145 research outputs found
Influence of Intergenerational Parenting on Gross Motor Skills Among Children Aged 3-6 Years Old
The stage of 3-6 years old is a critical period for the development of children\u27s gross motor skills, influencing their growth and development. Intergenerational parenting is a common way of family education in China. Studies have shown that the rough intergenerational parenting concept and limited energy of the elderly reduce the quality of life of the children under care (Qimeng Jiang, Nan Zhou, 2020). However, no research focused on the influence of intergenerational parenting on gross motor skills. This study aimed to explore the influence of different intergenerational parenting style on gross motor skills among children aged 3-6 years old. The participants were 62 children (25 boys and 37 girls) aged 3-6 years old from Liaoning Province. All of the participants were under intergenerational parenting. Gross motor skills were assessed using the Test of Gross Motor Development-Third Edition (TGMD-3), which includes locomotor skills and ball skills components. The intergenerational parenting status was divided into parent-dominated intergenerational parenting and grandparent-dominated intergenerational parenting according to a questionnaire (Lu Ye, 2020). The parenting style included authoritative, authoritarian and tolerant styles. The scores of three styles were determined by the grandparents-reported Chinese version of Parental Authority Questionnaire. Descriptive statistics, independent t-test and Pearson correlation were employed and the significant levels were set at 0.05. The results showed that participants had lower mean scores in both locomotor skills (Mboy = 12.58±4.42, Mgirl =13.51 ±3.03) and ball skills (Mboy = 8.46±2.9, Mgirl = 8.04±3.34) compared to the Chinese norm. There was no significant difference between parent-dominated and grandparent-dominated intergenerational parenting (M Parent-dominated =22.90±4.15, M grandparent-dominated = 20.48±4.47; t = 1.269, p = 0.209). Correlation analysis indicated a small association between the score of locomotor skills and authoritative style (r = 0.269, p \u3c 0.05). No significant relationship was found between other parenting styles and the scores of TGMD-3. It is concluded that intergenerational parenting may negatively influence children’s gross motor development. Parent-dominated and grandparent-dominated intergenerational parenting may not have differences in children’s motor development. The authoritative parenting style of intergenerational education has certain impacts on children\u27s gross motor skills, especially on children\u27s locomotor skills
A Bayesian Circadian Hidden Markov Model to Infer Rest-Activity Rhythms Using 24-hour Actigraphy Data
24-hour actigraphy data collected by wearable devices offer valuable insights
into physical activity types, intensity levels, and rest-activity rhythms
(RAR). RARs, or patterns of rest and activity exhibited over a 24-hour period,
are regulated by the body's circadian system, synchronizing physiological
processes with external cues like the light-dark cycle. Disruptions to these
rhythms, such as irregular sleep patterns, daytime drowsiness or shift work,
have been linked to adverse health outcomes including metabolic disorders,
cardiovascular disease, depression, and even cancer, making RARs a critical
area of health research.
In this study, we propose a Bayesian Circadian Hidden Markov Model (BCHMM)
that explicitly incorporates 24-hour circadian oscillators mirroring human
biological rhythms. The model assumes that observed activity counts are
conditional on hidden activity states through Gaussian emission densities, with
transition probabilities modeled by state-specific sinusoidal functions. Our
comprehensive simulation study reveals that BCHMM outperforms frequentist
approaches in identifying the underlying hidden states, particularly when the
activity states are difficult to separate. BCHMM also excels with smaller
Kullback-Leibler divergence on estimated densities. With the Bayesian
framework, we address the label-switching problem inherent to hidden Markov
models via a positive constraint on mean parameters. From the proposed BCHMM,
we can infer the 24-hour rest-activity profile via time-varying state
probabilities, to characterize the person-level RAR. We demonstrate the utility
of the proposed BCHMM using 2011-2014 National Health and Nutrition Examination
Survey (NHANES) data, where worsened RAR, indicated by lower probabilities in
low-activity state during the day and higher probabilities in high-activity
state at night, is associated with an increased risk of diabetes
Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping
High-resolution 3D object generation remains a challenging task primarily due
to the limited availability of comprehensive annotated training data. Recent
advancements have aimed to overcome this constraint by harnessing image
generative models, pretrained on extensive curated web datasets, using
knowledge transfer techniques like Score Distillation Sampling (SDS).
