Futures Quantitative Investment with Heterogeneous Continual Graph Neural Network

It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-temporal data. However, we cannot directly apply STGNNs to high-frequency future data because future investors have to consider both the long-term and short-term characteristics when doing decision-making. To capture both the long-term and short-term features, we exploit more label information by designing four heterogeneous tasks: price regression, price moving average regression, price gap regression (within a short interval), and change-point detection, which involve both long-term and short-term scenes. To make full use of these labels, we train our model in a continual manner. Traditional continual GNNs define the gradient of prices as the parameter important to overcome catastrophic forgetting (CF). Unfortunately, the losses of the four heterogeneous tasks lie in different spaces. Hence it is improper to calculate the parameter importance with their losses. We propose to calculate parameter importance with mutual information between original observations and the extracted features. The empirical results based on 49 commodity futures demonstrate that our model has higher prediction performance on capturing long-term or short-term dynamic change

Image Understands Point Cloud: Weakly Supervised 3D Semantic Segmentation via Association Learning

Weakly supervised point cloud semantic segmentation methods that require 1\% or fewer labels, hoping to realize almost the same performance as fully supervised approaches, which recently, have attracted extensive research attention. A typical solution in this framework is to use self-training or pseudo labeling to mine the supervision from the point cloud itself, but ignore the critical information from images. In fact, cameras widely exist in LiDAR scenarios and this complementary information seems to be greatly important for 3D applications. In this paper, we propose a novel cross-modality weakly supervised method for 3D segmentation, incorporating complementary information from unlabeled images. Basically, we design a dual-branch network equipped with an active labeling strategy, to maximize the power of tiny parts of labels and directly realize 2D-to-3D knowledge transfer. Afterwards, we establish a cross-modal self-training framework in an Expectation-Maximum (EM) perspective, which iterates between pseudo labels estimation and parameters updating. In the M-Step, we propose a cross-modal association learning to mine complementary supervision from images by reinforcing the cycle-consistency between 3D points and 2D superpixels. In the E-step, a pseudo label self-rectification mechanism is derived to filter noise labels thus providing more accurate labels for the networks to get fully trained. The extensive experimental results demonstrate that our method even outperforms the state-of-the-art fully supervised competitors with less than 1\% actively selected annotations

Economic Evaluation of Post-Combustion CO2 Capture Integration Technology in Natural Gas Combined Cycle Power Plant

[Introduction] In recent years, natural gas power generation has played an important role in the construction of clean energy system of China. By the end of the "14th Five-year Plan" in 2025, China's gas power installed capacity is expected to hit 150 million kilowatts. Carbon capture，utilization and storage (CCUS) is one of the key paths for gas power to achieve the carbon peaking and carbon neutrality goals. [Method] To this end, an integrated plant combining 600 MW natural gas combined cycle (NGCC) and CO2 post-combustion capture (PCC) were set up as the simulation object. [Result] The simulation study shows that the design captures all CO2 flue gas with 90% efficiency, the CO2 compression and purification rate is 99.5%, the total output of gas power generation decreases by about 16.05%, the auxiliary power ratio increases by 5.55%, and the demand for circulating cooling water increases by about 50.52%. [Conclusion] The economic analysis shows that the static investment cost of the integrated plant is 54.28% higher than that of the single power plant, and the levelized cost of energy (LCOE) increases by 15.96%, which brings great difficulties to the deployment and development of carbon dioxide capture. However, the natural gas price is still the most important factor affecting the operating cost of the power plant

Root growth adaptation is mediated by PYLs ABA receptor-PP2A protein phosphatase complex

Plant root architecture dynamically adapts to various environmental conditions, such as salt‐containing soil. The phytohormone abscisic acid (ABA) is involved among others also in these developmental adaptations, but the underlying molecular mechanism remains elusive. Here, a novel branch of the ABA signaling pathway in Arabidopsis involving PYR/PYL/RCAR (abbreviated as PYLs) receptor‐protein phosphatase 2A (PP2A) complex that acts in parallel to the canonical PYLs‐protein phosphatase 2C (PP2C) mechanism is identified. The PYLs‐PP2A signaling modulates root gravitropism and lateral root formation through regulating phytohormone auxin transport. In optimal conditions, PYLs ABA receptor interacts with the catalytic subunits of PP2A, increasing their phosphatase activity and thus counteracting PINOID (PID) kinase‐mediated phosphorylation of PIN‐FORMED (PIN) auxin transporters. By contrast, in salt and osmotic stress conditions, ABA binds to PYLs, inhibiting the PP2A activity, which leads to increased PIN phosphorylation and consequently modulated directional auxin transport leading to adapted root architecture. This work reveals an adaptive mechanism that may flexibly adjust plant root growth to withstand saline and osmotic stresses. It occurs via the cross‐talk between the stress hormone ABA and the versatile developmental regulator auxin

Upregulation of miR-196b Confers a Poor Prognosis in Glioblastoma Patients via Inducing a Proliferative Phenotype

PURPOSE: To explore the expression pattern, prognostic value and functional role of miR-196b in glioblastoma (GBM) patients using large cohorts. EXPERIMENTAL DESIGN: MiR-196b expression was measured using the Human v2.0 miRNA Expression BeadChip (Illumina) in 198 frozen glioma tissues. The expression levels of miR-196b were also validated in an independent cohort containing 128 formalin-fixed paraffin-embedded (FFPE) glioma samples using qRT-PCR. The presence of other molecular prognostic indicators was assessed centrally in the glioma samples. Whole genome gene profiling was performed to investigate the underlying biological behavior. MiR-196b functional analyses were performed in U87 and U251 cell lines. RESULTS: The expression levels of miR-196b were inversely correlated with overall survival in GBM patients. Gene set enrichment analysis (GSEA) showed that the gene sets relating to cell cycle were significantly enriched in the cases with miR-196b overexpression. Functional analyses in U87 and U251 cells revealed that miR-196b was involved in cell proliferation. CONCLUSIONS: MiR-196b is overexpressed and confers a poor prognosis via promoting cellular proliferation in GBM patients

Auxin Response Factor2 (ARF2) and Its Regulated Homeodomain Gene HB33 Mediate Abscisic Acid Response in Arabidopsis

The phytohormone abscisic acid (ABA) is an important regulator of plant development and response to environmental stresses. In this study, we identified two ABA overly sensitive mutant alleles in a gene encoding Auxin Response Factor2 (ARF2). The expression of ARF2 was induced by ABA treatment. The arf2 mutants showed enhanced ABA sensitivity in seed germination and primary root growth. In contrast, the primary root growth and seed germination of transgenic plants over-expressing ARF2 are less inhibited by ABA than that of the wild type. ARF2 negatively regulates the expression of a homeodomain gene HB33, the expression of which is reduced by ABA. Transgenic plants over-expressing HB33 are more sensitive, while transgenic plants reducing HB33 by RNAi are more resistant to ABA in the seed germination and primary root growth than the wild type. ABA treatment altered auxin distribution in the primary root tips and made the relative, but not absolute, auxin accumulation or auxin signal around quiescent centre cells and their surrounding columella stem cells to other cells stronger in arf2-101 than in the wild type. These results indicate that ARF2 and HB33 are novel regulators in the ABA signal pathway, which has crosstalk with auxin signal pathway in regulating plant growth

Effect of interface evolution on thermal conductivity of vacuum hot pressed SiC/Al composites

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