Sparse mobile crowdsensing is a cost-effective sensing paradigm that infers global data by sensing data from partial areas in a city. With the rapid development of diverse autonomous mobile agents such as unmanned aerial vehicles (UAVs) and ground vehicles, they have been widely applied in sparse mobile crowdsensing. However, existing works often predefine the role structures and behavioral preferences of these agents in tasks, which significantly limits their flexibility and adaptability, and making it difficult to fully exploit the collaborative potential of crowdsensing agents to efficiently achieve high-quality data sensing. In this paper, we propose an adaptive role learning framework for sparse mobile crowdsensing (ARL-SMCS), which focuses on role recognition for heterogeneous agents and role refinement among homogeneous agents. This framework, based on a multi-agent reinforcement learning model, introduces a variational autoencoder to learn the latent role representations of agents and uses maximum mean discrepancy to distinguish the functionalities of different types of agents. Additionally, ARL-SMCS incorporates an evolutionary algorithm to further refine task preferences among homogeneous agents. This framework overcomes the limitations of static role assignment in adapting to dynamic environments and task conflicts during task execution, significantly improving sensing quality and resource utilization efficiency. Extensive experiments on two real-world datasets demonstrate that ARL-SMCS consistently outperforms other baseline methods under various conditions, including different numbers, endurance, and decision interval lengths.

Sparse mobile crowdsensing (MCS) is a cost-effective data collection paradigm that aims to recruit users to collect data from a part of sensing subareas and infer the rest. In a more realistic scenario, users participate in real-time and collect data along the way. For missing data inference, the significance of data collected from different subareas often varies over time. However, since users’ trajectories are uncertain, recruiting users who can cover important spatio-temporal subareas presents a challenge. Additionally, how to segment the budget wisely during recruitment is another challenge. To tackle these challenges, we propose a dual reinforcement learning (RL)-based online user recruitment strategy with adaptive budget segmentation, called DualRL-U, which consists of two alternating decision steps, i.e., the user recruitment decision and the budget retention decision. Specifically, for the user recruitment decision, we use RL to connect the user with data inference accuracy to estimate their contributions. For the budget retention decision, we use RL to connect the budget with the number of times the user can sense to evaluate the cost effectiveness. In this way, a dual RL model is constructed to achieve effective recruitment by alternately executing user recruitment decisions and budget retention decisions. Extensive experiments on real-world sensing data sets show the effectiveness of DualRL-U.

Sparse crowdsensing collects data from a subset of the sensing area and infers data for unsensed areas, reducing data collection costs. Previous works have primarily focused on independently collecting and inferring single types of data. However, real-world scenarios often involve multiple types of data that can complement each other by providing missing spatiotemporal distribution information. In this paper, we fully consider both intra-data correlations among data of the same type and inter-data correlations among data of different types, enabling collaborative execution of various tasks. In addition, we enhance the adaptability in practical application scenarios by utilizing real-time collected sparse data to guide task execution. For this purpose, we propose a multi-task adaptive budgeting framework for online sparse crowdsensing, called MTAB-SC. This framework consists of three parts: training data updating, data inference, and data collection. Firstly, we propose a multi-task data updating method to keep models up-to-date. Secondly, we design a data inference network for multi-task data joint inference. Finally, to allocate suitable budgets for each task and facilitate collaborative data collection across multiple tasks, we propose an Adaptive Budgeting for Collaborative Data Collection model (AB-CoDC). The effectiveness of our proposals is demonstrated through extensive experiments on two real-world datasets.

Mobile Crowdsensing (MCS) is a sensing paradigm that enables large-scale smart city applications, such as environmental sensing and traffic monitoring. However, traditional MCS often suffers from performance degradation due to the limited spatiotemporal coverage of collected data. In this context, Sparse MCS has been proposed, which utilizes data inference algorithms to recover full data from sparse data collected by users. However, existing Sparse MCS approaches often overlook spatiotemporal fractures, where no data is observed either for a sensing subarea across all sensing time slots (temporal fracture), or for a sensing time slot in all sensing subarea (spatial fracture). Such spatiotemporal fractures pose great challenges to the data inference algorithms, as it is difficult to capture the complex spatiotemporal correlations of the sensing data from very limited observations. To address this issue, we propose a Graph-and Attention-based Matrix Completion (GAMC) method for the spatiotemporal fracture data inference problem in Sparse MCS. Specifically, we first pre-fill the general missing values using the classical Matrix Factorization (MF) technique. Then, we propose a neural network architecture based on Graph Attention Networks (GAT) and Transformer to capture complex spatiotemporal dependencies in the sensing data. Finally, we recover the complete data with a projection layer. We conduct extensive experiments on three real-world urban sensing datasets. The experimental results show the effectiveness of the proposed method.

