The Expressive Power of Graph Neural Networks: A Survey

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement

Application of improved YOLOv7-based sugarcane stem node recognition algorithm in complex environments

IntroductionSugarcane stem node detection is one of the key functions of a small intelligent sugarcane harvesting robot, but the accuracy of sugarcane stem node detection is severely degraded in complex field environments when the sugarcane is in the shadow of confusing backgrounds and other objects.MethodsTo address the problem of low accuracy of sugarcane arise node detection in complex environments, this paper proposes an improved sugarcane stem node detection model based on YOLOv7. First, the SimAM (A Simple Parameter-Free Attention Module for Convolutional Neural Networks) attention mechanism is added to solve the problem of feature loss due to the loss of image global context information in the convolution process, which improves the detection accuracy of the model in the case of image blurring; Second, the Deformable convolution Network is used to replace some of the traditional convolution layers in the original YOLOv7. Finally, a new bounding box regression loss function WIoU Loss is introduced to solve the problem of unbalanced sample quality, improve the model robustness and generalization ability, and accelerate the convergence speed of the network.ResultsThe experimental results show that the mAP of the improved algorithm model is 94.53% and the F1 value is 92.41, which are 3.43% and 2.21 respectively compared with the YOLOv7 model, and compared with the mAP of the SOTA method which is 94.1%, an improvement of 0.43% is achieved, which effectively improves the detection performance of the target detection model.DiscussionThis study provides a theoretical basis and technical support for the development of a small intelligent sugarcane harvesting robot, and may also provide a reference for the detection of other types of crops in similar environments

Polydatin Prevents Lipopolysaccharide (LPS)-Induced Parkinson's Disease via Regulation of the AKT/GSK3β-Nrf2/NF-κB Signaling Axis

Parkinson's disease (PD) is a common neurodegenerative disease characterized by selective loss of dopaminergic neurons in the substantia nigra (SN). Neuroinflammation induced by over-activation of microglia leads to the death of dopaminergic neurons in the pathogenesis of PD. Therefore, downregulation of microglial activation may aid in the treatment of PD. Polydatin (PLD) has been reported to pass through the blood-brain barrier and protect against motor degeneration in the SN. However, the molecular mechanisms underlying the effects of PLD in the treatment of PD remain unclear. The present study aimed to determine whether PLD protects against dopaminergic neurodegeneration by inhibiting the activation of microglia in a rat model of lipopolysaccharide (LPS)-induced PD. Our findings indicated that PLD treatment protected dopaminergic neurons and ameliorated motor dysfunction by inhibiting microglial activation and the release of pro-inflammatory mediators. Furthermore, PLD treatment significantly increased levels of p-AKT, p-GSK-3βSer9, and Nrf2, and suppressed the activation of NF-κB in the SN of rats with LPS-induced PD. To further explore the neuroprotective mechanism of PLD, we investigated the effect of PLD on activated microglial BV-2 cells. Our findings indicated that PLD inhibited the production of pro-inflammatory mediators and the activation of NF-κB pathways in LPS-induced BV-2 cells. Moreover, our results indicated that PLD enhanced levels of p-AKT, p-GSK-3βSer9, and Nrf2 in BV-2 cells. After BV-2 cells were pretreated with MK2206 (an inhibitor of AKT), NP-12 (an inhibitor of GSK-3β), or Brusatol (BT; an inhibitor of Nrf2), treatment with PLD suppressed the activation of NF-κB signaling pathways and the release of pro-inflammatory mediators in activated BV-2 cells via activation of the AKT/GSK3β-Nrf2 signaling axis. Taken together, our results are the first to demonstrate that PLD prevents dopaminergic neurodegeneration due to microglial activation via regulation of the AKT/GSK3β-Nrf2/NF-κB signaling axis