Efficiently addressing the requirements of high-resolution rendering often
necessitates the adoption of latent representation-based models, such as the
Latent Diffusion Model (LDM). In this framework, a significant challenge
arises: To compute gradients for individual image pixels, it is necessary to
backpropagate gradients from the designated latent space through the frozen
components of the image model, such as the VAE encoder used within LDM.
However, this gradient propagation pathway has never been optimized, remaining
uncontrolled during training. We find that the unregulated gradients adversely
affect the 3D model's capacity in acquiring texture-related information from
the image generative model, leading to poor quality appearance synthesis. To
address this overarching challenge, we propose an innovative operation termed
Pixel-wise Gradient Clipping (PGC) designed for seamless integration into
existing 3D generative models, thereby enhancing their synthesis quality.
Specifically, we control the magnitude of stochastic gradients by clipping the
pixel-wise gradients efficiently, while preserving crucial texture-related
gradient directions. Despite this simplicity and minimal extra cost, extensive
experiments demonstrate the efficacy of our PGC in enhancing the performance of
existing 3D generative models for high-resolution object rendering.Comment: Accepted at ICLR 2024. Project page:
https://fudan-zvg.github.io/PGC-3
SUIT: Learning Significance-guided Information for 3D Temporal Detection
3D object detection from LiDAR point cloud is of critical importance for
autonomous driving and robotics. While sequential point cloud has the potential
to enhance 3D perception through temporal information, utilizing these temporal
features effectively and efficiently remains a challenging problem. Based on
the observation that the foreground information is sparsely distributed in
LiDAR scenes, we believe sufficient knowledge can be provided by sparse format
rather than dense maps. To this end, we propose to learn Significance-gUided
Information for 3D Temporal detection (SUIT), which simplifies temporal
information as sparse features for information fusion across frames.
Specifically, we first introduce a significant sampling mechanism that extracts
information-rich yet sparse features based on predicted object centroids. On
top of that, we present an explicit geometric transformation learning
technique, which learns the object-centric transformations among sparse
features across frames. We evaluate our method on large-scale nuScenes and
Waymo dataset, where our SUIT not only significantly reduces the memory and
computation cost of temporal fusion, but also performs well over the
state-of-the-art baselines.Comment: Accepted to IROS 202
International Chinese learning app design for the 5G era
With the development of China, more and more foreigners are learning Chinese, and the post-epidemic period has brought the “Internet + Education” to a climax. This app combines “Chinese + vocational” education, including a multilingual HSK learning resource area and a “skills” community, and is an app that helps overseas Chinese learners to refine their vocational skills and HSK level
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A no-reference optical flow-based quality evaluator for stereoscopic videos in curvelet domain
Most of the existing 3D video quality assessment (3D-VQA/SVQA) methods only consider spatial information by directly using an image quality evaluation method. In addition, a few take the motion information of adjacent frames into consideration. In practice, one may assume that a single data-view is unlikely to be sufficient for effectively learning the video quality. Therefore, integration of multi-view information is both valuable and necessary. In this paper, we propose an effective multi-view feature learning metric for blind stereoscopic video quality assessment (BSVQA), which jointly focuses on spatial information, temporal information and inter-frame spatio-temporal information. In our study, a set of local binary patterns (LBP) statistical features extracted from a computed frame curvelet representation are used as spatial and spatio-temporal description, and the local flow statistical features based on the estimation of optical flow are used to describe the temporal distortion. Subsequently, a support vector regression (SVR) is utilized to map the feature vectors of each single view to subjective quality scores. Finally, the scores of multiple views are pooled into the final score according to their contribution rate. Experimental results demonstrate that the proposed metric significantly outperforms the existing metrics and can achieve higher consistency with subjective quality assessment
Us adolescent Rest-Activity Patterns: insights From Functional Principal Component analysis (Nhanes 2011-2014)
BACKGROUND: Suboptimal rest-activity patterns in adolescence are associated with worse health outcomes in adulthood. Understanding sociodemographic factors associated with rest-activity rhythms may help identify subgroups who may benefit from interventions. This study aimed to investigate the association of rest-activity rhythm with demographic and socioeconomic characteristics in adolescents.
METHODS: Using cross-sectional data from the nationally representative National Health and Nutrition Examination Survey (NHANES) 2011-2014 adolescents (N = 1814), this study derived rest-activity profiles from 7-day 24-hour accelerometer data using functional principal component analysis. Multiple linear regression was used to assess the association between participant characteristics and rest-activity profiles. Weekday and weekend specific analyses were performed in addition to the overall analysis.