The field of hematology is highly progressive and dynamic requiring researchers to commit significant amounts of time and effort towards staying abreast of the most crucial research areas. As such, in this work we assess the potential of ChatGPT for identifying research priorities within five key topics in hematology: acute lymphocytic leukemia, immunotherapy, targeted therapy, hematopoietic stem cell transplantation, and acute myeloid leukemia. After ChatGPT was employed to generate specific research questions in these areas, a panel of seven experienced hematologists independently reviewed and rated resultant research questions based on four parameters: relevance, originality, clarity, and specificity on a scale of 1 to 5, with 5 denoting the highest score. Excitingly, the mean and median grades of the four parameters were all above 4, indicating that the hematologists strongly agreed that the problems generated by ChatGPT were generally highly specific, clear, relevant, and original. As such, although further work will clearly be required, we suggest our current study indicates that Large Language Models (LLMs), such as ChatGPT, may very well represent valuable new tools for more efficiently identifying and prioritizing impactful research questions in the field of hematology.

In order to provide more efficient and reliable power services than the traditional grid, it is necessary for the smart grid to accurately predict the electric load. Recently, recurrent neural networks (RNNs) have attracted increasing attention in this task because it can discover the temporal correlation between current load data and those long-ago through the self-connection of the hidden layer. Unfortunately, the traditional RNN is prone to the vanishing or exploding gradient problem with the increase of memory depth, which leads to the degradation of predictive accuracy. Many RNN architectures address this problem at the expense of complex internal structures and increased network parameters. Motivated by this, this article proposes two adaptive modularized RNNs to tackle the challenge, which can not only solve the gradient problem effectively with a simple architecture, but also achieve better performance with fewer parameters than other popular RNNs.

To assist people's itinerary planning for viewing flowers, it is very meaningful to visualize the different stages of specific flowers with high spatio-temporal resolution. To achieve this goal, this paper realized a crowdsensing application called Hanami, which means ‘flower viewing’. The implementation of this application contains three modules: data sensing, flower classifier, and visualization. Particularly, the flower classification module utilized a residual network to identify the types and stages of flowers from crowdsensed photos. For the visualization module, a bilayer clustering view method was designed to aggregate the points on the map, which can be further clustered by different features of flowers. Experimental evaluation showed that Hanami can help users view flowers at their most beautiful moments.

The incidence of hypertension has recently shown a significant increase in young people, with aerobic exercise intervention being recognized as an effective approach to decrease blood pressure (BP). However, BP response to aerobic exercise can be highly individualized, and no research has been conducted on predicting the effect of aerobic exercise intervention for reducing BP in young hypertensive patients. In this work, we use the data generated from a cardiopulmonary exercise test (CPET) in young hypertensive patients (before aerobic exercise intervention) to derive information from multiple cardiopulmonary metabolic indices. The data, presented as time series, are then analyzed by a machine learning method to predict the effect of aerobic exercise intervention in lowering BP. This study provides several novel insights for making personalized aerobic exercise intervention programs for young adults with stage I hypertension.

High-sensitivity C-reactive protein (hs-CRP) performs important roles on the onset of metabolic syndrome and cardiovascular diseases (CVD), but little is known about association between hs-CRP and obesity-related metabolic abnormalities in young people without classical CVD risk factors. It thus motivated us to investigate association among hs-CRP, body fat mass (FM) distribution, and other cardiometabolic risk factors in young healthy women. Our approach is based on biological experiments, statistical analysis, and semantic search. Research outcomes in this study resulted in two novel discoveries. Discovery 1: There are four primary determinants for hs-CRP, i.e., central/abdominal FM (a.k.a. trunk FM) accumulation, leptin, high density lipoprotein cholesterol (HDL-C), and plasminogen activator inhibitior-1 (PAI-1). Discovery 2: Chronic inflammation may involve in adipocyte-cytokine interaction underlying the metabolic derangement in healthy young women.