Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph

Heterogeneous graph learning has drawn significant attentions in recent years, due to the success of graph neural networks (GNNs) and the broad applications of heterogeneous information networks. Various heterogeneous graph neural networks have been proposed to generalize GNNs for processing the heterogeneous graphs. Unfortunately, these approaches model the heterogeneity via various complicated modules. This paper aims to propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections. To optimize these relation embeddings and the other parameters simultaneously, a gradient scaling factor is proposed to constrain the embeddings to converge to suitable values. Besides, we theoretically demonstrate that our RE-GNNs have more expressive power than the meta-path based heterogeneous GNNs. Extensive experiments on the node classification tasks validate the effectiveness of our proposed method

Cross-Subject Domain Adaptation for Multi-Frame EEG Images

Working memory (WM) is a basic part of human cognition, which plays an important role in the study of human cognitive load. Among various brain imaging techniques, electroencephalography has shown its advantage on easy access and reliability. However, one of the critical challenges is that individual difference may cause the ineffective results, especially when the established model meets an unfamiliar subject. In this work, we propose a cross-subject deep adaptation model with spatial attention (CS-DASA) to generalize the workload classifications across subjects. First, we transform time-series EEG data into multi-frame EEG images incorporating more spatio-temporal information. First, the subject-shared module in CS-DASA receives multi-frame EEG image data from both source and target subjects and learns the common feature representations. Then, in subject-specific module, the maximum mean discrepancy is implemented to measure the domain distribution divergence in a reproducing kernel Hilbert space, which can add an effective penalty loss for domain adaptation. Additionally, the subject-to-subject spatial attention mechanism is employed to focus on the most discriminative spatial feature in EEG image data. Experiments conducted on a public WM EEG dataset containing 13 subjects show that the proposed model is capable of achieve better performance than existing state-of-the art methods

Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information networks because of the lack of ability to issue heterogeneity. At the same time, data has become a factor of production, playing an increasingly important role. Due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of HINs composed of scientific research teams and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated

Structure Diagram Recognition in Financial Announcements

Accurately extracting structured data from structure diagrams in financial announcements is of great practical importance for building financial knowledge graphs and further improving the efficiency of various financial applications. First, we proposed a new method for recognizing structure diagrams in financial announcements, which can better detect and extract different types of connecting lines, including straight lines, curves, and polylines of different orientations and angles. Second, we developed a two-stage method to efficiently generate the industry's first benchmark of structure diagrams from Chinese financial announcements, where a large number of diagrams were synthesized and annotated using an automated tool to train a preliminary recognition model with fairly good performance, and then a high-quality benchmark can be obtained by automatically annotating the real-world structure diagrams using the preliminary model and then making few manual corrections. Finally, we experimentally verified the significant performance advantage of our structure diagram recognition method over previous methods

Review on Applications of Lignin in Pavement Engineering: A Recent Survey

Lignin is the second-largest plant polymer on Earth after cellulose. About 98% of lignin produced in the papermaking and pulping industry is used for combustion heating or power generation. Less than 2% of lignin is used in more valuable fields, mainly in the formulation of dispersants, adhesives, and surfactants. Asphalt is one of the most important materials in pavement engineering. It is a dark brown complex mixture composed of hydrocarbons with different molecular weights and their non-metallic derivatives. Because the chemical structure of lignin is similar to that of asphalt, it is a carbon-based hydrocarbon material. More researchers studied the application of lignin in pavement engineering. In this paper, the structure, application, and extraction technology of lignin were summarized. This is a review article describing the different applications of lignin in pavement engineering and exploring the prospects of the application. There are three main types of pavement materials that can be used for lignin in pavement engineering, which are asphalt, asphalt mixture, and roadbed soil. In asphalt, lignin can be used as a modifier, extender, emulsifier, antioxidant, and coupling agent. In asphalt mixtures, lignin can be used as an additive. In road base soils, lignin can be used as a soil stabilizer. Furthermore, the article analyzed the application effects of lignin from the life cycle assessment. The conclusions suggest that lignin-modified asphalt exhibits more viscosity and hardness, and its high-temperature resistance and rutting resistance can be significantly improved compared with conventional asphalt. In addition, some lignin-modified asphalt binders exhibit reduced low-temperature crack resistance and fatigue resistance, which can be adjusted and selected according to the climate change in different regions. The performance of lignin as an asphalt mixture additive and asphalt extender has been proved to be feasible. Lignin can also produce good mechanical properties as well as environmental benefits as a soil stabilizer. In summary, lignin plays an important role in asphalt pavement and roadbed soil, and it is likely to be a development trend in the future due to its environmental friendliness and low cost. More research is needed to generalize the application of lignin in pavement engineering

