AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.Comment: Accepted by CIKM 202

Understanding Translationese in Cross-Lingual Summarization

Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS data, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese. Then we systematically investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Lastly, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.Comment: Accepted to the Findings of EMNLP 202

When to Pre-Train Graph Neural Networks? An Answer from Data Generation Perspective!

Recently, graph pre-training has attracted wide research attention, which aims to learn transferable knowledge from unlabeled graph data so as to improve downstream performance. Despite these recent attempts, the negative transfer is a major issue when applying graph pre-trained models to downstream tasks. Existing works made great efforts on the issue of what to pre-train and how to pre-train by designing a number of graph pre-training and fine-tuning strategies. However, there are indeed cases where no matter how advanced the strategy is, the "pre-train and fine-tune" paradigm still cannot achieve clear benefits. This paper introduces a generic framework W2PGNN to answer the crucial question of when to pre-train (i.e., in what situations could we take advantage of graph pre-training) before performing effortful pre-training or fine-tuning. We start from a new perspective to explore the complex generative mechanisms from the pre-training data to downstream data. In particular, W2PGNN first fits the pre-training data into graphon bases, each element of graphon basis (i.e., a graphon) identifies a fundamental transferable pattern shared by a collection of pre-training graphs. All convex combinations of graphon bases give rise to a generator space, from which graphs generated form the solution space for those downstream data that can benefit from pre-training. In this manner, the feasibility of pre-training can be quantified as the generation probability of the downstream data from any generator in the generator space. W2PGNN provides three broad applications, including providing the application scope of graph pre-trained models, quantifying the feasibility of performing pre-training, and helping select pre-training data to enhance downstream performance. We give a theoretically sound solution for the first application and extensive empirical justifications for the latter two applications

High-Resolution Boundary Detection for Medical Image Segmentation with Piece-Wise Two-Sample T-Test Augmented Loss

Deep learning methods have contributed substantially to the rapid advancement of medical image segmentation, the quality of which relies on the suitable design of loss functions. Popular loss functions, including the cross-entropy and dice losses, often fall short of boundary detection, thereby limiting high-resolution downstream applications such as automated diagnoses and procedures. We developed a novel loss function that is tailored to reflect the boundary information to enhance the boundary detection. As the contrast between segmentation and background regions along the classification boundary naturally induces heterogeneity over the pixels, we propose the piece-wise two-sample t-test augmented (PTA) loss that is infused with the statistical test for such heterogeneity. We demonstrate the improved boundary detection power of the PTA loss compared to benchmark losses without a t-test component

Anti-icing property of bio-inspired micro-structure superhydrophobic surfaces and heat transfer model

Ice accumulation is a thorny problem which may inflict serious damage even disasters in many areas, such as aircraft, power line maintenance, offshore oil platform and locators of ships. Recent researches have shed light on some promising bio-inspired anti-icing strategies to solve this problem. Inspired by typical plant surfaces with super-hydrophobic character such as lotus leaves and rose petals, structured superhydrophobic surface are prepared to discuss the anti-icing property. 7075 Al alloy, an extensively used materials in aircrafts and marine vessels, is employed as the substrates. As-prepared surfaces are acquired by laser processing after being modified by stearic acid for 1 h at room temperature. The surface morphology, chemical composition and wettability are characterized by means of SEM, XPS, Fourier transform infrared (FTIR) spectroscopy and contact angle measurements. The morphologies of structured as-prepared samples include round hump, square protuberance and mountain-range-like structure, and that the as-prepared structured surfaces shows an excellent superhydrophobic property with a WCA as high as 166 ± 2°. Furthermore, the anti-icing property of as-prepared surfaces was tested by a self-established apparatus, and the crystallization process of a cooling water on the sample was recorded. More importantly, we introduced a model to analyze heat transfer process between the droplet and the structured surfaces. This study offers an insight into understanding the heat transfer process of the superhydrophobic surface, so as to further research about its unique property against ice accumulation

Short-Term Load Forecasting Model of Electric Vehicle Charging Load Based on MCCNN-TCN

The large fluctuations in charging loads of electric vehicles (EVs) make short-term forecasting challenging. In order to improve the short-term load forecasting performance of EV charging load, a corresponding model-based multi-channel convolutional neural network and temporal convolutional network (MCCNN-TCN) are proposed. The multi-channel convolutional neural network (MCCNN) can extract the fluctuation characteristics of EV charging load at various time scales, while the temporal convolutional network (TCN) can build a time-series dependence between the fluctuation characteristics and the forecasted load. In addition, an additional BP network maps the selected meteorological and date features into a high-dimensional feature vector, which is spliced with the output of the TCN. According to experimental results employing urban charging station load data from a city in northern China, the proposed model is more accurate than artificial neural network (ANN), long short-term memory (LSTM), convolutional neural networks and long short-term memory (CNN-LSTM), and TCN models. The MCCNN-TCN model outperforms the ANN, LSTM, CNN-LSTM, and TCN by 14.09%, 25.13%, 27.32%, and 4.48%, respectively, in terms of the mean absolute percentage error

Regional Context, Market Transition, and Successful Aging: Results from Transitional China.

