PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User Engagement

Current advances in recommender systems have been remarkably successful in optimizing immediate engagement. However, long-term user engagement, a more desirable performance metric, remains difficult to improve. Meanwhile, recent reinforcement learning (RL) algorithms have shown their effectiveness in a variety of long-term goal optimization tasks. For this reason, RL is widely considered as a promising framework for optimizing long-term user engagement in recommendation. Though promising, the application of RL heavily relies on well-designed rewards, but designing rewards related to long-term user engagement is quite difficult. To mitigate the problem, we propose a novel paradigm, recommender systems with human preferences (or Preference-based Recommender systems), which allows RL recommender systems to learn from preferences about users historical behaviors rather than explicitly defined rewards. Such preferences are easily accessible through techniques such as crowdsourcing, as they do not require any expert knowledge. With PrefRec, we can fully exploit the advantages of RL in optimizing long-term goals, while avoiding complex reward engineering. PrefRec uses the preferences to automatically train a reward function in an end-to-end manner. The reward function is then used to generate learning signals to train the recommendation policy. Furthermore, we design an effective optimization method for PrefRec, which uses an additional value function, expectile regression and reward model pre-training to improve the performance. We conduct experiments on a variety of long-term user engagement optimization tasks. The results show that PrefRec significantly outperforms previous state-of-the-art methods in all the tasks

SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation

Expressive human pose and shape estimation (EHPS) unifies body, hands, and face motion capture with numerous applications. Despite encouraging progress, current state-of-the-art methods still depend largely on a confined set of training datasets. In this work, we investigate scaling up EHPS towards the first generalist foundation model (dubbed SMPLer-X), with up to ViT-Huge as the backbone and training with up to 4.5M instances from diverse data sources. With big data and the large model, SMPLer-X exhibits strong performance across diverse test benchmarks and excellent transferability to even unseen environments. 1) For the data scaling, we perform a systematic investigation on 32 EHPS datasets, including a wide range of scenarios that a model trained on any single dataset cannot handle. More importantly, capitalizing on insights obtained from the extensive benchmarking process, we optimize our training scheme and select datasets that lead to a significant leap in EHPS capabilities. 2) For the model scaling, we take advantage of vision transformers to study the scaling law of model sizes in EHPS. Moreover, our finetuning strategy turn SMPLer-X into specialist models, allowing them to achieve further performance boosts. Notably, our foundation model SMPLer-X consistently delivers state-of-the-art results on seven benchmarks such as AGORA (107.2 mm NMVE), UBody (57.4 mm PVE), EgoBody (63.6 mm PVE), and EHF (62.3 mm PVE without finetuning). Homepage: https://caizhongang.github.io/projects/SMPLer-X/Comment: Homepage: https://caizhongang.github.io/projects/SMPLer-X

Daily time-use patterns and obesity and mental health among primary school students in shanghai: a population-based cross-sectional study

Physical activity, screen viewing, sleep, and homework among children have been independently linked to health outcomes. However, few studies have assessed the independent associations between time spent in daily activities and children’s physical and mental health. This study describes time spent in four activities among primary school students in Shanghai, and examines the relationship between daily time-use patterns and obesity and mental health. The representative sample consists of 17,318 children aged 6–11 years in Shanghai. Time spent in moderate to vigorous physical activities (MVPA), screen viewing, sleep, and homework was measured by validated questionnaires. Logistic regressions were performed. We also fitted generalized additive models (GAM) and performed two-objective optimization to minimize the probability of poor mental health and obesity. In 2014, 33.7% of children spent ˂1 hour/day on MVPA, 15.6% spent ≥ 2 hours/day on screen viewing, 12.4% spent ˂ 9 hours/day on sleep, and 27.2% spent ≥ 2 hours/day on homework. The optimization results suggest that considering the 24-hour time limit, children face trade-offs when allocating time. A priority should be given to the duration of sleep and MVPA. Screen exposure should be minimized to save more time for sleep and other beneficial activities

Association between Dietary Patterns and Precocious Puberty in Children: A Population-Based Study

Objective. The aim of the present study was to investigate the association between dietary patterns and precocious puberty among Shanghai children. Methods. A cross-sectional study was conducted among Shanghai children by multistage stratified cluster random sampling in June 2014. Diet was assessed using a simplified food frequency questionnaire (FFQ). Height, weight, and Tanner stages of breast development, pubic hair growth, and testicular volume were carefully measured. Exploratory factor analysis was used to identify dietary patterns, and logistic regression analysis was used to assess the association between dietary patterns and precocious puberty. Results. Three distinct dietary patterns, “traditional diet,” “unhealthy diet,” and “protein diet,” were established. Neither the “traditional diet” pattern nor the “protein diet” pattern showed any association with precocious puberty, taking gender, BMI, and adjustment factors into consideration. The “unhealthy diet” pattern was significantly positively associated with precocious puberty in both boys (OR = 1.24, 95% CI = 1.02–1.51) and girls (OR = 1.31, 95% CI = 1.10–1.56). The relationship remained positive only for girls (OR = 1.25, 95% CI = 1.04–1.49) after adjustment for age and BMI but statistically nonsignificant after further adjustment for socioeconomic factors in both boys and girls. Conclusions. Dietary patterns were found to be related to precocious puberty among Shanghai children

Sleep and neuropsychological development among infants and toddlers

Abstract Sleep quantity and quality have been verified to influence cognitive function in adult and children, while little is known about such relation among young children. This study aims to explore the concurrent association between sleep and neuropsychological development in infants and toddlers. 1102 children aged 2-30 months old from 16 hospitals in 8 provinces of China were involved in the survey. Main caregivers were asked to fill out the Brief Infant Sleep Questionnaire (BISQ) and the Bayley Scales of Infant Development I (BSID-I) was conducted to evaluate children's neuropsychological development. Results showed that in average infants slept 12.40 (12.30-12.51) hours over 24 hours, woke up 1.77 (1.69-1.84) times per night and woke 0.50 (0.45-0.54) hour through the night. Prolonged night waking duration was negatively associated with the Mental Development Index (MDI) only among toddlers aged 1-2.5 years old (β=-0.10, p=0.045). No association between sleep and psychomotor development was found. This study underscores the high prevalent fragmental sleep in Chinese infants and toddlers that indicates for concurrent slow cognitive development