The Effects of MiFID II on Sell-Side Analysts, Buy-Side Analysts, and Firms

This paper provides early but broad empirical evidence on a major new investor protection regulation in Europe, MiFID II, which requires investment firms to unbundle investment research from other costs they charge to clients. We predict that the price separation resulting from unbundling and a hard-dollar system leads to a shrinking of the market for sell-side investment research, manifested in lower quantity of sell-side coverage that is of higher quality than before the regulation. We test our predictions in difference-in-differences matched-sample research designs with firm fixed effects. We find a decrease in the number of sell-side analysts covering European firms after MiFID II implementation, particularly for firms that are less important to the sell-side. However, research quality improves; specifically, individual analyst forecasts are more accurate and stock recommendations garner greater market reactions. In addition, sell-side analysts seem to cater more to the buy-side after MiFID II by providing industry recommendations along with stock recommendations. Importantly, we predict and find evidence that buy-side investment firms turn to more in-house research after MiFID II implementation. Equally interesting, buy-side analysts increase their participation and engagement in earnings conference calls compared to the control group. Finally, we find some evidence that stock-market liquidity decreases post-MiFID II. Our findings have implications beyond Europe, as investors are currently pressuring the U.S. Securities and Exchange Commission to adopt a similar regulation

The effects of MiFID II on sell-side analysts, buy-side analysts, and firms

This paper provides early but broad empirical evidence on MiFID II, which requires investment firms to unbundle investment research from other costs they charge to clients. Employing difference-in-differences matched-sample research designs with firm fixed effects, we find a decrease in the number of sell-side analysts covering European firms after MiFID II implementation, particularly for firms that are less important to the sell-side. However, research quality improves; specifically, individual analyst forecasts are more accurate and stock recommendations garner greater market reactions. In addition, sell-side analysts seem to cater more to the buy-side after MiFID II by providing industry recommendations along with stock recommendations. Importantly, we predict and find evidence that buy-side investment firms turn to more in-house research after MiFID II implementation. Equally interesting, buy-side analysts increase their participation and engagement in earnings conference calls, compared to the control group. We find some evidence that stock-market liquidity decreases post MiFID II

Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images

Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation

Predictive Current Control of Boost Three-Level and T-Type Inverters Cascaded in Wind Power Generation Systems

A topology structure based on boost three-level converters (BTL converters) and T-type three-level inverters for a direct-drive wind turbine in a wind power generation system is proposed. In this structure, the generator-side control can be realized by the boost-TL converter. Compared with the conventional boost converter, the boost-TL converter has a low inductor current ripple, which reduces the torque ripple of the generator, increases the converter’s capacity, and minimizes switching losses. The boost-TL converter can boost the DC output from the rectifier at low speeds. The principles of the boost-TL converter and the T-type three-level inverter are separately introduced. Based on the cascaded structure of the proposed BTL converter and three-level inverter, a model predictive current control (MPCC) method is adopted, and the optimization of the MPCC is presented. The prediction model is derived, and the simulation and experimental research are carried out. The results show that the algorithm based on the proposed cascaded structure is feasible and superior

Kisspeptin-10 Induces β-Casein Synthesis via GPR54 and Its Downstream Signaling Pathways in Bovine Mammary Epithelial Cells

Kisspeptins (Kps) play a key role in the regulation of GnRH axis and as an anti-metastasis agent by binding with GPR54. Recently, we observed that the expression of GPR54 was higher in the lactating mammary tissues of dairy cows with high-quality milk (0.81 ± 0.13 kg/day of milk protein yield; 1.07 ± 0.18 kg/day of milk fat yield) than in those with low-quality milk (0.51 ± 0.14 kg/day of milk protein yield; 0.67 ± 0.22 kg/day of milk fat yield). We hypothesized that Kp-10 might regulate the milk protein, β-casein (CSN2) synthesis via GPR54 and its downstream signaling. First, we isolated the bovine mammary epithelial cells (bMECs) from lactating Holstein dairy cows, and treated them with different concentrations of Kp-10. Compared with the control cells, the synthesis of CSN2 is significantly increased at a concentration of 100 nM of Kp-10. In addition, the increased effect of CSN2 synthesis was blocked when the cells were pre-treated with the selective inhibitor of GPR54 Peptide-234 (P-234). Mechanistic study revealed that Kp-10 activated ERK1/2, AKT, mTOR and STAT5 in bMECs. Moreover, inhibiting ERK1/2, AKT, mTOR and STAT5 with U0126, MK2206, Rapamycin and AG490 could block the effects of Kp-10. Together, these results demonstrate that Kp-10 facilitates the synthesis of CSN2 via GPR54 and its downstream signaling pathways mTOR, ERK1/2, STAT5 and AKT