The Politics of Memory, Diaspora, and Identity in Lillian Ng’s Silver Sister

Silver Sister, a biographical novel telling the unique life stories of the so-called “comb-ups,” represents China as a troubled homeland with turmoil, war, and painful memories. Silver, the protagonist of this novel, as a Chinese comb-up, has mixed and doubled identities as an illiterate Chinese female in diaspora. On one hand, she is imprinted with characteristics of Chineseness. On the other hand, the novel contests the notion of Chineseness by demonstrating the interactive relation between Silver’s personal remembrance and collective memory of common Chinese females at that time. In this essay, it is argued that this novel is more precisely about how traumatic memory transforms Chinese women’s diasporic identity in a global context, instead of only in a journey from China to Australia. Its meaning lies not only on the way of representing trauma and memory respectively, but the way how traumatic memories together with other diasporic memories function on influencing identity politics.

CGINet: graph convolutional network-based model for identifying chemical-gene interaction in an integrated multi-relational graph

Background Elucidation of interactive relation between chemicals and genes is of key relevance not only for discovering new drug leads in drug development but also for repositioning existing drugs to novel therapeutic targets. Recently, biological network-based approaches have been proven to be effective in predicting chemical-gene interactions. Results We present CGINet, a graph convolutional network-based method for identifying chemical-gene interactions in an integrated multi-relational graph containing three types of nodes: chemicals, genes, and pathways. We investigate two different perspectives on learning node embeddings. One is to view the graph as a whole, and the other is to adopt a subgraph view that initial node embeddings are learned from the binary association subgraphs and then transferred to the multi-interaction subgraph for more focused learning of higher-level target node representations. Besides, we reconstruct the topological structures of target nodes with the latent links captured by the designed substructures. CGINet adopts an end-to-end way that the encoder and the decoder are trained jointly with known chemical-gene interactions. We aim to predict unknown but potential associations between chemicals and genes as well as their interaction types. Conclusions We study three model implementations CGINet-1/2/3 with various components and compare them with baseline approaches. As the experimental results suggest, our models exhibit competitive performances on identifying chemical-gene interactions. Besides, the subgraph perspective and the latent link both play positive roles in learning much more informative node embeddings and can lead to improved prediction.

in Strategic and Security Studies "Regional and International", National Center for Research and Scientific Studies, Tripoli, Libya.

This study presents three main approaches; The rooting methodology for the subject of the study; Taking into account the analysis of the status of research and studies, and the importance that touches upon the very essence of community development. Not only that; But even in the renaissance and advancement of nations regionally and continentally. And also, to have a look at the reasons that stand stumbling block in failing to achieve that, and despite the existence of extensive research and studies in all fields of development, which is represented in the large gap between research institutions and policy makers in Arab States. But even at the continental level, that is why we find it flounder when make decisions, and low level of services provided by the governments. This is due to these decisions are not based on prior studies, to insure simulate the particularity of the decision with the purpose for which it was established, where we would like to clarify through this study to Touching the concept of scientific research, and to shows the importance of scientific research in the development of societies, and to recognize the reality of this interactive relation between the two directions; Research Institutions and decision-makers.

DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification

In dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers' intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately (Kim and Kim 2018). Most of the existing systems either treat them as separate tasks or just jointly model the two tasks by sharing parameters in an implicit way without explicitly modeling mutual interaction and relation. To address this problem, we propose a Deep Co-Interactive Relation Network (DCR-Net) to explicitly consider the cross-impact and model the interaction between the two tasks by introducing a co-interactive relation layer. In addition, the proposed relation layer can be stacked to gradually capture mutual knowledge with multiple steps of interaction. Especially, we thoroughly study different relation layers and their effects. Experimental results on two public datasets (Mastodon and Dailydialog) show that our model outperforms the state-of-the-art joint model by 4.3% and 3.4% in terms of F1 score on dialog act recognition task, 5.7% and 12.4% on sentiment classification respectively. Comprehensive analysis empirically verifies the effectiveness of explicitly modeling the relation between the two tasks and the multi-steps interaction mechanism. Finally, we employ the Bidirectional Encoder Representation from Transformer (BERT) in our framework, which can further boost our performance in both tasks.

Multidimensional Understanding of Religiosity/Spirituality: Relationship to Major Depression and Familial Risk

Background. Previous research has shown prospectively that religiosity/spirituality protects against depression, but these findings are commonly critiqued on two grounds, namely: 1) apparent religiosity/spirituality reflects merely an original absence of depression or elevated mood and 2) religiosity/spirituality too often is measured as a global construct. The current study investigates the relationship between depression and religiosity/spirituality by examining its multidimensional structural integrity. Method. Confirmatory factor analyses with a previously observed cross-cultural factor structure of religiosity/spirituality variables were conducted on an independent sample, diagnostic and familial risk subgroups from this sample, and a subsample of the original cross-cultural sample. Linear regressions onto a previous diagnosis of major depressive disorder (MDD) five years prior to assess potential attenuating impact of a previous depression were explored.Results. Across familial risk groups and clinical subgroups, each of the previously validated religiosity/spirituality domains was confirmed, namely: religious/spiritual commitment, contemplative practice, sense of interconnectedness, experience of love, and altruistic engagement. Previous MDD diagnosis was associated with lower religious/spiritual commitment among high risk individuals, higher contemplation among low risk individuals, and lower importance of religion or spirituality regardless of risk group. Conclusions. Structural integrity was found across familial risk groups and diagnostic history for a multidimensional structure of religiosity/spirituality. Differential associations between a previous diagnosis of MDD and level of religiosity/spirituality across domains suggest a complex and interactive relation between depression, familial risk, and religiosity/spirituality. Accounting for an empirically valid, multidimensional understanding of religiosity/spirituality may advance research on mechanisms underlying the relationship between religiosity/spirituality and mental health.

Compound Autoregressive Network for Prediction of Multivariate Time Series

The prediction information has effects on the emergency prevention and advanced control in various complex systems. There are obvious nonlinear, nonstationary, and complicated characteristics in the time series. Moreover, multiple variables in the time-series impact on each other to make the prediction more difficult. Then, a solution of time-series prediction for the multivariate was explored in this paper. Firstly, a compound neural network framework was designed with the primary and auxiliary networks. The framework attempted to extract the change features of the time series as well as the interactive relation of multiple related variables. Secondly, the structures of the primary and auxiliary networks were studied based on the nonlinear autoregressive model. The learning method was also introduced to obtain the available models. Thirdly, the prediction algorithm was concluded for the time series with multiple variables. Finally, the experiments on environment-monitoring data were conducted to verify the methods. The results prove that the proposed method can obtain the accurate prediction value in the short term.