Deep neural networks have recently been applied to long-term time series forecasting (LTSF) tasks, aiming to capture dynamic temporal features from historical data for more accurate predictions. However, time series data inevitably contain noise, influencing prediction accuracy. Recent denoising studies suggest that high-frequency components in the frequency domain are the source of noise, and these studies use filters to remove them. While these filters effectively suppress noise, they also unintentionally discard valuable high-frequency information. To tackle this problem, we propose a Wavelet-Enhanced Long Short-Term Memory (WE-LSTM). Specifically, we introduce an Enhanced Spectral Denoising module that combines Fourier Transform and Discrete Wavelet Transform (DWT) for joint denoising. This module achieves effective denoising through adaptive wavelet decomposition with multiple wavelet bases. Additionally, this work presents an Enhanced LSTM module, which integrates exponential gating units into LSTM to capture complex patterns and enhance LTSF capabilities. Extensive experiments on seven real-world datasets with varying noise levels demonstrate that the WE-LSTM architecture achieves performance comparable to current state-of-the-art models in LTSF tasks, validating the superiority of our model.

With the growing popularity of Mobile Crowdsensing (MCS), online matching has recently attracted considerable attention. However, most previous schemes focused on single-task matching, which limits their practicality in new MCS applications that require multi-task matching. Moreover, most MCS tasks require workers to share locations with the platform, which poses serious privacy concerns. To address this issue, we propose a privacy-preserving multi-task online matching algorithm in a snapshot-based mode (PMS). Specifically, the entire time period is divided into snapshots to reduce the waiting time for newly arrived tasks to be matched. In each snapshot, the planar Laplace-based privacy mechanism is applied to protect worker locations and ensure ε-geo-indistinguishability. Meanwhile, the Minimum-Cost Maximum-Flow (MCMF)-based multi-task matching mechanism is presented to maximize the task completion rate while minimizing the total travel cost. Experiments on real-world datasets demonstrate that PMS achieves superior task completion, reduced travel costs, and improved privacy preservation compared to existing algorithms.

Transformers have emerged as a crucial technology in long-term time series forecasting (LTSF). Recent studies have demonstrated that Transformers segment time series into patches, effectively capturing temporal patterns. However, single-scale patches still fail to adequately capture the complex short-term fluctuations and long-term trends in time series. To tackle this issue, we propose a novel framework, the Multi-scale Patch Parallel Prediction Network (MP3Net). Firstly, we propose a multi-scale patch module to extract local features and long-term correlations from the time series to effectively capture and identify data characteristics across different time scales. Secondly, we propose an adaptive fusion module to dynamically balance the weight of local detail features and long- term correlations from each branch. Thirdly, we introduce a hybrid loss function for model training aimed at enhancing the sensitivity and adaptability of the network towards different types of errors, consequently improving overall performance. Experiments conducted on five benchmark datasets demonstrate that MP3Net surpasses existing methods in LTSF and MP3Net presents an effective approach for addressing the challenge of modeling long-term dependencies in time series analysis.

Transformer has achieved excellent results in the field of time series forecasting. However, some recent studies have pointed out that the existing Transformer models are ineffective in preserving the temporal order information. To solve this problem, we propose a Linear Weighted Attention mechanism (LinWA), a new, simple, and effective attention weights calculation method. Linwa calculates the linear weights and the attention weights, respectively, and combines them to reduce the proportion of the original attention weights to use the order information effectively and improve the forecasting performance. Additionally, we introduce an adaptive parameter adjustment mechanism to adjust the proportion between linear weights and attention weights dynamically. Experimental results on six publicly available datasets show that LinWA preserves temporal order well and improves performance on many Transformer models.

Graph Neural Networks (GNNs) have been widely applied to link prediction tasks. GNN models generally follow a message passing scheme to recursively aggregate the attribute features of neighbor nodes. In this scheme, the GNN does not explicitly consider the structural information of the graph that is critical for link prediction. This inspires researchers to encode this information to consider the location of nodes and their roles. However, current studies mostly adopt a single encoding method, which does not sufficiently consider the structural information. In additional, the structural and attribute features are not effectively integrated. In this paper, we propose a novel framework named Hybrid Structure Encoding Graph neural networks with Attention mechanism (HSEGA) for link prediction. HSEGA uses PageRank, betweenness centrality, and node labeling for hybrid encoding of structural information to capture the importance, centrality, and location of graph nodes. Subsequently, the structural and attribute features are integrated as deep GNN inputs to learn from two domains. Finally, we use the attention mechanism to adaptively incorporate information. Extensive experiments on diverse benchmark datasets show that HSEGA consistently achieves state-of-the-art link prediction performance.

There is huge amount of biomedical and biological literature online or in digital libraries. Moreover, new research papers are published with an exponential growth in recent years. So it is pressing and challenging to mine meaningful topics from massive biomedical literature. The mined topics are helpful to researchers for literature exploration and topic discovery. However, latent topics inferred by traditional topic models are not always coherent and meaningful. In this work, we propose a new methodology to mine meaningful biomedical topics with a combination of several off-the-shelf text mining techniques such as part-of-speech tagging, base noun phrase chunking, K-means clustering and latent Dirichlet allocation, which endow our methodology with scalability and implementation simplicity. We conduct comprehensive experiments on a dataset collected from PubMed. The experimental results demonstrate that our method significantly outperforms a baseline method. We also perform a qualitative analysis and present meaningful biomedical topics and multi-word expressions.