory signals, presenting a promising avenue for further application.

Sleep monitoring typically requires the uncomfortable and expensive polysomnography (PSG) test to determine the sleep stages. Body movement and cardiopulmonary signals provide an alternative way to perform sleep staging. In recent years, long-short term memory (LSTM) networks and convolutional neural networks (CNN) have dominated automatic sleep staging due to their better learning ability than machine learning classifiers. However, LSTM may lose information when dealing with long sequences, while CNN is not good at sequence modeling. As an improvement, we develop a hierarchical attention-based deep learning method for sleep staging using body movement, electrocardiogram (ECG), and abdominal breathing signals. We apply the multi-head self-attention to model the global context of feature sequences and coupled it with CNN to achieve a hierarchical self-attention weight assignment. We evaluate the performance of the method using two public datasets. Our method outperforms other baselines in the three sleep stages, achieving an accuracy of 84.3$\%$, an F1 score of 0.8038, and a Cohen's Kappa coefficient of 0.7036. The result demonstrates the effectiveness of the hierarchical self-attention mechanism when processing feature sequences in the sleep stage classification problem. This paper provides new possibilities for long-term sleep monitoring using movement and cardiopulmonary signals obtained from non-invasive devices.

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

Existing end-to-end task-oriented dialog systems struggle to dynamically model long dialog context for interactions and effectively incorporate knowledge base (KB) information into dialog generation. To conquer these limitations, we propose a Dual Dynamic Memory Network (DDMN) for multi-turn dialog generation, which maintains two core components: dialog memory manager and KB memory manager. The dialog memory manager dynamically expands the dialog memory turn by turn and keeps track of dialog history with an updating mechanism, which encourages the model to filter irrelevant dialog history and memorize important newly coming information. The KB memory manager shares the structural KB triples throughout the whole conversation, and dynamically extracts KB information with a memory pointer at each turn. Experimental results on three benchmark datasets demonstrate that DDMN significantly outperforms the strong baselines in terms of both automatic evaluation and human evaluation. Our code is available at https://github.com/siat-nlp/DDMN.

Summary Item‐based collaborative filtering (CF) is a model‐based algorithm for making recommendations. In the algorithm, the similarity between items are calculated by using a number of similarity measures, and then these similarity values are used to predict ratings for users. However, if the number of items and users grows to millions, the scalability and the processing efficiency of item‐based CF can be hindered by some hardware constraints. To solve this problem, we propose an optimized MapReduce for item‐based CF algorithm integrated with empirical analysis. Through extensive experiments on real‐world datasets, we demonstrate the advantages of our approach by evaluating its execution time and by comparing its shuffle phase overhead with the conventional methods. The experimental results suggest that our approach has better performance when processing large‐scale datasets.

The cellular Potts model (CPM) has been widely used for biological simulations. However, most of current implementations are either sequential or approximate, which cannot be used for large scale complex 3D simulation. In this paper we present a hybrid parallel framework for CPM simulations. The time-consuming partial differential equation (PDE) solving, cell division, and cell reaction operation are distributed to clusters by using the message passing interface (MPI). The Monte Carlo lattice update is parallelized on shared-memory SMP system by using OpenMP. Since the Monte Carlo lattice update is much faster than the PDE solving and SMP systems are more and more common, this hybrid approach achieves good performance and high accuracy at the same time. Based on the parallel cellular Potts model, we have studied the avascular tumor growth by using a multiscale model. The application and performance analyses demonstrate that the hybrid parallel framework is quite efficient. The hybrid parallel CPM can be used for the large scale simulation (~ 108sites) of complex collective behavior of numerous cells (~ 106).

Finite element method (FEM)-based scientific numerical simulations are often computing-extensive and grid platform can speed up the simulation progress significantly. However, the coupling of FEM-based simulation tasks with grid platform isn't so straightforward. Adopting the service-oriented architecture (SOA), we develop a general service-oriented grid computing platform for FEM-based simulations (GSGCP-FEM). GSGCP-FEM provides users with the ability to make a general FEM-based simulation to be service-oriented. Basing on the services provided by GSGCP-FEM and grid middleware, users can deploy new scientific simulation applications easily. In this paper, we explain the design, architecture, and implementation of GSGCP-FEM in detail. We also deploy two practical applications based on GSGCP-FEM to demonstrate its usefulness