Distributed Estimation and Inference with Statistical Guarantees

This paper studies hypothesis testing and parameter estimation in the context of the divide and conquer algorithm. In a unified likelihood based framework, we propose new test statistics and point estimators obtained by aggregating various statistics from $k$ subsamples of size $n/k$, where $n$ is the sample size. In both low dimensional and high dimensional settings, we address the important question of how to choose $k$ as $n$ grows large, providing a theoretical upper bound on $k$ such that the information loss due to the divide and conquer algorithm is negligible. In other words, the resulting estimators have the same inferential efficiencies and estimation rates as a practically infeasible oracle with access to the full sample. Thorough numerical results are provided to back up the theory

Suicide Risk Prediction for Users with Depression in Question Answering Communities: A Design Based on Deep Learning

In the field of public health, suicide risk prediction is a central and urgent problem. Existing researches mainly focus on user’s current post but overlook historical post. In light of the psychological characteristics, we argue that it is valuable to consider users’ historical post in addition to current post for predicting suicide risk. Based on this rationale, we propose a deep learning-based suicide risk prediction framework - Dynamic Historical Information based Suicide Risk Prediction (DHISRP) - by considering the user’s current post content and historical post content. To capture the dynamic and complicated information of historical post, we design a unit based on long short-term memory (LSTM), named RNLSTM. We also conduct experiments to compare with the benchmark model to prove the effectiveness of our model, and perform ablation experiments to verify the significance of each component in the prediction framework in this study

Does giving and receiving helping behavior fit matter? : the role of neighboring behavior fit in working residents' mental health

Ecological systems theory suggests that for individuals, the three domains of community, family, and work are connected and transfer resources among each other. In the community, residents receive and give helping behavior from and to their neighbors. Neighboring behavior underlies interactions among residents in the community, thereby influencing the work and family domains. Building on ecological systems theory, the authors propose that the compatibility of receiving and giving helping behavior among working residents is related to their mental health. Additionally, the authors propose that this congruence effect functions through work-family interference and meaning in life. Using a two-stage field questionnaire survey, this study collected data from 220 full-time Chinese working residents. Using polynomial regression and response surface analysis, receiving-giving neighboring behavior fit was found to be positively associated with mental health. Furthermore, receiving-giving neighboring behavior fit enhances mental health by decreasing work-family interference and promoting meaning in life. When giving and receiving neighboring behavior are imbalanced, working residents have higher levels of mental health when they received more neighboring behavior than they gave, in comparison to the condition when they gave more neighboring behavior than they received. Work-family interference represents inter-role conflict in which pressures from the family and work domains are mutually incompatible. Including both work to family interference and family to work interference, work-family interferences reflect the stress that working residents experience in their family and work domains. By exploring the mediating role of work-family interference, this study shows how the spillover of the benefits of neighboring behavior into the family and work domains enhances working residents' mental health. This study highlights the importance of balancing receiving and giving neighboring behavior for maintaining mental health, thus contributing both theoretically and practically to ecological systems theory

Adsorption kinetics and thermodynamics of water-insoluble crosslinked β-cyclodextrin polymer for phenol in aqueous solution

A water-insoluble β-cyclodextrin (β-CD) polymer was synthesized by reacting β-CD with hexamethyl- ene diisocyanate, and its adsorption kinetics and thermodynamics for phenol from aqueous solution was investi- gated. The kinetics of adsorption followed the pseudo-second-order model and the adsorption isotherms could be well fitted by the Freundlich adsorption equation. The values of thermodynamic parameters demonstrated that the adsorption was a physisorption in a spontaneous and exothermic process

