Cross-relation Cross-bag Attention for Distantly-supervised Relation Extraction

Distant supervision leverages knowledge bases to automatically label instances, thus allowing us to train relation extractor without human annotations. However, the generated training data typically contain massive noise, and may result in poor performances with the vanilla supervised learning. In this paper, we propose to conduct multi-instance learning with a novel Cross-relation Cross-bag Selective Attention (C$^2$SA), which leads to noise-robust training for distant supervised relation extractor. Specifically, we employ the sentence-level selective attention to reduce the effect of noisy or mismatched sentences, while the correlation among relations were captured to improve the quality of attention weights. Moreover, instead of treating all entity-pairs equally, we try to pay more attention to entity-pairs with a higher quality. Similarly, we adopt the selective attention mechanism to achieve this goal. Experiments with two types of relation extractor demonstrate the superiority of the proposed approach over the state-of-the-art, while further ablation studies verify our intuitions and demonstrate the effectiveness of our proposed two techniques.Comment: AAAI 201

Joint Hand-object 3D Reconstruction from a Single Image with Cross-branch Feature Fusion

Accurate 3D reconstruction of the hand and object shape from a hand-object image is important for understanding human-object interaction as well as human daily activities. Different from bare hand pose estimation, hand-object interaction poses a strong constraint on both the hand and its manipulated object, which suggests that hand configuration may be crucial contextual information for the object, and vice versa. However, current approaches address this task by training a two-branch network to reconstruct the hand and object separately with little communication between the two branches. In this work, we propose to consider hand and object jointly in feature space and explore the reciprocity of the two branches. We extensively investigate cross-branch feature fusion architectures with MLP or LSTM units. Among the investigated architectures, a variant with LSTM units that enhances object feature with hand feature shows the best performance gain. Moreover, we employ an auxiliary depth estimation module to augment the input RGB image with the estimated depth map, which further improves the reconstruction accuracy. Experiments conducted on public datasets demonstrate that our approach significantly outperforms existing approaches in terms of the reconstruction accuracy of objects.Comment: Accepted by IEEE Transactions on Image Processing (TIP

Mixed-Variable PSO with Fairness on Multi-Objective Field Data Replication in Wireless Networks

Digital twins have shown a great potential in supporting the development of wireless networks. They are virtual representations of 5G/6G systems enabling the design of machine learning and optimization-based techniques. Field data replication is one of the critical aspects of building a simulation-based twin, where the objective is to calibrate the simulation to match field performance measurements. Since wireless networks involve a variety of key performance indicators (KPIs), the replication process becomes a multi-objective optimization problem in which the purpose is to minimize the error between the simulated and field data KPIs. Unlike previous works, we focus on designing a data-driven search method to calibrate the simulator and achieve accurate and reliable reproduction of field performance. This work proposes a search-based algorithm based on mixedvariable particle swarm optimization (PSO) to find the optimal simulation parameters. Furthermore, we extend this solution to account for potential conflicts between the KPIs using {\alpha}-fairness concept to adjust the importance attributed to each KPI during the search. Experiments on field data showcase the effectiveness of our approach to (i) improve the accuracy of the replication, (ii) enhance the fairness between the different KPIs, and (iii) guarantee faster convergence compared to other methods.Comment: Accepted in International Conference on Communications (ICC) 202

Evaluating Profitability Based on Integrated Method: A Case Study of Chinese Listed Airlines and Airports

Due to the fact that the development of Chinese aviation industry has slowed down since the world financial crisis in 2008, how to evaluate the airline profitability improvements has become a key issue. Many mathematical methods can be applied in comprehensive evaluations, but the different focal points of these methods and the choice of methods may lead to different evaluation results, even if they are based on the same data. Both the theory and methods to evaluate the profitability of the airlines need to be developed and improved. Therefore, in order to evaluate airlinemanagement and performance, this paper presents a new approach to evaluate the Chinese aviation industry and applies towards the evaluation of 12 listed airlines and airports in the Chinese Hushen stock market. This approach overcomes the one-sidedness of a single method and obtains more comprehensive, realistic and objective results

Auxin response factor 6A regulates photosynthesis, sugar accumulation, and fruit development in tomato.

Auxin response factors (ARFs) are involved in auxin-mediated transcriptional regulation in plants. In this study, we performed functional characterization of SlARF6A in tomato. SlARF6A is located in the nucleus and exhibits transcriptional activator activity. Overexpression of SlARF6A increased chlorophyll contents in the fruits and leaves of tomato plants, whereas downregulation of SlARF6A decreased chlorophyll contents compared with those of wild-type (WT) plants. Analysis of chloroplasts using transmission electron microscopy indicated increased sizes of chloroplasts in SlARF6A-overexpressing plants and decreased numbers of chloroplasts in SlARF6A-downregulated plants. Overexpression of SlARF6A increased the photosynthesis rate and accumulation of starch and soluble sugars, whereas knockdown of SlARF6A resulted in opposite phenotypes in tomato leaves and fruits. RNA-sequence analysis showed that regulation of SlARF6A expression altered the expression of genes involved in chlorophyll metabolism, photosynthesis and sugar metabolism. SlARF6A directly bound to the promoters of SlGLK1, CAB, and RbcS genes and positively regulated the expression of these genes. Overexpression of SlARF6A also inhibited fruit ripening and ethylene production, whereas downregulation of SlARF6A increased fruit ripening and ethylene production. SlARF6A directly bound to the SAMS1 promoter and negatively regulated SAMS1 expression. Taken together, these results expand our understanding of ARFs with regard to photosynthesis, sugar accumulation and fruit development and provide a potential target for genetic engineering to improve fruit nutrition in horticulture crops

