Effects of Soft Golf on Physical Fitness of Children Aged 4 to 5 Years

Playing golf is good for health. Previous studies found that golf training can improve performance such as muscle strength, endurance, balance, and agility. Constitution is the important foundation of national and social development, and children are the key period of constitution development. At present, there are few studies on the influence of golf on children\u27s physical fitness. This study explored the influence of 16-week soft golf exercise intervention on the physical health of 4–5-year-old children. Sixty children aged 4-5 years old were recruited from a kindergarten school in Shanghai, China with means of age at 4.56±0.54 and height at 112.72±3.96. Participants were randomly assigned into experimental group and control group. The physical fitness data were collected before and after the intervention. The intervention lasted 16 weeks of soft golf training with twice a week and 45 minutes each time. The control group did not do any training. The results showed that: (1) there were no significant differences between the experimental group and the control group in terms of lower limb strength and coordination ability. After intervention, Participants in the experimental group performed significantly better than those in the control group. (2) There were no significant differences in upper limb strength, endurance, and balance among participants between the experimental group and the control group. After intervention, Participants in the experimental group and the control group improved in physical fitness at varying degrees when compared with the pre-test, however the improvements were not statistically significant. This study showed that after 16 weeks of soft golf intervention, participants in the experimental group and the control group had different degrees of improvement in physical fitness test indicators. Especially in lower limb strength and coordination ability test indicators, participants in the experimental group performed significantly higher than those in the control group. It is suggested that the impact of soft golf on children\u27s physical fitness was positive. In China, research on golf and children\u27s physical fitness is still in its infancy. Soft golf retains the characteristics of traditional golf, and the development conditions are more convenient, which provide objective conditions for campus golf. The exercise effect of soft golf on physical fitness among different participants should be further explored in the future

Metadata-Driven Federated Learning of Connectional Brain Templates in Non-IID Multi-Domain Scenarios

A connectional brain template (CBT) is a holistic representation of a population of multi-view brain connectivity graphs, encoding shared patterns and normalizing typical variations across individuals. The federation of CBT learning allows for an inclusive estimation of the representative center of multi-domain brain connectivity datasets in a fully data-preserving manner. However, existing methods overlook the non-independent and identically distributed (non-IDD) issue stemming from multidomain brain connectivity heterogeneity, in which data domains are drawn from different hospitals and imaging modalities. To overcome this limitation, we unprecedentedly propose a metadata-driven federated learning framework, called MetaFedCBT, for cross-domain CBT learning. Given the data drawn from a specific domain (i.e., hospital), our model aims to learn metadata in a fully supervised manner by introducing a local client-based regressor network. The generated meta-data is forced to meet the statistical attributes (e.g., mean) of other domains, while preserving their privacy. Our supervised meta-data generation approach boosts the unsupervised learning of a more centered, representative, and holistic CBT of a particular brain state across diverse domains. As the federated learning progresses over multiple rounds, the learned metadata and associated generated connectivities are continuously updated to better approximate the target domain information. MetaFedCBT overcomes the non-IID issue of existing methods by generating informative brain connectivities for privacy-preserving holistic CBT learning with guidance using metadata. Extensive experiments on multi-view morphological brain networks of normal and patient subjects demonstrate that our MetaFedCBT is a superior federated CBT learning model and significantly advances the state-of-the-art performance.Comment: 10 page

DMAT: A Dynamic Mask-Aware Transformer for Human De-occlusion

Human de-occlusion, which aims to infer the appearance of invisible human parts from an occluded image, has great value in many human-related tasks, such as person re-id, and intention inference. To address this task, this paper proposes a dynamic mask-aware transformer (DMAT), which dynamically augments information from human regions and weakens that from occlusion. First, to enhance token representation, we design an expanded convolution head with enlarged kernels, which captures more local valid context and mitigates the influence of surrounding occlusion. To concentrate on the visible human parts, we propose a novel dynamic multi-head human-mask guided attention mechanism through integrating multiple masks, which can prevent the de-occluded regions from assimilating to the background. Besides, a region upsampling strategy is utilized to alleviate the impact of occlusion on interpolated images. During model learning, an amodal loss is developed to further emphasize the recovery effect of human regions, which also refines the model's convergence. Extensive experiments on the AHP dataset demonstrate its superior performance compared to recent state-of-the-art methods

Joint Satellite-Transmitter and Ground-Receiver Digital Pre-Distortion for Active Phased Arrays in LEO Satellite Communications

A novel joint satellite-transmitter and ground-receiver (JSG) digital pre-distortion (DPD) (JSG-DPD) technique is proposed to improve the linearity and power efficiency of the space-borne active phased arrays (APAs) in low Earth orbit (LEO) satellite communications. Different from the conventional DPD technique that requires a complex RF feedback loop, the DPD coefficients based on a generalized memory polynomial (GMP) model are extracted at the ground-receiver and then transmitted to the digital baseband front-end of the LEO satellite-transmitter via a satellite–ground bi-directional transmission link. The issue of the additive white Gaussian noise (AWGN) of the satellite–ground channel affecting the extraction of DPD coefficients is tackled using a superimposing training sequences (STS) method. The proposed technique has been experimentally verified using a 28 GHz phased array. The performance improvements in terms of error vector amplitude (EVM) and adjacent channel power ratio (ACPR) are 7.5% and 3.6 dB, respectively. Requiring limited space-borne resources, this technique offers a promising solution to achieve APA DPD for LEO satellite communications

