Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks

Large knowledge graphs often grow to store temporal facts that model the dynamic relations or interactions of entities along the timeline. Since such temporal knowledge graphs often suffer from incompleteness, it is important to develop time-aware representation learning models that help to infer the missing temporal facts. While the temporal facts are typically evolving, it is observed that many facts often show a repeated pattern along the timeline, such as economic crises and diplomatic activities. This observation indicates that a model could potentially learn much from the known facts appeared in history. To this end, we propose a new representation learning model for temporal knowledge graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism. CyGNet is not only able to predict future facts from the whole entity vocabulary, but also capable of identifying facts with repetition and accordingly predicting such future facts with reference to the known facts in the past. We evaluate the proposed method on the knowledge graph completion task using five benchmark datasets. Extensive experiments demonstrate the effectiveness of CyGNet for predicting future facts with repetition as well as de novo fact prediction.Comment: AAAI 2021; Updated in accordance with camera read

Dihydropyrano[2,3-c]pyrazole-induced apoptosis in lung cancer cells is associated with ROS generation and activation of p38/JNK pathway

Purpose: To investigate the effect of 2,4-dihydropyrano[2,3-c]pyrazole (DHPP) on lung cancer cells, and the associated mechanism.Methods: The effect of DHPP on cell proliferation was measured using sulphorhodamine B (SRB) assay. Apoptosis of cells was determined using Olympus IX71 inverted microscope connected to FITC and rhodamine filters.Results: DHPP significantly suppressed the proliferation of A549 and H1299 cells at doses of 0.5-8.0 μM, but did not affect normal cells (MRC5 and BEAS-2B). In DHPP-treated A549 and H1299 cells, caspase-3 activity was markedly enhanced. At 24 h of treatment with 8.0 μM DUPP, apoptosis in A549 and H1299 cells was increased to 67.89 and 61.35 %, respectively. Phosphorylation levels of JNK-1/2 and p38 in DHPP-treated A549 and H1299 cells were markedly enhanced. The p-ERK-1/2 expressions in DHPP-treated A549 and H1299 cells were suppressed significantly at 24 h. In DHPP-treated A549 and H1299 cells, DCF-fluorescence was increased 10 folds and 8.5 folds, respectively. Pretreatment with FeTMPyP, an antioxidant, effectively alleviated DHPP-induced increase in expressions of p-p38 and p-JNK, and suppression of expression of p-ERK-1/2. In FeTMPyP-pre-treated cells, the DHPPinduced increase in caspase-3 activity was markedly reduced.Conclusion: DHPP selectively inhibits lung cancer cell growth via oxidative stress which subsequently causes cell apoptosis. Moreover, it activates caspase-3 protein and p38/JNK signaling, with simultaneous inactivation of ERK-1/2. Therefore, DHPP has a potential to be developed for the treatment of lung cancer. However; more studies are required to confirm these findings. Keywords: Lung cancer, Anti-oxidant, Apoptosis, Caspase-3, Chemotherap

Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China

260-270The southern coastal area of Laizhou Bay is subjected to severe soil salinization due to saline groundwater. The degree of spatial variability is strongly affected by seasonal changes during an annual cycle. In this paper, the spatio-temporal variability of soil salinity in Laizhou Bay, China, was examined to ascertain the current situation of soil salinization in the study area and to reveal the characteristics of seasonal variation of soil salinity. The classical statistical methods and geostatistical methods were applied to soil salinity data collected from four soil layers, i.e., 0-30, 30-60, 60-90, and 0-100 cm, during summer and autumn in 2014. The results indicated that the variation of soil salinity of all the soil layers in summer and autumn was moderate. The soil salinity in the 0-30 cm layer showed a moderate spatial autocorrelation, whereas the spatial autocorrelations of soil salinity in other layers were strong. The overall spatial distribution of soil salinity showed a clear banding distribution and the degree of salinization in the eastern area was lower than that in the western and northern regions.A high ratio of evaporation/precipitation is one of the important reasons for the soil salinity in July is significantly higher than that in November. The rank of soil salinity under different land-use types was: salt pan > orchard > weeds > soybean > woods > cotton > maize > ginger > sweet potato. The research findings can provide theoretical guidance for accurate assessment and soil partition management of regional soil salinization

A Survey on Particle Swarm Optimization for Association Rule Mining

Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.publishedVersio

TTSVD: an efficient sparse decision making model with two-way trust recommendation in the AI enabled IoT systems

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making (so called Sparse Decision Making, SDM) will decrease the efficiency dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce so-called trust information. However, trust information also maybe face the difficulty of sparse trust evidence (a.k.a sparse trust problem). In our work, an accurate sparse decision making model with two-way trust recommendation in the AI enabled IoT systems is proposed by us, named TT-SVD. Our model incorporates both trust information and rating information more completely, which can efficiently alleviate the above mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the two-fold trust influences from both trustees and trustors, which can be represented by a factor called trust propensity. To this end, we propose a dual model, including the trustor model (TrustorSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state of the art including SVD and TrustSVD in both the ”all users” and ”cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse datasets. In a word, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems

Analysis of Road Traffic Network Cascade Failures with Coupled Map Lattice Method