RESULTS: Four rest-activity rhythm profiles were identified, which explained a total of 82.7% of variance in the study sample, including (1) High amplitude profile; (2) Early activity window profile; (3) Early activity peak profile; and (4) Prolonged activity/reduced rest window profile. The rest-activity profiles were associated with subgroups of age, sex, race/ethnicity, and household income. On average, older age was associated with a lower value for the high amplitude and early activity window profiles, but a higher value for the early activity peak and prolonged activity/reduced rest window profiles. Compared to boys, girls had a higher value for the prolonged activity/reduced rest window profiles. When compared to Non-Hispanic White adolescents, Asian showed a lower value for the high amplitude profile, Mexican American group showed a higher value for the early activity window profile, and the Non-Hispanic Black group showed a higher value for the prolonged activity/reduced rest window profiles. Adolescents reported the lowest household income had the lowest average value for the early activity window profile.
CONCLUSIONS: This study characterized main rest-activity profiles among the US adolescents, and demonstrated that demographic and socioeconomic status factors may shape rest-activity behaviors in this population
Artificial Light at Night and Social Vulnerability: an Environmental Justice analysis in the US 2012-2019
BACKGROUND: Artificial Light at Night (ALAN) is an emerging health risk factor that has been linked to a wide range of adverse health effects. Recent study suggested that disadvantaged neighborhoods may be exposed to higher levels of ALAN. Understanding how social disadvantage correlates with ALAN levels is essential for identifying the vulnerable populations and for informing lighting policy.
METHODS: We used satellite data from the National Aeronautics and Space Administration\u27s (NASA) Black Marble data product to quantify annual ALAN levels (2012-2019), and the Center for Disease Control and Prevention\u27s (CDC) Social Vulnerability Index (SVI) to quantify social disadvantage, both at the US census tract level. We examined the relationship between the ALAN and SVI (overall and domain-specific) in over 70,000 tracts in the Contiguous U.S., and investigated the heterogeneities in this relationship by the rural-urban status and US regions (i.e., Northeast, Midwest, South, West).
RESULTS: We found a significant positive relationship between SVI and ALAN levels. On average, the ALAN level in the top 20% most vulnerable communities was 2.46-fold higher than that in the 20% least vulnerable communities (beta coefficient (95% confidence interval) for log-transformed ALAN, 0.90 (0.88, 0.92)). Of the four SVI domains, minority and language status emerged as strong predictors of ALAN levels. Our stratified analysis showed considerable and complex heterogeneities across different rural-urban categories, with the association between greater vulnerability and higher ALAN primarily observed in urban cores and rural areas. We also found regional differences in the association between ALAN and both overall SVI and SVI domains.
CONCLUSIONS: Our study suggested ALAN as an environmental justice issue that may carry important public health implications. Funding National Aeronautics and Space Administration
S-NeRF++: Autonomous Driving Simulation via Neural Reconstruction and Generation
Autonomous driving simulation system plays a crucial role in enhancing
self-driving data and simulating complex and rare traffic scenarios, ensuring
navigation safety. However, traditional simulation systems, which often heavily
rely on manual modeling and 2D image editing, struggled with scaling to
extensive scenes and generating realistic simulation data. In this study, we
present S-NeRF++, an innovative autonomous driving simulation system based on
neural reconstruction. Trained on widely-used self-driving datasets such as
nuScenes and Waymo, S-NeRF++ can generate a large number of realistic street
scenes and foreground objects with high rendering quality as well as offering
considerable flexibility in manipulation and simulation. Specifically, S-NeRF++
is an enhanced neural radiance field for synthesizing large-scale scenes and
moving vehicles, with improved scene parameterization and camera pose learning.
The system effectively utilizes noisy and sparse LiDAR data to refine training
and address depth outliers, ensuring high quality reconstruction and novel-view
rendering. It also provides a diverse foreground asset bank through
reconstructing and generating different foreground vehicles to support
comprehensive scenario creation. Moreover, we have developed an advanced
foreground-background fusion pipeline that skillfully integrates illumination
and shadow effects, further enhancing the realism of our simulations. With the
high-quality simulated data provided by our S-NeRF++, we found the perception
methods enjoy performance boost on several autonomous driving downstream tasks,
which further demonstrate the effectiveness of our proposed simulator
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