Correction of UAV LiDAR-derived grassland canopy height based on scan angle

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25–65cm) and segmented (25–40cm, 40–50cm and 50–65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing

Predicting depressive symptom by cardiometabolic indicators in mid-aged and older adults in China: a population-based cross-sectional study

ObjectiveDepressive symptom is a serious mental illness often accompanied by physical and emotional problems. The prevalence of depressive symptom in older adults has become an increasingly important public health priority. Our study used cardiometabolic indicators to predict depressive symptom in middle-aged and older adults in China.MethodsThe data came from the China Health and Retirement Longitudinal Study 2011 (CHARLS2011), which was a cross-sectional study. The analytic sample included 8,942 participants aged 45 years or above. The study evaluated the relationship between cardiometabolic indicators and depression by measuring 13 indicators, including body mass index (BMI), waist circumference, waist-height ratio (WHtR), conicity index, visceral adiposity index (VAI), Chinese visceral adiposity index (CVAI), lipid accumulation product (LAP), a body shape index (ABSI), body roundness index (BRI), triglyceride glucose index (TyG-index) and its correlation index (TyG-BMI, TyG-waist circumference, TyG-WHtR). Binary logistic regression analysis was used to examine the association between thirteen cardiometabolic indicators and depressive symptom. In addition, the receiver operating characteristic (ROC) curve analysis and area under curve (AUC) were used to evaluate the predictive anthropometric index and to determine the optimum cut-off value.ResultsThe study included 8,942 participants, of whom 4,146 (46.37%) and 4,796 (53.63%) were male and female. The prevalence of depressive symptom in mid-aged and older adults in China was 41.12% in males and 55.05% in females. The results revealed that BMI [AUC = 0.440, 95%CI: 0.422–0.457], waist circumference [AUC = 0.443, 95%CI: 0.425–0.460], WHtR [AUC = 0.459, 95%CI: 0.441–0.476], LAP [AUC = 0.455, 95%CI: 0.437–0.472], BRI [AUC = 0.459, 95%CI: 0.441–0.476], CVAI [AUC = 0.449, 95%CI: 0.432–0.467], TyG-BMI [AUC = 0.447, 95%CI: 0.429–0.465], and TyG-waist circumference [AUC =0.452, 95%CI: 0.434–0.470] were weak predictors of depressive symptom (p < 0.05) in males. In females, BMI [AUC = 0.470, 95%CI: 0.453–0.486], LAP [AUC = 0.484, 95%CI: 0.467–0.500], TyG-BMI [AUC = 0.470, 95%CI: 0.454–0.487], and TyG-waist circumference [AUC =0.481, 95%CI: 0.465–0.498] were weak predictors of depressive symptom (p < 0.05). On the other side, VAI, ABSI, conicity index and TyG index could not predict depressive symptom in middle-aged and older adults.ConclusionMost cardiometabolic indicators have important value in predicting depressive symptom. Our results can provide measures for the early identification of depressive symptom in middle-aged and older adults in China to reduce the prevalence of depressive symptom and improve health

Historical trends of forest fires and carbon emissions in China from 1988 to 2012

A larger amount of carbon is stored in forest ecosystems than in the entire atmosphere. Thus, relatively small changes in forest carbon stocks can significantly impact net carbon exchange between the biosphere and atmosphere. Changes in forest stocks can result from various disturbances, such as insect pests, windstorms, flooding, and especially forest fires. Globally, the impact of forest fires has been enhanced due to ongoing warming of the climate. The current study reported an evaluation of carbon emissions from historical forest fires in China during 1988-2012 with observational data collected from national agriculture statistics. Historical fire trends and fire-induced carbon emissions were described over space and time at both national and regional levels. The results indicated that no significant increases in fire occurrence and carbon emissions were observed during the study period at the national level. However, at the regional level, there was a significant increasing trend in fire occurrence, and drought severity was a major driver of fire activity. Most carbon emissions were from north and northeast China, and these emissions contributed significantly to total carbon emissions. The results also showed that annual fire-induced emissions ranged from 0.04TgC to 7.22TgC, with an average of 1.03TgC. Large interannual and spatial variabilities of carbon emissions were also indicated, and these were attributed to spatial and temporal variations in fire regimes. The results improve understanding of fire characteristics and provide significant information for reducing model-related uncertainty of fire-induced carbon emissions