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

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

Hybrid SVM-CNN Classification Technique for Human–Vehicle Targets in an Automotive LFMCW Radar

Human–vehicle classification is an essential component to avoiding accidents in autonomous driving. The classification technique based on the automotive radar sensor has been paid more attention by related researchers, owing to its robustness to low-light conditions and severe weather. In the paper, we propose a hybrid support vector machine–convolutional neural network (SVM-CNN) approach to address the class-imbalance classification of vehicles and pedestrians with limited experimental radar data available. A two-stage scheme with the combination of feature-based SVM technique and deep learning-based CNN is employed. In the first stage, the modified SVM technique based on these distinct physical features is firstly used to recognize vehicles to effectively alleviate the imbalance ratio of vehicles to pedestrians in the data level. Then, the residual unclassified images will be used as inputs to the deep network for the subsequent classification, and we introduce a weighted false error function into deep network architectures to enhance the class-imbalance classification performance at the algorithm level. The proposed SVM-CNN approach takes full advantage of both the locations of underlying class in the entire Range-Doppler image and automatical local feature learning in the CNN with sliding filter bank to improve the classification performance. Experimental results demonstrate the superior performances of the proposed method with the F 1 score of 0.90 and area under the curve (AUC) of the receiver operating characteristic (ROC) of 0.99 over several state-of-the-art methods with limited experimental radar data available in a 77 GHz automotive radar

Targeted therapy using engineered extracellular vesicles: principles and strategies for membrane modification

Abstract Extracellular vesicles (EVs) are 30–150 nm membrane-bound vesicles naturally secreted by cells and play important roles in intercellular communication by delivering regulatory molecules such as proteins, lipids, nucleic acids and metabolites to recipient cells. As natural nano-carriers, EVs possess desirable properties such as high biocompatibility, biological barrier permeability, low toxicity, and low immunogenicity, making them potential therapeutic delivery vehicles. EVs derived from specific cells have inherent targeting capacity towards specific cell types, which is yet not satisfactory enough for targeted therapy development and needs to be improved. Surface modifications endow EVs with targeting abilities, significantly improving their therapeutic efficiency. Herein, we first briefly introduce the biogenesis, composition, uptake and function of EVs, and review the cargo loading approaches for EVs. Then, we summarize the recent advances in surface engineering strategies of EVs, focusing on the applications of engineered EVs for targeted therapy. Altogether, EVs hold great promise for targeted delivery of various cargos, and targeted modifications show promising effects on multiple diseases. Graphical Abstrac

Phase-type Fresnel zone plate with multi-wavelength imaging embedded in fluoroaluminate glass fabricated via ultraviolet femtosecond laser lithography

Herein, we report a novel optical glass material, fluoroaluminate (AlF3) glass, with excellent optical transmittance from ultraviolet to infrared wavelength ranges, which provides more options for application in optical devices. Based on its performance, the phase-type Fresnel zone plate (FZP) by ultraviolet femtosecond (fs) laser-inscribed lithography is achieved, which induces the refractive index change by fs-laser tailoring. The realization of ultraviolet fs-laser fabrication inside glass can benefit from the excellent optical performance of the AlF3 glass. Compared with traditional surface-etching micro-optical elements, the phase-type FZP based on AlF3 glass exhibits a clear and well-defined geometry and presents perfect environmental suitability without surface roughness problems. Additionally, optical focusing and multi-wavelength imaging can be easily obtained. Phase-type FZP embedded in AlF3 glass has great potential applications in the imaging and focusing in glass-integrated photonics, especially for the ultraviolet wavelength range.Published versio

Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions

Reweighted Robust Particle Filtering Approach for Target Tracking in Automotive Radar Application

In view of the decline of filtering accuracy caused by measured outliers in target tracking application, a novel reweighted robust particle filter is proposed to acquire accurate state estimates in an automotive radar system. To infer the importance of each entry in the multidimensional contaminated measurement vector, we employ a weight vector, which follows a Gamma distribution, to model the measured noise and carry out accurate state estimates. Additionally, the particle filter method is employed to perform approximate posterior inference of state estimates in the nonlinear model. The Cramer–Rao lower bound is provided for the performance evaluation of the proposed method. Both simulation and experimental results demonstrate the superiorities of the proposed algorithm over other robust solutions