Rule-Guided Joint Embedding Learning over Knowledge Graphs

Recent studies focus on embedding learning over knowledge graphs, which map entities and relations in knowledge graphs into low-dimensional vector spaces. While existing models mainly consider the aspect of graph structure, there exists a wealth of contextual and literal information that can be utilized for more effective embedding learning. This paper introduces a novel model that incorporates both contextual and literal information into entity and relation embeddings by utilizing graph convolutional networks. Specifically, for contextual information, we assess its significance through confidence and relatedness metrics. In addition, a unique rule-based method is developed to calculate the confidence metric, and the relatedness metric is derived from the literal information's representations. We validate our model performance with thorough experiments on two established benchmark datasets

Semantic Parsing for Question Answering over Knowledge Graphs

In this paper, we introduce a novel method with graph-to-segment mapping for question answering over knowledge graphs, which helps understanding question utterances. This method centers on semantic parsing, a key approach for interpreting these utterances. The challenges lie in comprehending implicit entities, relationships, and complex constraints like time, ordinality, and aggregation within questions, contextualized by the knowledge graph. Our framework employs a combination of rule-based and neural-based techniques to parse and construct highly accurate and comprehensive semantic segment sequences. These sequences form semantic query graphs, effectively representing question utterances. We approach question semantic parsing as a sequence generation task, utilizing an encoder-decoder neural network to transform natural language questions into semantic segments. Moreover, to enhance the parsing of implicit entities and relations, we incorporate a graph neural network that leverages the context of the knowledge graph to better understand question representations. Our experimental evaluations on two datasets demonstrate the effectiveness and superior performance of our model in semantic parsing for question answering.Comment: arXiv admin note: text overlap with arXiv:2401.0296

Gas pressure sintering of BN/Si3N4 wave-transparent material with Y2O3–MgO nanopowders addition

AbstractBN/Si3N4 ceramics performed as wave-transparent material in spacecraft were fabricated with boron nitride powders, silicon nitride powders and Y2O3–MgO nanopowders by gas pressure sintering at 1700°C under 6MPa in N2 atmosphere. The effects of Y2O3–MgO nanopowders on densification, phase evolution, microstructure and mechanical properties of BN/Si3N4 material were investigated. The addition of Y2O3–MgO nanopowders was found beneficial to the mechanical properties of BN/Si3N4 composites. The BN/Si3N4 ceramics with 8wt% Y2O3–MgO nanopowders showed a relative density of 80.2%, combining a fracture toughness of 4.6MPam1/2 with an acceptable flexural strength of 396.5MPa

A Fast, Efficient Domain Adaptation Technique for Cross-Domain Electroencephalography(EEG)-Based Emotion Recognition

Electroencephalography (EEG)-based emotion recognition is an important element in psychiatric health diagnosis for patients. However, the underlying EEG sensor signals are always non-stationary if they are sampled from different experimental sessions or subjects. This results in the deterioration of the classification performance. Domain adaptation methods offer an effective way to reduce the discrepancy of marginal distribution. However, for EEG sensor signals, both marginal and conditional distributions may be mismatched. In addition, the existing domain adaptation strategies always require a high level of additional computation. To address this problem, a novel strategy named adaptive subspace feature matching (ASFM) is proposed in this paper in order to integrate both the marginal and conditional distributions within a unified framework (without any labeled samples from target subjects). Specifically, we develop a linear transformation function which matches the marginal distributions of the source and target subspaces without a regularization term. This significantly decreases the time complexity of our domain adaptation procedure. As a result, both marginal and conditional distribution discrepancies between the source domain and unlabeled target domain can be reduced, and logistic regression (LR) can be applied to the new source domain in order to train a classifier for use in the target domain, since the aligned source domain follows a distribution which is similar to that of the target domain. We compare our ASFM method with six typical approaches using a public EEG dataset with three affective states: positive, neutral, and negative. Both offline and online evaluations were performed. The subject-to-subject offline experimental results demonstrate that our component achieves a mean accuracy and standard deviation of 80.46% and 6.84%, respectively, as compared with a state-of-the-art method, the subspace alignment auto-encoder (SAAE), which achieves values of 77.88% and 7.33% on average, respectively. For the online analysis, the average classification accuracy and standard deviation of ASFM in the subject-to-subject evaluation for all the 15 subjects in a dataset was 75.11% and 7.65%, respectively, gaining a significant performance improvement compared to the best baseline LR which achieves 56.38% and 7.48%, respectively. The experimental results confirm the effectiveness of the proposed method relative to state-of-the-art methods. Moreover, computational efficiency of the proposed ASFM method is much better than standard domain adaptation; if the numbers of training samples and test samples are controlled within certain range, it is suitable for real-time classification. It can be concluded that ASFM is a useful and effective tool for decreasing domain discrepancy and reducing performance degradation across subjects and sessions in the field of EEG-based emotion recognition

