THE ADJUSTMENT OF LEG STIFFNESS DURING DYNAMIC EXERCISE AND DOWNWARD STEPPING FOR ELDERLY

The purpose of the present study was to evaluate the ability of leg stiffness regulation during downward stepping and maximal Counter-Movement-Jump (CMJ) for the elderly. Ten healthy aged people (age: 68.6±5 years; height: 165.3±4.4cm; mass: 61.7±9.3kg) and 10 students (age: 24.3±2years; height: 171.5±4.6cm; mass: 65.9±8kg) volunteered as subjects. Kistler force platform (1200Hz) and Peak high-speed camera (120Hz) were used synchronously to record the ground reaction force and the kinematic parameters of the subjects performing CMJ and stepping down from different heights. The results revealed that the elderly group has a smaller joint range of motion and greater leg stiffness than the young group during stepping down. The force and the leg stiffness during CMJ were significantly smaller for the elderly. The leg stiffness during downward stepping is independent of dynamic leg stiffness during CMJ. With aging, the adjustment ability of leg stiffness for maximal dynamic voluntary contraction was decreased

The contribution of the smartphone use to reducing depressive symptoms of Chinese older adults: The mediating effect of social participation

BackgroundDepression is a prevalent mental health disorder. Although Internet use has been associated with depression, there is limited data on the association between smartphone use and depressive symptoms. The present study aimed to investigate the relationship between smartphone use and depressive symptoms among older individuals in China.Methods5,244 Chinese older individuals over the age of 60 were selected as the sample from the China Longitudinal Aging Social Survey (CLASS) 2018 dataset. The dependent variable “depression symptoms” was measured using the 9-item Center for Epidemiologic Studies-Depression (CES-D) scale. The study employed multiple linear regression to investigate the relationship between smartphone use (independent variable) and depressive symptoms in older people. Thorough analyses of robustness, sensitivity, and heterogeneity were conducted to ensure the robustness and sensitivity of the findings. Additionally, mediating effect analysis was performed to elucidate the mechanism through which the dependent and independent variables were related.ResultsEmpirical study indicated that smartphone use had a negative impact on depressive symptoms among older adults, specifically leading to a reduction in such symptoms. The above-mentioned result was verified through endogenous and robustness tests. The heterogeneity analysis revealed that older individuals aged 70 years and above, male, and residing in urban areas exhibited a stronger association between smartphone use and depressive symptoms. Furthermore, the mediating effect model indicated that political participation, voluntary participation, and active leisure participation mediated the relationship between smartphone use and lower levels of depression symptoms among the older adults. However, passive leisure participation had a suppressing effect on the relationship between smartphone use and reduced depressive symptoms among the older adults.LimitationsThe causal relationship between variables required further investigation with a longitudinal design.ConclusionThese findings suggested that smartphone use may be considered an intervention to reduce depression symptoms among older people by increasing levels of political participation, voluntary participation, and active leisure participation

Law Article-Enhanced Legal Case Matching: a Causal Learning Approach

Legal case matching, which automatically constructs a model to estimate the similarities between the source and target cases, has played an essential role in intelligent legal systems. Semantic text matching models have been applied to the task where the source and target legal cases are considered as long-form text documents. These general-purpose matching models make the predictions solely based on the texts in the legal cases, overlooking the essential role of the law articles in legal case matching. In the real world, the matching results (e.g., relevance labels) are dramatically affected by the law articles because the contents and the judgments of a legal case are radically formed on the basis of law. From the causal sense, a matching decision is affected by the mediation effect from the cited law articles by the legal cases, and the direct effect of the key circumstances (e.g., detailed fact descriptions) in the legal cases. In light of the observation, this paper proposes a model-agnostic causal learning framework called Law-Match, under which the legal case matching models are learned by respecting the corresponding law articles. Given a pair of legal cases and the related law articles, Law-Match considers the embeddings of the law articles as instrumental variables (IVs), and the embeddings of legal cases as treatments. Using IV regression, the treatments can be decomposed into law-related and law-unrelated parts, respectively reflecting the mediation and direct effects. These two parts are then combined with different weights to collectively support the final matching prediction. We show that the framework is model-agnostic, and a number of legal case matching models can be applied as the underlying models. Comprehensive experiments show that Law-Match can outperform state-of-the-art baselines on three public datasets.Comment: 10 pages accepted by SIGIR202

DORec: Decomposed Object Reconstruction Utilizing 2D Self-Supervised Features

Decomposing a target object from a complex background while reconstructing is challenging. Most approaches acquire the perception for object instances through the use of manual labels, but the annotation procedure is costly. The recent advancements in 2D self-supervised learning have brought new prospects to object-aware representation, yet it remains unclear how to leverage such noisy 2D features for clean decomposition. In this paper, we propose a Decomposed Object Reconstruction (DORec) network based on neural implicit representations. Our key idea is to transfer 2D self-supervised features into masks of two levels of granularity to supervise the decomposition, including a binary mask to indicate the foreground regions and a K-cluster mask to indicate the semantically similar regions. These two masks are complementary to each other and lead to robust decomposition. Experimental results show the superiority of DORec in segmenting and reconstructing the foreground object on various datasets

Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users' watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user's actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.Comment: Accepted by Recsys'2

Consistent responses of soil microbial taxonomic and functional attributes to mercury pollution across China

Background: The ecological consequences of mercury (Hg) pollution—one of the major pollutants worldwide—on microbial taxonomic and functional attributes remain poorly understood and largely unexplored. Using soils from two typical Hg-impacted regions across China, here, we evaluated the role of Hg pollution in regulating bacterial abundance, diversity, and co-occurrence network. We also investigated the associations between Hg contents and the relative abundance of microbial functional genes by analyzing the soil metagenomes from a subset of those sites. Results: We found that soil Hg largely influenced the taxonomic and functional attributes of microbial communities in the two studied regions. In general, Hg pollution was negatively related to bacterial abundance, but positively related to the diversity of bacteria in two separate regions. We also found some consistent associations between soil Hg contents and the community composition of bacteria. For example, soil total Hg content was positively related to the relative abundance of Firmicutes and Bacteroidetes in both paddy and upland soils. In contrast, the methylmercury (MeHg) concentration was negatively correlated to the relative abundance of Nitrospirae in the two types of soils. Increases in soil Hg pollution correlated with drastic changes in the relative abundance of ecological clusters within the co-occurrence network of bacterial communities for the two regions. Using metagenomic data, we were also able to detect the effect of Hg pollution on multiple functional genes relevant to key soil processes such as element cycles and Hg transformations (e.g., methylation and reduction). Conclusions: Together, our study provides solid evidence that Hg pollution has predictable and significant effects on multiple taxonomic and functional attributes including bacterial abundance, diversity, and the relative abundance of ecological clusters and functional genes. Our results suggest an increase in soil Hg pollution linked to human activities will lead to predictable shifts in the taxonomic and functional attributes in the Hg-impacted areas, with potential implications for sustainable management of agricultural ecosystems and elsewhere