Investigation on The Hydrodynamic Properties in The External Loop of Circulating Fluidized Bed With a Loop Seal

The pressure balance and mass balance are influenced by the characteristics of different components in the loop of a circulating fluidized bed (CFB). Experiments were conducted in a 4.3 m high cold laboratory CFB test rig with a loop seal. With a fixed bed inventory and superficial gas velocity, the pressure drop of the loop seal decreased with increasing aeration, thus causing an increase in the solid circulation flux (Gs). Correspondingly, the pressure drop in the riser became higher with increasing Gs; the pressure drop of the cyclone had a non-linear relationship with Gs, and the transition point was determined in the experiment. Using the laser fiber and gas tracer method, hydrodynamic characteristics in the standpipe were directly measured. It was found that the pressure gradient, voidage, and solid height in the standpipe were affected by the pressure balance in the whole loop. By adjusting the gas flow rate and direction in the standpipe, the gas-solid slip velocity and pressure gradient changed correspondingly. Therefore, the standpipe could maintain the pressure balance and realize self-equilibrium of the loop by absorbing the pressure drop variations of other parts in the system

Ligustrazine Inhibits the Migration and Invasion of Renal Cell Carcinoma

Ligustrazine is a Chinese herb (Chuanxiong) approved for use as a medical drug in China. Recent evidence suggests that ligustrazine has promising antitumor properties. Our preliminary results showed that ligustrazine could inhibit the growth of human renal cell carcinoma (RCC) cell lines. However, the complicated molecular mechanism has not been fully revealed. Therefore, the purpose of this study to investigate the mechanism of ligustrazine resistance in human RCC cells. Cell proliferation, migration, invasion, and colony-formation ability of RCC cells A498 were detected by MTT assay, clonal formation rates, and transwell chamber assay in vitro. The expression of epithelial–mesenchymal transition (EMT)–related proteins were analyzed using western blot test. The effect of ligustrazine on the growth of A498 cells in nude mice was investigated in vivo. Our results showed that ligustrazine could significantly inhibit the proliferation, migration, and invasion of A498 both in vivo and vitro. Western blot analysis showed that the expressions of EMT-related, N-cadherin, snail, and slug proteins were significantly decreased in A498 in the ligustrazine treatment group. This study indicated that ligustrazine could significantly inhibit the malignant biological behaviors of RCC cell lines, possibly by inhibiting the EMT process

An Optimized High Throughput Clean-Up Method Using Mixed-Mode SPE Plate for the Analysis of Free Arachidonic Acid in Plasma by LC-MS/MS

A high throughput sample preparation method was developed utilizing mixed-mode solid phase extraction (SPE) in 96-well plate format for the determination of free arachidonic acid in plasma by LC-MS/MS. Plasma was mixed with 3% aqueous ammonia and loaded into each well of 96-well plate. After washing with water and methanol sequentially, 3% of formic acid in acetonitrile was used to elute arachidonic acid. The collected fraction was injected onto a reversed phase column at 30°C with mobile phase of acetonitrile/water (70 : 30, v/v) and detected by LC-MS/MS coupled with electrospray ionization (ESI) in multiple reaction monitoring (MRM) mode. The calibration curve ranged from 10 to 2500 ng/mL with sufficient linearity (r2 = 0.9999). The recoveries were in the range of 99.38% to 103.21% with RSD less than 6%. The limit of detection is 3 ng/mL