In recent years, there is growing literature concerning the cascading failure of network characteristics. The object of this paper is to investigate the cascade failures on road traffic network, considering the aeolotropism of road traffic network topology and road congestion dissipation in traffic flow. An improved coupled map lattice (CML) model is proposed. Furthermore, in order to match the congestion dissipation, a recovery mechanism is put forward in this paper. With a real urban road traffic network in Beijing, the cascading failures are tested using different attack strategies, coupling strengths, external perturbations, and attacked road segment numbers. The impacts of different aspects on road traffic network are evaluated based on the simulation results. The findings confirmed the important roles that these characteristics played in the cascading failure propagation and dissipation on road traffic network. We hope these findings are helpful to find out the optimal road network topology and avoid cascading failure on road network

Hydrochemical Characteristics and Quality Assessment of Groundwater under the Impact of Seawater Intrusion and Anthropogenic Activity in the Coastal Areas of Zhejiang and Fujian Provinces, China

AbstractCoastal groundwater is an important resource in the developed region associated with human health and sustainable economic development. To identify the origins of salinity and evaluate the impact of water-rock interactions, seawater intrusion (SWI), and evaporation on groundwater in the coastal areas of Zhejiang and Fujian provinces, a comprehensive investigation was performed. Meanwhile, nitrate and fluoride indicators resulting from the anthropogenic activity and SWI were also considered. At last, the water quality index (WQI) of coastal groundwater was evaluated with geochemical and multivariate statistical methods. The results indicated that (1) the groundwater in coastal areas of Zhejiang and Fujian provinces has been affected by SWI to varying degrees. The analysis of selected ion ratios (Na+/Cl− and Br−/Cl−) and isotopic compositions showed that SWI is the predominant cause of increasing salinity in the groundwater of Zhejiang Province, while the cause is water-rock interactions (ion exchange and mineral weathering) in Fujian Province. The hydrochemical evolution path of groundwater in Zhejiang Province is Ca/Mg-HCO3 to Na-Cl, while a different pattern of Ca/Mg-HCO3 to Na (Mg/Ca)-Cl occurs in Fujian Province. However, the trend of SWI development in both provinces was freshening. (2) Nitrification, sewage infiltration, and SWI increased the NO3− content in groundwater. Some of the NO3− concentration in Fujian Province exceeds the standard, and the nitrogen pollution was more serious than in Zhejiang Province. The F− content in coastal groundwater was affected by SWI and mineral dissolution; the F− content in Zhejiang Province was higher than in Fujian Province, which was close to the groundwater standard limit. The average WQI value of Zhejiang was 103.61, and the WQI of Fujian was 61.69, indicating that the coastal groundwater quality in Fujian Province was better than in Zhejiang Province. The results of the study revealed the impact of SWI and anthropogenic activity on groundwater in the southern coastal zone of China and will be valuable for sustainable groundwater resource management

Statistical modeling of spatially stratified heterogeneous data

Spatial statistics is an important methodology for geospatial data analysis. It has evolved to handle spatially autocorrelated data and spatially (locally) heterogeneous data, which aim to capture the first and second laws of geography, respectively. Examples of spatially stratified heterogeneity (SSH) include climatic zones and land-use types. Methods for such data are relatively underdeveloped compared to the first two properties. The presence of SSH is evidence that nature is lawful and structured rather than purely random. This induces another “layer” of causality underlying variations observed in geographical data. In this article, we go beyond traditional cluster-based approaches and propose a unified approach for SSH in which we provide an equation for SSH, display how SSH is a source of bias in spatial sampling and confounding in spatial modeling, detect nonlinear stochastic causality inherited in SSH distribution, quantify general interaction identified by overlaying two SSH distributions, perform spatial prediction based on SSH, develop a new measure for spatial goodness of fit, and enhance global modeling by integrating them with an SSH q statistic. The research advances statistical theory and methods for dealing with SSH data, thereby offering a new toolbox for spatial data analysis

Distribution of 222Rn in Seawater Intrusion Area and Its Implications on Tracing Submarine Groundwater Discharge on the Upper Gulf of Thailand

AbstractRadon (222Rn) has been widely employed as a tracer for estimating submarine groundwater discharge (SGD). However, the uncertainty of the SGD estimation remains significant, due to the spatial variability of radon in groundwater. In this study, we analyzed the hydrochemical proprieties of seawater and coastal groundwater in the Upper Gulf of Thailand and discussed the distribution characteristics of 222Rn in aquifers in terms of aquifer lithology, groundwater system recharge conditions, and water retention time. The results suggested that the residence time of groundwater and the process of groundwater salinization have the greatest impact on the distribution of 222Rn activity. A 222Rn mass balance model, synthesizing the distribution characteristics of 222Rn in groundwater and tidal influences on SGD, was built to estimate the submarine groundwater discharge in the Upper Gulf of Thailand. The result showed that the SGD flux of the Upper Gulf of Thailand was 0.0203 m/d. Moreover, there is a positive correlation between tidal height and the activity of 222Rn in groundwater. The SGD observed during the low tide was about 1.25 times higher than that observed during the high tide. This may influence the marine geochemical cycles of elements and their impact on marine ecosystems