Fine structures of solar radio type III bursts and their possible relationship with coronal density turbulence

Solar radio type III bursts are believed to be the most sensitive signatures of near-relativistic electron beam propagation in the corona. A solar radio type IIIb-III pair burst with fine frequency structures, observed by the Low Frequency Array (LOFAR) with high temporal (~10 ms) and spectral (12.5 kHz) resolutions at 30–80 MHz, is presented. The observations show that the type III burst consists of many striae, which have a frequency scale of about 0.1 MHz in both the fundamental (plasma) and the harmonic (double plasma) emission. We investigate the effects of background density fluctuations based on the observation of striae structure to estimate the density perturbation in the solar corona. It is found that the spectral index of the density fluctuation spectrum is about −1.7, and the characteristic spatial scale of the density perturbation is around 700 km. This spectral index is very close to a Kolmogorov turbulence spectral index of −5/3, consistent with a turbulent cascade. This fact indicates that the coronal turbulence may play the important role of modulating the time structures of solar radio type III bursts, and the fine structure of radio type III bursts could provide a useful and unique tool to diagnose the turbulence in the solar corona

Simple and Efficient Partial Graph Adversarial Attack: A New Perspective

As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack methods treat all nodes in the graph as their attack targets. Although existing methods have achieved excellent results, there is still considerable space for improvement. The key problem is that the current approaches rigidly follow the definition of global attacks. They ignore an important issue, i.e., different nodes have different robustness and are not equally resilient to attacks. From a global attacker's view, we should arrange the attack budget wisely, rather than wasting them on highly robust nodes. To this end, we propose a totally new method named partial graph attack (PGA), which selects the vulnerable nodes as attack targets. First, to select the vulnerable items, we propose a hierarchical target selection policy, which allows attackers to only focus on easy-to-attack nodes. Then, we propose a cost-effective anchor-picking policy to pick the most promising anchors for adding or removing edges, and a more aggressive iterative greedy-based attack method to perform more efficient attacks. Extensive experimental results demonstrate that PGA can achieve significant improvements in both attack effect and attack efficiency compared to other existing graph global attack methods

Anti-cancer and antioxidant properties of phenolics isolated from Toona sinensis A Juss acetone leaf extract

Purpose: To investigate the antioxidant and anticancer activities of phenolics from the leaf extract of Toona sinensis (TS).Methods: Acetone leaf extract of TS was screened for total phenolic and flavanoid contents, and the flanonoids were subjected to high performance liquid  chromatographic (HPLC) analysis. Antioxidant properties were assessed via oxygen radical absorbance capacity (ORAC), peroxyl radical scavenging capacity (PSC) and cellular antioxidant activity (CAA), while anti-proliferative activity ins HepG2 cell line was assessed using methylene blue assay.Results: The extract contained 36.02 ± 0.24 mg of gallic acid equiv/g dry weight (DW) and 20.24 ± 1.73 mg of catechin equiv/g DW of total phenolic and total flavonoid, respectively. The levels of rutin and quercitrin were 0.51 and 19.55 mg/g, respectively. Epicatechin, gallic acid, quercitin, isoquercetin were not detected. The extract showed significant antioxidant potential and high anti-proliferation capacity with low cytotoxicity against HepG2 cell in vitro. The underlying mechanism of anti-proliferative effect was induction of apoptosis.Conclusion: TS leaf extract possesses significant in vitro antioxidant properties and anti-proliferative effect against HepG2 cells, which make it a potential anticancer drug source.Keywords: Toona sinensis, Phenolics, Antioxidants, HepG2 cells, Anti-proliferatio

DIFER: Differentiable Automated Feature Engineering

Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to Automated Feature Engineering (AutoFE) to replace expensive human labor. However, existing methods are computationally demanding due to treating AutoFE as a coarse-grained black-box optimization problem over a discrete space. In this work, we propose an efficient gradient-based method called DIFER to perform differentiable automated feature engineering in a continuous vector space. DIFER selects potential features based on evolutionary algorithm and leverages an encoder-predictor-decoder controller to optimize existing features. We map features into the continuous vector space via the encoder, optimize the embedding along the gradient direction induced by the predicted score, and recover better features from the optimized embedding by the decoder. Extensive experiments on classification and regression datasets demonstrate that DIFER can significantly improve the performance of various machine learning algorithms and outperform current state-of-the-art AutoFE methods in terms of both efficiency and performance.Comment: 8 pages, 5 figure