Recognizing the positive aspects of aging and exploring the ways older people age successfully has become an increasingly popular area of gerontology in recent years. Despite advances in successful aging research, there remain two important gaps in current research literature: First, most studies have been limited to the US or other developed Western countries. Relatively little is known concerning the ways many older people experience successful aging in developing countries. Second, the existing studies overemphasize the role of the individual in successful aging and largely ignore the role that social factors play in successful aging. This study seeks to address the gaps by establishing a macro-micro linkage in the understanding of successful aging and investigates the ways in which large-scale social changes influence the likelihood that Chinese older people will age successfully – over and above individual-level characteristics. I use individual-level data from 2000 Chinese Longitudinal Healthy Longevity Survey (CLHLS) and province-level data from administrative statistics to examine the effects of three dimensions of provincial-level market transition context – economic development, income inequality, and economic ownership restructuring – on individual successful aging in the transitional China. Two forms of market transition effects are examined: main effects of market transition context and interaction effects between market transition context and individual SES. Statistically, two-level random intercept logistic regression models are built for analyses. Findings reveal that objective measure successful aging is associated with both individual-level characteristics and province-level market transition characteristics including economic development, income inequality, and economic ownership restructuring; whereas subjective measure successful aging is only associated with individual-level characteristics. Moreover, the study finds that although economic development and economic ownership restructuring are positively associated with successful aging for all cases, the influences are not evenly distributed among groups: low SES older people benefit more from them than do high SES older adults. This study has significant theoretical and policy implications. On the theoretical side, this study is an important addition to the literature on the gerontological implications of market transition. From a policy perspective, this study calls for greater attention to the effects of social context on individual aging experiences.PHDSocial Work and Political ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/96089/1/jazhang_1.pd

Cigarette Smoking and Alcohol Consumption among Chinese Older Adults: Do Living Arrangements Matter?

This study used five waves of the Chinese Longitudinal Healthy Longevity Survey to examine the relationship between living arrangements, smoking, and drinking among older adults in China from 1998–2008. We found that living arrangements had strong implications for cigarette smoking and alcohol consumption among the elderly. First, the likelihood of smoking was lower among older men living with children, and older women living either with a spouse, or with both a spouse and children; and the likelihood of drinking was lower among both older men, and women living with both a spouse and children, compared with those living alone. Second, among dual consumers (i.e., being a drinker and a smoker), the amount of alcohol consumption was lower among male dual consumers living with children, while the number of cigarettes smoked was higher among female dual consumers living with others, compared with those living alone. Third, among non-smoking drinkers, the alcohol consumption was lower among non-smoking male drinkers in all types of co-residential arrangements (i.e., living with a spouse, living with children, living with both a spouse and children, or living with others), and non-smoking female drinkers living with others, compared with those living alone. Results highlighted the importance of living arrangements to cigarette smoking and alcohol consumption among Chinese elderly. Co-residential arrangements provided constraints on Chinese older adults’ health-risk behaviors, and had differential effects for men and women

The paeonol target gene autophagy-related 5 has a potential therapeutic value in psoriasis treatment

Background Paeonol is a potent therapy for psoriasis. This study aimed to screen out paeonol-targeted genes in psoriasis and validate the potential of using paeonol for the management of psoriasis. Methods Microarray datasets were obtained from the Gene Expression Omnibus. The differentially expressed genes (DEGs) in the lesional skin samples and the overlapping genes between DEGs and paeonol- and psoriasis-related genes were defined as potential targets for psoriasis. After being treated with si-ATG5 and pc-ATG5, human HaCaT cells were treated with 100 ng/ml IL-22 and 10 ng/ml TNF-α with and without paeonol. Cell proliferation, apoptosis, and expression of interleukin (IL)-6, IL-1β, Beclin 1, ATG5, and p62 in HaCaT cells were determined using ESLIA, PCR, and Western blot analysis. Results A total of 779 DEGs were identified in the lesional skin samples compared with the non-lesional tissues. The autophagy-related 5 (ATG5) gene was the only gene that overlapped between the DEGs and genes related to paeonol and psoriasis. Cell proliferation, inflammatory cytokines (IL-6 and IL-1β), and ATG5 expression were increased in IL-22/TNF-α-stimulated HaCaT (model) cells compared with control. Paeonol treatment rescued all changes. si-ATG5 transfection increased inflammation and apoptosis in model cells compared with controls. pc-ATG5 prevented IL-22/TNF-α-induced changes in HaCaT cells. Also, si-ATG5 decreased p62 and Beclin 1 proteins, while pc-ATG5 increased them both. Conclusions ATG5-dependent autophagy plays a crucial role in psoriasis. The ATG5 gene might be a therapeutic target for the management of in vitro psoriasis