YouTube AV 50K: An Annotated Corpus for Comments in Autonomous Vehicles

With one billion monthly viewers, and millions of users discussing and sharing opinions, comments below YouTube videos are rich sources of data for opinion mining and sentiment analysis. We introduce the YouTube AV 50K dataset, a freely-available collections of more than 50,000 YouTube comments and metadata below autonomous vehicle (AV)-related videos. We describe its creation process, its content and data format, and discuss its possible usages. Especially, we do a case study of the first self-driving car fatality to evaluate the dataset, and show how we can use this dataset to better understand public attitudes toward self-driving cars and public reactions to the accident. Future developments of the dataset are also discussed.Comment: in Proceedings of the Thirteenth International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP 2018

Interactive effects of family socioeconomic status and body mass index on depression in school-age children

[[abstract]]Depression is an important health problem in children and the onset of depression is occurring at a younger age than previously suggested. The associations of being overweight and low socioeconomic status in childhood depression have been well documented; nevertheless few studies have addressed the combined effects of socioeconomic status and body weight, with depression in school-age children. We intended to examine if the relationship between socioeconomic status and childhood depression could be modified by abnormal body weight. A cross-sectional study was performed with a total of 559 subjects from 29 elementary schools in Taiwan. A depression scale was used to determine the depression status. Children receiving governmental monetary assistance for after-school class were categorized as being in the lower socioeconomic group. Data for depression-related demographic characteristics, family and school variables were collected. Children in the lower socioeconomic status group have a higher prevalence of depression (23.5%) than those in higher socioeconomic status groups(16.4%). Being overweight demonstrates the opposite effect on depression risk in the different socioeconomic groups. In lower socioeconomic families, the risk of depression in overweight children is three times higher than that for normal weight children; whereas in higher socioeconomic families, overweight children have a lower risk for depression than normal weight children. We concluded that a qualitative interactive effect existed between being overweight and socioeconomic status with childhood depression. More attention should be paid to overweight children from lower socioeconomic status families to prevent depression in school-age children

interactive influences of family and school ecologies on the depression status among children in martial immigrant families

[[abstract]]The incidence of transnational marriage has increased significantly in Taiwan in recent years. Children born in immigrant families are predisposed to acculturation and learning problems. We aimed to determine if the children of marital immigrants are more depressed than children from native families, and examine the individual and joint effects of various factors on their depression risk. A cross-sectional study was performed to investigate the depression status of elementary school children in MiaoLi County, Taiwan. A total of 676 participants, including 157 children from families in which the mother was an immigrant and the father native to Taiwan, were recruited from 29 schools. A modified depression scale “Depression Screen Scale for Children and Adolescents” for domestic school children was used to determine depression status. Data which might relate to depression, including demographic, family and school variables, were collected with a structured questionnaire and analyzed with multivariate and stratification methods. The results show that 20.4% of children from immigrant mother families and 17.1% of children from native families exhibited depressive symptoms. The child–parent relationship, peer relationship and academic performance in school were found by logistic regression to be the main predictors of depression in immigrant family children. With further stratification analysis, synergistic effects in immigrant families were found between child–parent relationship and family climate and between peer relationship and academic performance, raising the risk of depression in children of marital immigrants by 7.26- and 7.71-fold, respectively. This synergistic effect was not observed in native families. This study provides significant evidence of synergistic effects between family variables and school variables which increase, up to more than 7-fold, the risk of depression in children of marital immigrants. The results provide hints to parents and teachers for improving the mental health of children in immigrant families by reducing the occurrence of depression

Multi-representations Space Separation based Graph-level Anomaly-aware Detection

Graph structure patterns are widely used to model different area data recently. How to detect anomalous graph information on these graph data has become a popular research problem. The objective of this research is centered on the particular issue that how to detect abnormal graphs within a graph set. The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies. Furthermore, abnormal graphs that have subtle differences from normal graphs are easily escaped detection by the existing methods. Thus, we propose a multi-representations space separation based graph-level anomaly-aware detection framework in this paper. To consider the different importance of node-level and graph-level anomalies, we design an anomaly-aware module to learn the specific weight between them in the abnormal graph evaluation process. In addition, we learn strictly separate normal and abnormal graph representation spaces by four types of weighted graph representations against each other including anchor normal graphs, anchor abnormal graphs, training normal graphs, and training abnormal graphs. Based on the distance error between the graph representations of the test graph and both normal and abnormal graph representation spaces, we can accurately determine whether the test graph is anomalous. Our approach has been extensively evaluated against baseline methods using ten public graph datasets, and the results demonstrate its effectiveness.Comment: 11 pages, 12 figure

Structural, stereochemical, and bioactive studies of cembranoids from Chinese soft coral Sarcophyton trocheliophorum

LBK

Reinforced Multi-Teacher Selection for Knowledge Distillation

In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popular method for model compression, knowledge distillation transfers knowledge from one or multiple large (teacher) models to a small (student) model. When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation. Furthermore, most of the existing methods allocate an equal weight to every teacher model. In this paper, we observe that, due to the complexity of training examples and the differences in student model capability, learning differentially from teacher models can lead to better performance of student models distilled. We systematically develop a reinforced method to dynamically assign weights to teacher models for different training instances and optimize the performance of student model. Our extensive experimental results on several NLP tasks clearly verify the feasibility and effectiveness of our approach.Comment: AAAI 202

Bioluminescence imaging of hepatitis B virus enhancer and promoter activities in mice

AbstractBy bioluminescence imaging and hydrodynamic gene transfer technology, the activities of hepatitis B virus (HBV) promoters and the effects of HBV enhancers on these promoters in mice under true physiological conditions have been assessed. Our studies reveal that either of the two HBV enhancers can stimulate HBV major promoter activity in hepa 1–6 cells (in vitro) and in mouse liver (in vivo), and the enhancer effects on the three promoters (S1, S2 and X promoter) are markedly greater in vivo than in vitro. The two HBV enhancers have no cooperative action on HBV promoters in vitro or in vivo

Low-energy Scattering of (D∗Dˉ∗)±(D^{*}\bar{D}^{*})^\pm System and the Resonance-like Structure Zc(4025)Z_c(4025)

In this paper, low-energy scattering of the $(D^{*}\bar{D}^{*})^\pm$ meson system is studied within L\"uscher's finite-size formalism using $N_{f}=2$ twisted mass gauge field configurations. With three different pion mass values, the $s$-wave threshold scattering parameters, namely the scattering length $a_0$ and the effective range $r_0$, are extracted in $J^P=1^+$ channel. Our results indicate that, in this particular channel, the interaction between the two vector charmed mesons is weakly repulsive in nature hence do not support the possibility of a shallow bound state for the two mesons, at least for the pion mass values being studied. This study provides some useful information on the nature of the newly discovered resonance-like structure $Z_c(4025)$ observed in various experiments.Comment: 11 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:1403.131