E2 regulates MMP-13 via targeting miR-140 in IL-1β-induced extracellular matrix degradation in human chondrocytes

Background: Estrogen deficiency is closely related to the development of menopausal arthritis. Estrogen replacement therapy (ERT) shows a protective effect against the osteoarthritis. However, the underlying mechanism of this protective effect is unknown. This study aimed to determine the role of miR-140 in the estrogen-dependent regulation of MMP-13 in human chondrocytes. Methods: Primary human articular chondrocytes were obtained from female OA patients undergoing knee replacement surgery. Normal articular chondrocytes were isolated from the knee joints of female donors after trauma and treated with interleukin-1 beta (IL-1 beta). Gene expression levels of miR-140, MMP-13, and ADAMTS-5 were detected by quantitative real-time PCR (qRT-PCR). miR-140 levels were upregulated or downregulated by transfecting cells with a miRNA mimic and inhibitor, respectively, prior to treatment with IL-1 beta. MMP-13 expression was then evaluated by Western blotting and immunofluorescence. Luciferase reporter assays were performed to verify the interaction between miR-140 and ER. Results: 17-beta-estradiol (E2) suppressed MMP-13 expression in human articular chondrocytes. miR-140 expression was upregulated after estrogen treatment. Knockdown of miR-140 expression abolished the inhibitory effect of estrogen on MMP-13. In addition, the estrogen/ER/miR-140 pathway showed an inhibitory effect on IL-1 beta-induced cartilage matrix degradation. Conclusions: This study suggests that estrogen acts via ER and miR-140 to inhibit the catabolic activity of proteases within the chondrocyte extracellular matrix. These findings provide new insight into the mechanism of menopausal arthritis and indicate that the ER/miR-140 signaling pathway may be a potential target for therapeutic interventions for menopausal arthritis.National Natural Science Foundation of China [81572198, 81260161]; Natural Science Foundation of Guangdong Province, China [2015A030313772]; Shenzhen Science and Technology Innovation Committee [JSGG20140519105550503, JSGG20151030140325149, JCYJ20140414170821200, JCYJ20140414170821160]SCI(E)[email protected]; [email protected]

Green manure (Ophiopogon japonicus) cover promotes tea plant growth by regulating soil carbon cycling

IntroductionIn mountainous tea plantations, which are the primary mode of tea cultivation in China, issues such as soil erosion and declining soil fertility are particularly severe. Although green manure cover is an effective agricultural measure for restoring soil fertility, its application in mountainous tea plantations has been relatively understudied.MethodsThis study investigated the effects of continuous green manure cover using the slope-protecting plant Ophiopogon japonicus on tea plant growth and soil microbial community structure. We implemented three treatments: 1 year of green manure coverage, 2 years of coverage, and a control, to study their effects on tea plant growth, soil physicochemical properties, and soil bacterial and fungal communities.ResultsResults demonstrate that green manure coverage significantly promote the growth of tea plants, enhanced organic matter and pH levels in soil, and various enzyme activities, including peroxidases and cellulases. Further functional prediction results indicate that green manure coverage markedly promoted several carbon cycling functions in soil microbes, including xylanolysis, cellulolysis, degradation of aromatic compounds, and saprotrophic processes. LEfSe analysis indicated that under green manure cover, the soil tends to enrich more beneficial microbial communities with degradation functions, such as Sphingomonas, Sinomonas, and Haliangium (bacteria), and Penicillium, Apiotrichum, and Talaromyce (fungi). In addition. Random forest and structural equation models indicated that carbon cycling, as a significant differentiating factor, has a significant promoting effect on tea plant growth.DiscussionIn the management practices of mountainous tea plantations, further utilizing slope-protecting plants as green manure can significantly influence the soil microbial community structure and function, enriching microbes involved in the degradation of organic matter and aromatic compounds, thereby positively impacting tea tree growth and soil nutrient levels

