Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge Graphs

Node Importance Estimation (NIE) is a task of inferring importance scores of the nodes in a graph. Due to the availability of richer data and knowledge, recent research interests of NIE have been dedicating to knowledge graphs for predicting future or missing node importance scores. Existing state-of-the-art NIE methods train the model by available labels, and they consider every interested node equally before training. However, the nodes with higher importance often require or receive more attention in real-world scenarios, e.g., people may care more about the movies or webpages with higher importance. To this end, we introduce Label Informed ContrAstive Pretraining (LICAP) to the NIE problem for being better aware of the nodes with high importance scores. Specifically, LICAP is a novel type of contrastive learning framework that aims to fully utilize the continuous labels to generate contrastive samples for pretraining embeddings. Considering the NIE problem, LICAP adopts a novel sampling strategy called top nodes preferred hierarchical sampling to first group all interested nodes into a top bin and a non-top bin based on node importance scores, and then divide the nodes within top bin into several finer bins also based on the scores. The contrastive samples are generated from those bins, and are then used to pretrain node embeddings of knowledge graphs via a newly proposed Predicate-aware Graph Attention Networks (PreGAT), so as to better separate the top nodes from non-top nodes, and distinguish the top nodes within top bin by keeping the relative order among finer bins. Extensive experiments demonstrate that the LICAP pretrained embeddings can further boost the performance of existing NIE methods and achieve the new state-of-the-art performance regarding both regression and ranking metrics. The source code for reproducibility is available at https://github.com/zhangtia16/LICAPComment: Accepted by IEEE TNNL

Research progress in the relationship between mitochondrial dysfunction and osteoporosis

Osteoporosis (OP) is a chronic senile bone disease characterized by decreased bone mass and increased bone fragility. There are many inducing factors and the pathogenesis is complex. To explore the mechanism of OP and improve clinical efficacy has always been a hot topic in life science. In recent years, it has been found that mitochondria play an important role in the pathogenesis of OP. Functional abnormalities such as mitochondrial energy metabolism, mitochondrial oxidative stress, mitochondrial autophagy, mitochondrial-mediated apoptosis and mitochondrial dynamics can interfere with the differentiation of bone marrow mesenchymal stem cells through different signal pathways, cytokines and protein expression to regulate osteoblast activity, proliferation and differentiation, and start the process of osteoclast apoptosis. Therefore, taking mitochondria as the target, regulating the functions of mitochondrial energy metabolism, oxidative stress, autophagy and kinetics, inducing osteogenic differentiation of bone marrow mesenchymal stem cells, promoting osteoblast differentiation and mineralization, and inducing osteoclast apoptosis are potential strategies for the prevention and treatment of OP. In this article, the mechanism of mitochondrial dysfunction in OP was reviewed by referring to relevant literature at home and abroad, in order to lay a foundation for further research

A Review of Current Methodologies for Regional Evapotranspiration Estimation from Remotely Sensed Data

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements

Concurrent Topological Structure and Cross-Infill Angle Optimization for Material Extrusion Polymer Additive Manufacturing with Microstructure Modeling

This paper contributes a concurrent topological structure and cross-infill angle optimization method for material extrusion type additive manufacturing (AM). This method features in modeling the process-induced material anisotropy through microscopic geometric modeling obtained by scanning electron micrographs. Numerical homogenization is performed to evaluate the equivalent effective properties of the 100-percentage cross-infilled local microstructures, and by introducing fitting functions, the relationship between equivalent effective material properties and varying cross-infill angles is empirically constructed. Then, optimization problems involving cross-infill angles as design variables are formulated, including concurrent optimization formulation. Numerical and experimental studies are conducted to illustrate the effectiveness of the proposed method. Both the numerical and experimental results demonstrate that the structural stiffness obtained by our proposed method has evidently improved

Satellite-derived aridity index reveals China's drying in recent two decades

Summary: The expansion of dryland has caused a huge impact on the natural environment and human society. Aridity index (AI) can effectively reflect the degree of dryness, but spatiotemporally continuous estimation of AI is still challenging. In this study, we develop an ensemble learning algorithm to retrieve AIs from MODIS satellite data in China from 2003 to 2020. The validation proves the high match between these satellite AIs and their corresponding station estimates with a root-mean-square error of 0.21, bias of −0.01, and correlation coefficient of 0.87. The analysis results indicate China has been drying in recent two decades. Moreover, the North China Plain is undergoing an intense drying process, whereas the Southeastern China is becoming significantly more humid. On the national scale, China's dryland area shows a slight expansion, while the hyper arid area has a decreasing trend. These understandings have contributed to China's drought assessment and mitigation

Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data

Rooftop solar photovoltaics (PV) play increasing role in the global sustainable energy transition. This raises the challenge of accurate and high-resolution geospatial assessment of PV technical potential in policymaking and power system planning. To address the challenge, we propose a general framework that combines multi-resource satellite images and deep learning models to provide estimates of rooftop PV power generation. We apply deep learning based inversion model to estimate hourly solar radiation based on geostationary satellite images, and automatic segmentation model to extract building footprint from high-resolution satellite images. The framework enables precise survey of available rooftop resources and detailed simulation of power generation on an hourly basis with a spatial resolution of 100 m. The case study in Jiangsu Province demonstrates that the framework is applicable for large areas and scalable between precise locations and arbitrary regions across multiple temporal scales. Our estimates show that rooftop resources across the province have a potential installed capacity of 245.17 GW, corresponding to an annual power generation of 290.66 TWh. This highlights the huge space for carbon emissions reduction through developing rooftop PVs

