A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction

The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of multiple driving (exogenous) series, has been studied for decades. Despite the fact that various NARX models have been developed, few of them can capture the long-term temporal dependencies appropriately and select the relevant driving series to make predictions. In this paper, we propose a dual-stage attention-based recurrent neural network (DA-RNN) to address these two issues. In the first stage, we introduce an input attention mechanism to adaptively extract relevant driving series (a.k.a., input features) at each time step by referring to the previous encoder hidden state. In the second stage, we use a temporal attention mechanism to select relevant encoder hidden states across all time steps. With this dual-stage attention scheme, our model can not only make predictions effectively, but can also be easily interpreted. Thorough empirical studies based upon the SML 2010 dataset and the NASDAQ 100 Stock dataset demonstrate that the DA-RNN can outperform state-of-the-art methods for time series prediction.Comment: International Joint Conference on Artificial Intelligence (IJCAI), 201

Hydrogen sensing performance of silica microfiber elaborated with Pd nanoparticles

A hydrogen sensor has been proposed by coating Pd nanoparticles-PMMA composite organic sol on silica microfiber independent on any expensive or complex chemical process. The thickness of cladding layer and the diameter of elaborated microfiber were determined as ∼20 μm and ∼57.93 μm, respectively. Due to the evanescent wave excited by silica microfiber and the amorphous structure of PMMA film, the Pd nanoparticles effectively absorbed the hydrogen molecules and resulted in the shift of resonance wavelength. The experimental results match well with an exponential curve with an average sensitivity of 5.58 nm/%, which is comparable to other electrochemical hydrogen sensors reported recently.The authors thank the support from the National Natural Science Foundation of China (NSFC) under Grants (61405032, 61403074, 61605031); and Doctoral Scientific Research Startup Foundation of Liaoning Province under Grant (201501144); and Fundamental Research Funds for the Central Universities under Grants (N150404022, N150401001); and China Scholarship Council (201606085023)

Vulnerability and Resilience in the Wake of COVID-19: Family Resources and Children’s Well-being in China

The present study uses data from a 2020 survey conducted in Shaanxi Province during the COVID-19 outbreak to examine the family resources and psychological well-being of four major groups of Chinese children (urban, migrant, rural nonmigrant, and rural left-behind children). The results highlight the complex ways in which family resources intersect with the pandemic to affect these different groups of children. Family economic resources have generally declined across all groups, but left-behind children have suffered the most severe economic shock. However, parent-child relationships for all children have improved across the board during the pandemic. Diminished economic resources act as a risk factor, while improved family relationships play a protective role in children’s psychological well-being. Parent-child relationships have had a more pronounced positive impact on psychological outcomes for migrant and left-behind children, who are the most deprived of parental input under normal circumstances, than for other groups of children. Because of these processes, migrant children and left-behind children fare similarly to urban children in terms of their resilience to the COVID-19 crisis. Among children enjoying especially favorable parent-child relationships, migrant children and left-behind children even appear to have higher psychological well-being than urban children during the pandemic. In comparison to this social impact, the impact of family economic resources is more moderate in magnitude and does not vary systematically across different groups of children. As a result, the positive impact of improved parent-child relationships largely outweighs the adverse effect of reduced family economic resources. Overall, the findings provide new insight into the relationship among disasters, family resources, and child well-being in the context of the COVID-19 crisis in China

Family Structure, Family Instability, and Child Psychological Well-being in the Context of Migration: Evidence from Sequence Analysis in China

This study conceptualizes parental migration as a dynamic family process that exposes children to parental absence and family instability. Using detailed migration histories, this study identifies the left-behind trajectories of rural Chinese children throughout childhood (age 1-12) and examines the impact on psychological well-being (N=3,961). Results indicate heterogeneity in children’s experience of parental migration, which is characterized by both persistence (prolonged parental absence) and instability (repeated parental migration). A quarter of rural children experienced prolonged parental migration, and for half of these, by both parents. Another 50% of rural children experienced repeated parental migration. Children continuously left behind by both parents and children who experienced substantial family instability both fared worse in psychological development than those in stable two-parent families

Modulation of the vitamin D receptor by traditional Chinese medicines and bioactive compounds: potential therapeutic applications in VDR-dependent diseases

The Vitamin D receptor (VDR) is a crucial nuclear receptor that plays a vital role in various physiological functions. To a larger extent, the genomic effects of VDR maintain general wellbeing, and its modulation holds implications for multiple diseases. Current evidence regarding using vitamin D or its synthetic analogs to treat non-communicable diseases is insufficient, though observational studies suggest potential benefits. Traditional Chinese medicines (TCMs) and bioactive compounds derived from natural sources have garnered increasing attention. Interestingly, TCM formulae and TCM-derived bioactive compounds have shown promise in modulating VDR activities. This review explores the intriguing potential of TCM and bioactive compounds in modulating VDR activity. We first emphasize the latest information on the genetic expression, function, and structure of VDR, providing a comprehensive understanding of this crucial receptor. Following this, we review several TCM formulae and herbs known to influence VDR alongside the mechanisms underpinning their action. Similarly, we also discuss TCM-based bioactive compounds that target VDR, offering insights into their roles and modes of action

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial?

The past decade has witnessed the flourishing of a new profession as media content creators, who rely on revenue streams from online content recommendation platforms. The reward mechanism employed by these platforms creates a competitive environment among creators which affect their production choices and, consequently, content distribution and system welfare. It is thus crucial to design the platform's reward mechanism in order to steer the creators' competition towards a desirable welfare outcome in the long run. This work makes two major contributions in this regard: first, we uncover a fundamental limit about a class of widely adopted mechanisms, coined Merit-based Monotone Mechanisms, by showing that they inevitably lead to a constant fraction loss of the optimal welfare. To circumvent this limitation, we introduce Backward Rewarding Mechanisms (BRMs) and show that the competition game resultant from BRMs possesses a potential game structure. BRMs thus naturally induce strategic creators' collective behaviors towards optimizing the potential function, which can be designed to match any given welfare metric. In addition, the BRM class can be parameterized to allow the platform to directly optimize welfare within the feasible mechanism space even when the welfare metric is not explicitly defined