On Tree-Based Neural Sentence Modeling

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is open-source and available at https://github.com/ExplorerFreda/TreeEnc.Comment: To Appear at EMNLP 201

Exploration of a New Model of "Highway + Tourism" Development from the Perspective of 5G——Taking the Yichang Road Tourism Economic Belt as an Example

The innovation of 5G technology is the characteristics of the progress and development of the times, now Yibin City Yichang Road construction has been basically perfect, along the way tourism in this network era and the epidemic epidemic intersection can be described as ready to go, in the typical mode of "highway + tourism", with the help of 5G advantages, Yichang Road tourism economic belt innovation into "5G + highway + tourist attractions + tourism services" integrated tourism comprehensive development model, adapt to the development of the times and the diversification characteristics of tourists, explore new directions of industry development, draw a new main line for high-quality tourism development. 5G is a mobile communication technology, but also a trend of future development, in the development of Yibin Yichang Road tourism, seize the new opportunities of 5G mobile communication development, rely on the "Internet of Things", "cloud computing" and "smart city" common development, combined with the current situation, with the help of "highway + tourism" typical model, strive to explore a new development model for Yichang Road tourism economy

Appling an Improved Method Based on ARIMA Model to Predict the Short-Term Electricity Consumption Transmitted by the Internet of Things (IoT)

The rapid development of the Internet of Things (IoT) has brought a data explosion and a new set of challenges. It has been an emergency to construct a more robust and precise model to predict the electricity consumption data collected from the Internet of Things (IoT). Accurately forecasting the electricity consumption is a crucial technology for the planning of the energy resource which could lead to remarkable conservation of the building electricity consumption. This paper is focused on the electricity consumption forecasting of an office building with a small-scale dataset, and 117 daily electricity consumption of the building are involved in the dataset, among which 89 values are selected as the training dataset and the remaining 28 values as the testing dataset. The hybrid model ARIMA (autoregression integrated moving average)-SVR (support vector regression) is proposed to predict the electricity consumption with different prediction horizons ranging from 1 day to 28 days. The model performances are assessed by three evaluation indicators, respectively, are the mean squared error (MSE), the root mean square error (RMSE), and the mean absolute percentage error (MAPE). The proposed model ARIMA-SVR is compared with the other four models, respectively, are the ARIMA, ARIMA-GBR (gradient boosting regression), LSTM (long short-term memory), and GRU (gated recurrent unit) models. The experiment result shows that the ARIMA-SVR model has lower prediction errors when the prediction horizon is within 20 days, and the ARIMA model is better when the prediction horizon is in the interval of 20 to 28 days. The provided method ARIMA-SVR has higher flexibility, and it is a great choice for electricity consumption prediction with more accurate results

I was determined to breastfeed, and I always found a solution: Successful Experiences of Exclusive Breastfeeding Among Chinese Mothers in Ireland

Background: The prevalence of exclusive breastfeeding for at least 4 months was previously found to be very low among Chinese immigrants in Ireland, at 5.8% (Zhou et al., Front Public Health 6:351, 2018). This study investigates the successful experiences of Chinese mothers living in Ireland who exclusively breastfeed for between four and 6 months. Methods: Participants were recruited from the sample of the Ireland Chinese Mother Survey. Qualitative in-depth interviews were conducted with fourteen participants in their homes or public places. Results: A content analysis revealed that various factors contributed to a successful experience of exclusive breastfeeding among the group of Chinese immigrant mothers, including strong self-determination; appropriate physical conditions; awareness of the benefits of exclusive breastfeeding; a lack of time constraints; and family, professional and policy support. The barriers that the mothers faced included the difficulty of balancing breastfeeding and employment, infant health issues, language barriers, an inability to consume the traditional Chinese postpartum diet and a lack of public breastfeeding facilities. Measures taken to overcome these barriers included seeking family support, resting during the lactation period, and pumping breast milk to feed from a bottle when outside the home. Conclusions: This study highlights unique factors affecting exclusive breastfeeding among Chinese mothers in Ireland, which may be useful to health care professionals working with Chinese immigrant women internationally

Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing

This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries. This task is challenging because only overall labels indicating the video events are provided for training. However, an event might be labeled but not appear in one of the modalities, which results in a modality-specific noisy label problem. In this work, we propose a training strategy to identify and remove modality-specific noisy labels dynamically. It is motivated by two key observations: 1) networks tend to learn clean samples first; and 2) a labeled event would appear in at least one modality. Specifically, we sort the losses of all instances within a mini-batch individually in each modality, and then select noisy samples according to the relationships between intra-modal and inter-modal losses. Besides, we also propose a simple but valid noise ratio estimation method by calculating the proportion of instances whose confidence is below a preset threshold. Our method makes large improvements over the previous state of the arts (e.g. from 60.0\% to 63.8\% in segment-level visual metric), which demonstrates the effectiveness of our approach. Code and trained models are publicly available at \url{https://github.com/MCG-NJU/JoMoLD}.Comment: Accepted by ECCV 202

Knowledge, Attitude and Practices (KAP) towards Diet and Health among International Students in Dublin: A Cross-Sectional Study

International students may have difficulties in dietary acculturation. This study aimed to evaluate the knowledge, attitude and practices (KAP) of diet and health during the acculturation of international students. A cross-sectional survey was conducted among a convenience sample of 473 international students in Dublin. Knowledge, attitude and practices towards diet and health were evaluated by a questionnaire with open- and closed-ended questions. It was found that 45.3% of participants had a broad concept of a healthy diet, while few knew its specific contents. Furthermore, 75.3% of participants could explain the term functional food, and among them, 62.1% knew the appropriate definition of functional food. Participants who perceived their health very good and excellent were more likely to believe that their health status was determined by their own control. The consumption rate of functional food varied among regions and South and Central America students had the highest usage rate (44.5%) and Asian students had the highest daily usage rate (52.7%). Participants who were younger, single, from African and South and Central American countries, or who were in Ireland for less than one year were more likely to report dietary change after immigration. In conclusion, insufficient knowledge and self-perception towards diet and health as well as unhealthily dietary changes exist among international students living in Dublin

How to Promote Exclusive Breastfeeding in Ireland: a Qualitative Study on Views of Chinese Immigrant Mothers

Background The exclusive breastfeeding rate in Ireland is very low with extremely slow annual growth.The population of immigrants in Ireland is increasing. Improving exclusive breastfeeding practice amongimmigrants may contribute to the overall improvement of exclusive breastfeeding rates in Ireland. Thisstudy was conducted to elicit recommendations on improving exclusive breastfeeding rate for six monthsamong Chinese immigrants in Ireland. Methods Fourteen semi-structured in-depth individual interviewswere conducted with Chinese immigrant mothers resident in Ireland, who breastfed exclusively for four to six months

Light-driven C-H bond activation mediated by 2D transition metal dichalcogenides

C-H bond activation enables the facile synthesis of new chemicals. While C-H activation in short-chain alkanes has been widely investigated, it remains largely unexplored for long-chain organic molecules. Here, we report light-driven C-H activation in complex organic materials mediated by 2D transition metal dichalcogenides (TMDCs) and the resultant solid-state synthesis of luminescent carbon dots in a spatially-resolved fashion. We unravel the efficient H adsorption and a lowered energy barrier of C-C coupling mediated by 2D TMDCs to promote C-H activation. Our results shed light on 2D materials for C-H activation in organic compounds for applications in organic chemistry, environmental remediation, and photonic materials