Methane Adsorption Rate and Diffusion Characteristics in Marine Shale Samples from Yangtze Platform, South China

Knowledge of the gas adsorption rate and diffusion characteristics in shale are very important to evaluate the gas transport properties. However, research on methane adsorption rate characteristics and diffusion behavior in shale is not well established. In this study, high-pressure methane adsorption isotherms and methane adsorption rate data from four marine shale samples were obtained by recording the pressure changes against time at 1-s intervals for 12 pressure steps. Seven pressure steps were selected for modelling, and three pressure steps of low (~0.4 MPa), medium (~4.0 MPa), and high (~7.0 MPa) were selected for display. According to the results of study, the methane adsorption under low pressure attained equilibrium much more quickly than that under medium and high pressure, and the adsorption rate behavior varied between different pressure steps. By fitting the diffusion models to the methane adsorption rate data, the unipore diffusion model based upon unimodal pore size distribution failed to describe the methane adsorption rate, while the bidisperse diffusion model could reasonably describe most of the experimental adsorption rate data, with the exception of sample YY2-1 at high pressure steps. This phenomenon may be related to the restricted assumption on pore size distribution and linear adsorption isotherm. The diffusion parameters α and β/α obtained from the bidisperse model indicated that both macro- and micropore diffusion controlled the methane adsorption rate in shale samples, as well as the relative importance and influence of micropore diffusion and adsorption to adsorption rate and total adsorption increased with increasing pressure. This made the inflection points, or two-stage process, at higher pressure steps not as evident as at low pressure steps, and the adsorption rate curves became less steep with increasing pressure. This conclusion was also supported by the decreasing difference values with increasing pressures between macro- and micropore diffusivities obtained using the bidisperse model, which is roughly from 10−3 to 100, and 10−3 to 10−1, respectively. Additionally, an evident negative correlation between macropore diffusivities and pressure lower than 3–4 MPa was observed, while the micropore diffusivities only showed a gentle decreasing trend with pressure. A mirror image relationship between the variation in the value of macropore diffusivity and adsorption isotherms was observed, indicating the negative correlation between surface coverage and gas diffusivity. The negative correlation of methane diffusivity with pressure and surface coverage may be related to the increasing degree of pore blockage and the decreasing concentration gradient of methane adsorption. Finally, due to the significant deviation between the unipore model and experimental adsorption rate data, a new estimation method based upon the bidisperse model is proposed here

Nestin Positive Bone Marrow Derived Cells Responded to Injury Mobilize into Peripheral Circulation and Participate in Skin Defect Healing.

Exogenously infused mesenchymal stem cells (MSCs) are thought to migrate to injury site through peripheral blood stream and participate in tissue repair. However, whether and how endogenous bone marrow MSCs mobilized to circulating and targeted to tissue injury has raised some controversy, and related studies were restricted by the difficulty of MSCs identifying in vivo. Nestin, a kind of intermediate filament protein initially identified in neuroepithelial stem cells, was recently reported as a credible criteria for MSCs in bone marrow. In this study, we used a green fluorescent protein (GFP) labeled bone marrow replacement model to trace the nestin positive bone marrow derived cells (BMDCs) of skin defected-mice. We found that after skin injured, numbers of nestin+ cells in peripheral blood and bone marrow both increased. A remarkable concentration of nestin+ BMDCs around skin wound was detected, while few of these cells could be observed in uninjured skin or other organs. This recruitment effect could not be promoted by granulocyte colony-stimulating factor (G-CSF), suggests a different mobilization mechanism from ones G-CSF takes effect on hematopoietic cells. Our results proposed nestin+ BMDCs as mobilized candidates in skin injury repair, which provide a new insight of endogenous MSCs therapy

RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL

Relational structures such as schema linking and schema encoding have been validated as a key component to qualitatively translating natural language into SQL queries. However, introducing these structural relations comes with prices: they often result in a specialized model structure, which largely prohibits the use of large pretrained models in text-to-SQL. To address this problem, we propose RASAT: a Transformer seq2seq architecture augmented with relation-aware self-attention that could leverage a variety of relational structures while at the meantime being able to effectively inherit the pretrained parameters from the T5 model. Our model is able to incorporate almost all types of existing relations in the literature, and in addition, we propose to introduce co-reference relations for the multi-turn scenario. Experimental results on three widely used text-to-SQL datasets, covering both single-turn and multi-turn scenarios, have shown that RASAT could achieve competitive results in all three benchmarks, achieving state-of-the-art performance in execution accuracy (80.5\% EX on Spider, 53.1\% IEX on SParC, and 37.5\% IEX on CoSQL).Comment: 9 pages, first versio