Building Earthquake Damage Analysis Using Terrestrial Laser Scanning Data

Terrestrial laser scanners (TLSs) can acquire high-precision three-dimensional point cloud data for earthquake-damaged buildings. In this study, we collected TLS data in the Wenchuan earthquake zone and developed the TLS-BSAM (terrestrial laser scanning-based building shape analysis model) to carry out a building earthquake damage analysis. This model involves equidistance polygon array extraction, shape dispersion parameter calculations, irregular building clustering segmentation, and damage analysis. We chose 21 buildings as samples for the experiments. The results show that when using an equidistance polygon array to depict a three-dimensional building, 0.5 m is a reasonable sampling interval for building earthquake damage analysis. Using certain characteristic parameters to carry out K-means clustering, one can efficiently divide irregular buildings into regular blocks. Then, by weighted averages, the shape dispersion parameters can be calculated to express the damage extent to buildings. Among the shape dispersion parameters, at least the weighted average standard deviations of the tilt direction, rectangularity, compactness, and center point are suitable to reflect the damage extent. Higher values reflect more serious damage. On the basis of existing data, the weighted average standard deviations of the tilt direction and center point can be used to establish discriminant functions that can effectively distinguish the damage extent

Two-dimensional suprawavelength periodic surface structuring of a ZnO single crystal with a UV femtosecond laser

Herein, we report on the one-step formation of a novel microstructure on the surface of crystalline ZnO in ambient air excited by a single femtosecond laser beam (central wavelength 400 nm, pulse duration 35fs), which has photon energy close to the bandgap of ZnO. A two-dimensional surface structure with a controlled period of ∼2-6 μm is observed, with its orientation independent on the status of laser polarization (linear, circular, or elliptical polarization). We find that the orientation of this two-dimensional structure is defined by the direction of the crystal a and c axes. This structural period of ∼2-6 micrometers and the independence of its orientation on the laser polarization are in sharp contrast with the traditional laser induced periodic surface structure (LIPSS). In the meantime, surface cracks with a feature size of ∼30 nm are observed at the bottom of the valley of the two-dimensional structure and theoretical results show there exists strong electric field enhancement on the cracks under 400 nm femtosecond laser irradiation. In view of these unusual features, we attribute the formation of this two-dimensional structure to the mechanical cracking of the ZnO crystal along its (11-20) and (0001) planes induced by the multiple-cyclic heating due to linear absorption of the femtosecond pulses.</jats:p

Building Earthquake Damage Analysis Using Terrestrial Laser Scanning Data

Terrestrial laser scanners (TLSs) can acquire high-precision three-dimensional point cloud data for earthquake-damaged buildings. In this study, we collected TLS data in the Wenchuan earthquake zone and developed the TLS-BSAM (terrestrial laser scanning-based building shape analysis model) to carry out a building earthquake damage analysis. This model involves equidistance polygon array extraction, shape dispersion parameter calculations, irregular building clustering segmentation, and damage analysis. We chose 21 buildings as samples for the experiments. The results show that when using an equidistance polygon array to depict a three-dimensional building, 0.5 m is a reasonable sampling interval for building earthquake damage analysis. Using certain characteristic parameters to carry out K-means clustering, one can efficiently divide irregular buildings into regular blocks. Then, by weighted averages, the shape dispersion parameters can be calculated to express the damage extent to buildings. Among the shape dispersion parameters, at least the weighted average standard deviations of the tilt direction, rectangularity, compactness, and center point are suitable to reflect the damage extent. Higher values reflect more serious damage. On the basis of existing data, the weighted average standard deviations of the tilt direction and center point can be used to establish discriminant functions that can effectively distinguish the damage extent.</jats:p