Transformer and Snowball Graph Convolution Learning for Biomedical Graph Classification

Graph or network has been widely used for describing and modeling complex systems in biomedicine. Deep learning methods, especially graph neural networks (GNNs), have been developed to learn and predict with such structured data. In this paper, we proposed a novel transformer and snowball encoding networks (TSEN) for biomedical graph classification, which introduced transformer architecture with graph snowball connection into GNNs for learning whole-graph representation. TSEN combined graph snowball connection with graph transformer by snowball encoding layers, which enhanced the power to capture multi-scale information and global patterns to learn the whole-graph features. On the other hand, TSEN also used snowball graph convolution as position embedding in transformer structure, which was a simple yet effective method for capturing local patterns naturally. Results of experiments using four graph classification datasets demonstrated that TSEN outperformed the state-of-the-art typical GNN models and the graph-transformer based GNN models.Comment: Prepared for submitting to TB

Does the Short Term Fluctuation of Mineral Element Concentrations in the Closed Hydroponic Experimental Facilities Affect the Mineral Concentrations in Cucumber Plants Exposed to Elevated CO\u3csub\u3e2\u3c/sub\u3e?

Aims Studies dealing with plants’ mineral nutrient status under elevated atmospheric CO2concentration (eCO2) are usually conducted in closed hydroponic systems, in which nutrient solutions are entirely renewed every several days. Here, we investigated the contribution of the fluctuation of concentrations of N ([N]), P ([P]), and K ([K]) in nutrient solutions in this short period on their concentrations in cucumber plants exposed to different [CO2] and N levels. Methods Cucumber (Cucumis sativus L.) plants were hydroponically grown under two [CO2] and three N levels. [N], [P], and [K] in nutrient solutions and cucumber plants were analyzed. Results The transpiration rate (Tr) was significantly inhibited by eCO2, whereas Tr per plant was increased due to the larger leaf area. Elevated [CO2] significantly decreased [N] in low N nutrient solutions, which imposed an additional decrease in [N] in plants. [P] in nutrient solutions fluctuated slightly, so the change of [P] in plants might be attributed to the dilution effect and the demand change under eCO2. [K] in moderate and high N nutrient solutions were significantly decreased, which exacerbated the [K] decrease in plants under eCO2. Conclusions The short-term fluctuation of [N] and [K] in nutrient solutions is caused by the asynchronous uptakes of N, K, and water under eCO2, which has an appreciable influence on [N] and [K] in plants besides the dilution effect. This defect of the closed hydroponic system may let us exaggerate the negative impact of eCO2 itself on [N] and [K] in plants

Bridging the Domain Gap for Multi-Agent Perception

Existing multi-agent perception algorithms usually select to share deep neural features extracted from raw sensing data between agents, achieving a trade-off between accuracy and communication bandwidth limit. However, these methods assume all agents have identical neural networks, which might not be practical in the real world. The transmitted features can have a large domain gap when the models differ, leading to a dramatic performance drop in multi-agent perception. In this paper, we propose the first lightweight framework to bridge such domain gaps for multi-agent perception, which can be a plug-in module for most existing systems while maintaining confidentiality. Our framework consists of a learnable feature resizer to align features in multiple dimensions and a sparse cross-domain transformer for domain adaption. Extensive experiments on the public multi-agent perception dataset V2XSet have demonstrated that our method can effectively bridge the gap for features from different domains and outperform other baseline methods significantly by at least 8% for point-cloud-based 3D object detection.Comment: Accepted by ICRA2023.Code: https://github.com/DerrickXuNu/MPD

Inverse Problem Solution and Regularization Parameter Selection for Current Distribution Reconstruction in Switching Arcs by Inverting Magnetic Fields

Current density distribution in electric arcs inside low voltage circuit breakers is a crucial parameter for us to understand the complex physical behavior during the arcing process. In this paper, we investigate the inverse problem of reconstructing the current density distribution in arcs by inverting the magnetic fields. A simplified 2D arc chamber is considered. The aim of this paper is the computational side of the regularization method, regularization parameter selection strategies, and the estimation of systematic error. To address the ill-posedness of the inverse problem, Tikhonov regularization is analyzed, with the regularization parameter chosen by Morozov’s discrepancy principle, the L-curve, the generalized cross-validation, and the quasi-optimality criteria. The provided range of regularization parameter selection strategies is much wider than in the previous works. Effects of several features on the performance of these criteria have been investigated, including the signal-to-noise ratio, dimension of measurement space, and the measurement distance. The numerical simulations show that the generalized cross-validation and quasi-optimality criteria provide a more satisfactory performance on the robustness and accuracy. Moreover, an optimal measurement distance can be expected when using a planner sensor array to perform magnetic measurements

Lithospheric modification at the onset of the destruction of the North China Craton: Evidence from Late Triassic mafic dykes

Mantle-derived magmatism provides important insights for understanding the mechanism of lithospheric thinning. Here we report the results of an integrated geochronological and geochemical study of Late Triassic mafic dykes in Eastern Hebei, northern North China Craton. In situ zircon U-Pb dating shows that the dykes were emplaced between 238 and 223 Ma; the coeval Gaojiadian and Mataizi dykes intruded Precambrian basement at 238–234 Ma and the Saheqiao dyke was emplaced into Neoarchean supracrustal rocks later at 223 ± 4 Ma (2s). Bulk-rock geochemistry indicates that the Late Triassic dykes in Eastern Hebei were produced by melting of ancient lithospheric mantle within the garnet-spinel transition zone (~70–80 km), heated by upwelling asthenosphere. This ancient lithospheric mantle had been metasomatized during previous subduction events. The Gaojiadian and Mataizi dykes resulted from higher degrees of partial melting at slightly lower pressures than the Saheqiao dyke. The melting depth of Late Triassic dykes in Eastern Hebei indicates that the intact ancient lithospheric mantle had been at least locally modified/thinned to ~70–80 km by the Late Triassic. The intrusion of these Late Triassic dykes took place at the onset of the lithospheric thinning of the North China Craton, caused by post-collisional extension after subduction and collision of neighboring blocks with the North China Craton

Interactive Effects of the CO\u3csub\u3e2\u3c/sub\u3e Enrichment and Nitrogen Supply on the Biomass Accumulation, Gas Exchange Properties, and Mineral Elements Concentrations in Cucumber Plants at Different Growth Stages

The concentration changes of mineral elements in plants at different CO2 concentrations ([CO2]) and nitrogen (N) supplies and the mechanisms which control such changes are not clear. Hydroponic trials on cucumber plants with three [CO2] (400, 625, and 1200 µmol mol−1) and five N supply levels (2, 4, 7, 14, and 21 mmol L−1) were conducted. When plants were in high N supply, the increase in total biomass by elevated [CO2] was 51.7% and 70.1% at the seedling and initial fruiting stages, respectively. An increase in net photosynthetic rate (Pn) by more than 60%, a decrease in stomatal conductance (Gs) by 21.2–27.7%, and a decrease in transpiration rate (Tr) by 22.9–31.9% under elevated [CO2] were also observed. High N supplies could further improve the Pn and offset the decrease of Gs and Tr by elevated [CO2]. According to the mineral concentrations and the correlation results, we concluded the main factors affecting these changes. The dilution effect was the main factor driving the reduction of all mineral elements, whereas Tr also had a great impact on the decrease of [N], [K], [Ca], and [Mg] except [P]. In addition, the demand changes of N, Ca, and Mg influenced the corresponding element concentrations in cucumber plants