Research on Tracking and Synchronization of Uncertain Chaotic Systems

The tracking and synchronization problem of uncertain chaotic system, which is considered to be applied in secure communication in the future by many researchers, is considered in this paper. A double integral sliding mode controller is adopted to cope with the uncertainties of the chaotic system. Adaptive and robust strategies, such as Nussbaum gain method, are used to solve the unmodeled dynamic problem and unknown control direction problem. Meanwhile, the stability of the whole system is guaranteed by constructing of a big Lyapunov function for the whole system. Finally, a four dimension super-chaotic system is used as an example to do the numerical simulation and it testifies the rightness and effectiveness of the proposed method

Headwater streams contain amounts of heavy metal in an alpine forest in the upper reaches of the Yangtze River

Headwater streams are an essential link in the source and sink dynamics of heavy metals between terrestrial and aquatic ecosystems and are also critically important for downstream ecosystem processes and water quality. However, there is little available information about headwater streams. Therefore, the stream storage and distribution patterns of Cd, Pb, Ni, Cr, Cu, Mn and Zn were investigated in ten headwater streams of an Alpine forest located in the upper Yangtze River during the rainy season. The results indicated that the heavy metal storage per unit area of the investigated streams was as follows: 0.95 mg·m-2 for Cd, 8.36 mg m-2 for Pb, 1.98 mg m-2 for Ni, 136.98 mg m-2 for Cr, 9.29 mg m-2 for Cu, 433.39 mg m-2 for Mn and 29.07 mg m-2 for Zn; while the heavy metal storage per unit area of the catchment was as follows: 1.19 mg hm-2 for Cd, 10.47 mg hm-2 for Pb, 2.48 mg hm-2 for Ni, 171.62 mg hm-2 for Cr, 11.64 mg hm-2 for Cu, 542.99 mg hm-2 for Mn and 36.42 mg hm-2 for Zn. Headwater streams present remarkable potential for contamination, and plant debris from riparian forests may be the most important source of heavy metals, while the stream sediment acts as a significant sink for heavy metals. These results provide new perspectives and data for understanding the ecological links between alpine forests and watersheds

WGIT*: Workspace-Guided Informed Tree for Motion Planning in Restricted Environments

The motion planning of robots faces formidable challenges in restricted environments, particularly in the aspects of rapidly searching feasible solutions and converging towards optimal solutions. This paper proposes Workspace-guided Informed Tree (WGIT*) to improve planning efficiency and ensure high-quality solutions in restricted environments. Specifically, WGIT* preprocesses the workspace by constructing a hierarchical structure to obtain critical restricted regions and connectivity information sequentially. The refined workspace information guides the sampling and exploration of WGIT*, increasing the sample density in restricted areas and prioritizing the search tree exploration in promising directions, respectively. Furthermore, WGIT* utilizes gradually enriched configuration space information as feedback to rectify the guidance from the workspace and balance the information of the two spaces, which leads to efficient convergence toward the optimal solution. The theoretical analysis highlights the valuable properties of the proposed WGIT*. Finally, a series of simulations and experiments verify the ability of WGIT* to quickly find initial solutions and converge towards optimal solutions

WGIT*: Workspace-Guided Informed Tree for Motion Planning in Restricted Environments

The motion planning of robots faces formidable challenges in restricted environments, particularly in the aspects of rapidly searching feasible solutions and converging towards optimal solutions. This paper proposes Workspace-guided Informed Tree (WGIT*) to improve planning efficiency and ensure high-quality solutions in restricted environments. Specifically, WGIT* preprocesses the workspace by constructing a hierarchical structure to obtain critical restricted regions and connectivity information sequentially. The refined workspace information guides the sampling and exploration of WGIT*, increasing the sample density in restricted areas and prioritizing the search tree exploration in promising directions, respectively. Furthermore, WGIT* utilizes gradually enriched configuration space information as feedback to rectify the guidance from the workspace and balance the information of the two spaces, which leads to efficient convergence toward the optimal solution. The theoretical analysis highlights the valuable properties of the proposed WGIT*. Finally, a series of simulations and experiments verify the ability of WGIT* to quickly find initial solutions and converge towards optimal solutions