A Joint Design for Full-duplex OFDM AF Relay System with Precoded Short Guard Interval

In-band full-duplex relay (FDR) has attracted much attention as an effective solution to improve the coverage and spectral efficiency in wireless communication networks. The basic problem for FDR transmission is how to eliminate the inherent self-interference and re-use the residual self-interference (RSI) at the relay to improve the end-to-end performance. Considering the RSI at the FDR, the overall equivalent channel can be modeled as an infinite impulse response (IIR) channel. For this IIR channel, a joint design for precoding, power gain control and equalization of cooperative OFDM relay systems is presented. Compared with the traditional OFDM systems, the length of the guard interval for the proposed design can be distinctly reduced, thereby improving the spectral efficiency. By analyzing the noise sources, this paper evaluates the signal to noise ratio (SNR) of the proposed scheme and presents a power gain control algorithm at the FDR. Compared with the existing schemes, the proposed scheme shows a superior bit error rate (BER) performance.Comment: 16 pages, 5 figure

Recursively Summarizing Enables Long-Term Dialogue Memory in Large Language Models

Most open-domain dialogue systems suffer from forgetting important information, especially in a long-term conversation. Existing works usually train the specific retriever or summarizer to obtain key information from the past, which is time-consuming and highly depends on the quality of labeled data. To alleviate this problem, we propose to recursively generate summaries/ memory using large language models (LLMs) to enhance long-term memory ability. Specifically, our method first stimulates LLMs to memorize small dialogue contexts and then recursively produce new memory using previous memory and following contexts. Finally, the LLM can easily generate a highly consistent response with the help of the latest memory. We evaluate our method using ChatGPT and text-davinci-003, and the experiments on the widely-used public dataset show that our method can generate more consistent responses in a long-context conversation. Notably, our method is a potential solution to enable the LLM to model the extremely long context. Code and scripts will be released later

Fast cultivation and harvesting of oil-producing microalgae Ankistrodesmus falcatus var. acicularis fed with anaerobic digestion liquor via biogranulation in addition to nutrients removal

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Unleashing the Potential of SAM for Medical Adaptation via Hierarchical Decoding

The Segment Anything Model (SAM) has garnered significant attention for its versatile segmentation abilities and intuitive prompt-based interface. However, its application in medical imaging presents challenges, requiring either substantial training costs and extensive medical datasets for full model fine-tuning or high-quality prompts for optimal performance. This paper introduces H-SAM: a prompt-free adaptation of SAM tailored for efficient fine-tuning of medical images via a two-stage hierarchical decoding procedure. In the initial stage, H-SAM employs SAM's original decoder to generate a prior probabilistic mask, guiding a more intricate decoding process in the second stage. Specifically, we propose two key designs: 1) A class-balanced, mask-guided self-attention mechanism addressing the unbalanced label distribution, enhancing image embedding; 2) A learnable mask cross-attention mechanism spatially modulating the interplay among different image regions based on the prior mask. Moreover, the inclusion of a hierarchical pixel decoder in H-SAM enhances its proficiency in capturing fine-grained and localized details. This approach enables SAM to effectively integrate learned medical priors, facilitating enhanced adaptation for medical image segmentation with limited samples. Our H-SAM demonstrates a 4.78% improvement in average Dice compared to existing prompt-free SAM variants for multi-organ segmentation using only 10% of 2D slices. Notably, without using any unlabeled data, H-SAM even outperforms state-of-the-art semi-supervised models relying on extensive unlabeled training data across various medical datasets. Our code is available at https://github.com/Cccccczh404/H-SAM.Comment: CVPR 202

Measuring and evaluating progress towards Universal Health Coverage in China.

BACKGROUND: This paper aims to develop a Chinese version of Universal Health Coverage (UHC) indices and to measure China's progress towards UHC. METHODS: Nineteen indicators were selected based on expert consultations to construct indices of accessibility and affordability to measure UHC. Data were drawn from health statistics yearbooks, nationally representative surveys, and health system reform surveillance. The index of accessibility includes absolute accessibility (to essential health services), relative accessibility (to hospital care) and people's subjective perceptions. The index of affordability includes absolute affordability (the incidence of catastrophic health expenditure, CHE), relative affordability (the composition of health expenditure), and people's subjective perceptions. RESULTS: The indices of accessibility and affordability both showed steady increases over the 17 years considered. Absolute accessibility had the most significant improvement (from 23.6 in 2002 to 73.8 in 2018), while the index of relative accessibility decreased from 81.4 in 2002 to 67.3 in 2018. The index of absolute affordability decreased significantly from 46.6 in 2002 to 30.5 in 2010 and then exhibited an increasing trend afterwards, reaching 52.1 in 2018. The index of relative affordability continuously increased during the observation period, from 35.3 to 75.4. CONCLUSIONS: China has made great progress in increasing the accessibility and affordability of health services since the health system reforms in 2009. However, integrating primary health care and hospital care and containing escalating medical expenditure to further reduce patients' financial burdens are key challenges for strengthening the Chinese health system